TY  - JOUR
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5288
AB  - In this research, control of the Direct Current motor is accomplished using
a neuro controller in the Internal Model Control scheme. Two Feed
Forward Neural Networks are trained using historical input-output data.
The first neural network is trained to identify the object's dynamic
behavior, and that model is used as an internal model in the control
scheme. The second neural network is trained to obtain an inverse model
of the object, which is applied as a neuro controller. Experiment is
conducted on the real direct current motor in laboratory conditions.
Obtained results are compared to those achieved by implementing the
Direct Inverse Control method with the same neuro controller. It was
demonstrated that the proposed control method is simple to implement and
the system robustness is achieved, which is a great benefit, aside from the
fact that no mathematical model of the system is necessary to synthesize
the controller of the real object.
PB  - University of Belgrade - Faculty of Mechanical Engineering
T2  - FME Transactions
T1  - Control of Direct Current Motor by Using Artificial Neural Networks in Internal Model Control Scheme
EP  - 116
IS  - 1
SP  - 109
VL  - 51
DO  - 10.5937/fme2301109P
ER  - 

@article{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2023",
abstract = "In this research, control of the Direct Current motor is accomplished using
a neuro controller in the Internal Model Control scheme. Two Feed
Forward Neural Networks are trained using historical input-output data.
The first neural network is trained to identify the object's dynamic
behavior, and that model is used as an internal model in the control
scheme. The second neural network is trained to obtain an inverse model
of the object, which is applied as a neuro controller. Experiment is
conducted on the real direct current motor in laboratory conditions.
Obtained results are compared to those achieved by implementing the
Direct Inverse Control method with the same neuro controller. It was
demonstrated that the proposed control method is simple to implement and
the system robustness is achieved, which is a great benefit, aside from the
fact that no mathematical model of the system is necessary to synthesize
the controller of the real object.",
publisher = "University of Belgrade - Faculty of Mechanical Engineering",
journal = "FME Transactions",
title = "Control of Direct Current Motor by Using Artificial Neural Networks in Internal Model Control Scheme",
pages = "116-109",
number = "1",
volume = "51",
doi = "10.5937/fme2301109P"
}

Perišić, N.,& Jovanović, R.. (2023). Control of Direct Current Motor by Using Artificial Neural Networks in Internal Model Control Scheme. in FME Transactions
University of Belgrade - Faculty of Mechanical Engineering., 51(1), 109-116.
https://doi.org/10.5937/fme2301109P

Perišić N, Jovanović R. Control of Direct Current Motor by Using Artificial Neural Networks in Internal Model Control Scheme. in FME Transactions. 2023;51(1):109-116.
doi:10.5937/fme2301109P .

Perišić, Natalija, Jovanović, Radiša, "Control of Direct Current Motor by Using Artificial Neural Networks in Internal Model Control Scheme" in FME Transactions, 51, no. 1 (2023):109-116,
https://doi.org/10.5937/fme2301109P . .

TY  - JOUR
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7232
AB  - Konvolucione neuronske mreže su vrsta dubokih neuronskih mreža koje se koriste u zadacima klasifikacije, identifikacije, predikcije i detekcije objekata, a pogodne su za rad sa ulaznim podacima različitih dimenzija, kao što su signali, slike, video zapisi. O njihovom značaju svedoči činjenica da su u upotrebi više od bilo koje druge vrste dubokih mreža. Upravo zbog toga se konstantno radi na razvoju novih algoritama koji usavršavaju postojeće modele ili kreiranju novih modela koji ubrzavaju ili poboljšavaju proces učenja. Primenu ostvaruju u najrazličitijim oblastima nauke i industrije zbog mogućnosti postizanja visoke tačnosti i jednostavnosti implementacije. U ovom radu se predstavlja struktura konvolucionih mreža, a naročito se razmatraju novosti u sferi istraživanja konvolucionog sloja, gde se tumače različiti tipovi konvolucija. Takođe, posebno se obraća pažnja na upotrebu ovih mreža u sistemima automatskog upravljanja poslednjih godina, kao rezultata pojave Industrije 4.0. Prilikom analiziranja naučnih radova, primena konvolucionih mreža je razgraničena prema dimenzionalnosti ulaznih podataka, odnosno prema dimenzionalnosti mreža i zadacima koje je moguće rešiti pomoću njih.
PB  - Beograd: Savez inženjera i tehničara Srbije
T2  - Tehnika
T1  - Konvolucione neuronske mreže u sistemima automatskog upravljanja - pregled stanja u oblasti istraživanja
EP  - 441
IS  - 4
SP  - 433
VL  - 78
DO  - 10.5937/tehnika2304433P
ER  - 

@article{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2023",
abstract = "Konvolucione neuronske mreže su vrsta dubokih neuronskih mreža koje se koriste u zadacima klasifikacije, identifikacije, predikcije i detekcije objekata, a pogodne su za rad sa ulaznim podacima različitih dimenzija, kao što su signali, slike, video zapisi. O njihovom značaju svedoči činjenica da su u upotrebi više od bilo koje druge vrste dubokih mreža. Upravo zbog toga se konstantno radi na razvoju novih algoritama koji usavršavaju postojeće modele ili kreiranju novih modela koji ubrzavaju ili poboljšavaju proces učenja. Primenu ostvaruju u najrazličitijim oblastima nauke i industrije zbog mogućnosti postizanja visoke tačnosti i jednostavnosti implementacije. U ovom radu se predstavlja struktura konvolucionih mreža, a naročito se razmatraju novosti u sferi istraživanja konvolucionog sloja, gde se tumače različiti tipovi konvolucija. Takođe, posebno se obraća pažnja na upotrebu ovih mreža u sistemima automatskog upravljanja poslednjih godina, kao rezultata pojave Industrije 4.0. Prilikom analiziranja naučnih radova, primena konvolucionih mreža je razgraničena prema dimenzionalnosti ulaznih podataka, odnosno prema dimenzionalnosti mreža i zadacima koje je moguće rešiti pomoću njih.",
publisher = "Beograd: Savez inženjera i tehničara Srbije",
journal = "Tehnika",
title = "Konvolucione neuronske mreže u sistemima automatskog upravljanja - pregled stanja u oblasti istraživanja",
pages = "441-433",
number = "4",
volume = "78",
doi = "10.5937/tehnika2304433P"
}

Perišić, N.,& Jovanović, R.. (2023). Konvolucione neuronske mreže u sistemima automatskog upravljanja - pregled stanja u oblasti istraživanja. in Tehnika
Beograd: Savez inženjera i tehničara Srbije., 78(4), 433-441.
https://doi.org/10.5937/tehnika2304433P

Perišić N, Jovanović R. Konvolucione neuronske mreže u sistemima automatskog upravljanja - pregled stanja u oblasti istraživanja. in Tehnika. 2023;78(4):433-441.
doi:10.5937/tehnika2304433P .

Perišić, Natalija, Jovanović, Radiša, "Konvolucione neuronske mreže u sistemima automatskog upravljanja - pregled stanja u oblasti istraživanja" in Tehnika, 78, no. 4 (2023):433-441,
https://doi.org/10.5937/tehnika2304433P . .

TY  - CONF
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
AU  - Vesović, Mitra
AU  - Sretenović, Aleksandra
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7298
AB  - The use of artificial intelligence in modern agriculture is on the rise, due to the fact that it provides a possibility for more efficient production, better decision making and reduction of the costs. This research takes into consideration the use of the convolutional neural networks for diagnosing plant illnesses based on the leaf image. Detection of plant diseases in the early phase can improve the quality of the food products and minimize the loses. Convolutional neural networks are a type of deep learning method that is one of the most used models for solving image recognition, classification and detection tasks. Therefore, it is justified to anticipate that they can be very effectively applied in the agriculture sector. This paper covers plant species that are the most significant for Serbian production. Various models have been presented and analyzed, while highlighting their advantages and disadvantages when applied for solving this task.
C3  - ISAE 2023
T1  - Overview of the use of convolutional neural networks in plant desease recognition based on the leaf image
EP  - 22
SP  - 13
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7298
ER  - 

@conference{
author = "Perišić, Natalija and Jovanović, Radiša and Vesović, Mitra and Sretenović, Aleksandra",
year = "2023",
abstract = "The use of artificial intelligence in modern agriculture is on the rise, due to the fact that it provides a possibility for more efficient production, better decision making and reduction of the costs. This research takes into consideration the use of the convolutional neural networks for diagnosing plant illnesses based on the leaf image. Detection of plant diseases in the early phase can improve the quality of the food products and minimize the loses. Convolutional neural networks are a type of deep learning method that is one of the most used models for solving image recognition, classification and detection tasks. Therefore, it is justified to anticipate that they can be very effectively applied in the agriculture sector. This paper covers plant species that are the most significant for Serbian production. Various models have been presented and analyzed, while highlighting their advantages and disadvantages when applied for solving this task.",
journal = "ISAE 2023",
title = "Overview of the use of convolutional neural networks in plant desease recognition based on the leaf image",
pages = "22-13",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7298"
}

Perišić, N., Jovanović, R., Vesović, M.,& Sretenović, A.. (2023). Overview of the use of convolutional neural networks in plant desease recognition based on the leaf image. in ISAE 2023, 13-22.
https://hdl.handle.net/21.15107/rcub_machinery_7298

Perišić N, Jovanović R, Vesović M, Sretenović A. Overview of the use of convolutional neural networks in plant desease recognition based on the leaf image. in ISAE 2023. 2023;:13-22.
https://hdl.handle.net/21.15107/rcub_machinery_7298 .

Perišić, Natalija, Jovanović, Radiša, Vesović, Mitra, Sretenović, Aleksandra, "Overview of the use of convolutional neural networks in plant desease recognition based on the leaf image" in ISAE 2023 (2023):13-22,
https://hdl.handle.net/21.15107/rcub_machinery_7298 .

TY  - CONF
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
AU  - Perišić, Natalija
AU  - Sretenović, Aleksandra
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7297
AB  - Chamber dryers are widely used in various industries in order to remove the moisture from solid materials efficiently. Optimizing the design and operational parameters of chamber dryers plays a crucial role in enhancing their performance and energy efficiency. In order to maintain the temperature at the desired level, it is necessary to implement a good control system. To be able to facilitate the process of finding and setting parameters of the controller, for many control algorithms it is essential to make the reliable model of the object. The aim is to develop both reliable and accurate predictive model that can assist in optimizing the design, structures, and inspection processes of chamber dryers, which will lead to enhanced energy efficiency, harvesting and improved drying performance.
In this paper, the authors propose a novel approach for modeling heat flow transfer in chamber dryers using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The Quanser chamber was selected as the object of the research because of how closely its geometry, material choice, and air flow resemble the structural properties of a dryer. To obtain the most realistic model possible, parameters of ANFIS were found using Particle Swarm Optimization algorithm. By incorporating historical operational data of experimental measurements, the ANFIS model can learn and adapt to the dynamic behavior of the dryer system.
C3  - ISAE 2023 - Proceedings ; The 6th International Symposium on Agricultural Engineering - ISAE 2023
19th - 21st October 2023, Belgrade, Serbia
T1  - Modelling heat-flow prototype dryer using ANFIS optimized by PSO
EP  - 228
SP  - 219
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7297
ER  - 

@conference{
author = "Vesović, Mitra and Jovanović, Radiša and Perišić, Natalija and Sretenović, Aleksandra",
year = "2023",
abstract = "Chamber dryers are widely used in various industries in order to remove the moisture from solid materials efficiently. Optimizing the design and operational parameters of chamber dryers plays a crucial role in enhancing their performance and energy efficiency. In order to maintain the temperature at the desired level, it is necessary to implement a good control system. To be able to facilitate the process of finding and setting parameters of the controller, for many control algorithms it is essential to make the reliable model of the object. The aim is to develop both reliable and accurate predictive model that can assist in optimizing the design, structures, and inspection processes of chamber dryers, which will lead to enhanced energy efficiency, harvesting and improved drying performance.
In this paper, the authors propose a novel approach for modeling heat flow transfer in chamber dryers using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The Quanser chamber was selected as the object of the research because of how closely its geometry, material choice, and air flow resemble the structural properties of a dryer. To obtain the most realistic model possible, parameters of ANFIS were found using Particle Swarm Optimization algorithm. By incorporating historical operational data of experimental measurements, the ANFIS model can learn and adapt to the dynamic behavior of the dryer system.",
journal = "ISAE 2023 - Proceedings ; The 6th International Symposium on Agricultural Engineering - ISAE 2023
19th - 21st October 2023, Belgrade, Serbia",
title = "Modelling heat-flow prototype dryer using ANFIS optimized by PSO",
pages = "228-219",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7297"
}

Vesović, M., Jovanović, R., Perišić, N.,& Sretenović, A.. (2023). Modelling heat-flow prototype dryer using ANFIS optimized by PSO. in ISAE 2023 - Proceedings ; The 6th International Symposium on Agricultural Engineering - ISAE 2023
19th - 21st October 2023, Belgrade, Serbia, 219-228.
https://hdl.handle.net/21.15107/rcub_machinery_7297

Vesović M, Jovanović R, Perišić N, Sretenović A. Modelling heat-flow prototype dryer using ANFIS optimized by PSO. in ISAE 2023 - Proceedings ; The 6th International Symposium on Agricultural Engineering - ISAE 2023
19th - 21st October 2023, Belgrade, Serbia. 2023;:219-228.
https://hdl.handle.net/21.15107/rcub_machinery_7297 .

Vesović, Mitra, Jovanović, Radiša, Perišić, Natalija, Sretenović, Aleksandra, "Modelling heat-flow prototype dryer using ANFIS optimized by PSO" in ISAE 2023 - Proceedings ; The 6th International Symposium on Agricultural Engineering - ISAE 2023
19th - 21st October 2023, Belgrade, Serbia (2023):219-228,
https://hdl.handle.net/21.15107/rcub_machinery_7297 .

TY  - CONF
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6957
AB  - This paper provides a nonlinear technique that uses a fuzzy inference system and neural networks for the identification purposes of heat flow transfer in the chamber. Firstly, linear models are obtained by transfer functions with delay using Matlab identification tools for heat exchange.
Three different transfer functions are provided (for three sensors in different positions along the chamber), and after it has been concluded that the second model has the smallest error, it is tested using different input. In this case, the linear model failed to represent the behaviour of the system precisely, making the error more than 1.5 C in the steady state. This was expected because linear models are trustworthy only around certain operating ranges. In order to make the new model, which will be unique and valid in the whole state space, another identification method using an adaptive neuro-fuzzy inference system (ANFIS) was presented. Furthermore, for the best performance, the ANFIS architecture was found using one of the most famous population-based optimizations: the genetic evolutionary algorithm. With two inputs and 70 parameters found by optimization (40 premises and 30 consequent) ANFIS greatly outperforms standard identification technique in terms of the mean square error. This nonlinear model was also tested on the different input, which was not used in the training process, and it was concluded that the nonlinear model identifies the real object with a neglectable error, which is 45 times smaller than the linear one.
PB  - Belgrade : Singidunum University
C3  - Sinteza 2023 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2023, pp. 44-51
T1  - Heat Flow Process Identification Using ANFIS-GA Model
EP  - 51
SP  - 44
VL  - Computer Science and Artificial Intelligence Session
DO  - 10.15308/Sinteza-2023-44-51
ER  - 

@conference{
author = "Vesović, Mitra and Jovanović, Radiša",
year = "2023",
abstract = "This paper provides a nonlinear technique that uses a fuzzy inference system and neural networks for the identification purposes of heat flow transfer in the chamber. Firstly, linear models are obtained by transfer functions with delay using Matlab identification tools for heat exchange.
Three different transfer functions are provided (for three sensors in different positions along the chamber), and after it has been concluded that the second model has the smallest error, it is tested using different input. In this case, the linear model failed to represent the behaviour of the system precisely, making the error more than 1.5 C in the steady state. This was expected because linear models are trustworthy only around certain operating ranges. In order to make the new model, which will be unique and valid in the whole state space, another identification method using an adaptive neuro-fuzzy inference system (ANFIS) was presented. Furthermore, for the best performance, the ANFIS architecture was found using one of the most famous population-based optimizations: the genetic evolutionary algorithm. With two inputs and 70 parameters found by optimization (40 premises and 30 consequent) ANFIS greatly outperforms standard identification technique in terms of the mean square error. This nonlinear model was also tested on the different input, which was not used in the training process, and it was concluded that the nonlinear model identifies the real object with a neglectable error, which is 45 times smaller than the linear one.",
publisher = "Belgrade : Singidunum University",
journal = "Sinteza 2023 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2023, pp. 44-51",
title = "Heat Flow Process Identification Using ANFIS-GA Model",
pages = "51-44",
volume = "Computer Science and Artificial Intelligence Session",
doi = "10.15308/Sinteza-2023-44-51"
}

Vesović, M.,& Jovanović, R.. (2023). Heat Flow Process Identification Using ANFIS-GA Model. in Sinteza 2023 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2023, pp. 44-51
Belgrade : Singidunum University., Computer Science and Artificial Intelligence Session, 44-51.
https://doi.org/10.15308/Sinteza-2023-44-51

Vesović M, Jovanović R. Heat Flow Process Identification Using ANFIS-GA Model. in Sinteza 2023 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2023, pp. 44-51. 2023;Computer Science and Artificial Intelligence Session:44-51.
doi:10.15308/Sinteza-2023-44-51 .

Vesović, Mitra, Jovanović, Radiša, "Heat Flow Process Identification Using ANFIS-GA Model" in Sinteza 2023 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2023, pp. 44-51, Computer Science and Artificial Intelligence Session (2023):44-51,
https://doi.org/10.15308/Sinteza-2023-44-51 . .

TY  - CONF
AU  - Sretenović, Aleksandra
AU  - Jovanović, Radiša
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7296
AB  - This paper covers the application of artificial intelligence in agriculture. Technology development has enabled measurement, collecting and processing high quality big data. These data can be successfully used to significantly improve numerous segments of agriculture sector. The accent in this paper is given on the models used for energy use prediction. The application of Artificial Neural Networks with different structure is presented, such as Feedforward Neural Network, Radial basis Function Network and Adaptive Neuro-Fuzzy Inference System. Support Vector Machine model is also shown. The improvements of individual models are elaborated, through the analysis of the ensemble and hybrid approach. All of the proposed models are capable of solving complex problem of prediction of energy use based on real, measured data. Ensemble and hybrid models are promising, as it has been shown that the prediction accuracy is improved by combining different single models in proposed manner.
C3  - ISAE 2023
T1  - Artificial intelligence methods for energy use prediction
SP  - 42
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7296
ER  - 

@conference{
author = "Sretenović, Aleksandra and Jovanović, Radiša",
year = "2023",
abstract = "This paper covers the application of artificial intelligence in agriculture. Technology development has enabled measurement, collecting and processing high quality big data. These data can be successfully used to significantly improve numerous segments of agriculture sector. The accent in this paper is given on the models used for energy use prediction. The application of Artificial Neural Networks with different structure is presented, such as Feedforward Neural Network, Radial basis Function Network and Adaptive Neuro-Fuzzy Inference System. Support Vector Machine model is also shown. The improvements of individual models are elaborated, through the analysis of the ensemble and hybrid approach. All of the proposed models are capable of solving complex problem of prediction of energy use based on real, measured data. Ensemble and hybrid models are promising, as it has been shown that the prediction accuracy is improved by combining different single models in proposed manner.",
journal = "ISAE 2023",
title = "Artificial intelligence methods for energy use prediction",
pages = "42",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7296"
}

Sretenović, A.,& Jovanović, R.. (2023). Artificial intelligence methods for energy use prediction. in ISAE 2023, 42.
https://hdl.handle.net/21.15107/rcub_machinery_7296

Sretenović A, Jovanović R. Artificial intelligence methods for energy use prediction. in ISAE 2023. 2023;:42.
https://hdl.handle.net/21.15107/rcub_machinery_7296 .

Sretenović, Aleksandra, Jovanović, Radiša, "Artificial intelligence methods for energy use prediction" in ISAE 2023 (2023):42,
https://hdl.handle.net/21.15107/rcub_machinery_7296 .

TY  - CONF
AU  - Kerčov, Anton
AU  - Jovanović, Radiša
AU  - Bajc, Tamara
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/7076
AB  - Compared to methodology provided by standards concerning thermal comfort, by using
models based on various approximation methods or artificial intelligence, it may be possible
to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of
this study is to compare Predicted Mean Vote (PMV) computation model established by using
multiple linear regression and trained artificial neural network, from the standpoint of
accuracy. Both models are established on the basis of the same dataset which consists of 400
combinations of 4 thermal comfort parameters. These parameters are the air temperature,
mean radiant temperature, relative humidity and clothing resistance, while activity level and
air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are
considered constant values for selected type of indoor environment. Clothing resistance is
adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature,
mean radiant temperature and relative humidity are values which are randomly generated
within appropriately selected ranges. Taking into account that coefficients of determination
which correspond to it are over 95%, resulting first degree polynomial relation obtained by
using multiple linear regression can be considered a satisfactory approximation of PMV
model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value
combinations for which PMV values obtained by using this model coincide with the ones
calculated by using algorithm which is provided by standard. However, results obtained by
using trained neural network with one hidden layer coincide with PMV values calculated on
the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from
the standpoint of accuracy, it is concluded that neural network provides significantly better
approximation of PMV model.
PB  - Faculty of Mechanical Engineering and Naval Architecture, Zagreb
C3  - 18th conference on sustainable development of energy, water and environment systems
T1  - Thermal comfort indices analysis using multiple linear regression and neural network
UR  - https://hdl.handle.net/21.15107/rcub_machinery_7076
ER  - 

@conference{
author = "Kerčov, Anton and Jovanović, Radiša and Bajc, Tamara",
year = "2023",
abstract = "Compared to methodology provided by standards concerning thermal comfort, by using
models based on various approximation methods or artificial intelligence, it may be possible
to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of
this study is to compare Predicted Mean Vote (PMV) computation model established by using
multiple linear regression and trained artificial neural network, from the standpoint of
accuracy. Both models are established on the basis of the same dataset which consists of 400
combinations of 4 thermal comfort parameters. These parameters are the air temperature,
mean radiant temperature, relative humidity and clothing resistance, while activity level and
air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are
considered constant values for selected type of indoor environment. Clothing resistance is
adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature,
mean radiant temperature and relative humidity are values which are randomly generated
within appropriately selected ranges. Taking into account that coefficients of determination
which correspond to it are over 95%, resulting first degree polynomial relation obtained by
using multiple linear regression can be considered a satisfactory approximation of PMV
model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value
combinations for which PMV values obtained by using this model coincide with the ones
calculated by using algorithm which is provided by standard. However, results obtained by
using trained neural network with one hidden layer coincide with PMV values calculated on
the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from
the standpoint of accuracy, it is concluded that neural network provides significantly better
approximation of PMV model.",
publisher = "Faculty of Mechanical Engineering and Naval Architecture, Zagreb",
journal = "18th conference on sustainable development of energy, water and environment systems",
title = "Thermal comfort indices analysis using multiple linear regression and neural network",
url = "https://hdl.handle.net/21.15107/rcub_machinery_7076"
}

Kerčov, A., Jovanović, R.,& Bajc, T.. (2023). Thermal comfort indices analysis using multiple linear regression and neural network. in 18th conference on sustainable development of energy, water and environment systems
Faculty of Mechanical Engineering and Naval Architecture, Zagreb..
https://hdl.handle.net/21.15107/rcub_machinery_7076

Kerčov A, Jovanović R, Bajc T. Thermal comfort indices analysis using multiple linear regression and neural network. in 18th conference on sustainable development of energy, water and environment systems. 2023;.
https://hdl.handle.net/21.15107/rcub_machinery_7076 .

Kerčov, Anton, Jovanović, Radiša, Bajc, Tamara, "Thermal comfort indices analysis using multiple linear regression and neural network" in 18th conference on sustainable development of energy, water and environment systems (2023),
https://hdl.handle.net/21.15107/rcub_machinery_7076 .

TY  - CONF
AU  - Perišić, Natalija
AU  - Spasojević Brkić, Vesna
AU  - Jovanović, Radiša
AU  - Mihajlović, Ivan
AU  - Perišić, Martina
PY  - 2023
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6863
AB  - A company’s development performance and growth may be impacted by a wide range
of different factors, which unquestionably affect number of the employees in the loop. Taking into
account all influencing factors, companies would benefit if have possibility to predict the degree of
change in the number of employees in future period in order to adjust their internal strategy or to
make appropriate decisions that enable the survival and progress of the company in the market. The
aim of this research is to predict the change in number of employees based on current state of
contingency and quality management factors, using information obtained from a survey of 67
different companies from Serbia. In the first part of the research, a correlation analysis is used with
the aim to identify the specific contingency and quality management factors that are most closely
associated to the subject of interest, which is, in this case, degree of change in the number of
employes. The second part of the research involves feedforward neural network training for
prediction of the degree of change in number of employees based on feature extraction of main
factors. The training accuracy that proposed network achieved is 77.36%, while testing accuracy
amounts 71.43%.
PB  - Bor : University of Belgrade, Technical Faculty, Department of Engineering Management
C3  - International May Conference on Strategic Management – IMCSM23 May, 2023, Bor, Serbia
T1  - PREDICTION OF THE CHANGE IN NUMBER OF EMPLOYEES IN SERBIAN COMPANIES BASED ON CONTINGENCY AND QUALITY MANAGEMENT FACTORS
EP  - 48
IS  - 1
SP  - 39
VL  - 19
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6863
ER  - 

@conference{
author = "Perišić, Natalija and Spasojević Brkić, Vesna and Jovanović, Radiša and Mihajlović, Ivan and Perišić, Martina",
year = "2023",
abstract = "A company’s development performance and growth may be impacted by a wide range
of different factors, which unquestionably affect number of the employees in the loop. Taking into
account all influencing factors, companies would benefit if have possibility to predict the degree of
change in the number of employees in future period in order to adjust their internal strategy or to
make appropriate decisions that enable the survival and progress of the company in the market. The
aim of this research is to predict the change in number of employees based on current state of
contingency and quality management factors, using information obtained from a survey of 67
different companies from Serbia. In the first part of the research, a correlation analysis is used with
the aim to identify the specific contingency and quality management factors that are most closely
associated to the subject of interest, which is, in this case, degree of change in the number of
employes. The second part of the research involves feedforward neural network training for
prediction of the degree of change in number of employees based on feature extraction of main
factors. The training accuracy that proposed network achieved is 77.36%, while testing accuracy
amounts 71.43%.",
publisher = "Bor : University of Belgrade, Technical Faculty, Department of Engineering Management",
journal = "International May Conference on Strategic Management – IMCSM23 May, 2023, Bor, Serbia",
title = "PREDICTION OF THE CHANGE IN NUMBER OF EMPLOYEES IN SERBIAN COMPANIES BASED ON CONTINGENCY AND QUALITY MANAGEMENT FACTORS",
pages = "48-39",
number = "1",
volume = "19",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6863"
}

Perišić, N., Spasojević Brkić, V., Jovanović, R., Mihajlović, I.,& Perišić, M.. (2023). PREDICTION OF THE CHANGE IN NUMBER OF EMPLOYEES IN SERBIAN COMPANIES BASED ON CONTINGENCY AND QUALITY MANAGEMENT FACTORS. in International May Conference on Strategic Management – IMCSM23 May, 2023, Bor, Serbia
Bor : University of Belgrade, Technical Faculty, Department of Engineering Management., 19(1), 39-48.
https://hdl.handle.net/21.15107/rcub_machinery_6863

Perišić N, Spasojević Brkić V, Jovanović R, Mihajlović I, Perišić M. PREDICTION OF THE CHANGE IN NUMBER OF EMPLOYEES IN SERBIAN COMPANIES BASED ON CONTINGENCY AND QUALITY MANAGEMENT FACTORS. in International May Conference on Strategic Management – IMCSM23 May, 2023, Bor, Serbia. 2023;19(1):39-48.
https://hdl.handle.net/21.15107/rcub_machinery_6863 .

Perišić, Natalija, Spasojević Brkić, Vesna, Jovanović, Radiša, Mihajlović, Ivan, Perišić, Martina, "PREDICTION OF THE CHANGE IN NUMBER OF EMPLOYEES IN SERBIAN COMPANIES BASED ON CONTINGENCY AND QUALITY MANAGEMENT FACTORS" in International May Conference on Strategic Management – IMCSM23 May, 2023, Bor, Serbia, 19, no. 1 (2023):39-48,
https://hdl.handle.net/21.15107/rcub_machinery_6863 .

TY  - JOUR
AU  - Jovanović, Radiša
AU  - Zarić, Vladimir
AU  - Bučevac, Zoran
AU  - Bugarić, Uglješa
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4524
AB  - The study proposes a novel method for synthesis of a discrete-time parallel distributed
compensation (PDC) controller for the nonlinear discrete-time Takagi–Sugeno (TS) fuzzy plant model. For each of the fuzzy plant model linear subsystems, a local linear first order proportional-sum (PS) controller is designed. The algebraic technique is used in two-dimensional parameter space, utilizing the characteristic polynomial of the row nondegenerate full transfer function matrix. Each system’s relative stability is accomplished in relation to the selected damping coefficient. The supplementary criterion is the minimal value of the performance index in the form of the sum of squared errors (SSE). However, unlike the traditional technique, output error is influenced by all simultaneous actions on the system: nonzero inputs and nonzero initial conditions. The full transfer function matrix of the system allows for the treatment of simultaneous actions of the input vector and unknown unpredictable initial conditions. In order to show the improvement caused by the application of a new optimization method that considers nonzero initial conditions, a comparison of PDC controllers designed under zero and nonzero initial conditions is given, where the system in both cases starts from the same nonzero initial conditions, which is often the case in practice. The simulation and experimental results on a DC servo motor are shown to demonstrate the suggested method efficiency.
PB  - MDPI
T2  - Applied Sciences
T1  - Discrete-time system conditional optimization based on Takagi-Sugeno fuzzy model using the full transfer function
EP  - 22
IS  - 7705
SP  - 1
VL  - 12
DO  - 10.3390/app12157705
ER  - 

@article{
author = "Jovanović, Radiša and Zarić, Vladimir and Bučevac, Zoran and Bugarić, Uglješa",
year = "2022",
abstract = "The study proposes a novel method for synthesis of a discrete-time parallel distributed
compensation (PDC) controller for the nonlinear discrete-time Takagi–Sugeno (TS) fuzzy plant model. For each of the fuzzy plant model linear subsystems, a local linear first order proportional-sum (PS) controller is designed. The algebraic technique is used in two-dimensional parameter space, utilizing the characteristic polynomial of the row nondegenerate full transfer function matrix. Each system’s relative stability is accomplished in relation to the selected damping coefficient. The supplementary criterion is the minimal value of the performance index in the form of the sum of squared errors (SSE). However, unlike the traditional technique, output error is influenced by all simultaneous actions on the system: nonzero inputs and nonzero initial conditions. The full transfer function matrix of the system allows for the treatment of simultaneous actions of the input vector and unknown unpredictable initial conditions. In order to show the improvement caused by the application of a new optimization method that considers nonzero initial conditions, a comparison of PDC controllers designed under zero and nonzero initial conditions is given, where the system in both cases starts from the same nonzero initial conditions, which is often the case in practice. The simulation and experimental results on a DC servo motor are shown to demonstrate the suggested method efficiency.",
publisher = "MDPI",
journal = "Applied Sciences",
title = "Discrete-time system conditional optimization based on Takagi-Sugeno fuzzy model using the full transfer function",
pages = "22-1",
number = "7705",
volume = "12",
doi = "10.3390/app12157705"
}

Jovanović, R., Zarić, V., Bučevac, Z.,& Bugarić, U.. (2022). Discrete-time system conditional optimization based on Takagi-Sugeno fuzzy model using the full transfer function. in Applied Sciences
MDPI., 12(7705), 1-22.
https://doi.org/10.3390/app12157705

Jovanović R, Zarić V, Bučevac Z, Bugarić U. Discrete-time system conditional optimization based on Takagi-Sugeno fuzzy model using the full transfer function. in Applied Sciences. 2022;12(7705):1-22.
doi:10.3390/app12157705 .

Jovanović, Radiša, Zarić, Vladimir, Bučevac, Zoran, Bugarić, Uglješa, "Discrete-time system conditional optimization based on Takagi-Sugeno fuzzy model using the full transfer function" in Applied Sciences, 12, no. 7705 (2022):1-22,
https://doi.org/10.3390/app12157705 . .

TY  - JOUR
AU  - Jovanović, Radiša
AU  - Zarić, Vladimir
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3781
AB  - Even though, it is mostly used by process control engineers, the temperature control remains an important task for researchers. This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found. From these identified models a Takagi-Sugeno model is obtained using fixed membership functions in the premises of the rules. According to the chosen objective function, parameters in the premise part of Takagi-Sugeno fuzzy model were optimized using the grey wolf algorithm. Furthermore, by using the parallel distributed compensation a fuzzy controller is developed via the fuzzy blending of three proportional + sum controllers designed for each of the operating points. In order to evaluate performance, a comparison is made between the fuzzy controller and local linear controllers. Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared. The experimental results on a heat flow platform are presented to validate efficiency of the proposed method.
PB  - Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd
T2  - Thermal Science
T1  - Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimizer
EP  - 2286
IS  - 3
SP  - 2275
VL  - 26
DO  - 10.2298/TSCI210825324J
ER  - 

@article{
author = "Jovanović, Radiša and Zarić, Vladimir",
year = "2022",
abstract = "Even though, it is mostly used by process control engineers, the temperature control remains an important task for researchers. This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found. From these identified models a Takagi-Sugeno model is obtained using fixed membership functions in the premises of the rules. According to the chosen objective function, parameters in the premise part of Takagi-Sugeno fuzzy model were optimized using the grey wolf algorithm. Furthermore, by using the parallel distributed compensation a fuzzy controller is developed via the fuzzy blending of three proportional + sum controllers designed for each of the operating points. In order to evaluate performance, a comparison is made between the fuzzy controller and local linear controllers. Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared. The experimental results on a heat flow platform are presented to validate efficiency of the proposed method.",
publisher = "Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd",
journal = "Thermal Science",
title = "Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimizer",
pages = "2286-2275",
number = "3",
volume = "26",
doi = "10.2298/TSCI210825324J"
}

Jovanović, R.,& Zarić, V.. (2022). Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimizer. in Thermal Science
Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd., 26(3), 2275-2286.
https://doi.org/10.2298/TSCI210825324J

Jovanović R, Zarić V. Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimizer. in Thermal Science. 2022;26(3):2275-2286.
doi:10.2298/TSCI210825324J .

Jovanović, Radiša, Zarić, Vladimir, "Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimizer" in Thermal Science, 26, no. 3 (2022):2275-2286,
https://doi.org/10.2298/TSCI210825324J . .

TY  - JOUR
AU  - Zarić, Vladimir
AU  - Bučevac, Zoran M.
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3771
AB  - The paper presents a new approach to the design of the classical PD controller for the plant in the closed loop control system. Proposed controller has two unknown adjustable parameters which are designed by the algebraic method in the two dimensional parameter space, by using the newly discovered characteristic polynomial of the row nondegenerate full transfer function matrix. The system relative stability, in regard to the chosen damping coefficient is achieved. The optimality criterion is the minimal value of the performance index and sum of squared error is taken as an objective function. The output error used in the performance index is influenced by all actions on the system at the same time: by nonzero initial conditions and by nonzero inputs. In the classical approach the output error is influenced only by nonzero inputs. Experimental results confirm that by taking into account nonzero initial conditions, an optimal solution is obtained, which is not the case with the classical method where optimization is performed at zero initial conditions.
PB  - Univ Osijek, Tech Fac, Slavonski Brod
T2  - Tehnički vjesnik
T1  - Discrete-Time System Conditional Optimization in the Parameter Space with Nonzero Initial Conditions
EP  - 207
IS  - 1
SP  - 200
VL  - 29
DO  - 10.17559/TV-20191031145233
ER  - 

@article{
author = "Zarić, Vladimir and Bučevac, Zoran M. and Jovanović, Radiša",
year = "2022",
abstract = "The paper presents a new approach to the design of the classical PD controller for the plant in the closed loop control system. Proposed controller has two unknown adjustable parameters which are designed by the algebraic method in the two dimensional parameter space, by using the newly discovered characteristic polynomial of the row nondegenerate full transfer function matrix. The system relative stability, in regard to the chosen damping coefficient is achieved. The optimality criterion is the minimal value of the performance index and sum of squared error is taken as an objective function. The output error used in the performance index is influenced by all actions on the system at the same time: by nonzero initial conditions and by nonzero inputs. In the classical approach the output error is influenced only by nonzero inputs. Experimental results confirm that by taking into account nonzero initial conditions, an optimal solution is obtained, which is not the case with the classical method where optimization is performed at zero initial conditions.",
publisher = "Univ Osijek, Tech Fac, Slavonski Brod",
journal = "Tehnički vjesnik",
title = "Discrete-Time System Conditional Optimization in the Parameter Space with Nonzero Initial Conditions",
pages = "207-200",
number = "1",
volume = "29",
doi = "10.17559/TV-20191031145233"
}

Zarić, V., Bučevac, Z. M.,& Jovanović, R.. (2022). Discrete-Time System Conditional Optimization in the Parameter Space with Nonzero Initial Conditions. in Tehnički vjesnik
Univ Osijek, Tech Fac, Slavonski Brod., 29(1), 200-207.
https://doi.org/10.17559/TV-20191031145233

Zarić V, Bučevac ZM, Jovanović R. Discrete-Time System Conditional Optimization in the Parameter Space with Nonzero Initial Conditions. in Tehnički vjesnik. 2022;29(1):200-207.
doi:10.17559/TV-20191031145233 .

Zarić, Vladimir, Bučevac, Zoran M., Jovanović, Radiša, "Discrete-Time System Conditional Optimization in the Parameter Space with Nonzero Initial Conditions" in Tehnički vjesnik, 29, no. 1 (2022):200-207,
https://doi.org/10.17559/TV-20191031145233 . .

TY  - JOUR
AU  - Sretenović, Aleksandra
AU  - Jovanović, Radiša
AU  - Novaković, Vojislav M.
AU  - Nord, Nataša M.
AU  - Živković, Branislav
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3702
AB  - Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.
PB  - Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd
T2  - Thermal Science
T1  - Hybrid artificial intelligence model for prediction of heating energy use
EP  - 716
IS  - 1
SP  - 705
VL  - 26
DO  - 10.2298/TSCI210303152S
ER  - 

@article{
author = "Sretenović, Aleksandra and Jovanović, Radiša and Novaković, Vojislav M. and Nord, Nataša M. and Živković, Branislav",
year = "2022",
abstract = "Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using (Afferent statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple linear regression was selected for the linear modelling, while the non-linear part was predicted using feedforward and radial basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual feedforward and radial basis neural networks and multiple linear regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.",
publisher = "Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd",
journal = "Thermal Science",
title = "Hybrid artificial intelligence model for prediction of heating energy use",
pages = "716-705",
number = "1",
volume = "26",
doi = "10.2298/TSCI210303152S"
}

Sretenović, A., Jovanović, R., Novaković, V. M., Nord, N. M.,& Živković, B.. (2022). Hybrid artificial intelligence model for prediction of heating energy use. in Thermal Science
Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd., 26(1), 705-716.
https://doi.org/10.2298/TSCI210303152S

Sretenović A, Jovanović R, Novaković VM, Nord NM, Živković B. Hybrid artificial intelligence model for prediction of heating energy use. in Thermal Science. 2022;26(1):705-716.
doi:10.2298/TSCI210303152S .

Sretenović, Aleksandra, Jovanović, Radiša, Novaković, Vojislav M., Nord, Nataša M., Živković, Branislav, "Hybrid artificial intelligence model for prediction of heating energy use" in Thermal Science, 26, no. 1 (2022):705-716,
https://doi.org/10.2298/TSCI210303152S . .

TY  - JOUR
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
AU  - Trišović, Nataša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3770
AB  - The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System's model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.
PB  - Sage Publications Ltd, London
T2  - Advances in Mechanical Engineering
T1  - Control of a DC motor using feedback linearization and gray wolf optimization algorithm
IS  - 3
VL  - 14
DO  - 10.1177/16878132221085324
ER  - 

@article{
author = "Vesović, Mitra and Jovanović, Radiša and Trišović, Nataša",
year = "2022",
abstract = "The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System's model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.",
publisher = "Sage Publications Ltd, London",
journal = "Advances in Mechanical Engineering",
title = "Control of a DC motor using feedback linearization and gray wolf optimization algorithm",
number = "3",
volume = "14",
doi = "10.1177/16878132221085324"
}

Vesović, M., Jovanović, R.,& Trišović, N.. (2022). Control of a DC motor using feedback linearization and gray wolf optimization algorithm. in Advances in Mechanical Engineering
Sage Publications Ltd, London., 14(3).
https://doi.org/10.1177/16878132221085324

Vesović M, Jovanović R, Trišović N. Control of a DC motor using feedback linearization and gray wolf optimization algorithm. in Advances in Mechanical Engineering. 2022;14(3).
doi:10.1177/16878132221085324 .

Vesović, Mitra, Jovanović, Radiša, Trišović, Nataša, "Control of a DC motor using feedback linearization and gray wolf optimization algorithm" in Advances in Mechanical Engineering, 14, no. 3 (2022),
https://doi.org/10.1177/16878132221085324 . .

TY  - CONF
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4546
AB  - Several studies dealing with position control of the DC motor have reported 
issues concerning friction force. This article demonstrates a nonlinear control 
and optimization strategy for position control of a series servo motor. Once it is 
empirically verified that the linear model does not adequately reflect the system, 
the model is upgraded from linear to nonlinear. In the course of the research, 
the nonlinear feedback linearizing the controller's behavior is examined. A grey 
wolf metaheuristic optimization algorithm is used to find the coefficients of the 
controller's gains. In this way, modern methods are applied to take a fresh look 
at the existing problem.  Furthermore, performance for various targeted output 
signals is compared to show the approach proposed in the study. Also, a compara-
tive analysis with whale optimization algorithm is performed. The experimental 
results acquired on the stated system are shown, and they validate the usage of the 
nonlinear control, demonstrating the effectiveness of using optimum feedback 
linearization in electrical machines.
PB  - Belgrade :  Singidunum University
C3  - Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research
T1  - GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD
EP  - 43
SP  - THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36
DO  - 10.15308/Sinteza-2022-36-43
ER  - 

@conference{
author = "Vesović, Mitra and Jovanović, Radiša",
year = "2022",
abstract = "Several studies dealing with position control of the DC motor have reported 
issues concerning friction force. This article demonstrates a nonlinear control 
and optimization strategy for position control of a series servo motor. Once it is 
empirically verified that the linear model does not adequately reflect the system, 
the model is upgraded from linear to nonlinear. In the course of the research, 
the nonlinear feedback linearizing the controller's behavior is examined. A grey 
wolf metaheuristic optimization algorithm is used to find the coefficients of the 
controller's gains. In this way, modern methods are applied to take a fresh look 
at the existing problem.  Furthermore, performance for various targeted output 
signals is compared to show the approach proposed in the study. Also, a compara-
tive analysis with whale optimization algorithm is performed. The experimental 
results acquired on the stated system are shown, and they validate the usage of the 
nonlinear control, demonstrating the effectiveness of using optimum feedback 
linearization in electrical machines.",
publisher = "Belgrade :  Singidunum University",
journal = "Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research",
title = "GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD",
pages = "43-THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36",
doi = "10.15308/Sinteza-2022-36-43"
}

Vesović, M.,& Jovanović, R.. (2022). GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD. in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research
Belgrade :  Singidunum University., THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43.
https://doi.org/10.15308/Sinteza-2022-36-43

Vesović M, Jovanović R. GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD. in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research. 2022;:THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43.
doi:10.15308/Sinteza-2022-36-43 .

Vesović, Mitra, Jovanović, Radiša, "GREY WOLF OPTIMIZATION FOR POSITION CONTROL OF A DIRECT CURRENT MOTOR DRIVEN  BY FEEDBACK LINEARIZATION METHOD" in Sinteza 2022-International Scientific Conference on Information Technology and Data Related Research (2022):THEORETICAL COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE SESSION pp. 36-43,
https://doi.org/10.15308/Sinteza-2022-36-43 . .

TY  - CONF
AU  - Jovanović, Radiša
AU  - Bugarić, Uglješa
AU  - Vesović, Mitra
AU  - Perišić, Natalija
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4544
AB  - Рад на пројекту Deep Machine Learning and Swarm Intelligence based Optimization Algorithms for Control and Scheduling of Cyber‐Physical Systems in Industry 4.0 – MISSION4.0 је у оквиру једног од радних пакета подразумевао истраживање у области нелинеарних проблема оптимизације и управљања кретања захватног механизама у присуству ограничења. У циљу постизања задате путање врха захватног механизма, пројектовани су управљачки системи засновани на фази логици у управљању, чији параметри су оптимизовани применом различитих метахеуристичких метода оптимизације. Примена предложених техника управљања приказана је на примеру захватног механизма аутономног мобилног робота. Други део истраживања односио се на управљање брзине и позиције мотора једносмерене струје, као главног покретача захватног механизма, где су примењене различите технике управљања: фази управљање, техника feedback линеаризације, као и њихове оптимизоване верзије различитим метахеуристичким алгоритмима. У овом раду даје се преглед једног дела резултата пројекта MISSION4.0, објављених у различитим међународним и националним часописима и конференцијама, као и преглед резултата приказаних у техничком решењу.
AB  - One of the work packages in the project Deep Machine Learning and Swarm Intelligence based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0 - MISSION4.0 included research in the field of nonlinear optimization problems and control of the motion of the gripping mechanism in the presence of constraints. In order to achieve the proposed path of the gripping mechanism, control systems based on fuzzy logic, were designed and their parameters were optimized using various metaheuristic optimization methods. The application of the proposed control techniques is shown on the example of the gripping mechanism of an autonomous mobile robot. The second part of the research was related to the control of the speed and position of the direct current motor, as the main actuator of the gripping mechanism, where different control techniques were applied: fuzzy control, feedback linearization technique, as well as their optimized versions with different metaheuristic algorithms. This paper provides an overview of a part of the results of the MISSION4.0 project, published in various international and national journals and conferences, as well as an overview of the results presented in the technical report.
PB  - Univerzitet u Beogradu - Mašinski fakultet
C3  - 43. JUPITER KONFERENCIJA sa međunarodnim učešćem ZBORNIK RADOVA/ 43rd JUPITER CONFERENCE with foreign participants PROCEEDINGS / 39. simpozijum: NU - ROBOTI - FTS
T1  - ФАЗИ И НЕЛИНЕАРНО УПРАВЉАЊЕ ЗАХВАТНОГ МЕХАНИЗМА И МОТОРА ЈЕДНОСМЕРНЕ СТРУЈЕ - ПРЕГЛЕД РЕЗУЛТАТА ИСТРАЖИВАЊА У ОКВИРУ ПРОЈЕКТА MISSION4.0
EP  - 3.38
SP  - NU - ROBOTI - FTS pp. 3.26
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4544
ER  - 

@conference{
author = "Jovanović, Radiša and Bugarić, Uglješa and Vesović, Mitra and Perišić, Natalija",
year = "2022",
abstract = "Рад на пројекту Deep Machine Learning and Swarm Intelligence based Optimization Algorithms for Control and Scheduling of Cyber‐Physical Systems in Industry 4.0 – MISSION4.0 је у оквиру једног од радних пакета подразумевао истраживање у области нелинеарних проблема оптимизације и управљања кретања захватног механизама у присуству ограничења. У циљу постизања задате путање врха захватног механизма, пројектовани су управљачки системи засновани на фази логици у управљању, чији параметри су оптимизовани применом различитих метахеуристичких метода оптимизације. Примена предложених техника управљања приказана је на примеру захватног механизма аутономног мобилног робота. Други део истраживања односио се на управљање брзине и позиције мотора једносмерене струје, као главног покретача захватног механизма, где су примењене различите технике управљања: фази управљање, техника feedback линеаризације, као и њихове оптимизоване верзије различитим метахеуристичким алгоритмима. У овом раду даје се преглед једног дела резултата пројекта MISSION4.0, објављених у различитим међународним и националним часописима и конференцијама, као и преглед резултата приказаних у техничком решењу., One of the work packages in the project Deep Machine Learning and Swarm Intelligence based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0 - MISSION4.0 included research in the field of nonlinear optimization problems and control of the motion of the gripping mechanism in the presence of constraints. In order to achieve the proposed path of the gripping mechanism, control systems based on fuzzy logic, were designed and their parameters were optimized using various metaheuristic optimization methods. The application of the proposed control techniques is shown on the example of the gripping mechanism of an autonomous mobile robot. The second part of the research was related to the control of the speed and position of the direct current motor, as the main actuator of the gripping mechanism, where different control techniques were applied: fuzzy control, feedback linearization technique, as well as their optimized versions with different metaheuristic algorithms. This paper provides an overview of a part of the results of the MISSION4.0 project, published in various international and national journals and conferences, as well as an overview of the results presented in the technical report.",
publisher = "Univerzitet u Beogradu - Mašinski fakultet",
journal = "43. JUPITER KONFERENCIJA sa međunarodnim učešćem ZBORNIK RADOVA/ 43rd JUPITER CONFERENCE with foreign participants PROCEEDINGS / 39. simpozijum: NU - ROBOTI - FTS",
title = "ФАЗИ И НЕЛИНЕАРНО УПРАВЉАЊЕ ЗАХВАТНОГ МЕХАНИЗМА И МОТОРА ЈЕДНОСМЕРНЕ СТРУЈЕ - ПРЕГЛЕД РЕЗУЛТАТА ИСТРАЖИВАЊА У ОКВИРУ ПРОЈЕКТА MISSION4.0",
pages = "3.38-NU - ROBOTI - FTS pp. 3.26",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4544"
}

Jovanović, R., Bugarić, U., Vesović, M.,& Perišić, N.. (2022). ФАЗИ И НЕЛИНЕАРНО УПРАВЉАЊЕ ЗАХВАТНОГ МЕХАНИЗМА И МОТОРА ЈЕДНОСМЕРНЕ СТРУЈЕ - ПРЕГЛЕД РЕЗУЛТАТА ИСТРАЖИВАЊА У ОКВИРУ ПРОЈЕКТА MISSION4.0. in 43. JUPITER KONFERENCIJA sa međunarodnim učešćem ZBORNIK RADOVA/ 43rd JUPITER CONFERENCE with foreign participants PROCEEDINGS / 39. simpozijum: NU - ROBOTI - FTS
Univerzitet u Beogradu - Mašinski fakultet., NU - ROBOTI - FTS pp. 3.26-3.38.
https://hdl.handle.net/21.15107/rcub_machinery_4544

Jovanović R, Bugarić U, Vesović M, Perišić N. ФАЗИ И НЕЛИНЕАРНО УПРАВЉАЊЕ ЗАХВАТНОГ МЕХАНИЗМА И МОТОРА ЈЕДНОСМЕРНЕ СТРУЈЕ - ПРЕГЛЕД РЕЗУЛТАТА ИСТРАЖИВАЊА У ОКВИРУ ПРОЈЕКТА MISSION4.0. in 43. JUPITER KONFERENCIJA sa međunarodnim učešćem ZBORNIK RADOVA/ 43rd JUPITER CONFERENCE with foreign participants PROCEEDINGS / 39. simpozijum: NU - ROBOTI - FTS. 2022;:NU - ROBOTI - FTS pp. 3.26-3.38.
https://hdl.handle.net/21.15107/rcub_machinery_4544 .

Jovanović, Radiša, Bugarić, Uglješa, Vesović, Mitra, Perišić, Natalija, "ФАЗИ И НЕЛИНЕАРНО УПРАВЉАЊЕ ЗАХВАТНОГ МЕХАНИЗМА И МОТОРА ЈЕДНОСМЕРНЕ СТРУЈЕ - ПРЕГЛЕД РЕЗУЛТАТА ИСТРАЖИВАЊА У ОКВИРУ ПРОЈЕКТА MISSION4.0" in 43. JUPITER KONFERENCIJA sa međunarodnim učešćem ZBORNIK RADOVA/ 43rd JUPITER CONFERENCE with foreign participants PROCEEDINGS / 39. simpozijum: NU - ROBOTI - FTS (2022):NU - ROBOTI - FTS pp. 3.26-3.38,
https://hdl.handle.net/21.15107/rcub_machinery_4544 .

TY  - CONF
AU  - Jovanović, Radiša
AU  - Vesović, Mitra
AU  - Perišić, Natalija
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4501
AB  - In this paper a novel metaheuristic method, artificial gorilla troops optimizer, is used in order to optimize classical proportional-integral controller for liquid level system, that has wide application in many industries. In optimization process nonlinear model of the system is used. Obtained results are provided. It is shown that optimized controller represents superior solution compared to classical controller.
PB  - University of Belgrade Faculty of Mechanical Engineering
C3  - 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
T1  - PI controller optimization by artificial gorilla troops for liquid level control
EP  - 93
SP  - 90
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4501
ER  - 

@conference{
author = "Jovanović, Radiša and Vesović, Mitra and Perišić, Natalija",
year = "2022",
abstract = "In this paper a novel metaheuristic method, artificial gorilla troops optimizer, is used in order to optimize classical proportional-integral controller for liquid level system, that has wide application in many industries. In optimization process nonlinear model of the system is used. Obtained results are provided. It is shown that optimized controller represents superior solution compared to classical controller.",
publisher = "University of Belgrade Faculty of Mechanical Engineering",
journal = "8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia",
title = "PI controller optimization by artificial gorilla troops for liquid level control",
pages = "93-90",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4501"
}

Jovanović, R., Vesović, M.,& Perišić, N.. (2022). PI controller optimization by artificial gorilla troops for liquid level control. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
University of Belgrade Faculty of Mechanical Engineering., 90-93.
https://hdl.handle.net/21.15107/rcub_machinery_4501

Jovanović R, Vesović M, Perišić N. PI controller optimization by artificial gorilla troops for liquid level control. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia. 2022;:90-93.
https://hdl.handle.net/21.15107/rcub_machinery_4501 .

Jovanović, Radiša, Vesović, Mitra, Perišić, Natalija, "PI controller optimization by artificial gorilla troops for liquid level control" in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia (2022):90-93,
https://hdl.handle.net/21.15107/rcub_machinery_4501 .

TY  - JOUR
AU  - Jovanović, Radiša
AU  - Bugarić, Uglješa
AU  - Vesović, Mitra 
AU  - Perišić, Natalija
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4516
AB  - This paper presents the proportional–derivative fuzzy controller for
trajectory tracking of the gripping mechanism with two degrees of
freedom. Aiming to achieve movement of the gripping mechanism without
sudden starting and stopping, a polynomial velocity profile is utilized. The
African vultures optimization, as one of the latest metaheuristic
algorithms, is used to obtain the optimal input/output scaling gains of the
proposed fuzzy controller according to the selected fitness function. The
results obtained by this algorithm are compared with the other three new
and popular metaheuristic algorithms: the whale optimization, the ant lion
optimization and the sine cosine algorithm. Moreover, a simulation study
was done for the defined initial position and for the scenario where there is
a certain deviation because the gripping mechanism is not at its original
initial position. Finally, the robustness of the controller is tested for the
case when the masses of the segments increase three times. The results
revealed that the suggested controller was capable of dealing with
nonlinearities of the gripping mechanism, initial position and parameter
changes. The movement of the gripping mechanism is smooth and follows
the defined trajectory.
PB  - University of Belgrade - Faculty of Mechanical Engineering
T2  - FME Transactions
T1  - Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism
EP  - 501
IS  - 3
SP  - 491
VL  - 50
DO  - 10.5937/fme2203491J
ER  - 

@article{
author = "Jovanović, Radiša and Bugarić, Uglješa and Vesović, Mitra  and Perišić, Natalija",
year = "2022",
abstract = "This paper presents the proportional–derivative fuzzy controller for
trajectory tracking of the gripping mechanism with two degrees of
freedom. Aiming to achieve movement of the gripping mechanism without
sudden starting and stopping, a polynomial velocity profile is utilized. The
African vultures optimization, as one of the latest metaheuristic
algorithms, is used to obtain the optimal input/output scaling gains of the
proposed fuzzy controller according to the selected fitness function. The
results obtained by this algorithm are compared with the other three new
and popular metaheuristic algorithms: the whale optimization, the ant lion
optimization and the sine cosine algorithm. Moreover, a simulation study
was done for the defined initial position and for the scenario where there is
a certain deviation because the gripping mechanism is not at its original
initial position. Finally, the robustness of the controller is tested for the
case when the masses of the segments increase three times. The results
revealed that the suggested controller was capable of dealing with
nonlinearities of the gripping mechanism, initial position and parameter
changes. The movement of the gripping mechanism is smooth and follows
the defined trajectory.",
publisher = "University of Belgrade - Faculty of Mechanical Engineering",
journal = "FME Transactions",
title = "Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism",
pages = "501-491",
number = "3",
volume = "50",
doi = "10.5937/fme2203491J"
}

Jovanović, R., Bugarić, U., Vesović, M.,& Perišić, N.. (2022). Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism. in FME Transactions
University of Belgrade - Faculty of Mechanical Engineering., 50(3), 491-501.
https://doi.org/10.5937/fme2203491J

Jovanović R, Bugarić U, Vesović M, Perišić N. Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism. in FME Transactions. 2022;50(3):491-501.
doi:10.5937/fme2203491J .

Jovanović, Radiša, Bugarić, Uglješa, Vesović, Mitra , Perišić, Natalija, "Fuzzy Controller Optimized by the African Vultures Algorithm for Trajectory Tracking of a Two–Link Gripping Mechanism" in FME Transactions, 50, no. 3 (2022):491-501,
https://doi.org/10.5937/fme2203491J . .

TY  - CONF
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4498
AB  - In this paper artificial neural network is proposed as a method to classify defective and non-defective casting products in order to improve quality inspection process. Three different models of convolutional neural networks are trained and tested on dataset of submersible pump impeller images which has uneven number of image samples in each class. In order to inspect if slightly imbalanced classes have impact on result, two experiments are done. All of the models are ImageNet pre-trained networks, InceptionV3, Xception and MobileNetV2, where transfer learning method is applied with fine-tuning. Stochastic gradient descent algorithm is implemented for optimization. Obtained results of all models are presented and comparison is made.
PB  - Belgrade: University of Belgrade Faculty of Mechanical Engineering
C3  - 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
T1  - Application of deep learning in quality inspection of casting products
EP  - 151
SP  - 148
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4498
ER  - 

@conference{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2022",
abstract = "In this paper artificial neural network is proposed as a method to classify defective and non-defective casting products in order to improve quality inspection process. Three different models of convolutional neural networks are trained and tested on dataset of submersible pump impeller images which has uneven number of image samples in each class. In order to inspect if slightly imbalanced classes have impact on result, two experiments are done. All of the models are ImageNet pre-trained networks, InceptionV3, Xception and MobileNetV2, where transfer learning method is applied with fine-tuning. Stochastic gradient descent algorithm is implemented for optimization. Obtained results of all models are presented and comparison is made.",
publisher = "Belgrade: University of Belgrade Faculty of Mechanical Engineering",
journal = "8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia",
title = "Application of deep learning in quality inspection of casting products",
pages = "151-148",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4498"
}

Perišić, N.,& Jovanović, R.. (2022). Application of deep learning in quality inspection of casting products. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia
Belgrade: University of Belgrade Faculty of Mechanical Engineering., 148-151.
https://hdl.handle.net/21.15107/rcub_machinery_4498

Perišić N, Jovanović R. Application of deep learning in quality inspection of casting products. in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia. 2022;:148-151.
https://hdl.handle.net/21.15107/rcub_machinery_4498 .

Perišić, Natalija, Jovanović, Radiša, "Application of deep learning in quality inspection of casting products" in 8th International Conference of Industrial Engineering SIE 2022 : Proceedings, 29th-30th September, 2022, Belgrade, Serbia (2022):148-151,
https://hdl.handle.net/21.15107/rcub_machinery_4498 .

TY  - CONF
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4494
AB  - This paper deals with the problem of classifying images of real and fake faces as it
is impossible to distinguish them with the bare eye. Two different convolutional
neural networks architecture models are applied. The first one is pre-trained
VGG16 model, where transfer learning method is applied on our dataset. The
architecture of the second model is based on VGG16 and represents its smaller
and lighter version. Techniques such as learning rate decay, dropout and batch
normalization was applied in training process. Comparison of obtained results
of both models is made.
PB  - Belgrade: Singidunum University
C3  - Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research
T1  - Convolutional Neural Networks for Real and Fake Face Classification
EP  - 35
SP  - 29
DO  - 10.15308/Sinteza-2022-29-35
ER  - 

@conference{
author = "Perišić, Natalija and Jovanović, Radiša",
year = "2022",
abstract = "This paper deals with the problem of classifying images of real and fake faces as it
is impossible to distinguish them with the bare eye. Two different convolutional
neural networks architecture models are applied. The first one is pre-trained
VGG16 model, where transfer learning method is applied on our dataset. The
architecture of the second model is based on VGG16 and represents its smaller
and lighter version. Techniques such as learning rate decay, dropout and batch
normalization was applied in training process. Comparison of obtained results
of both models is made.",
publisher = "Belgrade: Singidunum University",
journal = "Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research",
title = "Convolutional Neural Networks for Real and Fake Face Classification",
pages = "35-29",
doi = "10.15308/Sinteza-2022-29-35"
}

Perišić, N.,& Jovanović, R.. (2022). Convolutional Neural Networks for Real and Fake Face Classification. in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research
Belgrade: Singidunum University., 29-35.
https://doi.org/10.15308/Sinteza-2022-29-35

Perišić N, Jovanović R. Convolutional Neural Networks for Real and Fake Face Classification. in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research. 2022;:29-35.
doi:10.15308/Sinteza-2022-29-35 .

Perišić, Natalija, Jovanović, Radiša, "Convolutional Neural Networks for Real and Fake Face Classification" in Sinteza 2022 - International Scientific Conference on Information Technology and Data Related Research (2022):29-35,
https://doi.org/10.15308/Sinteza-2022-29-35 . .

TY  - JOUR
AU  - Vesović, Mitra
AU  - Jovanović, Radiša
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4547
AB  - Adaptivni neuro fazi sistemi zaključivanja (eng. Adaptive Neural Fuzzy Inference Systems) ANFIS imaju sve veću tendenciju upotrebe u naučnim istraživanjima i praktičnim primenama. Digitalizacija proizvodnje i pojava Industrije 4.0 omogućila je razvoj ovog trenda, pre svega, zbog sposobnosti prilagođavanja zadatku integrisanjem veštačkih neuronskih mreža i fazi logike, čime se potencijalno mogu iskoristiti prednosti obe tehnike u jedinstvenim okvirima. Ovaj pristup olakšao je procese modelovanja, analize podataka, klasifikacije i upravljanja. Pogodnost ANFIS sistema, u odnosu na konvencionalne metode, se ogleda u mogućnosti predviđanja izlaza na osnovu skupa ulaza i baze pravila. Takođe, ovi sistemi su pogodni za korišćenje u upravljanju, jer pružaju mogućnost za podešavanje parametara upravljačkog sistema. U ovom radu je predstavljena struktura ANFIS sistema i dat je detaljan prikaz dosadašnjih dostignuća, kroz komparativnu analizu, pri čemu su istaknute neke moguće sfere interdisciplinarne primene. Razmatrane su mogućnosti za varijacije, poboljšanja i inovacije algoritma, kao i smanjenja složenosti same arhitekture mreže. Prikazani su predlozi za neke nove, još neiskorišćene kombinacije sa metaheurističkim metodama optimizacije. Konačno, date su bitne smernice o tome kada i gde je korisno primeniti ANFIS sisteme.
PB  - Beograd : Savez inženjera i tehničara Srbije
T2  - Tehnika
T1  - Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja
EP  - 446
IS  - 4
SP  - 439
VL  - 77
DO  - 10.5937/tehnika2204439V
ER  - 

@article{
author = "Vesović, Mitra and Jovanović, Radiša",
year = "2022",
abstract = "Adaptivni neuro fazi sistemi zaključivanja (eng. Adaptive Neural Fuzzy Inference Systems) ANFIS imaju sve veću tendenciju upotrebe u naučnim istraživanjima i praktičnim primenama. Digitalizacija proizvodnje i pojava Industrije 4.0 omogućila je razvoj ovog trenda, pre svega, zbog sposobnosti prilagođavanja zadatku integrisanjem veštačkih neuronskih mreža i fazi logike, čime se potencijalno mogu iskoristiti prednosti obe tehnike u jedinstvenim okvirima. Ovaj pristup olakšao je procese modelovanja, analize podataka, klasifikacije i upravljanja. Pogodnost ANFIS sistema, u odnosu na konvencionalne metode, se ogleda u mogućnosti predviđanja izlaza na osnovu skupa ulaza i baze pravila. Takođe, ovi sistemi su pogodni za korišćenje u upravljanju, jer pružaju mogućnost za podešavanje parametara upravljačkog sistema. U ovom radu je predstavljena struktura ANFIS sistema i dat je detaljan prikaz dosadašnjih dostignuća, kroz komparativnu analizu, pri čemu su istaknute neke moguće sfere interdisciplinarne primene. Razmatrane su mogućnosti za varijacije, poboljšanja i inovacije algoritma, kao i smanjenja složenosti same arhitekture mreže. Prikazani su predlozi za neke nove, još neiskorišćene kombinacije sa metaheurističkim metodama optimizacije. Konačno, date su bitne smernice o tome kada i gde je korisno primeniti ANFIS sisteme.",
publisher = "Beograd : Savez inženjera i tehničara Srbije",
journal = "Tehnika",
title = "Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja",
pages = "446-439",
number = "4",
volume = "77",
doi = "10.5937/tehnika2204439V"
}

Vesović, M.,& Jovanović, R.. (2022). Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja. in Tehnika
Beograd : Savez inženjera i tehničara Srbije., 77(4), 439-446.
https://doi.org/10.5937/tehnika2204439V

Vesović M, Jovanović R. Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja. in Tehnika. 2022;77(4):439-446.
doi:10.5937/tehnika2204439V .

Vesović, Mitra, Jovanović, Radiša, "Adaptivni neuro fazi sistemi u identifikaciji, modelovanju i upravljanju - pregled stanja u oblasti istraživanja" in Tehnika, 77, no. 4 (2022):439-446,
https://doi.org/10.5937/tehnika2204439V . .

TY  - CONF
AU  - Zarić, Vladimir
AU  - Perišić, Natalija
AU  - Jovanović, Radiša
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/4510
AB  - This paper deals with liquid level control as one of the frequent problems in industry. Several classical methods for tuning a PID like controller were applied. Furthermore, parameters for the controller were optimized using grey wolf optimizer. In addition to the classical controller, fuzzy PID like controller has also been designed and optimized using the same optimization algorithm. Experimental results obtained on the tank system are provided.
PB  - Kraljevo: Faculty of Mechanical and Civil Engineering
C3  - The Tennth International Triennial Conference Heavy Machinery HM 2021: Proceedings, Vrnjačka Banja, June 23-25
T1  - Control of a Liquid Level System Based on Classical and Fuzzy PID Like Controller Using the Grey Wolf Optimization Algorithm
EP  - C30
SP  - C23
UR  - https://hdl.handle.net/21.15107/rcub_machinery_4510
ER  - 

@conference{
author = "Zarić, Vladimir and Perišić, Natalija and Jovanović, Radiša",
year = "2021",
abstract = "This paper deals with liquid level control as one of the frequent problems in industry. Several classical methods for tuning a PID like controller were applied. Furthermore, parameters for the controller were optimized using grey wolf optimizer. In addition to the classical controller, fuzzy PID like controller has also been designed and optimized using the same optimization algorithm. Experimental results obtained on the tank system are provided.",
publisher = "Kraljevo: Faculty of Mechanical and Civil Engineering",
journal = "The Tennth International Triennial Conference Heavy Machinery HM 2021: Proceedings, Vrnjačka Banja, June 23-25",
title = "Control of a Liquid Level System Based on Classical and Fuzzy PID Like Controller Using the Grey Wolf Optimization Algorithm",
pages = "C30-C23",
url = "https://hdl.handle.net/21.15107/rcub_machinery_4510"
}

Zarić, V., Perišić, N.,& Jovanović, R.. (2021). Control of a Liquid Level System Based on Classical and Fuzzy PID Like Controller Using the Grey Wolf Optimization Algorithm. in The Tennth International Triennial Conference Heavy Machinery HM 2021: Proceedings, Vrnjačka Banja, June 23-25
Kraljevo: Faculty of Mechanical and Civil Engineering., C23-C30.
https://hdl.handle.net/21.15107/rcub_machinery_4510

Zarić V, Perišić N, Jovanović R. Control of a Liquid Level System Based on Classical and Fuzzy PID Like Controller Using the Grey Wolf Optimization Algorithm. in The Tennth International Triennial Conference Heavy Machinery HM 2021: Proceedings, Vrnjačka Banja, June 23-25. 2021;:C23-C30.
https://hdl.handle.net/21.15107/rcub_machinery_4510 .

Zarić, Vladimir, Perišić, Natalija, Jovanović, Radiša, "Control of a Liquid Level System Based on Classical and Fuzzy PID Like Controller Using the Grey Wolf Optimization Algorithm" in The Tennth International Triennial Conference Heavy Machinery HM 2021: Proceedings, Vrnjačka Banja, June 23-25 (2021):C23-C30,
https://hdl.handle.net/21.15107/rcub_machinery_4510 .

TY  - JOUR
AU  - Gruyitch, Lyubomir
AU  - Bučevac, Zoran M.
AU  - Jovanović, Radiša
AU  - Zarić, Vladimir
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3516
AB  - Dynamic systems operate under the simultaneous influence of both the initial conditions and the input vector. There is neither physical nor mathematical justification for ignoring the initial conditions, e.g., in the control optimisation. This paper gives a response to the following question: Is a set of controller parameters which is optimal for the operation of a control system under zero initial conditions also optimal for its operation under non-zero initial conditions? The paper presents a new approach to the design of a classical proportional-differencesum (PDS) controller for a plant in a closed loop control system. The system relative stability with respect to a desired damping coefficient is accomplished. The minimal value of the performance index in the form of the sum of squared errors is the optimality criterion. Unlike the classical approach, the output error used in the performance index is influenced by all actions performed on the system at the same time.
PB  - Univ Zagreb Fac Mechanical Engineering & Naval Architecture, Zagreb
T2  - Transactions of Famena
T1  - Discrete-time system conditional optimisation in the parameter space via the full transfer function matrix
EP  - 62
IS  - 3
SP  - 45
VL  - 45
DO  - 10.21278/TOF.453014220
ER  - 

@article{
author = "Gruyitch, Lyubomir and Bučevac, Zoran M. and Jovanović, Radiša and Zarić, Vladimir",
year = "2021",
abstract = "Dynamic systems operate under the simultaneous influence of both the initial conditions and the input vector. There is neither physical nor mathematical justification for ignoring the initial conditions, e.g., in the control optimisation. This paper gives a response to the following question: Is a set of controller parameters which is optimal for the operation of a control system under zero initial conditions also optimal for its operation under non-zero initial conditions? The paper presents a new approach to the design of a classical proportional-differencesum (PDS) controller for a plant in a closed loop control system. The system relative stability with respect to a desired damping coefficient is accomplished. The minimal value of the performance index in the form of the sum of squared errors is the optimality criterion. Unlike the classical approach, the output error used in the performance index is influenced by all actions performed on the system at the same time.",
publisher = "Univ Zagreb Fac Mechanical Engineering & Naval Architecture, Zagreb",
journal = "Transactions of Famena",
title = "Discrete-time system conditional optimisation in the parameter space via the full transfer function matrix",
pages = "62-45",
number = "3",
volume = "45",
doi = "10.21278/TOF.453014220"
}

Gruyitch, L., Bučevac, Z. M., Jovanović, R.,& Zarić, V.. (2021). Discrete-time system conditional optimisation in the parameter space via the full transfer function matrix. in Transactions of Famena
Univ Zagreb Fac Mechanical Engineering & Naval Architecture, Zagreb., 45(3), 45-62.
https://doi.org/10.21278/TOF.453014220

Gruyitch L, Bučevac ZM, Jovanović R, Zarić V. Discrete-time system conditional optimisation in the parameter space via the full transfer function matrix. in Transactions of Famena. 2021;45(3):45-62.
doi:10.21278/TOF.453014220 .

Gruyitch, Lyubomir, Bučevac, Zoran M., Jovanović, Radiša, Zarić, Vladimir, "Discrete-time system conditional optimisation in the parameter space via the full transfer function matrix" in Transactions of Famena, 45, no. 3 (2021):45-62,
https://doi.org/10.21278/TOF.453014220 . .

TY  - GEN
AU  - Jovanović, Radiša
AU  - Bugarić, Uglješa
AU  - Vesović, Mitra
AU  - Laban, Lara
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5648
AB  - Техничко решење се односи на решавање проблема оптималног управљања у смислу одређивања оптималне трајекторије, односно профила брзине кретања механизама транспортних машина, као и синтезу управљања за праћење задате оптималне трајекторије. Проблем оптималног кретања механизма захватног уређаја (његовог врха) спада у категорију нелинеарних проблема оптимизације у присуству ограничења. У циљу његовог решававања, неопходно је било урадити следеће: 1) математички моделовати динамичко понашање механизма захватног уређаја мобилног робота; 2) одредити оптималну трајекторију, односно профил брзине кретања механизма захватног уређаја; 3) пројектовати управљачки систем који омогућава праћење задате, оптималне трајекторије; 4) оптимизовати параметре управљачког система у циљу остваривања што је могуће мање грешке праћења. Применом методе принципа максимума одређен је оптимални профил брзине, односно оптимална трајекторија кретања, у смислу минимизације времена кретања механизама транспортних машина (захватних уређаја, целих машина или њених делова). Потом је имплементирано интелигентно, фази управљање кретања механизма у циљу остваривања дефинисане оптималне трајекторије, при чему је за оптимизацију параметара фази алгоритма управљања коришћена метахеуристичка метода оптимизације заснована на интелигенцији јата китова, као једна од најсавременијих. Експериментална верификација предложене методологије у овом техничком решењу приказана је на примеру аутономног мобилног робота, а добијени резултати су показали да је рад оптимизованог фази управљачког система веома задовољавајући, тако да се ова метода може успешно користити за задатке управљања у различитим техничким областима.
T2  - Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер
T1  - Оптимално управљање кретања захватног уређаја
T1  - Оптимално управљање кретања захватног уређаја
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5648
ER  - 

@misc{
author = "Jovanović, Radiša and Bugarić, Uglješa and Vesović, Mitra and Laban, Lara",
year = "2021",
abstract = "Техничко решење се односи на решавање проблема оптималног управљања у смислу одређивања оптималне трајекторије, односно профила брзине кретања механизама транспортних машина, као и синтезу управљања за праћење задате оптималне трајекторије. Проблем оптималног кретања механизма захватног уређаја (његовог врха) спада у категорију нелинеарних проблема оптимизације у присуству ограничења. У циљу његовог решававања, неопходно је било урадити следеће: 1) математички моделовати динамичко понашање механизма захватног уређаја мобилног робота; 2) одредити оптималну трајекторију, односно профил брзине кретања механизма захватног уређаја; 3) пројектовати управљачки систем који омогућава праћење задате, оптималне трајекторије; 4) оптимизовати параметре управљачког система у циљу остваривања што је могуће мање грешке праћења. Применом методе принципа максимума одређен је оптимални профил брзине, односно оптимална трајекторија кретања, у смислу минимизације времена кретања механизама транспортних машина (захватних уређаја, целих машина или њених делова). Потом је имплементирано интелигентно, фази управљање кретања механизма у циљу остваривања дефинисане оптималне трајекторије, при чему је за оптимизацију параметара фази алгоритма управљања коришћена метахеуристичка метода оптимизације заснована на интелигенцији јата китова, као једна од најсавременијих. Експериментална верификација предложене методологије у овом техничком решењу приказана је на примеру аутономног мобилног робота, а добијени резултати су показали да је рад оптимизованог фази управљачког система веома задовољавајући, тако да се ова метода може успешно користити за задатке управљања у различитим техничким областима.",
journal = "Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер",
title = "Оптимално управљање кретања захватног уређаја, Оптимално управљање кретања захватног уређаја",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5648"
}

Jovanović, R., Bugarić, U., Vesović, M.,& Laban, L.. (2021). Оптимално управљање кретања захватног уређаја. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер.
https://hdl.handle.net/21.15107/rcub_machinery_5648

Jovanović R, Bugarić U, Vesović M, Laban L. Оптимално управљање кретања захватног уређаја. in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер. 2021;.
https://hdl.handle.net/21.15107/rcub_machinery_5648 .

Jovanović, Radiša, Bugarić, Uglješa, Vesović, Mitra, Laban, Lara, "Оптимално управљање кретања захватног уређаја" in Техничко решење (M85) је прихваћено од стране Матичног научног одбора за машинство и индустријски софтвер (2021),
https://hdl.handle.net/21.15107/rcub_machinery_5648 .

TY  - BOOK
AU  - Jovanović, Radiša
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/5569
AB  - Данас, Matlab представља један од готово незаобилазних алата у научној и инжењерској пракси. Књига Matlab и Simulink у аутоматском управљању је конципирана тако да обухвата области предвиђене наставним планом и програмом предмета Програмирање у аутоматском управљању, који се слуша на Основним академским студијама на Машинском факултету у Београду. Међутим, узимајући у обзир актуелност и значај упознавања са неким од софтверских алата за различите врсте инжњерских прорачуна и анализа (а Matlab је један од најзаступљенијих), књига је написана тако да могу да је користе и студенти са других факултета у чијим наставним плановима и програмима је заступљена ова проблематика. У том смислу, програмски језик Matlab и његов пакет Simulink представљени су у прва три дела књиге. Четврти и пети део књиге се односе на њихову примену у области система аутоматског управљања, и добрим делом представљају подршку настави из предмета Основе аутоматског управљања који је обавезан предмет на основним академским студијама на Машинском факултету у Београду, као и већини предмета на мастер академским студијама модула за аутоматско управљање на Машинском факултету у Београду. Изложени материјал је илустрован бројним примерима. Тамо где је неопходно, дати су и резултати извршавања одређених наредби, скриптова, а одређени број примера, посебно у петом делу, урађен је и аналитичким путем.
PB  - Mašinski fakultet Univerziteta u Beogradu
T1  - Matlab и Simulink у аутоматском управљању
T1  - Matlab and Simulink in automatic control
EP  - 319
IS  - 2
SP  - 1
UR  - https://hdl.handle.net/21.15107/rcub_machinery_5569
ER  - 

@book{
author = "Jovanović, Radiša",
year = "2021",
abstract = "Данас, Matlab представља један од готово незаобилазних алата у научној и инжењерској пракси. Књига Matlab и Simulink у аутоматском управљању је конципирана тако да обухвата области предвиђене наставним планом и програмом предмета Програмирање у аутоматском управљању, који се слуша на Основним академским студијама на Машинском факултету у Београду. Међутим, узимајући у обзир актуелност и значај упознавања са неким од софтверских алата за различите врсте инжњерских прорачуна и анализа (а Matlab је један од најзаступљенијих), књига је написана тако да могу да је користе и студенти са других факултета у чијим наставним плановима и програмима је заступљена ова проблематика. У том смислу, програмски језик Matlab и његов пакет Simulink представљени су у прва три дела књиге. Четврти и пети део књиге се односе на њихову примену у области система аутоматског управљања, и добрим делом представљају подршку настави из предмета Основе аутоматског управљања који је обавезан предмет на основним академским студијама на Машинском факултету у Београду, као и већини предмета на мастер академским студијама модула за аутоматско управљање на Машинском факултету у Београду. Изложени материјал је илустрован бројним примерима. Тамо где је неопходно, дати су и резултати извршавања одређених наредби, скриптова, а одређени број примера, посебно у петом делу, урађен је и аналитичким путем.",
publisher = "Mašinski fakultet Univerziteta u Beogradu",
title = "Matlab и Simulink у аутоматском управљању, Matlab and Simulink in automatic control",
pages = "319-1",
number = "2",
url = "https://hdl.handle.net/21.15107/rcub_machinery_5569"
}

Jovanović, R.. (2021). Matlab и Simulink у аутоматском управљању. 
Mašinski fakultet Univerziteta u Beogradu.(2), 1-319.
https://hdl.handle.net/21.15107/rcub_machinery_5569

Jovanović R. Matlab и Simulink у аутоматском управљању. 2021;(2):1-319.
https://hdl.handle.net/21.15107/rcub_machinery_5569 .

Jovanović, Radiša, "Matlab и Simulink у аутоматском управљању", no. 2 (2021):1-319,
https://hdl.handle.net/21.15107/rcub_machinery_5569 .

TY  - GEN
AU  - Петковић, Александар
AU  - Божић, Иван
AU  - Гајић, Александар
AU  - Јовановић, Радиша
AU  - Илић, Јован
PY  - 2021
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/6380
AB  - Предметним техничким решењем је решаван проблем оптималног унутарстаничног диспечинга (ОУД). Развијен је метод за унутарстаничну оптимизацију сваке од асиметричних хидроелектрана (ХЕ) из система каскадних Власинских хидроелектрана .
ОУД једне хидроелектране решава проблем да се при задатој коти горње воде и стању расположивости агрегата за погон, одређује такав састав ангажованих агрегата и расподела оптерећења на њих, тако да се на ХЕ радни процес остварује најрационалније, а уз задовољење свих припадајућих ограничења.
Посебне специфичности при тражењу оптималног решења су: вредности параметара у оптимизационом проблему, оптимизациони критеријум (функција циља), функционална ограничењима и скуп расположивих агрегата.
AB  - Развијен је метод за решавање проблема ОУД за универзалну диспозицију доводно-одводних органа сваке од четири хидроелектране у систему Власинских ХЕ, које имају асиметричне унутарстаничне енергетске карактеристике.  Резултати су применљиви за разне услове расположивости агрегата за погон, коте воде у акумулацији, вредности ротирајуће резерве и маневарности ХЕ у целини. 
Компаративне анализе стварно реализованих режима и ОУД-режима квантификовале су уштеду погонске воде која преостаје за допунску производњу енергије на свакој од четири ХЕ. Спроведене анализе указују да се могућности уштеде воде за реализовање додатне производње налазе у опсегу 1÷2,5% годишње производње, а збирно за целокупни систем Власинских ХЕ око 2% годишње производње.
T2  - Министарство просвете, науке и технолошког развоја, Матични научни одбор за енергетику, рударство и енергетску ефикасност
T1  - Унутарстанична оптимизација радних режима хидроелектрана
IS  - ТР0100-033/2021
UR  - https://hdl.handle.net/21.15107/rcub_machinery_6380
ER  - 

@misc{
author = "Петковић, Александар and Божић, Иван and Гајић, Александар and Јовановић, Радиша and Илић, Јован",
year = "2021",
abstract = "Предметним техничким решењем је решаван проблем оптималног унутарстаничног диспечинга (ОУД). Развијен је метод за унутарстаничну оптимизацију сваке од асиметричних хидроелектрана (ХЕ) из система каскадних Власинских хидроелектрана .
ОУД једне хидроелектране решава проблем да се при задатој коти горње воде и стању расположивости агрегата за погон, одређује такав састав ангажованих агрегата и расподела оптерећења на њих, тако да се на ХЕ радни процес остварује најрационалније, а уз задовољење свих припадајућих ограничења.
Посебне специфичности при тражењу оптималног решења су: вредности параметара у оптимизационом проблему, оптимизациони критеријум (функција циља), функционална ограничењима и скуп расположивих агрегата., Развијен је метод за решавање проблема ОУД за универзалну диспозицију доводно-одводних органа сваке од четири хидроелектране у систему Власинских ХЕ, које имају асиметричне унутарстаничне енергетске карактеристике.  Резултати су применљиви за разне услове расположивости агрегата за погон, коте воде у акумулацији, вредности ротирајуће резерве и маневарности ХЕ у целини. 
Компаративне анализе стварно реализованих режима и ОУД-режима квантификовале су уштеду погонске воде која преостаје за допунску производњу енергије на свакој од четири ХЕ. Спроведене анализе указују да се могућности уштеде воде за реализовање додатне производње налазе у опсегу 1÷2,5% годишње производње, а збирно за целокупни систем Власинских ХЕ око 2% годишње производње.",
journal = "Министарство просвете, науке и технолошког развоја, Матични научни одбор за енергетику, рударство и енергетску ефикасност",
title = "Унутарстанична оптимизација радних режима хидроелектрана",
number = "ТР0100-033/2021",
url = "https://hdl.handle.net/21.15107/rcub_machinery_6380"
}

Петковић, А., Божић, И., Гајић, А., Јовановић, Р.,& Илић, Ј.. (2021). Унутарстанична оптимизација радних режима хидроелектрана. in Министарство просвете, науке и технолошког развоја, Матични научни одбор за енергетику, рударство и енергетску ефикасност(ТР0100-033/2021).
https://hdl.handle.net/21.15107/rcub_machinery_6380

Петковић А, Божић И, Гајић А, Јовановић Р, Илић Ј. Унутарстанична оптимизација радних режима хидроелектрана. in Министарство просвете, науке и технолошког развоја, Матични научни одбор за енергетику, рударство и енергетску ефикасност. 2021;(ТР0100-033/2021).
https://hdl.handle.net/21.15107/rcub_machinery_6380 .

Петковић, Александар, Божић, Иван, Гајић, Александар, Јовановић, Радиша, Илић, Јован, "Унутарстанична оптимизација радних режима хидроелектрана" in Министарство просвете, науке и технолошког развоја, Матични научни одбор за енергетику, рударство и енергетску ефикасност, no. ТР0100-033/2021 (2021),
https://hdl.handle.net/21.15107/rcub_machinery_6380 .