2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第9页

A Data-Driven Near-Optimization Approach for Smart Parking Management Platforms 基于数据驱动的智能停车管理平台近优化方法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702219

Mingyan Bai, Shenghua Zhong, Pengyu Yan, Zhibin Chen, Zhixian Zhang

引用次数: 1

Chaotic Lévy Whale Optimization Algorithm with Simulated Annealing and Differential Evolution 基于模拟退火和差分进化的混沌lsamvy鲸优化算法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702200

J. Bi, Wenduo Gu, Haitao Yuan

{"title":"Chaotic Lévy Whale Optimization Algorithm with Simulated Annealing and Differential Evolution","authors":"J. Bi, Wenduo Gu, Haitao Yuan","doi":"10.1109/ICNSC52481.2021.9702200","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702200","url":null,"abstract":"Typical Whale Optimization Algorithm (WOA) suffers from slow convergence, early trapping into local optima, and weak exploration ability. This work aims to solve these problems by combining Differential Evolution (DE), chaos theory, Lévy flight, and Simulated Annealing (SA). This work designs a Chaotic Differential Whale optimization based on Simulated annealing and Levy flight (CDWSL). CDWSL improves randomness of solutions, and reduces the possibility of falling into local optima. CDWSL is evaluated with ten typical functions, two composite ones and a real-world one of computation offloading in vehicular edge computing. In addition, experiments on higher- dimension problems are also conducted to evaluate the performance of CDWSL. The comparison of experimental results shows that CDWSL achieves superior performance over its typical state-of-the-art peers in solving both low-dimension and high- dimension simple benchmark problems. Furthermore, CDWSL yields better results for both composite benchmark functions and the real-world computation offloading problem than some typical algorithms.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"5 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":"124589665","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

Investigation on the Optimization Method of Polygonal Line Loading Path for Tube Hydroforming with Radial Crushing 管件径向挤压液压成形多角线加载路径优化方法研究

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702189

Guolin Hu, C. Pan

{"title":"Investigation on the Optimization Method of Polygonal Line Loading Path for Tube Hydroforming with Radial Crushing","authors":"Guolin Hu, C. Pan","doi":"10.1109/ICNSC52481.2021.9702189","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702189","url":null,"abstract":"The loading path is an important factor that affects the hydroforming performance of the tube and the quality of the formed parts. A reasonable loading path can effectively avoid forming failures and significantly improve the forming quality. In this paper, by studying the deformation behavior of the tube during radial pressure bulging, the influence of the loading path on the forming performance of the tube is revealed. Using the compound optimization strategy of genetic algorithm and dichotomy, the index to evaluate the forming quality is proposed, the index is weighted according to the degree of difficulty, and a multi-objective function that takes into account each index is constructed. The effectiveness and stability of the compound optimization strategy are analyzed through the forming quality of the bulging part, the number of iterations of the optimization strategy and the global optimization. The research results show that the tube has relatively optimal comprehensive forming quality under the action of the optimal loading path, and the optimization strategy has fewer iterations and good global optimization. It shows that the optimization method in this paper is effective and the optimization strategy is stable.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"38 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":"116086739","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

Real-Time Sensor Fault Detection, Isolation and Accommodation for Industrial Digital Twins 工业数字孪生的实时传感器故障检测、隔离和调节

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702175

Hossein Darvishi, D. Ciuonzo, P. Rossi

引用次数: 10

Improving the Accuracy of Load Forecasting for Campus Buildings Based on Federated Learning 基于联邦学习提高校园建筑负荷预测的准确性

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702205

Shi Zhang, Zhezhuang Xu, Jinlong Wang, Jian Chen, Yuxiong Xia

引用次数: 3

Creator2Vec: Creator Feature Embedding for Deep Learning Recommender System Creator2Vec:深度学习推荐系统的创建者特征嵌入

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702156

Zhengrong Wen, Kehua Miao, Yuling Fan, Yuxin Shang

{"title":"Creator2Vec: Creator Feature Embedding for Deep Learning Recommender System","authors":"Zhengrong Wen, Kehua Miao, Yuling Fan, Yuxin Shang","doi":"10.1109/ICNSC52481.2021.9702156","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702156","url":null,"abstract":"The current embedding method neglects to deeply explore the associations between the items created by the same item creator, and directly puts the long-tail distributed categorical creator feature into the model for end-to-end training. Because the tail data provides too little information, it is difficult to train stable and meaningful embedding vectors. To this end, we propose an improved embedding method Creator2Vec used for recommender systems, which is based on the items produced by the creator to characterize the creator. Specifically, we first use Word2Vec to generate item embedding, and take the average of item embedding created by the creator as creator embedding. Then, according to the quality of item comments, we design a feature extraction method weighted to mine the information of item comments and comments on item comments. Benefit from the proposed method, we not only use item ratings as features, but are also concern that item comments with the same rating and different qualities have different recommendation effects. Finally, we uses the number of likes of item reviews as a standard to measure the quality of item comments. The empirical results on a practical dataset show that Creator2Vec and weighted cumulative item rating features, as the input layer of common deep learning models, have good effects on binary classification recommendation tasks.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"151 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":"133564613","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 Conflict-Reducing Path Planning Algorithm in Automated Warehouses 自动化仓库中减少冲突的路径规划算法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702216

Ligen Feng, Xinyu Chen, Tingqi Zhang, Weimin Wu

引用次数: 0

Multi-Stage PSO-Based Cost Minimization for Computation Offloading in Vehicular Edge Networks 基于多阶段pso的车辆边缘网络计算卸载成本最小化

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702184

Yihan Wen, Qiuyue Zhang, Haitao Yuan, J. Bi

引用次数: 3

Group Role Assignment with a Training Plan 有培训计划的小组角色分配

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702183

Libo Zhang, Zhihang Yu, Haibin Zhu, Yin Sheng

引用次数: 2

A Virtual Wafer-based Scheduling Method for Dual-arm Cluster Tools with Chamber Cleaning Requirements 具有腔室清洗要求的双臂集群工具的虚拟晶圆调度方法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702173

Yan Qiao, Jie Li, Yanjun Lu, SiWei Zhang, N. Wu, Bin Liu

{"title":"A Virtual Wafer-based Scheduling Method for Dual-arm Cluster Tools with Chamber Cleaning Requirements","authors":"Yan Qiao, Jie Li, Yanjun Lu, SiWei Zhang, N. Wu, Bin Liu","doi":"10.1109/ICNSC52481.2021.9702173","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702173","url":null,"abstract":"Cluster tools play a significant role in the entire process of wafer fabrication. Wafer residency time constraints and chamber cleaning requirements are commonly seen in etching, chemical vapor deposition, coating processes, etc. They make the scheduling problem of cluster tools more challenging. This work aims to provide a solution for dual-arm cluster tools with wafer residency time constraints and chamber cleaning requirements. To do so, it proposes a novel virtual wafer-based scheduling method. By this method, under a steady state, a process module (PM) processes either a real or virtual wafer at a time. When a PM processes a virtual one, its chamber performs a cleaning operation. In this way, we can meet not only the strict residency time constraints for real wafers, but also innovatively performs chamber cleaning operations as required. Based on such a novel scheduling method, an efficient binary integer programming model is established to maximize the throughput of cluster tools. Finally, experiments are performed to show the efficiency and effectiveness of the proposed method.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"102 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":"115907633","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