Acta Polytechnica Hungarica最新文献_第7页

The Effect of Engineering Education, on Spatial Ability, in Virtual Environments 虚拟环境中工程教育对空间能力的影响

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.5.2023.5.2

Tibor Guzsvinecz, É. Orbán-Mihálykó, Erika Perge, C. Sik-Lányi

引用次数: 0

Optimal Fuzzy Controller, using a Genetic Algorithm for a Ball on Wheel System 基于遗传算法的球对轮系统最优模糊控制器

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.6.2023.6.4

Péter Menich, J. Kopják

引用次数: 1

Controlling the Spindle Speed when Milling Free-Form Surfaces using Ball-End Milling Cutter 球头铣刀铣削自由曲面时主轴转速的控制

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.6.2023.6.8

Abdul W. Mgherony, Balázs Mikó

引用次数: 0

Convergence Rate for Distributed Macro Calibration of Sensor Networks based on Consensus 基于共识的传感器网络分布式宏观标定收敛速度研究

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.4.2023.4.9

引用次数: 0

Cognitive Information Systems and Related Architecture Issues 认知信息系统和相关架构问题

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.5.2023.5.7

Dóra Mattyasovszky-Philipp, B. Molnár

引用次数: 0

A Simulation System for Testing Side Crashes, in Non-Traditional Seating Positions, for Self-Driving Cars 用于测试自动驾驶汽车非传统座位位置侧碰撞的模拟系统

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.7.2023.7.4

Laszlo Porkolab, István Lakatos

{"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}

引用次数: 0

Petri Net-based S3PR Models of Automated Manufacturing Systems with Resources and Their Deadlock Prevention 基于Petri网的资源自动化制造系统S3PR模型及其死锁预防

IF 1.7 4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.6.2023.6.5

F. Capkovic

引用次数: 0

A Hybrid Machine Learning-based Control Strategy for Autonomous Driving Optimization 基于混合机器学习的自动驾驶优化控制策略

4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.9.2023.9.10

Ahmad Reda, Rabab Benotsmane, Ahmed Bouzid, József Vásárhelyi

{"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}

引用次数: 0

Demand-Supply Balancing in Energy Systems with High Photovoltaic Penetration, using Flexibility of Nuclear Power Plants 利用核电站灵活性的高光伏渗透率能源系统供需平衡

4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.11.2023.11.8

Illia Diahovchenko, Ihor Yevtushenko, Michal Kolcun, Zsolt Čonka, Tetiana Zahorodnia, Petro Vasyleha

引用次数: 0

The Long-Range Macroeconomic Effects of Sars-Covid-19 Pandemic in Hungary: a Conceptual Framework and Methodology - Focusing Approach 匈牙利新冠肺炎大流行的长期宏观经济影响:概念框架和方法聚焦方法

4区工程技术

Acta Polytechnica Hungarica Pub Date : 2023-01-01 DOI: 10.12700/aph.20.9.2023.9.1

Éva Szendrő, Zoltán Lakner

引用次数: 0