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  - 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  - 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  - 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 .