2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第3页

An Improved Deep Multiple-input and Single-output PointNet for 3D Model Retrieval 一种用于三维模型检索的改进深度多输入单输出点网

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238062

Junwen Tong, Jiabao Zhao, Jiaoyang Jin, Weisong Qiao, Haitong Li

引用次数: 0

Semi-Supervised Incremental Three-Way Decision Using Convolutional Neural Network 基于卷积神经网络的半监督增量三向决策

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238069

Yuwei Liang, Huaxiong Li, Bing Huang, Zhuohuai Guan, Pei Yang

{"title":"Semi-Supervised Incremental Three-Way Decision Using Convolutional Neural Network","authors":"Yuwei Liang, Huaxiong Li, Bing Huang, Zhuohuai Guan, Pei Yang","doi":"10.1109/ICNSC48988.2020.9238069","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238069","url":null,"abstract":"This paper aims to develop a novel cost-sensitive face recognition framework, which can gain the desirable recognition results with the least total cost. By combining two recently rising techniques: deep convolutional neural networks (CNNs) and sequential three-way decision (3WD) method, our framework can automatically label new samples and incorporates the delayed decision into decision-making process. We first explore the semi-supervised face recognition method in the case of the scarcity of labeled training data. By learning the class estimation and the deep convolution feature extraction of the unlabeled data jointly, the CNN trained by both labeled training data and unlabeled data is generated. Then, rather than getting a lower recognition error rate, we focus on seeking the minimum cost of misclassification at each decision step. For this purpose, we introduce the method of sequential 3WD in our cost-sensitive face recognition framework, which take each iteration of semi-supervised learning as a decision-making step. When there are insufficient labeled samples, a delayed decision will be adopted to reduce the decision cost. Finally, the test cost is also considered in the decision-making process, and the sum of misclassification cost and test cost is taken as the total cost. Using the total cost as the objective function, optimizing the performance indicators, training to get the classifier with the smallest total cost. In short, the model strives to get an optimal decision step, so that the reliable identification result can be obtained with only a small number of labeled data. The work value of this paper is to prove the effectiveness of our method in two face datasets.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"517 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":"133565766","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

Small-Sample Information Diffusion and Applications 小样本信息扩散与应用

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238067

Shanshan Yuan

引用次数: 1

A Lightweight Time Synchronisation for Wireless Sensor Networks 无线传感器网络的轻量级时间同步

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238056

C. Zhang, Shuanghua Yang

引用次数: 0

Selection of raw material manufacturers for hotel room consumables based on combined effects 选择酒店客房耗材原料厂家的综合效应

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238058

Z. Liu, Xianzhong Zhou, Wenting Hu

{"title":"Selection of raw material manufacturers for hotel room consumables based on combined effects","authors":"Z. Liu, Xianzhong Zhou, Wenting Hu","doi":"10.1109/ICNSC48988.2020.9238058","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238058","url":null,"abstract":"With the continuous improvement of China's economy and life, the hotel industry is booming simultaneously. As a major management business for hotels, purchasing management of room consumables is crucial. In reality, hotels usually purchase multiple types of room consumables through supply middlemen, and there may be combined effects between these raw material products of consumables during the procurement process. This type of combined effects can enable hotels to increase efficiency and reduce costs in purchasing activities, and thereby influence their choice of manufacturer. Therefore, by studying the combined effect of purchasing room consumables and selecting the optimal manufacturer, the purpose of reducing hotel procurement costs and increasing revenue is achieved. This paper proposes a manufacturer selection algorithm for hotel room consumables based on combined effects. The raw material products can be combined into several groups with similar attributes by analyzing the combined degree of raw material products. Finally, the optimal manufacturer can be determined which meets the requirements based on the attributes of the combined groups.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"78 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":"121848752","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

Biologically Inspired Smart Contract: A Blockchain-Based DDoS Detection System 生物启发的智能合约:基于区块链的DDoS检测系统

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238104

Xu Han, Rongbai Zhang, Xingzi Liu, Frank Jiang

{"title":"Biologically Inspired Smart Contract: A Blockchain-Based DDoS Detection System","authors":"Xu Han, Rongbai Zhang, Xingzi Liu, Frank Jiang","doi":"10.1109/ICNSC48988.2020.9238104","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238104","url":null,"abstract":"With the increase of Internet usage, the identification and recovery from cyber-attacks become the major concerns for cyber industries. Therefore, the harm caused by network attacks has caused widespread concern. Distributed Denial of Service (DDoS) attack is a very common destructive cyber attack. This is a network attack that destroys the network and can cause multiple computers to be attacked at the same time, failing to perform services properly. Therefore, based on the understanding of blockchain structure and DDoS characteristics, a blockchain-based DDoS detection model framework is proposed to form a blockchain-based collaborative detection system. We use the blockchain consortium chain structure to treat all participants as part of the private chain in the system. Each participating organization has its own channel, and other organizations cannot access its information, thus fully protecting the privacy of each participant. Our experimental results show that smart contracts can detect DDoS data and generate anomalous chains on each node. The time required to generate an exception chain and information sharing is very short, which indicates that the system can protect the privacy of user data. While sharing data in time, good results can be obtained as a collaborative detection system.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"36 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":"129281759","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

Percussion-based Detection of Bolt Looseness Using Speech Recognition Technology and Least Square Support Vector Machine 基于语音识别技术和最小二乘支持向量机的冲击螺栓松动检测

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238108

Furui Wang, Xuemin Chen, G. Song

引用次数: 2

A Deep Learning Approach to Large-Scale Light Curve Prediction and Real-Time Anomaly Detection with Grubbs Criterion 基于Grubbs准则的大规模光曲线预测和实时异常检测的深度学习方法

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238105

Xiaodong Huang, Lei Peng, Cheng Lu, J. Bi, Haitao Yuan

引用次数: 0

Adversarial Transform Networks for Unsupervised Transfer Learning 无监督迁移学习的对抗变换网络

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238125

Guanyu Cai, Yuqin Wang, Lianghua He, Mengchu Zhou

{"title":"Adversarial Transform Networks for Unsupervised Transfer Learning","authors":"Guanyu Cai, Yuqin Wang, Lianghua He, Mengchu Zhou","doi":"10.1109/ICNSC48988.2020.9238125","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238125","url":null,"abstract":"Transfer learning, especially unsupervised domain adaptation, is a crucial technology for sample-efficient learning. Recently, deep adversarial domain adaptation methods perform remarkably well in various tasks, which introduce a domain classifier to promote domain-invariant representation. However, previous methods either constrain the representative ability with an identical feature extractor for both domains or ignore the relationship between domains with separate extractors. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Transform Network (ATN) to both enhance the representative ability and transfer general information between domains. Residual connections are used to share features in the bottom layers, which deliver transferrable features to boost generalization performance. Moreover, a regularizer is proposed to alleviate a vanishing gradient problem, thus stabilizing the optimization procedure. Extensive experiments are conducted to show that the proposed ATN is comparable with the methods of the state-of-the-art and effectively deals with the vanishing gradient problem.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"6 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":"126945764","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 Dual-Path Deep Neural Network for Sonar Image Quality Evaluation

2020 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2020-10-30 DOI: 10.1109/ICNSC48988.2020.9238081

Huiqing Zhang, Shuo Li, Donghao Li

引用次数: 2