2017 13th IEEE Conference on Automation Science and Engineering (CASE)最新文献_第4页

On time-optimal behavior scheduling of robotic swarms for achieving multiple goals 面向多目标的机器人群体时间最优行为调度研究

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256323

S. Nagavalli, N. Chakraborty, K. Sycara

{"title":"On time-optimal behavior scheduling of robotic swarms for achieving multiple goals","authors":"S. Nagavalli, N. Chakraborty, K. Sycara","doi":"10.1109/COASE.2017.8256323","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256323","url":null,"abstract":"Robotic swarms are multi-robot systems whose global behaviors emerge from local interactions between individual robots. Each robot obeys a local control law that can be activated depending on an operator's choice of global swarm behavior. Real missions occur in uncontrolled environments with dynamically arising objectives and require combinations of behaviors. Given a library of swarm behaviors, a supervisory operator commanding the swarm must choose a sequence of behaviors to execute and their execution durations in order to accomplish a particular task during a mission composed of many tasks. In this paper, we address the following problem: given a library of swarm behaviors, the swarm initial state, the final goal and an unordered set of intermediate goals the operator wants to achieve, the objective is to identify a behavior schedule comprised of selected behaviors and associated time intervals of application of the behavior so that the total time to reach the final goal is minimized. Our contributions are as follows: (a) formalization of the problem of behavior scheduling to achieve multiple unordered goals with a robotic swarm, (b) an algorithm that produces a behavior schedule to achieve all intermediate goals and the final goal in minimum time such that the behavior durations are locally optimal and given which the goal sequence has bounded suboptimality and (c) application of this algorithm to configuration control of robot swarms.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"35 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133489329","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

Visual guided deep learning scheme for fall detection 视觉引导深度学习跌倒检测方案

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256202

N. Lu, Xiaodong Ren, Jinbo Song, Yidan Wu

{"title":"Visual guided deep learning scheme for fall detection","authors":"N. Lu, Xiaodong Ren, Jinbo Song, Yidan Wu","doi":"10.1109/COASE.2017.8256202","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256202","url":null,"abstract":"Fall detection is an important problem in the field of public health care, which is especially crucial for instant medical service delivery to the injured elderly due to falls. Ambient camera based fall detection has been a recognized non-intrusive and publicly acceptable method, where video data is employed to discriminate fall event from daily activities. Fall detection with videos usually requires a large dataset to extract features and train the classifier. However, it is hard to collect free-living environment fall data and instead simulated falls by young people have been collected to construct the training dataset, which is controlled intentional behavior and restricted to limited quantity of samples. In addition, the existing video based fall detection methods need segment the subject first, which is inclined to be influenced by image noise, illumination variation and occlusion. To address these problems, a three dimensional convolutional neural network (3D CNN) based method for fall detection is developed which only uses kinetic data to train an automatic feature extractor. Besides the spatial feature in 2D image, the motion information from the video could also be encoded by the three dimensional convolutions over the frames. A LSTM based spatial visual attention scheme is then incorporated, which could enable the network to focus on the key regions. Sports dataset Sports-1M with no fall examples is employed to train the 3D CNN and the visual attention model is trained on the small Multiple Cameras Fall Dataset. Then the visual attention based 3D CNN is employed to extract the features from the videos with fall event and implement fall detection. Experiments have shown the superior performance of the proposed scheme on fall dataset with high detection accuracy of 100%.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"32 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":"132751011","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: 12

Rolling horizon planning with engineering activities in semiconductor supply chains 半导体供应链工程活动的滚动地平线规划

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256237

Timm Ziarnetzky, L. Mönch, Thomas Ponsignon, H. Ehm

引用次数: 6

A ferrofluid-based robotic sheet for liquid manipulation by using vibration control 一种基于铁磁流体的振动控制液体操纵机器人片

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256198

Tadayuki Tone, Kenji Suzuki

引用次数: 2

Selecting the best system using transient means with sequential sampling constraints 用时序抽样约束的暂态方法选择最优系统

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256243

Hui Xiao

引用次数: 0

Feature-based systematic approach development for inconsistency resolution in automated production system design 自动化生产系统设计中不一致解决的基于特征的系统方法开发

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256183

M. Zou, B. Vogel‐Heuser

{"title":"Feature-based systematic approach development for inconsistency resolution in automated production system design","authors":"M. Zou, B. Vogel‐Heuser","doi":"10.1109/COASE.2017.8256183","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256183","url":null,"abstract":"Inconsistency management is important and challenging in automated production system design (APSD), because consistency among interdisciplinary models from different stakeholders cannot always be maintained in different design phases. Hence, (semi-)automated techniques assisting domain-specific engineers to resolve arising inconsistencies are essential to increase system reliability and design efficiency. However, few efforts have been made on inconsistency resolution in APSD. This study proposes a systematic method to develop an inconsistency resolution approach from problem definition to approach formulation. The problem addressed is firstly formalized, in order to reach a clear and common understanding across disciplines. Then, the solution space is framed with the feature diagram, which draws on mostly primary features in resolving inconsistencies. The feature diagram also allows approaches in different domains to be compared. As a result, a conceptual approach based on the feature diagram and oriented by the formalized problem is put forward. It is capable to plan resolutions semi-automatically for inter-model inconsistencies in APSD.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"38 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":"124570592","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: 8

Genetic algorithms for a single-machine multiple orders per job scheduling problem with a common due date 具有共同到期日的单机多订单作业调度问题的遗传算法

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256240

Jens Rocholl, L. Mönch

引用次数: 1

Maximizing the long-run average expected profit of a periodic-review assemble-to-order system 使定期评审的按订单组装系统的长期平均预期利润最大化

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256181

Yaping Zhao, Xiaoyun Xu, Haidong Li

引用次数: 1

Toward opportunistic services for the industrial Internet of Things 面向工业物联网的机会主义服务

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256205

G. Fortino, Claudio Savaglio, Mengchu Zhou

{"title":"Toward opportunistic services for the industrial Internet of Things","authors":"G. Fortino, Claudio Savaglio, Mengchu Zhou","doi":"10.1109/COASE.2017.8256205","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256205","url":null,"abstract":"Services are expected to represent the real drivers for all Internet of Things (IoT) application scenarios, including Industrial IoT (IIoT). Indeed, rather than focusing only on products, enterprises are now investing on product-service hybrids to improve operational efficiency, boost productivity and, most importantly, build new markets, hence diversifying their revenues. In this direction, IoT service modeling is a crucial activity requiring a multi-facet in-depth examination. In this paper, we first discuss the importance of IoT services by eliciting related benefits and challenges. Then, we tackle service modeling from both a developer-oriented (comprising highlevel descriptive metamodels and ontologies) and an enterprise-oriented (comprising operational models subject to formal verification and execution, such as extensions of the conventional Business Process Models, Petri Nets and WorkFlows) perspective, providing a conceptual mapping between these two complementary approaches. The proposed new paradigm of “Opportunistic IoT Service” extends the available models by explicitly considering essential features for service provision, i.e., dynamicity, context-awareness, co-location and transience.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"2 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":"114886947","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: 38

Big data analytic for multivariate fault detection and classification in semiconductor manufacturing 半导体制造中多变量故障检测与分类的大数据分析

2017 13th IEEE Conference on Automation Science and Engineering (CASE) Pub Date : 2017-08-01 DOI: 10.1109/COASE.2017.8256190

Ying-Jen Chen, Bo-Cheng Wang, Jei-Zheng Wu, Yi-Chia Wu, Chen-Fu Chien

{"title":"Big data analytic for multivariate fault detection and classification in semiconductor manufacturing","authors":"Ying-Jen Chen, Bo-Cheng Wang, Jei-Zheng Wu, Yi-Chia Wu, Chen-Fu Chien","doi":"10.1109/COASE.2017.8256190","DOIUrl":"https://doi.org/10.1109/COASE.2017.8256190","url":null,"abstract":"Nowadays, there are more attentions on cost control and yield enhancement in the semiconductor industry. Many manufacturers have the ability to collect the physical data called Status Variables Identification (SVID) by sensors embedded in the advanced machines during the manufacturing process. To maintain the competitive advantages, process monitoring and quick response to yield problem are pivotal in detecting the cause of the faults with the help of the sensor data. To state the physical nature of certain SVID, we usually transform SVID into Fault Detection and Classification parameters (FDC parameters) using statistical indicators. The data containing FDC parameters is called FDC data. This study aims to develop a multivariate analysis model to find out the crucial factors which may lead to process excursion among a large amount of FDC data. We proposed a 2-phase multivariate analysis framework: (1) the Least Absolute Shrinkage and Selection Operator (LASSO) is applied for key operation screening. (2) And Random Forest (RF) is used to rank the FDC parameters based on the key operations. Based on the results, domain engineers can quickly take actions responding to low yield problems.","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":"114961064","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