Pub Date : 2023-01-01DOI: 10.12700/aph.20.5.2023.5.2
Tibor Guzsvinecz, É. Orbán-Mihálykó, Erika Perge, C. Sik-Lányi
{"title":"The Effect of Engineering Education, on Spatial Ability, in Virtual Environments","authors":"Tibor Guzsvinecz, É. Orbán-Mihálykó, Erika Perge, C. Sik-Lányi","doi":"10.12700/aph.20.5.2023.5.2","DOIUrl":"https://doi.org/10.12700/aph.20.5.2023.5.2","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66136192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.6.2023.6.4
Péter Menich, J. Kopják
{"title":"Optimal Fuzzy Controller, using a Genetic Algorithm for a Ball on Wheel System","authors":"Péter Menich, J. Kopják","doi":"10.12700/aph.20.6.2023.6.4","DOIUrl":"https://doi.org/10.12700/aph.20.6.2023.6.4","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66137076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.6.2023.6.8
Abdul W. Mgherony, Balázs Mikó
: Ball-end tools are widely used in industries such as die/mould, automobile
{"title":"Controlling the Spindle Speed when Milling Free-Form Surfaces using Ball-End Milling Cutter","authors":"Abdul W. Mgherony, Balázs Mikó","doi":"10.12700/aph.20.6.2023.6.8","DOIUrl":"https://doi.org/10.12700/aph.20.6.2023.6.8","url":null,"abstract":": Ball-end tools are widely used in industries such as die/mould, automobile","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66137462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.4.2023.4.9
{"title":"Convergence Rate for Distributed Macro Calibration of Sensor Networks based on Consensus","authors":"","doi":"10.12700/aph.20.4.2023.4.9","DOIUrl":"https://doi.org/10.12700/aph.20.4.2023.4.9","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66135618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.5.2023.5.7
Dóra Mattyasovszky-Philipp, B. Molnár
{"title":"Cognitive Information Systems and Related Architecture Issues","authors":"Dóra Mattyasovszky-Philipp, B. Molnár","doi":"10.12700/aph.20.5.2023.5.7","DOIUrl":"https://doi.org/10.12700/aph.20.5.2023.5.7","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66136352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.7.2023.7.4
Laszlo Porkolab, István Lakatos
: Historically, vehicle safety reflects the current state of the art and new innovations will continue to make our cars even safer in the future. Since its invention, the car has enjoyed a unique, triumphal procession. Safety plays a central role in the development of a car model today. The number of accidents has also risen, in line with the growth in traffic. In addition to carelessness or distraction at the wheel, the most common causes of accidents are excessive speed, risky maneuvers and disregard of traffic rules. The introduction of the speed limit on rural roads and the obligation to wear seat belts, were particularly important positive milestones. Two different methods are also used to check the effect of the individual technical options and safety, in the event of accidents. First are the crash tests. Here, an accident situation is simulated in practice, under realistic conditions. The other solution is the simulation. The Finite Element Method, behind this term, lies the virtual calculation of various consequences of an accident, on the basis of mathematical differential equations. The degree of deformation of various components or the entire car, as a whole, is examined by calculation.
{"title":"A Simulation System for Testing Side Crashes, in Non-Traditional Seating Positions, for Self-Driving Cars","authors":"Laszlo Porkolab, István Lakatos","doi":"10.12700/aph.20.7.2023.7.4","DOIUrl":"https://doi.org/10.12700/aph.20.7.2023.7.4","url":null,"abstract":": Historically, vehicle safety reflects the current state of the art and new innovations will continue to make our cars even safer in the future. Since its invention, the car has enjoyed a unique, triumphal procession. Safety plays a central role in the development of a car model today. The number of accidents has also risen, in line with the growth in traffic. In addition to carelessness or distraction at the wheel, the most common causes of accidents are excessive speed, risky maneuvers and disregard of traffic rules. The introduction of the speed limit on rural roads and the obligation to wear seat belts, were particularly important positive milestones. Two different methods are also used to check the effect of the individual technical options and safety, in the event of accidents. First are the crash tests. Here, an accident situation is simulated in practice, under realistic conditions. The other solution is the simulation. The Finite Element Method, behind this term, lies the virtual calculation of various consequences of an accident, on the basis of mathematical differential equations. The degree of deformation of various components or the entire car, as a whole, is examined by calculation.","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66139482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.6.2023.6.5
F. Capkovic
{"title":"Petri Net-based S3PR Models of Automated Manufacturing Systems with Resources and Their Deadlock Prevention","authors":"F. Capkovic","doi":"10.12700/aph.20.6.2023.6.5","DOIUrl":"https://doi.org/10.12700/aph.20.6.2023.6.5","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66137149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.11.2023.11.8
Illia Diahovchenko, Ihor Yevtushenko, Michal Kolcun, Zsolt Čonka, Tetiana Zahorodnia, Petro Vasyleha
{"title":"Demand-Supply Balancing in Energy Systems with High Photovoltaic Penetration, using Flexibility of Nuclear Power Plants","authors":"Illia Diahovchenko, Ihor Yevtushenko, Michal Kolcun, Zsolt Čonka, Tetiana Zahorodnia, Petro Vasyleha","doi":"10.12700/aph.20.11.2023.11.8","DOIUrl":"https://doi.org/10.12700/aph.20.11.2023.11.8","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135560492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.9.2023.9.1
Éva Szendrő, Zoltán Lakner
{"title":"The Long-Range Macroeconomic Effects of Sars-Covid-19 Pandemic in Hungary: a Conceptual Framework and Methodology - Focusing Approach","authors":"Éva Szendrő, Zoltán Lakner","doi":"10.12700/aph.20.9.2023.9.1","DOIUrl":"https://doi.org/10.12700/aph.20.9.2023.9.1","url":null,"abstract":"","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135750356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.12700/aph.20.9.2023.9.10
Ahmad Reda, Rabab Benotsmane, Ahmed Bouzid, József Vásárhelyi
: Developing autonomous vehicles is a highly important topic in the field of intelligent transportation systems. Automated steering is a crucial function in the autonomous vehicle. Therefore, it is urgent to either develop a new effective control strategy or improve existing ones. A variety of control strategies are used for this purpose, most with limitations related to their computing capabilities with the highly complex systems or to lack of efficacy related to maintaining the balance between driving performance and driving smoothness. In this paper, three different machine learning-based models were developed to perform an autonomous driving task: a supervised learning model (Deep Neural Network, DNN), a reinforcement Deep Q-learning model (DQN), and a hybrid model. The DNN model was trained based on the behavior of the classical MPC controller. The DQN was designed with the same structure as the DNN and trained by directly interacting with the driving environment. The hybrid model is a combination of supervised and reinforcement learning algorithms, where the trained DNN model is used as a decision-maker (Actor) in a deep deterministic policy gradient reinforcement learning model. The behavior of the designed models was compared based on several performance indicators, including the ability to drive the vehicle along the desired trajectory, the response time, and the smoothness of the driving system. The results show that the DNN model was able to imitate the behavior of the traditional MP Controller efficiently and all three machine learning models successfully drive the vehicle along the desired path. The hybrid model achieves the best results and improved the smoothness of the driving system with a reasonable response time.
{"title":"A Hybrid Machine Learning-based Control Strategy for Autonomous Driving Optimization","authors":"Ahmad Reda, Rabab Benotsmane, Ahmed Bouzid, József Vásárhelyi","doi":"10.12700/aph.20.9.2023.9.10","DOIUrl":"https://doi.org/10.12700/aph.20.9.2023.9.10","url":null,"abstract":": Developing autonomous vehicles is a highly important topic in the field of intelligent transportation systems. Automated steering is a crucial function in the autonomous vehicle. Therefore, it is urgent to either develop a new effective control strategy or improve existing ones. A variety of control strategies are used for this purpose, most with limitations related to their computing capabilities with the highly complex systems or to lack of efficacy related to maintaining the balance between driving performance and driving smoothness. In this paper, three different machine learning-based models were developed to perform an autonomous driving task: a supervised learning model (Deep Neural Network, DNN), a reinforcement Deep Q-learning model (DQN), and a hybrid model. The DNN model was trained based on the behavior of the classical MPC controller. The DQN was designed with the same structure as the DNN and trained by directly interacting with the driving environment. The hybrid model is a combination of supervised and reinforcement learning algorithms, where the trained DNN model is used as a decision-maker (Actor) in a deep deterministic policy gradient reinforcement learning model. The behavior of the designed models was compared based on several performance indicators, including the ability to drive the vehicle along the desired trajectory, the response time, and the smoothness of the driving system. The results show that the DNN model was able to imitate the behavior of the traditional MP Controller efficiently and all three machine learning models successfully drive the vehicle along the desired path. The hybrid model achieves the best results and improved the smoothness of the driving system with a reasonable response time.","PeriodicalId":50884,"journal":{"name":"Acta Polytechnica Hungarica","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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