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2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献

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Dispatching and Path Planning of Automated Guided Vehicles based on Petri Nets and Deep Reinforcement Learning 基于Petri网和深度强化学习的自动引导车辆调度与路径规划
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702196
Hongbin Zhang, Jiliang Luo, Xinjie Lin, KaiCheng Tan, Chunrong Pan
{"title":"Dispatching and Path Planning of Automated Guided Vehicles based on Petri Nets and Deep Reinforcement Learning","authors":"Hongbin Zhang, Jiliang Luo, Xinjie Lin, KaiCheng Tan, Chunrong Pan","doi":"10.1109/ICNSC52481.2021.9702196","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702196","url":null,"abstract":"A formal approach is proposed for scheduling a team of automated guided vehicles (AGVs). First, a team of AGVs and their environment are modeled as a place timed Petri net (P-timed PN). Second, a method is presented to design controlling structures to avoid collisions and reduce deadlocks of P-timed PNs. Third, a multi-AGV path planning problem is formulated as a Markov decision process, and a neural network is trained based on a corresponding reinforcement learning with the PN model to estimate the action reward function for a given Multi-AGV system. Finally, an approach is obtained to dispatch transportation tasks among and to plan routes for AGVs according to rewards calculated by the neural network. An example is utilized to illustrate the proposed methods.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133153160","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
Parallel 3D ICP Based on Conditionally Constrained Corresponding Points and Applications 基于条件约束对应点的并行三维ICP及其应用
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702130
Tun-Dong Liu, Fan Zhen Kong, Miao He, X. M. Wu, G. Shao
{"title":"Parallel 3D ICP Based on Conditionally Constrained Corresponding Points and Applications","authors":"Tun-Dong Liu, Fan Zhen Kong, Miao He, X. M. Wu, G. Shao","doi":"10.1109/ICNSC52481.2021.9702130","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702130","url":null,"abstract":"The iterative closest point (ICP) tends to fall into local optimality due to inaccurate initial poses during the registration of the multi-view 3D cloud. Therefore, this paper proposes a parallel ICP algorithm with conditional constraint corresponding points. To achieve a fine registration of the point cloud, the corresponding point set is first filtered by adding the normal and color information. Then the OpenMP is introduced to accelerate the program in parallel for ICP. To verify the effectiveness of our algorithm, in the V-Rep simulation environment, the multi-view point cloud data of the scene is obtained by the RGB-D cameras from different angles point cloud. The results show that our algorithm can fuse the multi-view point cloud, improve the accuracy and real-time performance of ICP. Furthermore, in a large-scale calculation, the average single iteration time is less than 0.1s, and the RMSE (root mean square error) is about 0.1, which meets the need of target recognition and sorting in a three-dimensional industrial scene.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116349356","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: 0
A Collision Avoidance Algorithm for Idle Robots in Multi-robot System 多机器人系统中空闲机器人的避碰算法
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702220
Zichao Xing, Weimin Wu, Xingkai Wang, Ruifen Hu
{"title":"A Collision Avoidance Algorithm for Idle Robots in Multi-robot System","authors":"Zichao Xing, Weimin Wu, Xingkai Wang, Ruifen Hu","doi":"10.1109/ICNSC52481.2021.9702220","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702220","url":null,"abstract":"Multi-robot systems (MRSs) play vital roles in manufacturing systems and logistics systems. In order to improve the efficiency of MRSs, we propose an idle robot collision avoidance algorithm(IRCAA) for idle robots to give way for working robots. In the proposed method, idle robots stop in place for new tasks without returning to robot stations. Once an idle robot gets in the way of working robots, IRCAA will find an avoidance point (AP) for the idle robot to give way. Additionally, no new block or deadlock occurs when the idle robot arrives at the AP. The proposed method in this paper is effective and able to improve the efficiency of MRSs, which is verified by both simulation experiments and actual industrial projects.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332395","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: 0
Hierarchical Learning-based Particle Swarm Optimizer 基于分层学习的粒子群优化器
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702243
Huanyi Liu, Junqi Zhang, Mengchu Zhou
{"title":"Hierarchical Learning-based Particle Swarm Optimizer","authors":"Huanyi Liu, Junqi Zhang, Mengchu Zhou","doi":"10.1109/ICNSC52481.2021.9702243","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702243","url":null,"abstract":"Particle Swarm Optimization (PSO) is an optimization technique that has been applied to solve various optimization problems. Its traditional strategy adopts elitism, in which only the personal and global best positions are utilized as leaders to guide all particles’ update and discard other potentially excellent positions around which the global optimum may be found. In a human society, people fall into different classes. They tend to learn from better ones, not just from the best ones in the whole society. Inspired from the learning behavior in a human society, this work considers particles in a swarm as people belonging to different classes and proposes a hierarchical learning-based particle swarm optimizer (HLPSO). In it, particles hierarchically learn from the ones in either the same or upper level ones. The levels of particles are updated according to their fitness after each iteration. Since all particles determine respective leaders according to their own levels, the population hierarchically learns from a large number of potentially excellent positions, which greatly maintains the diversity of population and brings HLPSO a powerful exploration capability. The diversity analyses of HLPSO reveal that the hierarchical utilization of diversified leaders maintains population diversity. HLPSO and eight popular PSO contenders are tested on 28 CEC2013 benchmark functions. Experimental results indicate its high effectiveness and efficiency.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"45 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131726249","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: 0
A Partitioning Algorithm for Bi-modal Road Networks 一种双模式道路网络的划分算法
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702174
Saifei Chen, Yan Qiao, N. Wu, Hui Fu, Yefei Wang
{"title":"A Partitioning Algorithm for Bi-modal Road Networks","authors":"Saifei Chen, Yan Qiao, N. Wu, Hui Fu, Yefei Wang","doi":"10.1109/ICNSC52481.2021.9702174","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702174","url":null,"abstract":"The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or called 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the significance of large occupancy vehicles such as buses contributing to passenger flows. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized in the further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. This bi-modal partition is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjustment is applied by changing the region to which a boundary belongs to get the optimal result. Multi-sensor data collected from Shenzhen, China are utilized to verify the effectiveness of the proposed partition method.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125342980","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: 0
Research and Evaluation of Intelligent Threat Detection Under Industrial Internet 工业互联网下智能威胁检测的研究与评价
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702185
Linfeng Wu, Wei Ruan, Keda Sun, Liang Chen, Liu Yang, Tingting Ye
{"title":"Research and Evaluation of Intelligent Threat Detection Under Industrial Internet","authors":"Linfeng Wu, Wei Ruan, Keda Sun, Liang Chen, Liu Yang, Tingting Ye","doi":"10.1109/ICNSC52481.2021.9702185","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702185","url":null,"abstract":"At present, the security situation in the industrial internet is becoming more and more serious. Various threats such as network attacks, malicious code and vulnerability utilization are gradually increasing. Consequently, it is urgent to study industrial threat detection methods. In order to tackle typical network attacks, system vulnerabilities and malicious operations, a real-time intelligent industrial threat detection method is proposed by analyzing the network data in the industrial control system. Particularly, artificial intelligence technique, adversarial sample generation technique and deep learning model are used in the method. Besides, the proposed method is achieved in the real network, and the corresponding industrial threat detection platform is developed. The results show that the developed threat detection platform can detect a variety of typical network attacks, system vulnerabilities, malicious code, etc. At the same time, the platform has good throughput and compatibility and is suitable for the actual industrial environment.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121424783","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: 0
Overnight Charging Scheduling of Battery Electric Bus Considering Peak-to-valley Electricity Prices* 考虑峰谷电价的纯电动客车夜间充电调度*
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702236
Feifeng Zheng, Zhixin Wang, Yinfeng Xu, Ming Liu
{"title":"Overnight Charging Scheduling of Battery Electric Bus Considering Peak-to-valley Electricity Prices*","authors":"Feifeng Zheng, Zhixin Wang, Yinfeng Xu, Ming Liu","doi":"10.1109/ICNSC52481.2021.9702236","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702236","url":null,"abstract":"As a kind of public transportation with great potential, battery electric buses play a key role in protecting the ecological environment. However, there are many challenges involved in the process of transforming traditional fuel vehicles to battery electric buses. To face such challenges and solve a series of problems, we investigate a overnight charging problem of battery electric buses. Based on discrete-time optimization, the impact of power costs, battery damage cost and peak-to valley prices on the charging schedule are considered to establish a mixed integer liner programming model. For verifying the effectiveness of the proposed model, a case study is also conducted in our work. Numerical results demonstrate that the proposed model has good performance in making the optimal charging scheduling scheme, which carries the potential for using in large-scale real-world bus networks.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243680","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
Normalized Neural Network for Belief Propagation LDPC Decoding 信念传播的归一化神经网络LDPC译码
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702213
Yiduo Tang, Lin Zhou, Shuying Zhang, Chen Chen, Lin Wang
{"title":"Normalized Neural Network for Belief Propagation LDPC Decoding","authors":"Yiduo Tang, Lin Zhou, Shuying Zhang, Chen Chen, Lin Wang","doi":"10.1109/ICNSC52481.2021.9702213","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702213","url":null,"abstract":"BP decoding algorithm is one of the commonly used decoding algorithms for LDPC codes. To adapt LDPC codes to different 5G scenarios and further improve the decoding performance of short LDPC codes, a scheme combining model-driven deep learning with a traditional BP decoding algorithm is proposed. With the advantages of model-driven, this solution expands the decoding iteration process between the check node and the variable node into a neural network and proposes a parameter normalization optimization solution to solve the problem of the program with many training parameters, the edge weights of the optimized Tanner graph are re-assigned and bound. Simulation results show that the proposed scheme can improve the decoding performance of LDPC codes with short lengths.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319157","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: 3
Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles 基于神经网络和ANFIS的自适应MPC自动驾驶汽车路径跟踪
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702227
Yassine Kebbati, N. A. Oufroukh, V. Vigneron, D. Ichalal
{"title":"Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles","authors":"Yassine Kebbati, N. A. Oufroukh, V. Vigneron, D. Ichalal","doi":"10.1109/ICNSC52481.2021.9702227","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702227","url":null,"abstract":"Self-driving cars operate in constantly changing environments and are exposed to a variety of uncertainties and disturbances. These factors render classical controllers ineffective, especially for the lateral control in such vehicles. Therefore, an adaptive MPC controller is designed in this paper for the path tracking task, the developed controller is tuned by an improved particle swarm optimization algorithm. Furthermore, online parameter adaption is performed using Neural Networks and ANFIS. The designed controller showed promising results and adaptation capability against the standard MPC in a triple lane change scenario and a general trajectory test.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114225515","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: 5
Brain-Computer Interface Interaction Design Based on Fuzzy Petri Nets 基于模糊Petri网的脑机接口交互设计
2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702258
Wenfeng Chen, Huijuan Fang
{"title":"Brain-Computer Interface Interaction Design Based on Fuzzy Petri Nets","authors":"Wenfeng Chen, Huijuan Fang","doi":"10.1109/ICNSC52481.2021.9702258","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702258","url":null,"abstract":"Brain-computer interaction is a new type of interaction between brain-computer interface (BCI) technology and external devices. Aiming at the situation of variable environment and unclear user intention, a brain-computer interface interaction design based on fuzzy Petri net is proposed. This paper designs a brain-computer interaction method suitable for changing scenarios. The robot can gradually ask the user questions in a dialogue way according to the change of the system state in the process of movement, to continuously clarify the user intention. In addition, the fuzzy Petri net model of the brain-computer interaction system is constructed. The feasibility and rationality are verified by building the experimental platform of the brain-computer interaction system. The results show that the brain-computer interaction based on fuzzy Petri nets is suitable for environments and tasks with uncertain tasks, and can improve the efficiency of brain-controlled robots.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"33 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116728424","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: 0
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