Aquaculture Cloud Management System
Vol. 4 No. 2 (2014): JITA – APEIRON Yu-Min Yang, Chao-Tsong Fang-Tsou Aquaculture Cloud Management System Original scientific paper DOI: https://doi.org/10.7251/JIT1402075Y Download Article PDF Abstract This study proposes an aquaculture system combining wireless sensors with the Internet of Things and expert system concepts. Built on the accumulated expertise and experience of professionals and researchers, the knowledge base advises aqua-farms on relevant farming practices. We hope that this system will conserve resources and secure product quality. The system also provides production data to consumers, thus facilitating information transparency and allowing consumers to purchase products with full knowledge and guarantee of food safety. Keywords: Expert system, Internet of Things, Cloud services. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Hybrid Methodology of Nonlinear Goal Programming
Vol. 4 No. 2 (2014): JITA – APEIRON Lazo Roljić Hybrid Methodology of Nonlinear Goal Programming Original scientific paper DOI: https://doi.org/10.7251/JIT1402068R Download Article PDF Abstract What we demonstrate here is a nonlinear goal-programming (NGP) algorithm based on hybrid connection of the modified simplex method of goal programming, gradient method of feasible directions and method of optimal displacement size finding-called HNGPM. Iterative methodology is given in five steps: (1) linearization the set of nonlinear constraints at particular point, (2) solving the problem of normalized linear goal programming, (3) feasible direction computation, (4) calculating optimal step length displacement, and (5) testing out convergence problem. Our idea was to apply Euler’s theorem for the “total” linearization of the nonlinear constraints (in the space) around particular point. According to Euler’s theorem, it is possible to apply this methodology to solve the problems of NGP whether the nonlinear constraint functions are linearly or positively homogeneous. Keywords: Non-linear goal programming, Cobb- Douglas’s production function, Euler’s homogeneous function theorem, feasible directions method. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
E-textbook Development Capacities Within the Current Context in the Republic of Serbia
Vol. 4 No. 2 (2014): JITA – APEIRON Željko Stanković, Ljiljana Tešmanović E-textbook Development Capacities Within the Current Context in the Republic of Serbia Original scientific paper DOI: https://doi.org/10.7251/JIT1402062S Download Article PDF Abstract The study is a short sublimation of the e-book and e-textbook development. In digital age and with the adoption of new technologies, new educational digital platform has become an integral part of our everyday life and education which requires adjustments and changes in the educational system structure. In order to make the students be equal and functional members of the society and to prepare them for contemporary digital era, it is the entire society’s most important responsibility to enable educational system to provide, in most optimal and proficient way, equal opportunities for each and every student to gain knowledge. Expensive process of a book digitalization will, in time, become economically acceptable for all in the broader community. Keywords: traditional book / textbook,e-book, digital textbook. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Using 3D Models for Improving Face Recognition
Vol. 4 No. 2 (2014): JITA – APEIRON Zoran Bikicki, Ivan Milenković, Dušan Starčević Using 3D Models for Improving Face Recognition Original scientific paper DOI: https://doi.org/10.7251/JIT1402055B Download Article PDF Abstract Face recognition algorithm Principal Component Analysis (PCA) has a significant performance drop when comparing photographs taken from different angle. In this paper a 3D model was used for improving that performance. Model enables us to transform the face image which is taken from certain angle to en face. Model has been tested against biometric database formed at the Faculty of Organizational Sciences. Image rotation based on the model was performed before matching with the en face images from the database. Study results show that algorithm precision on biometric verification and identification has been seriously improved. Keywords: biometrics, face recognition, 3D graphics, PCA. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Prospects of High Technologies in the Remote Diagnosis of the Track
Vol. 5 No. 1 (2015): JITA – APEIRON Boris A. Lievin, Boris L. Nedorchuk Prospects of High Technologies in the Remote Diagnosis of the Track Original scientific paper DOI: https://doi.org/10.7251/JIT1501065L Download Article PDF Abstract The article assesses trends of development of devices for control and diagnostics of railway tracks, highlights the growing importance of advanced technologies that make use of more sophisticated methods of remote monitoring of the technical condition and ensure safe operation of railroad bed. In particular, the authors analyze in detail the results of their own developments, with an emphasis on options for optical control with the use of aircraft and video recording, significantly expanding the possibilities of monitoring and quality of observations, and at the same time forecast (considering experimental data and economic factors) promising areas of engineering research. Keywords: railway, track monitoring, remote diagnostics, aerial photography, optical sensors, polarization of refl ected light, infrared technology, satellite communications. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Control Systems for Automated Vessel Piloting Through Local Stationary Obstacles
Vol. 5 No. 1 (2015): JITA – APEIRON Dovgobrod Georgy Moiseevich, Klyachko Lev Mikhaylovich Control Systems for Automated Vessel Piloting Through Local Stationary Obstacles Original scientific paper DOI: https://doi.org/10.7251/JIT1501061M Download Article PDF Abstract To reduce the “human factor” component in the causes of accidents during pilot age of vessels along areas of fairways with local stationary obstacles we propose a device which provides: a) real-time presentation on a graphical display of information on current and predicted positions of a vessel with regard to a stationary obstacle; b) automated or semi-automated piloting of a vessel in to a straight path for safe passage of an obstacle. Specified device will allow reducing the risk of accidents while piloting a vessel along difficult parts of fairways. Keywords: control systems, navigation, microcontroller. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Water treeing in extruded cable insulation as Rehbinder electrical effect
Vol. 5 No. 1 (2015): JITA – APEIRON Iz. B. Peshkov, M. Yu. Shuvalov, V. L. Ovsienko Water treeing in extruded cable insulation as Rehbinder electrical effect Original scientific paper DOI: https://doi.org/10.7251/JIT1501055P Download Article PDF Abstract The paper contains systematic comparison of signs and properties of the water treeing phenomenon (the basic mechanism of degradation of medium voltage electric cable extrudered insulation which develops under combined action of electric stress and water) and Rehbinder Effect – the reduction of mechanical strength of solids due to physical and chemical action of liquid medium.The analysis of the published data permits to distinguish 13 indications of the Rehbinder Effect. The authors show successively the direct analogy of the water treeing and the Rehbinder Effect using the above mentioned indications, including decrease of work of the development of new surfaces in the course of destruction, chemical specificity, role of material defects, two-stage destruction nature, etc. The analogy obtained is accepted as a working hypothesis which permits to bring certain order into theoretical and experimental studies of the water treeing. Keywords: water treeing, Rehbinder Effect, destruction, medium, defects. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Safety and Risk Management
Vol. 5 No. 1 (2015): JITA – APEIRON V. M. Lisenkov, P. F. Bestemyanov Safety and Risk Management Original scientific paper DOI: https://doi.org/10.7251/JIT1501042L Download Article PDF Abstract The article is devoted to the problem of creating a system of security and risk management. Formulated in relation to the process of movement of trains:– factor of safety of the train– the probability of traversing the trains on a particular route without transfer of its movement in a dangerous condition;– a measure of risk of the transfer movement of the train in a dangerous state– transition probability of motion in a dangerous state when the movement of trains on a given route.The objectives of security and risk management are: to provide values of their indicators are not worse than normative, namely, the values of the performance security shall be not less than the normative, and the values of indicators of risk – not more than normative. Proposed functional framework and organizational structure for the management of safety and risks. Keywords: safety index, a measure of risk, a dangerous condition, standard indicators of safety and risk. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
The Perspective of High-Temperature Superconductivity Eltctrical Equipment Application for Traction Power Supply and the Problems of Electromagnetic Compatibility
Vol. 5 No. 1 (2015): JITA – APEIRON M. P. Badjor, Yu. M. Inkov The Perspective of High-Temperature Superconductivity Eltctrical Equipment Application for Traction Power Supply and the Problems of Electromagnetic Compatibility Original scientific paper DOI: https://doi.org/10.7251/JIT1501033B Download Article PDF Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
A new Approach to Computer Analysis of Queuing Systems Without Programming
Vol. 5 No. 1 (2015): JITA – APEIRON Zoran Ž. Avramović, Radomir Z. Radojičić, Saša D. Mirković A new Approach to Computer Analysis of Queuing Systems Without Programming Original scientific paper DOI: https://doi.org/10.7251/JIT1501025A Download Article PDF Abstract The paper presents original object oriented programming system ARS for modelling and simulation queuing systems. Programming system was developed in programming language C++. It establishes connection with intrinsic, but also with other Windows programming packages, in a simple way, through object oriented environment. Basic characteristics and possibilities of programming system, as well as comparative analysis of simulators: mathematical model (analytical solution) – GPSS/H – ARS, on the example of closed queuing network in the paper is given. The significant application for computer performance evaluation is reported. Keywords: simulation, queuing system, programming system, computer performance evaluation. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.