2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)最新文献_第4页

Internet Time as Virtual Time for Real-Time Session Routing Internet时间作为实时会话路由的虚拟时间

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147175

S. Orzen, L. Kovács

引用次数: 0

Autonomous pedestrian crossing in smart city environment 智慧城市环境下的自动人行横道

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147188

Monika Pogátsnik, David Fischer, Laszlo Nagy, Sebestyen Dóra

引用次数: 0

Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering 系统工程系统运行时互操作性的自编码器对齐方法

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147168

Jacob Nilsson, J. Delsing, Fredrik Sandin

{"title":"Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering","authors":"Jacob Nilsson, J. Delsing, Fredrik Sandin","doi":"10.1109/INES49302.2020.9147168","DOIUrl":"https://doi.org/10.1109/INES49302.2020.9147168","url":null,"abstract":"We formulate the challenging problem to establish information interoperability within a system of systems (SoS) as a machine-learning task, where autoencoder embeddings are aligned using message data and metadata to automate message translation. An SoS requires communication and collaboration between otherwise independently operating systems, which are subject to different standards, changing conditions, and hidden assumptions. Thus, interoperability approaches that are based on standardization and symbolic inference will have limited generalization and scalability in the SoS engineering domain. We present simulation experiments performed with message data generated using heating and ventilation system simulations. While the unsupervised learning approach proposed here remains unsolved in general, we obtained up to 75% translation accuracy with autoencoders aligned by back-translation after investigating seven different models with different training protocols and hyperparameters. For comparison, we obtain 100% translation accuracy on the same task with supervised learning, but the need for a labeled dataset makes that approach less interesting. We discuss possibilities to extend the proposed unsupervised learning approach to reach higher translation accuracy.","PeriodicalId":175830,"journal":{"name":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Organized Driving Intellectual Content to Assist Situation Recognition 有组织的驾驶知识内容，以协助情况识别

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147178

L. Horváth

{"title":"Organized Driving Intellectual Content to Assist Situation Recognition","authors":"L. Horváth","doi":"10.1109/INES49302.2020.9147178","DOIUrl":"https://doi.org/10.1109/INES49302.2020.9147178","url":null,"abstract":"Situation is one of the recent key issues in engineering mainly because smart cyber physical system (CPS) use it as direct driving context at decision on physical activities. Definition, representation, and recognition of situation and its application as driving context have become essential in smart CPS. Recognized situation is context for decision on real time executed physical activities in smart CPS environment where dozens or even hundreds of cooperating systems operate industrial or commercial product. Owing to quick development of virtual technology during the past two decades, lifecycle engineering of complete industrial product can be done using a single autonomous model system (AMS). AMS is continuously developed and applied during the integrated innovation, production, and life cycle of CPS. Therefore, development of AMS to eligible for smart CPS is actual issue. Former relevant publications by the author introduced various purposed driving content structures which purpose was to provide organized driving contexts for engineering model systems and cyber units of CPS. This paper introduces the organized driving intellectual content (ODIC) structure specifically to support situation recognition at model based engineering. Before introducing the ODIC structure, new model of the thoroughly changed engineering scenario is introduced and applied to explain the main problem solution for which the contribution is conceptualized in this paper. Situation awareness related issues are discussed. Methodological essentials of ODIC is followed by situation related structural units of the proposed ODIC. Finally, some circumstances around implementation are outlined.","PeriodicalId":175830,"journal":{"name":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124999865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

INES 2020 Authors Index INES 2020作者索引

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/ines49302.2020.9147176

引用次数: 0

Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks 基于CNN和LSTM网络的强化学习在高速公路上驾驶

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147185

Lászlo Szőke, S. Aradi, Tamás Bécsi, P. Gáspár

{"title":"Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks","authors":"Lászlo Szőke, S. Aradi, Tamás Bécsi, P. Gáspár","doi":"10.1109/INES49302.2020.9147185","DOIUrl":"https://doi.org/10.1109/INES49302.2020.9147185","url":null,"abstract":"This work presents a powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. Our goal is to create an agent that is capable of navigating safely in changing highway traffic and successfully accomplish to get through the defined section keeping the reference speed. Meanwhile, creating a state representation that is capable of extracting information from images based on the actual highway situation. The algorithm uses Convolutional Neural Network (CNN) with Long-Short Term Memory (LSTM) layers as a function approximator for the agent with discrete action space on the control level, e.g., acceleration and lane change. Simulation of Urban MObility (SUMO), an open-source microscopic traffic simulator is chosen as our simulation environment. It is integrated with an open interface to interact with the agent in real-time. The agent can learn from numerous driving and highway situations that are created and fed to it. The representation becomes more general by randomizing and customizing the behavior of the other road users in the simulation, thus the experience of the agent can be much more diverse. The article briefly describes the modeling environment, the details on the learning agent, and the rewarding scheme. After evaluating the experiences gained from the training, some further plans and optimization ideas are briefed.","PeriodicalId":175830,"journal":{"name":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","volume":"59 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126080727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Parallel and Distributed Training of Deep Neural Networks: A brief overview 深度神经网络的并行和分布式训练:简要概述

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147123

Attila Farkas, Gábor Kertész, R. Lovas

引用次数: 9

Evaluation of Questionnaires by Combining Fuzzy Signatures, Factor Analysis and Least Squares Method 结合模糊特征、因子分析和最小二乘法的问卷评价

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/ines49302.2020.9147125

D. Susnienė, O. Purvinis, L. Kóczy, Márta Konczosné Szombathelyi, Szabolcs Rámháp

引用次数: 0

Computation of continuous sequential reference paths from discrete optimal paths for mobile robots 移动机器人离散最优路径的连续顺序参考路径计算

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-07-01 DOI: 10.1109/INES49302.2020.9147180

Matevž Bošnak, I. Škrjanc

引用次数: 0

Fake News Detection with Generated Comments for News Articles 假新闻检测与生成评论的新闻文章

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) Pub Date : 2020-04-17 DOI: 10.1109/INES49302.2020.9147195

Y. Yanagi, R. Orihara, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga

{"title":"Fake News Detection with Generated Comments for News Articles","authors":"Y. Yanagi, R. Orihara, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1109/INES49302.2020.9147195","DOIUrl":"https://doi.org/10.1109/INES49302.2020.9147195","url":null,"abstract":"Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes make social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diffusion. Automatic detection of fake news is a popular researching topic. It is confirmed that considering not only articles but also social contexts(i.e. likes, retweets, replies, comments) supports to spot fake news correctly. However, the social contexts are naturally unavailable when an article comes out, making early fake news detection by means of the social context useless. We propose a fake news detector with the ability to generate fake social contexts, aiming to detect fake news in the early stage of its diffusion where few social contexts are available. The fake context generation is based on a fake news generator model. This model is trained to generate comments using a dataset which consists of news articles and their social contexts. In addition, we also trained a classify model. This used news articles, real-posted comments, and generated comments. To measure our detector’s effectiveness, we examined the performance of the generated comments for articles with real comments and generated ones by the classifying model. As a result, we conclude that considering a generated comment help detect more fake news than considering real comments only. It suggests that our proposed detector will be effective to spot fake news on social networks.","PeriodicalId":175830,"journal":{"name":"2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121515406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17