2017 13th IEEE Conference on Automation Science and Engineering (CASE)最新文献_第3页

Analysis string stability of a new car-following model considering response time 考虑响应时间的新型汽车跟随模型的字符串稳定性分析

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256210

Junjie Zhang, Yunpeng Wang, G. Lu, Wenmin Long

引用次数: 2

Extracting grasping, contact points and objects motion from assembly demonstration 从装配演示中提取抓取、接触点和物体运动

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256252

Damien Petit, I. Ramirez-Alpizar, K. Harada, N. Yamanobe, Weiwei Wan, K. Nagata

引用次数: 1

State-dependent M/G/1/K queuing model for hard disk drives 硬盘驱动器的状态依赖M/G/1/K队列模型

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256206

Mingzhou Xie, L. Xia, Jun Xu

引用次数: 4

Multiple binary classifiers to analyse decision of non-compliance: For automated evaluation of piping layout 多二元分类器分析不符合决策:用于管道布置自动化评价

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256079

Wei-Chian Tan, I. Chen, H. K. Tan

{"title":"Multiple binary classifiers to analyse decision of non-compliance: For automated evaluation of piping layout","authors":"Wei-Chian Tan, I. Chen, H. K. Tan","doi":"10.1109/COASE.2017.8256079","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256079","url":null,"abstract":"This paper presents an approach to analyse decision from existing framework on automated evaluation of piping layout or design for reason of non-compliance. On top of Histogram of Connectivity and linear Support Vector Machines based approach for prediction if a design is compliant or non-compliant, multiple binary classifiers are trained using linear Support Vector Machines to classify a non-compliant design further according to nature of non-compliance, in space of Histogram of Connectivity. Non-compliant designs in existing dataset of Regulation 12, Annex I, International Convention for the Prevention of Pollution from Ships are further divided into separate categories according to reason of non-compliance. For each sub-category of non-compliance, a binary classifier is trained using linear Support Vector Machines by taking all non-compliant designs belonging to current category as positive and all others as negative class. Existing dataset of 1318 non-compliant designs is divided into seven sub-categories. Developed method has demonstrated encouraging performance on existing dataset of International Convention for the Prevention of Pollution from Ships.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125650838","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

A nonparametric adaptive sampling strategy for online monitoring of big data streams 大数据流在线监测的非参数自适应采样策略

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1080/00401706.2017.1317291

Xiaochen Xian, Andi Wang, Kaibo Liu

引用次数: 36

A framework of credit assurance mechanism for manufacturing services under social manufacturing context 社会化制造背景下制造业服务业信用保障机制框架

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256072

Jiajun Liu, P. Jiang, Jiewu Leng

{"title":"A framework of credit assurance mechanism for manufacturing services under social manufacturing context","authors":"Jiajun Liu, P. Jiang, Jiewu Leng","doi":"10.1109/COASE.2017.8256072","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256072","url":null,"abstract":"Increasing production personalization demand requires manufacturing enterprises to gain higher flexibility and faster market response. Enterprises have to share their manufacturing resources and cooperate with each other to win out in the fierce market competition. Under the circumstances, Social Manufacturing (SocialM) mode is proposed. In this mode, Social Manufacturing Network (SMN) integrates distributed Socialized Manufacturing Resources (SMRs) to provide more precise and professional service for customers. As a decentralized network, SMN cannot ensure the cross-enterprise collaborations because there is no trusted third party as supervisor. In this paper, a blockchain-based Production Credit Mechanism (PCM) for manufacturing services is put forward to regulate the cross-enterprise collaborations among SMRs. The frame and concept of PCM are firstly given, followed by four key enabling technologies supporting the implementation of the mechanism. It is expected that the PCM proposed in this paper will provide a possible way to normalize and regulate cross-enterprise collaborations under social manufacturing mode.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965589","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: 28

An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions 一种基于物理和参数学习的船舶变工况能量预测方法

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256263

Xingjian Lai, Xiaoning Jin, Xi Gu

{"title":"An integrated physical-based and parameter learning method for ship energy prediction under varying operating conditions","authors":"Xingjian Lai, Xiaoning Jin, Xi Gu","doi":"10.1109/COASE.2017.8256263","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256263","url":null,"abstract":"The efficiency of energy consumption of an engineering system dynamically changes during the its operation when the operational and environmental conditions vary in time. Various methods have been developed to monitor the energy consumption rate and predict the consumption efficiency for a given operating condition. The main challenges to maintain the accuracy of modeling and prediction stem from the great diversity of operational and environmental inputs that affect the energy consumption rate dynamically, as well as the lack of a full understanding of the physical relationship between energy efficiency and operation parameters of the system. Operating condition is a key component in system modeling and state identification in many applications because not only the system parameters, but also the structure and complexity of a model might vary significantly during different operation modes. This paper investigates a novel method that integrates a physics-based hydrodynamic model and dynamic parameter learning and estimation, using energy consumption monitoring data and operating condition data, in purpose of improving the prediction accuracy of energy consumption. By leveraging the strengths of both the physics-based models and data-driven parameter learning methods, the proposed method is advantageous when the complex system physics is not perfectly known and the performance of system is affected by the environmental operating condition, while abundant monitoring data are available. We demonstrate the model on a ship propulsion system for fuel consumption prediction, which achieves higher prediction accuracy compared with models without operating condition adaption and tuning.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131796545","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 collaborative scheduling strategy for twin fab 双晶圆厂协同调度策略

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256188

Hao She, Qingyun Yu, Zhihong Min, Li Li

引用次数: 0

Reliability evaluation of AC/DC hybrid power grid considering transient security constraints 考虑暂态安全约束的交直流混合电网可靠性评估

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256270

Can Wang, Haipeng Xie, Z. Bie, Chao-Bo Yan, Yanling Lin

{"title":"Reliability evaluation of AC/DC hybrid power grid considering transient security constraints","authors":"Can Wang, Haipeng Xie, Z. Bie, Chao-Bo Yan, Yanling Lin","doi":"10.1109/COASE.2017.8256270","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256270","url":null,"abstract":"With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130842164","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: 6

Using the distributed proximal alternating direction method of multipliers for smart grid monitoring 利用乘法器的分布式近交变方向法进行智能电网监测

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256140

Raffaele Carli, M. Dotoli

引用次数: 2