2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第5页

Deep Learning Based Anomaly Detection in Water Distribution Systems 基于深度学习的配水系统异常检测

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238099

Kai Qian, Jie Jiang, Yulong Ding, Shuanghua Yang

{"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}

引用次数: 10

Improved Artificial Bee Colony Algorithm for Solving a Single-Objective Sequence-dependent Disassembly Line Balancing Problem 求解单目标序列相关拆解线平衡问题的改进人工蜂群算法

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238075

Wenhong Luo, Mengchu Zhou, Xiwang Guo, Haiping Wei, Liang Qi, Ziyan Zhao

{"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}

引用次数: 5

Switch Control Used to Coordinate Different Demand Response Resources 用于协调不同需求响应资源的切换控制

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238053

Zhidong Ding, Mingyu Huang, Haitao Liu, Yaping Li, Zhetong Ding, Kaifeng Zhang

引用次数: 0

Identification of Electrical Equipment Based on Faster LSTM-CNN Network 基于更快LSTM-CNN网络的电气设备识别

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238109

Xiaoping Xiong, Shuang Xu, Wenliang Wu, Deran Tu, Jie Zhang, Zhi Wei

引用次数: 1

ss5:A Neural Network-based Energy Consumption Prediction Model for Feature Selection and Paremeter Optimization of Winders [5]基于神经网络的卷绕机能耗预测模型

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238073

Bobo Wang, Xiaohu Zheng, Jinsong Bao, Jie Li

{"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}

引用次数: 0

Deployment Decision of Flexible Umanned Platform Based on Meta Model 基于元模型的柔性无人平台部署决策

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238086

Yuxiang Sun, Qinlin Xiang, Xiaopeng Huang, Xianzhong Zhou

引用次数: 0

An Initialization Method of Deep Q-network for Learning Acceleration of Robotic Grasp 机器人抓取加速学习的深度q网络初始化方法

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238061

Yanxu Hou, Jun Li, Zihan Fang, Xuechao Zhang

{"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}

引用次数: 0

Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds 云环境中能源成本与性能敏感的双目标任务调度

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238080

Haitao Yuan, J. Bi, Mengchu Zhou

引用次数: 0

Generative Adversarial Nets for Cost-Sensitive Face Recognition 成本敏感人脸识别的生成对抗网络

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238101

Zihao Chen, Huaxiong Li, Yunsen Zhou, Jun Wu

{"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}

引用次数: 0

[ICNSC 2020 Front Matter] [ICNSC 2020前沿事项]

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/icnsc48988.2020.9311391

引用次数: 0