2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)最新文献_第6页

Requirements for adopting digital B2B platforms for manufacturing capacity finding and sharing 采用数字化B2B平台寻找和共享制造能力的需求

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869016

E. Arica, M. Oliveira

引用次数: 2

A Framework to Design and Implement Real-time Multicore Schedulers using Machine Learning 利用机器学习设计和实现实时多核调度程序的框架

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869545

Leonardo Passig Horstmann, José Luís Conradi Hoffmann, A. A. Fröhlich

{"title":"A Framework to Design and Implement Real-time Multicore Schedulers using Machine Learning","authors":"Leonardo Passig Horstmann, José Luís Conradi Hoffmann, A. A. Fröhlich","doi":"10.1109/ETFA.2019.8869545","DOIUrl":"https://doi.org/10.1109/ETFA.2019.8869545","url":null,"abstract":"In this paper, we introduce a Framework to Design and Implement Real-time Multicore Schedulers using Machine Learning techniques applied to the very own data such systems produce as they operate. The framework builds on sensors and event counters present in modern hardware platforms and on variables kept by the operating system to capture run-time data that are subsequently subjected to ML tools to produce scheduling heuristics targeting specific optimization goals. It provides non-intrusive mechanisms to collect such data while the system runs real task sets with real workloads, thus preserving the quality of the captured data. It abstracts the Performance Monitoring Unit, thermal sensing, energy monitoring, and Dynamic Voltage and Frequency Scaling available on such platforms through a lean, architecture-independent API. After describing the framework in details, we demonstrate its applicability with the implementation of an energy-efficient, load balancing, real-time, multicore heuristic for a PEDF scheduler. The measured overhead imposed by the framework on the tasks it schedule is at most 0,0003583% and the maximum added jitter is less than 40μs, corroborating the ability of the framework to support the development of effective domain-specific schedulers using machine learning techniques.","PeriodicalId":6682,"journal":{"name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"1 1","pages":"251-258"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89833699","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

Towards Support of Global Views on Common Concepts employing Local Views 使用局部视图支持通用概念的全局视图

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869239

Felix Rinker, Laura Waltersdorfer, Kristof Meixner, S. Biffl

引用次数: 8

Modular Signal Processing Unit for Motion Control Applications Based on System-on-Chip with FPGA 基于FPGA的片上系统运动控制模块信号处理单元

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869121

Vlastimil Setka, Ondřej Ježek, Rihards Novickis

{"title":"Modular Signal Processing Unit for Motion Control Applications Based on System-on-Chip with FPGA","authors":"Vlastimil Setka, Ondřej Ježek, Rihards Novickis","doi":"10.1109/ETFA.2019.8869121","DOIUrl":"https://doi.org/10.1109/ETFA.2019.8869121","url":null,"abstract":"Motion control systems with distributed architecture where multiple input/output devices are connected to the upper layer controller by fast digital communication (fieldbus) became an industrial standard. This paper presents design of a modular input/output device which can process signals from multiple sensors, drive multiple actuators and act as a Slave or Master node in EtherCAT fieldbus network.User-defined algorithms can be easily implemented to preprocess input signals, combine multiple signals or close local control loops with extremely high sampling rates which makes the difference to standard off-the-shelf solutions. To meet these requirements and simplify hardware design, our device is based on System-on-Chip with both programmable logic (FPGA) and classic processor (CPU) ARM cores. Data processing including user algorithms can be done entirely in FPGA which provides very low latency and no jitter, and also on CPU for more complex computations with advantage of tight integration between FPGA and CPU. In this paper we provide description of hardware design, system architecture and typical applications.","PeriodicalId":6682,"journal":{"name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"25 1","pages":"857-863"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86639214","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

Incremental Abstraction for Diagnosability Verification of Modular Systems 模块化系统可诊断性验证的增量抽象

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869382

Mona Noori Hosseini, B. Lennartson

引用次数: 0

Superpixel Description and Indexing for Visual Loop Closure Detection 视觉闭环检测的超像素描述和索引

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869091

Rihem El Euch, E. Garcia-Fidalgo, A. Ortiz, F. Chaabane, A. Ghazel

引用次数: 0

A KPI-based Condition Monitoring System for the Beer Brewing Process 基于kpi的啤酒酿造过程状态监测系统

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869184

Faruk Pasic, B. Wohlers, Stefan Dziwok, Matthias Becker, Matthias Heinrich

{"title":"A KPI-based Condition Monitoring System for the Beer Brewing Process","authors":"Faruk Pasic, B. Wohlers, Stefan Dziwok, Matthias Becker, Matthias Heinrich","doi":"10.1109/ETFA.2019.8869184","DOIUrl":"https://doi.org/10.1109/ETFA.2019.8869184","url":null,"abstract":"The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.","PeriodicalId":6682,"journal":{"name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"44 1","pages":"1469-1472"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79010444","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

Modelling and simulation of a magnetically coupled multiport dc-dc converter 磁耦合多端口dc-dc变换器的建模与仿真

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869249

R. Griñó, A. Dòria-Cerezo

引用次数: 6

Diagnosability of Linear Hybrid Systems with discrete dynamics modelled by Petri nets* 用Petri网建模的离散动力学线性混合系统的可诊断性

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869020

A. F. Pissos, C. Vázquez, M. Marcos, A. Ramírez-Treviño

引用次数: 0

Dynamic scheduling in modern processing systems using expert-guided distributed reinforcement learning 基于专家引导的分布式强化学习的现代加工系统动态调度

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Pub Date : 2019-09-01 DOI: 10.1109/ETFA.2019.8869023

Shuhui Qu, Jie Wang, J. Jasperneite

{"title":"Dynamic scheduling in modern processing systems using expert-guided distributed reinforcement learning","authors":"Shuhui Qu, Jie Wang, J. Jasperneite","doi":"10.1109/ETFA.2019.8869023","DOIUrl":"https://doi.org/10.1109/ETFA.2019.8869023","url":null,"abstract":"In the environment of modern processing systems, one topic of great interest is how to optimally schedule (i.e., allocate) jobs with different requirements for the systems to meet various objectives. Methods using distributed reinforcement learning (DRL) have recently achieved great success with large-scale dynamic scheduling problems. However, most DRL methods require a huge amount of computational time and a large amount of data for the DRL agents for a control policy. Meanwhile, various scheduling experts have already developed several scheduling policies (i.e., dispatching rules) that can successfully fulfill different objectives with acceptable performance for a processing system based on their understandings of the processing systems characteristics. In this paper, we propose to learn from experts to reduce the learning and searching costs of a good policy in large-scale dynamic scheduling problems. In the learning process, our DRL agents select the experts who have better performance in the scheduling environment, observe the experts’ actions, and learn a scheduling policy guided by these experts’ demonstrations. Our realistic simulations results demonstrate that this expert-guided DRL (EGDRL) approach outperforms DRL methods without expert guidance, as well as some other reinforcement learning from demonstration (RLfD) methods in several systems. To the best of our knowledge, our research is one of the first works that incorporates existing expert policies to guide the learning of optimal policies for large-scale dynamic scheduling problems.","PeriodicalId":6682,"journal":{"name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"98 1","pages":"459-466"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81121858","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