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2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献

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Aspect-level Sentiment Classification with Multi-head-attention-based Multi-channel Graph Convolutional Networks 基于多头注意的多通道图卷积网络的方面级情感分类
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004113
Can Cui, Luyuan Xu, Bo-Lan Liu, Jing Chen, Shuaiyu Yao, Qi Kang
{"title":"Aspect-level Sentiment Classification with Multi-head-attention-based Multi-channel Graph Convolutional Networks","authors":"Can Cui, Luyuan Xu, Bo-Lan Liu, Jing Chen, Shuaiyu Yao, Qi Kang","doi":"10.1109/ICNSC55942.2022.10004113","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004113","url":null,"abstract":"Aspect-level sentiment classification (ALSC) has received extensive attention due to its fine-grained characteristics. However, ALSC tasks are often plagued by complex sentence structures, and existing attention-based methods cannot fully utilize implicit syntactic information to deconstruct sentences. Therefore, we propose a model named MAMC-GCN (Multi-head-attention-based Multi-channel Graph Convolutional Networks), which integrates the dependency relations of sentences and uses the multi-head attention mechanism to improve the ability to extract key information related to aspect terms. Experiments on five benchmarks prove that our model can make excellent performance under a short training duration.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116268317","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
Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization 基于数据去噪和特征优化的云制造系统异常检测技术
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004139
Longbo Zhao, Bo Li, Juan Jia, Tongkun Wu
{"title":"Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization","authors":"Longbo Zhao, Bo Li, Juan Jia, Tongkun Wu","doi":"10.1109/ICNSC55942.2022.10004139","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004139","url":null,"abstract":"Aiming at the problem that the traditional anomaly detection method based on threshold cannot effectively detect sensor numerical anomalies in cloud manufacturing system, this work proposes a new method to detect some sensor numerical anomalies form the industrial control system. It is the central part of a cloud manufacturing system. Firstly, this work constructs a Savitzky-Golay (S-G) filter to reduce data noises. Furthermore, an extreme learning machine based on genetic algorithm (GA-ELM) model is proposed to detect sensor numerical anomalies form the industrial control system. The genetic algorithm (GA) is used to reduce feature dimensions from 51 to 10 and the extreme learning machine algorithm (ELM) is used for classification to achieve the purpose of anomaly detection. Finally, using the public dataset called Secure Water Treatment (SWaT), the classification accuracy is 98.96%. It shows a better performance of the proposed method.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114864310","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
Distributionally robust lane reservation under uncertain road travel times 不确定道路行驶时间下的分布鲁棒车道保留
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004120
Xinyi Zhang, Peng Wu
{"title":"Distributionally robust lane reservation under uncertain road travel times","authors":"Xinyi Zhang, Peng Wu","doi":"10.1109/ICNSC55942.2022.10004120","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004120","url":null,"abstract":"In this paper, we investigate a new stochastic lane reservation problem under uncertain road travel times. The problem needs to determine an optimal location of reserved lanes in a transportation network and design reserved lanes-based routes for special time-crucial transport tasks under the condition that the travel time is uncertain, but partial information, i.e., mean and covariance matrix are known. For the problem, we develop a service-oriented distributionally robust optimization model. The objective is to maximize the service satisfaction, which is measured by the probability of completing the tasks on time. To solve it, the widely used sample average approximation (SAA) method is first adapted. However, the SAA method is time-consuming to the NP-hardness of the problem. Thus, by analyzing the characteristics of the problem, we propose a new method based on approximate mixed integer second-order cone programming (MI-SOCP). The efficiency and effectiveness of the proposed method are verified by the results of a real case as compared with the SAA method.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278836","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
An Improved Hunter-prey Optimization Algorithm and Its Application* 一种改进的猎-猎物优化算法及其应用*
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004114
Mingxin Fu, Qiang Liu
{"title":"An Improved Hunter-prey Optimization Algorithm and Its Application*","authors":"Mingxin Fu, Qiang Liu","doi":"10.1109/ICNSC55942.2022.10004114","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004114","url":null,"abstract":"Hunter-prey optimization (HPO) algorithm is a new optimization algorithm proposed to simulate the behavior of leopards, lions and other predators in hunting deer and antelope. In order to solve the problems such as insufficient global optimization capability of HPO, easy to fall into local optimization, and low optimization accuracy, an improved Hunter-prey optimization (IHPO) algorithm is proposed. Firstly, Tent chaotic map is used to generate the initial population and increase the diversity of individuals, Secondly, in order to balance the ability of global search in the early stage and local search in the late stage, the enhanced sine cosine algorithm (ESCA) is integrated to adaptively select the population location update mode according to the conversion probability, Finally, Cauchy mutation strategy is adopted in the later stage of iteration to disturb the population position and enhance the ability of the algorithm to jump out of precocity. The simulation results of benchmark functions show that IHPO algorithm has better convergence accuracy and convergence speed. The effectiveness of the improved algorithm is further verified by the simulation experiment of pipe routing examples","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121968656","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
Grey Wolf Algorithm for Human-Robot Collaborative Disassembly Line Balancing Problem Subject to Dangerous Components 考虑危险部件的人机协同拆解线平衡问题的灰狼算法
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004166
Chong Li, Xiwang Guo, Jiacun Wang, Shujin Qin, Liang Qi, Ying Tang
{"title":"Grey Wolf Algorithm for Human-Robot Collaborative Disassembly Line Balancing Problem Subject to Dangerous Components","authors":"Chong Li, Xiwang Guo, Jiacun Wang, Shujin Qin, Liang Qi, Ying Tang","doi":"10.1109/ICNSC55942.2022.10004166","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004166","url":null,"abstract":"Disassembly is a critical remanufacturing process to obtain reusable components from discarded products. Due to the limitation of disassembly by humans or robots alone, the human-robot collaborative disassembly method is used to obtain components. Three types of components are considered in this paper: dangerous, delicate and normal. Robots disassemble dangerous components, humans disassemble delicate components, and both humans and robots can disassemble normal components. A mathematical model that maximizes disassembly profit is established. An improved gray wolf optimizer algorithm to solve the single-product disassembly line balancing problem is proposed. The algorithm is compared with the migratory bird optimization algorithm and the brain storming optimization algorithm to test its performance. Experimental results show that the proposed algorithm has a faster convergence speed.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326477","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
A New Density Clustering Method based on Dynamic Local Density 一种基于动态局部密度的密度聚类方法
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004074
Jian-chun Lu, Quanwang Wu, Chunling Wu
{"title":"A New Density Clustering Method based on Dynamic Local Density","authors":"Jian-chun Lu, Quanwang Wu, Chunling Wu","doi":"10.1109/ICNSC55942.2022.10004074","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004074","url":null,"abstract":"Density-based clustering is an important research direction of data mining because density-based clustering algorithms can find clusters with arbitrary shape and are robust to noises. Local density estimation is widely used in the task of clustering and outlier detection. Traditionally, distance-based and statistic-based local density estimation methods are usually adopted for data mining. However, the effectiveness and stability of local density estimation models for clustering are still to be improved. In this paper, we propose a novel density clustering method based on dynamic local density estimation. First, we show Poisson distribution to fit the distribution of reverse $k$ nearest neighbor counts and describe the model of dynamic local density estimation. Second, we construct a cluster order based on dynamic local density. Finally, decision graph is developed for density clustering and the identified break points partition the cluster order into clusters. Experiment results show that the new dynamic local density estimation model is effective and can help improve the performance of density clustering.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123834362","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
An Improved Marine Predators Algorithm Based on Group Learning 一种改进的基于群学习的海洋捕食者算法
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004096
Liying Li, Jian Zhao, Jiacun Wang, Xiwang Guo
{"title":"An Improved Marine Predators Algorithm Based on Group Learning","authors":"Liying Li, Jian Zhao, Jiacun Wang, Xiwang Guo","doi":"10.1109/ICNSC55942.2022.10004096","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004096","url":null,"abstract":"To address the shortcomings of the marine predators algorithm (MPA) in solving complex problems, such as low optimization accuracy and easily falling into local optimization, this paper proposes an improved marine predators algorithm based on group learning (GLMPA). An opposition-based learning method is adopted to enhance the quality of the initial solutions. Then, a group learning strategy is used to diversify the population. Two subgroups are produced by fitness evaluation and employing different updating mechanisms. In addition, a new position-updating rule is used to help the proposed algorithm escape from the local optima in the later stage of iteration. Finally, six test functions are utilized to test the GLMPA, and the simulation results verify the effectiveness of the proposed algorithm when compared with other famous algorithms.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671769","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
Supervisory Control for Petri Nets Based on Partial Order Techniques 基于偏序技术的Petri网监控
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004085
Ziliang Zhang, Depei Zhang, Gaiyun Liu, Yonglai Wang
{"title":"Supervisory Control for Petri Nets Based on Partial Order Techniques","authors":"Ziliang Zhang, Depei Zhang, Gaiyun Liu, Yonglai Wang","doi":"10.1109/ICNSC55942.2022.10004085","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004085","url":null,"abstract":"Supervisory control strategies for discrete event systems based on reachability graphs of Petri nets are in general subject to the state explosion problem. In order to deal with this problem, this paper designs a liveness-enforcing supervisor based on a partial order technique. First, the concept of persistent step graphs is introduced to acquire the partial state information of a system. Based on the persistent step graph analysis, a liveness-enforcing supervisor is obtained by iterately removing the first-met bad markings in the graph. Avoiding the traversal of a system's reachable space, the proposed strategy reduces the computational complexity of reachability graph-based methods, and also achieves the optimal/sub-optimal behavior of the system. Examples are presented to demonstrate the proposed policy.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489978","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
Heuristic Scheduling Method for FMSs Based on P-timed Petri Nets and Deep Learning 基于p时间Petri网和深度学习的fms启发式调度方法
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004193
Jun Li, Jiliang Luo, Xuhang Li, Sijia Yi, Chunrong Pan
{"title":"Heuristic Scheduling Method for FMSs Based on P-timed Petri Nets and Deep Learning","authors":"Jun Li, Jiliang Luo, Xuhang Li, Sijia Yi, Chunrong Pan","doi":"10.1109/ICNSC55942.2022.10004193","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004193","url":null,"abstract":"As for the scheduling issue of flexible manufacturing systems (FMS), a heuristic method is proposed based on Petri nets and deep learning. First, an algorithm is presented to generate a heuristic data set by means of the operation rules of P-timed Petri nets. Second, a deep neural network (DNN) is designed to learn the heuristics of Petri net behavior from the data set. Third, the DNN is used as a heuristic function in a dynamic window search (DWS) algorithm to obtain an optimal or near-optimal schedule strategy for an FMS. Finally, a mechanical arm handling system is taken as an example, and numerical experiments are carried out. The results show that the DNN can represent a heuristic function with high precision, and its average estimation error is less than 0.05%, and that the proposed DWS algorithm is very efficient to resolve a given FMS schedul issue.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132703214","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 Breif Review on Data-driven Battery Health Estimation Methods for Energy Storage Systems 基于数据驱动的储能系统电池健康评估方法综述
2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004051
Minzhi Chen, Hao Wu
{"title":"A Breif Review on Data-driven Battery Health Estimation Methods for Energy Storage Systems","authors":"Minzhi Chen, Hao Wu","doi":"10.1109/ICNSC55942.2022.10004051","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004051","url":null,"abstract":"Battery degradation has an impact on the safety and sustain ability of energy storage systems, which is a consequence of multiple coupled ageing mechanisms. The caused factors include battery chemistry and manufacturing, as well as environmental and operating conditions. Hence, the ageing mechanisms are highly complicated to characterize. The state of health (SOH) of a battery is a commonly used metric to evaluate its aging level. Monitoring battery SOH can realize safety and reliable operation of battery management systems. Data-driven methods for battery health estimation and prediction are gaining increasing attention in both academia and industry due to the advantage of avoiding complex physical models. Hence, this paper reviews current state-of-the-art data-driven SOH estimation methods published in 2018–2022, where the variants and extensions of each method existing in current papers are introduced. Finally, the current faced challenges and the solutions are analyzed.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132920574","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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