2017 13th IEEE Conference on Automation Science and Engineering (CASE)最新文献_第9页

A geometric errors analysis method integrated clamping error and wear out error over working space 一种综合夹持误差和工作空间磨损误差的几何误差分析方法

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256178

Xiaojian Liu, Chenrui Wu, Le-miao Qiu, Yang Wang, Shuyou Zhang

{"title":"A geometric errors analysis method integrated clamping error and wear out error over working space","authors":"Xiaojian Liu, Chenrui Wu, Le-miao Qiu, Yang Wang, Shuyou Zhang","doi":"10.1109/COASE.2017.8256178","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256178","url":null,"abstract":"Machining accuracy is one of the major parameters of machine tools which is determined by geometric accuracy design to a large extent. In order to improve the comprehensiveness and veracity in geometric accuracy design, this paper proposed a geometric errors analysis method integrated clamping error and wear out error over working space. A multi-rigid-body model which included the cutting tool's wear out error and work-piece's clamping error is established to represent the position relationships of machine tools' working components. The expression of geometric error was converted from matrix form to screw form through a screw mapping method so that geometric error of all the six degree of freedom in global coordinate frame can be straightly expressed. Based on this, the key geometric errors that affecting the machining accuracy were identified through the improved sensitivity analysis in which motion rules through working space were considered. Finally, a case study on geometric accuracy design stage of a horizontal boring machine was carried out which highlights the advantages of the proposed methodology.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 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":"128643982","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 design approach for event-driven optimization in complex air conditioning systems 复杂空调系统事件驱动优化设计方法

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256219

Junqi Wang, S. Lou, Pei Zhou, G. Huang

{"title":"A design approach for event-driven optimization in complex air conditioning systems","authors":"Junqi Wang, S. Lou, Pei Zhou, G. Huang","doi":"10.1109/COASE.2017.8256219","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256219","url":null,"abstract":"Air conditioning (AC) systems take up the major proportion of total building energy consumption. While online optimal control is regarded as an efficient tool to improve the operating efficiency of AC systems, traditional online optimal control schemes utilize a so-called time-driven optimization (TDO) scheme. Although it works well for simple AC systems, several limitations are encountered when systems become more and more complex. TDO is basically a periodic scheme, which may lead to inefficient actions (e.g. delayed or unnecessary actions) in response to aperiodic or stochastic operational changes. TDO is also not efficient in balancing the optimization performance and computing load. Recently, an event-driven optimization (EDO) scheme has been proposed to solve these limitations. However, as the EDO in the building sector is quite a new topic, the corresponding EDO design methodology remains blank. Thus, this paper presents a feasible design methodology for EDO. The effectiveness of the design methodology is validated by the case study of a commercial AC system. Results show that the EDO (with optimized events) achieves better computational efficiency without sacrificing energy performance compared with the conventional TDO.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"16 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":"130827206","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

Exploring functional variant using a deep learning framework 使用深度学习框架探索功能变体

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256086

Tianyi Sun, Zhuo Liu, Xingming Zhao, R. Jiang

引用次数: 0

Secure state estimation for DC microgrids control 直流微电网控制的安全状态估计

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256334

Gabriella Fiore, A. Iovine, E. D. Santis, M. D. Benedetto

引用次数: 11

Smart control of indoor thermal environment based on online learned thermal comfort model using infrared thermal imaging 基于红外热成像在线学习热舒适模型的室内热环境智能控制

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256221

Fulin Wang, Binruo Zhu, Rui Li, Dianshan Han, Zeyun Sun, Saejin Moon, Ziyang Gong, Wenhong Yu

引用次数: 11

Multi-fidelity modeling for analysis of serial production lines 多保真度建模方法在生产线分析中的应用

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256071

Yunyi Kang, L. Mathesen, Giulia Pedrielli, Feng Ju

{"title":"Multi-fidelity modeling for analysis of serial production lines","authors":"Yunyi Kang, L. Mathesen, Giulia Pedrielli, Feng Ju","doi":"10.1109/COASE.2017.8256071","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256071","url":null,"abstract":"Analytical and simulation models are two common types of approaches used to estimate and predict the performance of complex production systems. Typically analytical models are fast to run but can have reduced accuracy. On the other hand simulation models can achieve high accuracy, but only at the cost of large simulation time and number of replications. Traditionally, the research has been focusing on the development of models able to achieve a satisfactory trade off between accuracy and computational effort. Nevertheless, such an approach implies the choice of a single model to approximate the system behavior. There is still lack of a generic model that can deliver high accuracy and low computational cost for production systems. In this paper, we attempt to address this issue and present a multi-fidelity modeling approach, utilizing both analytical models and simulation models at different levels of fidelity, to efficiently and effectively estimate the performance of asynchronous serial lines with exponential machines. Experimental results show that the multi-fidelity model provides better estimation of the production rate of the studied example Such a model has demonstrated potential in evaluating a large number of solutions with limited computational budget.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"94 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":"130012752","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: 4

Generalized Haar filter based CNN for object detection in traffic scenes 基于广义Haar滤波的CNN交通场景目标检测

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256342

Keyu Lu, Jian Li, X. An, Hangen He, Xiping Hu

{"title":"Generalized Haar filter based CNN for object detection in traffic scenes","authors":"Keyu Lu, Jian Li, X. An, Hangen He, Xiping Hu","doi":"10.1109/COASE.2017.8256342","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256342","url":null,"abstract":"Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). Meanwhile, it also poses to be a demanding task due to the diversity of traffic scenes and resource limitations of the platforms for traffic scene applications. To address these issues, we present a generalized Haar filter based CNN (Convolutional Neural Network) which is suitable for the object detection tasks in traffic scenes. In this approach, we first decompose an object detection task into multiple local regression tasks. Thereafter, we handle these local regression tasks using several light and efficient networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of these deep networks are constrained to the form of generalized Haar filters. Finally, we carry out various experiments to evaluate the performance of our proposed approach in traffic scene datasets. Experimental results demonstrate that our object detection system is light and effective in comparison with the state-of-the-art.","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":"131123280","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: 3

A cloud-supported cps approach to control decision of process manufacturing: 3D ONoC 一种云支持的过程制造控制决策cps方法:3D ONoC

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256147

Zhaolong Ning, Weigang Hou, Xiping Hu, Xiaoxue Gong

引用次数: 5

Vacuity aware falsification for MTL request-response specifications 用于MTL请求-响应规范的空感知伪造

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256286

Adel Dokhanchi, Shakiba Yaghoubi, Bardh Hoxha, Georgios Fainekos

引用次数: 14

Load identification based on Factorial Hidden Markov Model and online performance analysis 基于阶乘隐马尔可夫模型的负荷识别及在线性能分析

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256272

Siyun Chen, F. Gao, Ting Liu

{"title":"Load identification based on Factorial Hidden Markov Model and online performance analysis","authors":"Siyun Chen, F. Gao, Ting Liu","doi":"10.1109/COASE.2017.8256272","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256272","url":null,"abstract":"Load identification is important for the tasks such as load forecasting, demand response and energy management in smart buildings. The accuracy of the traditional methods depends on the dimension of load signatures, the sampling frequency and the stability of load profile. In this paper, a Factorial Hidden Markov Model (FHMM)-based method is proposed to analyze the aggregate load profile and identify the individual device. We extend the Viterbi algorithm to solve the FHMM directly, and this process is more efficient than the solution of the equivalent HMM by using the conventional Viterbi algorithm. The proposed method is insensitive to the stability and accuracy of power data, so it is suitable for the devices in buildings, even for the continuously variable loads. Two experiments with real power data are evaluated to illustrate the proposed method. Meanwhile, we focus on the online performance of the Viterbi algorithm. It is found that the states decoded by Viterbi are unreliable when the observed data are inside a confusing zone. Through analyzing the mechanism of the Viterbi algorithm, the judgment conditions the boundary of the confusing zone are given. We hope this work brings insight to the research on load identification and HMM.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"1 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":"126053119","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: 3