Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238099
Kai Qian, Jie Jiang, Yulong Ding, Shuanghua Yang
Water distribution system (WDS) is one of the most essential infrastructures all over the world. However, incidents such as natural disasters, accidents and intentional damages are endangering the safety of drinking water. With the advance of sensor technologies, different kinds of sensors are being deployed to monitor operative and quality indicators such as flow rate, pH, turbidity, the amount of chlorine dioxide etc. This brings the possibility to detect anomalies in real time based on the data collected from the sensors and different kinds of methods have been applied to tackle this task such as the traditional machine learning methods (e.g. logistic regression, support vector machine, random forest). Recently, researchers tried to apply the deep learning methods (e.g. RNN, CNN) for WDS anomaly detection but the results are worse than that of the traditional machine learning methods. In this paper, by taking into account the characteristics of the WDS monitoring data, we integrate sequence-to-point learning and data balancing with the deep learning model Long Short-term Memory (LSTM) for the task of anomaly detection in WDSs. With a public data set, we show that by choosing an appropriate input length and balance the training data our approach achieves better F1 score than the state-of-the-art method in the literature.
{"title":"Deep Learning Based Anomaly Detection in Water Distribution Systems","authors":"Kai Qian, Jie Jiang, Yulong Ding, Shuanghua Yang","doi":"10.1109/ICNSC48988.2020.9238099","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238099","url":null,"abstract":"Water distribution system (WDS) is one of the most essential infrastructures all over the world. However, incidents such as natural disasters, accidents and intentional damages are endangering the safety of drinking water. With the advance of sensor technologies, different kinds of sensors are being deployed to monitor operative and quality indicators such as flow rate, pH, turbidity, the amount of chlorine dioxide etc. This brings the possibility to detect anomalies in real time based on the data collected from the sensors and different kinds of methods have been applied to tackle this task such as the traditional machine learning methods (e.g. logistic regression, support vector machine, random forest). Recently, researchers tried to apply the deep learning methods (e.g. RNN, CNN) for WDS anomaly detection but the results are worse than that of the traditional machine learning methods. In this paper, by taking into account the characteristics of the WDS monitoring data, we integrate sequence-to-point learning and data balancing with the deep learning model Long Short-term Memory (LSTM) for the task of anomaly detection in WDSs. With a public data set, we show that by choosing an appropriate input length and balance the training data our approach achieves better F1 score than the state-of-the-art method in the literature.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128583869","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}
The circular economy follows the principle of reducing resource usage and energy consumption, reusing usable resources including subassemblies and components in discarded or used products, and recycling usable materials. It is guided by saving resources, improving the utilization rate of resources, reducing pollution, and protecting an ecological environment. Effective product disassembly planning methods can improve recovery efficiency and promote the circular economy. However, the existing studies pay little attention to sequential dependency disassembly, which makes it difficult to implement the existing planning methods under the constraints of limited disassembly methods and tools. In this paper, a single-objective sequence-dependent disassembly line balancing problem (SDLB) is studied. This problem requires that disassembly tasks are assigned to a group of orderly disassembly workstations to obtain the near optimal solution while meeting a disassembly priority constraint. Because solution complexity increases with the number of parts in a product, an improved artificial bee colony method (IABC) is proposed to solve the problem. Through experiments and compared with a genetic algorithm, the effectiveness of the proposed algorithm is verified.
{"title":"Improved Artificial Bee Colony Algorithm for Solving a Single-Objective Sequence-dependent Disassembly Line Balancing Problem","authors":"Wenhong Luo, Mengchu Zhou, Xiwang Guo, Haiping Wei, Liang Qi, Ziyan Zhao","doi":"10.1109/ICNSC48988.2020.9238075","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238075","url":null,"abstract":"The circular economy follows the principle of reducing resource usage and energy consumption, reusing usable resources including subassemblies and components in discarded or used products, and recycling usable materials. It is guided by saving resources, improving the utilization rate of resources, reducing pollution, and protecting an ecological environment. Effective product disassembly planning methods can improve recovery efficiency and promote the circular economy. However, the existing studies pay little attention to sequential dependency disassembly, which makes it difficult to implement the existing planning methods under the constraints of limited disassembly methods and tools. In this paper, a single-objective sequence-dependent disassembly line balancing problem (SDLB) is studied. This problem requires that disassembly tasks are assigned to a group of orderly disassembly workstations to obtain the near optimal solution while meeting a disassembly priority constraint. Because solution complexity increases with the number of parts in a product, an improved artificial bee colony method (IABC) is proposed to solve the problem. Through experiments and compared with a genetic algorithm, the effectiveness of the proposed algorithm is verified.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126585415","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}
with a variety of new energy access to the power grid, its frequency control is facing many new challenges, and the demand response resources play an increasingly important role in FM (frequency modulation). In this paper, the intermittent characteristics of demand response resource is described, which is determined by the user's comfort and the physical characteristics of the demand response resources themselves. For example, air conditionings are disconnected for a period of time to maintain constant room temperature. Electric vehicle need to charge themselves after participating in the frequency response. The intermittent characteristics will reduce the performance of frequency control without coordination. Thus the coordination strategy will be designed for diffident demand response resources based on switch control method. And the simulation results show the effectiveness of the coordination strategy.
{"title":"Switch Control Used to Coordinate Different Demand Response Resources","authors":"Zhidong Ding, Mingyu Huang, Haitao Liu, Yaping Li, Zhetong Ding, Kaifeng Zhang","doi":"10.1109/ICNSC48988.2020.9238053","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238053","url":null,"abstract":"with a variety of new energy access to the power grid, its frequency control is facing many new challenges, and the demand response resources play an increasingly important role in FM (frequency modulation). In this paper, the intermittent characteristics of demand response resource is described, which is determined by the user's comfort and the physical characteristics of the demand response resources themselves. For example, air conditionings are disconnected for a period of time to maintain constant room temperature. Electric vehicle need to charge themselves after participating in the frequency response. The intermittent characteristics will reduce the performance of frequency control without coordination. Thus the coordination strategy will be designed for diffident demand response resources based on switch control method. And the simulation results show the effectiveness of the coordination strategy.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131720670","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}
Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.
{"title":"Identification of Electrical Equipment Based on Faster LSTM-CNN Network","authors":"Xiaoping Xiong, Shuang Xu, Wenliang Wu, Deran Tu, Jie Zhang, Zhi Wei","doi":"10.1109/ICNSC48988.2020.9238109","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238109","url":null,"abstract":"Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115438157","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}
Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238073
Bobo Wang, Xiaohu Zheng, Jinsong Bao, Jie Li
Textile industry has become the third largest energy consuming industry after engineering and chemical sectors. In order to reduce the energy consumption in the textile industry, a neural network is used to establish the energy consumption prediction model of the winder. In this research, the model is specially designed as the objective function to optimize the energy consumption of the winders. Firstly, the neural network error back propagation is analyzed and the absolute values of the weight coefficient matrix product are used to approximate the influence of input parameters on the model output. The values are also used to select the core parameters to optimize the model. Secondly, the single-dimensional search method is applied for a set of parameter values within a reasonable interval of the whole input parameters to reduce the energy consumption. Experimental results indicate that a set of core parameters can be determined to remodel after the training of the neural network model. In addition, a set of parameter values obtained by single-dimensional search can also be used to effectively reduce the energy consumption of the winders. The proposed method effectively solves the problem and is efficient and straightforward. The feasibility of the proposed approach is validated through the comparative analysis.
{"title":"ss5:A Neural Network-based Energy Consumption Prediction Model for Feature Selection and Paremeter Optimization of Winders","authors":"Bobo Wang, Xiaohu Zheng, Jinsong Bao, Jie Li","doi":"10.1109/ICNSC48988.2020.9238073","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238073","url":null,"abstract":"Textile industry has become the third largest energy consuming industry after engineering and chemical sectors. In order to reduce the energy consumption in the textile industry, a neural network is used to establish the energy consumption prediction model of the winder. In this research, the model is specially designed as the objective function to optimize the energy consumption of the winders. Firstly, the neural network error back propagation is analyzed and the absolute values of the weight coefficient matrix product are used to approximate the influence of input parameters on the model output. The values are also used to select the core parameters to optimize the model. Secondly, the single-dimensional search method is applied for a set of parameter values within a reasonable interval of the whole input parameters to reduce the energy consumption. Experimental results indicate that a set of core parameters can be determined to remodel after the training of the neural network model. In addition, a set of parameter values obtained by single-dimensional search can also be used to effectively reduce the energy consumption of the winders. The proposed method effectively solves the problem and is efficient and straightforward. The feasibility of the proposed approach is validated through the comparative analysis.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125302434","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}
In order to coordinate the effective collaboration and collaboration of unmanned platforms, the flexible reorganization of future multi-unmanned platforms system is studied. In this paper, a three-tier organizational structure of networked unmanned platform integration system is established by using meta-model modeling technology, and the flexible reorganization of networked unmanned platform integration system is realized. A flexible architecture based on meta-model is proposed. The service-based loosely coupled and distributed architecture is adopted for unmanned platform system. The final unmanned platform system will use dynamic task decomposition, assignment and reorganization to achieve operational business. A flexible, scalable, standardized and unified architecture is designed, which provides a way to realize the construction of the integrated architecture of flexible unmanned platform.
{"title":"Deployment Decision of Flexible Umanned Platform Based on Meta Model","authors":"Yuxiang Sun, Qinlin Xiang, Xiaopeng Huang, Xianzhong Zhou","doi":"10.1109/ICNSC48988.2020.9238086","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238086","url":null,"abstract":"In order to coordinate the effective collaboration and collaboration of unmanned platforms, the flexible reorganization of future multi-unmanned platforms system is studied. In this paper, a three-tier organizational structure of networked unmanned platform integration system is established by using meta-model modeling technology, and the flexible reorganization of networked unmanned platform integration system is realized. A flexible architecture based on meta-model is proposed. The service-based loosely coupled and distributed architecture is adopted for unmanned platform system. The final unmanned platform system will use dynamic task decomposition, assignment and reorganization to achieve operational business. A flexible, scalable, standardized and unified architecture is designed, which provides a way to realize the construction of the integrated architecture of flexible unmanned platform.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760817","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}
Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238061
Yanxu Hou, Jun Li, Zihan Fang, Xuechao Zhang
Generally, self-supervised learning of robotic grasp utilizes a model-free Reinforcement Learning method, e.g., a Deep Q-network (DQN). A DQN makes use of a high-dimensional Q-network to infer dense pixel-wise probability maps of affordances for grasping actions. Unfortunately, it usually leads to a time-consuming training process. Inspired by the initialization thought of optimization algorithms, we propose a method of initialization for accelerating self-supervised learning of robotic grasp. It pre-trains the Q-network by the supervised learning of affordance maps before the robotic grasp training. When applying the pre-trained Q-network a robot can be trained through self-supervised trial-and-error in a purposeful style to avoid meaningless grasping in empty regions. The Q-network is pre-trained by supervised learning on a small dataset with coarse-grained labels. We test the proposed method with Mean Square Error, Smooth L1, and Kullback-Leibler Divergence (KLD) as loss functions in the pre-training phase. The results indicate that the KLD loss function can predict accurately affordances with less noise in the empty regions. Also, our method is able to accelerate the self-supervised learning significantly in the early stage and shows little relevance to the sparsity of objects in the workspace.
{"title":"An Initialization Method of Deep Q-network for Learning Acceleration of Robotic Grasp","authors":"Yanxu Hou, Jun Li, Zihan Fang, Xuechao Zhang","doi":"10.1109/ICNSC48988.2020.9238061","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238061","url":null,"abstract":"Generally, self-supervised learning of robotic grasp utilizes a model-free Reinforcement Learning method, e.g., a Deep Q-network (DQN). A DQN makes use of a high-dimensional Q-network to infer dense pixel-wise probability maps of affordances for grasping actions. Unfortunately, it usually leads to a time-consuming training process. Inspired by the initialization thought of optimization algorithms, we propose a method of initialization for accelerating self-supervised learning of robotic grasp. It pre-trains the Q-network by the supervised learning of affordance maps before the robotic grasp training. When applying the pre-trained Q-network a robot can be trained through self-supervised trial-and-error in a purposeful style to avoid meaningless grasping in empty regions. The Q-network is pre-trained by supervised learning on a small dataset with coarse-grained labels. We test the proposed method with Mean Square Error, Smooth L1, and Kullback-Leibler Divergence (KLD) as loss functions in the pre-training phase. The results indicate that the KLD loss function can predict accurately affordances with less noise in the empty regions. Also, our method is able to accelerate the self-supervised learning significantly in the early stage and shows little relevance to the sparsity of objects in the workspace.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121538285","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}
Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238080
Haitao Yuan, J. Bi, Mengchu Zhou
Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.
{"title":"Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds","authors":"Haitao Yuan, J. Bi, Mengchu Zhou","doi":"10.1109/ICNSC48988.2020.9238080","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238080","url":null,"abstract":"Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127075253","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}
Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238101
Zihao Chen, Huaxiong Li, Yunsen Zhou, Jun Wu
Most face recognition studies are based on standard frontal face databases, but in real life, the images we obtain are profile face images of any angle in most instances. In this case, the traditional face recognition methods cannot achieve the lowest recognition cost. Therefore, how to use the obtained profile face images to synthesize the corresponding frontal face images is important in the face recognition system. Besides, most traditional face recognition systems are try to find an accurate classifier to achieve the lowest error rate, implicitly assuming that all misclassification costs are equal. It is an unreasonable assumption because almost in all face recognition systems, different types of misclassification errors often lead to different misclassification costs. To address the two issues, we propose a cost-sensitive face recognition method based on generative adversarial nets. First, generate frontal face images using the two-channel generative adversarial nets, and then introduce cost-sensitive learning in the recognition process to consider the cost imbalance problem. The experimental results demonstrate the effectiveness of the proposed method.
{"title":"Generative Adversarial Nets for Cost-Sensitive Face Recognition","authors":"Zihao Chen, Huaxiong Li, Yunsen Zhou, Jun Wu","doi":"10.1109/ICNSC48988.2020.9238101","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238101","url":null,"abstract":"Most face recognition studies are based on standard frontal face databases, but in real life, the images we obtain are profile face images of any angle in most instances. In this case, the traditional face recognition methods cannot achieve the lowest recognition cost. Therefore, how to use the obtained profile face images to synthesize the corresponding frontal face images is important in the face recognition system. Besides, most traditional face recognition systems are try to find an accurate classifier to achieve the lowest error rate, implicitly assuming that all misclassification costs are equal. It is an unreasonable assumption because almost in all face recognition systems, different types of misclassification errors often lead to different misclassification costs. To address the two issues, we propose a cost-sensitive face recognition method based on generative adversarial nets. First, generate frontal face images using the two-channel generative adversarial nets, and then introduce cost-sensitive learning in the recognition process to consider the cost imbalance problem. The experimental results demonstrate the effectiveness of the proposed method.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122125175","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}
Pub Date : 2020-10-30DOI: 10.1109/icnsc48988.2020.9311391
{"title":"[ICNSC 2020 Front Matter]","authors":"","doi":"10.1109/icnsc48988.2020.9311391","DOIUrl":"https://doi.org/10.1109/icnsc48988.2020.9311391","url":null,"abstract":"","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129774705","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}
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