2019 IEEE 17th International Conference on Industrial Informatics (INDIN)最新文献_第10页

Virtual Gateway in TCMS Execution Environment based on an Integrated Architecture 基于集成体系结构的TCMS执行环境中的虚拟网关

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972294

Hongjie Fang, R. Obermaisser

{"title":"Virtual Gateway in TCMS Execution Environment based on an Integrated Architecture","authors":"Hongjie Fang, R. Obermaisser","doi":"10.1109/INDIN41052.2019.8972294","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972294","url":null,"abstract":"In the railway domain, the development trend is evolving from federated to integrated architectures, which promise massive cost reduction through higher functional integration of Train Control and Monitoring Systems (TCMS). An integrated architecture means that components are integrated on a computing node, which formerly were allocated to separate computing nodes. Therefore, the execution environment needs to support communication between these components on a computing node. Based on an integrated architecture, a virtual switch supporting time-space isolation and dynamic configuration has been proposed in previous work to address the mixed criticality of TCMS applications and dynamic coupling of vehicles. However, controlled information import and export between different data flows is not addressed in the state of the art.In this paper, we propose a virtual gateway residing in the TCMS execution environment to resolve property mismatches between different data flows and prevent fault propagation between applications of different safety critical levels. We capture the properties of data flows in the gateway specification and realise the gateway services by leveraging a database in the gateway. The experimental results show the isolation and the controlled information transportation between data flows.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885990","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

Developing a Secure, Smart Microgrid Energy Market using Distributed Ledger Technologies 利用分布式账本技术开发安全、智能的微电网能源市场

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972018

Lehlogonolo P. I. Ledwaba, G. Hancke, S. Isaac, H. Venter

引用次数: 8

Power Consumption Optimization of a Wireless Temperature Sensor Node Using Unidirectional Communication 基于单向通信的无线温度传感器节点功耗优化

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972226

R. Taherkhani, S. Nihtianov

引用次数: 1

Towards Formal Modeling and Analysis of SystemJ GALS Systems using Coloured Petri Nets 基于彩色Petri网的SystemJ GALS系统形式化建模与分析

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972025

Weiyi Zhang, Z. Salcic, Avinash Malik

引用次数: 1

Detection of Machine Tool Anomalies from Bayesian Changepoint Recurrence Estimation 基于贝叶斯变点递归估计的机床异常检测

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972164

Christian Reich, Christina Nicolaou, Ahmad Mansour, Kristof Van Laerhoven

{"title":"Detection of Machine Tool Anomalies from Bayesian Changepoint Recurrence Estimation","authors":"Christian Reich, Christina Nicolaou, Ahmad Mansour, Kristof Van Laerhoven","doi":"10.1109/INDIN41052.2019.8972164","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972164","url":null,"abstract":"In this study, we consider the problem of detecting process-related anomalies for machine tools. The similar shape of successive sensor signals, which arises due to the same process step sequence applied to each workpiece, suggests extracting shape-related features. In recent years, shapelets dominated the field of shape-related features. Unfortunately, they involve a high computational burden due to hyperparameter optimization.We introduce alternative shape-related features relying on abrupt signal changes (changepoints) reflecting the changes of process steps. During normal operation, changepoints follow a highly recurrent pattern, i.e., appear at similar locations. Thus, being able to distinguish regular, recurrent from abnormal, non-recurrent changepoints allows detecting process anomalies.For changepoint recurrence estimation, we extend the Bayesian Online Changepoint Detection (BOCPD) method. The extension allows distinguishing normal and abnormal changepoints relying on empirical estimates of the changepoint recurrence distribution. Subsequently, changepoint-related features are introduced and compared to shapelets and wavelet-based features in a case study comprising real-world machine tool data.Qualitative results verify changepoint locations being comparable to shapelet locations found by the FLAG shapelet approach. Furthermore, quantitative results suggest superior classification performance both to shapelets and wavelet-based features.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617644","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 Thermal Camera-based Activity Recognition Using Discriminant Skeleton Features and RNN 基于分辨骨架特征和RNN的热像仪活动识别

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972082

Md. Zia Uddin, W. Khaksar, J. Tørresen

{"title":"A Thermal Camera-based Activity Recognition Using Discriminant Skeleton Features and RNN","authors":"Md. Zia Uddin, W. Khaksar, J. Tørresen","doi":"10.1109/INDIN41052.2019.8972082","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972082","url":null,"abstract":"Recognizing human activities from sensor data is one of the key areas of image processing, computer vision, and pattern recognition researches today. The target of human activity recognition (HAR) is usually to detect and analyze distinguished activities from the data acquired via different sensors (e.g. thermal cameras). This work proposes a HAR approach from videos recorded via a thermal camera. Skeletons of human bodies are extracted from thermal frames using an opensource deep convolutional neural network (CNN)-based approach named OpenPose. It is generally applied on videos of typical color cameras. However, this work adopts OpenPose on thermal images to extract useful features so that the HAR system can be deployed in environments with low lights as well. Once skeletons of human silhouettes are obtained from the thermal images, robust spatiotemporal features are extracted followed by discriminant analysis. Finally, the discriminant features are fed into a deep recurrent neural network (RNN) for activity training and recognition. The proposed HAR method can be applied to monitor the users such as elderly in both bright and dark environments to prolong their independent life, unlike other typical color cameras which are generally applied in bright environments.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126177932","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

The Exchange Network: An Architecture for the Negotiation of Non-Repudiable Token Exchanges 交换网络:不可抵赖代币交换协商的架构

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972031

Emanuel Palm, O. Schelén, Ulf Bodin, Richard Hedman

引用次数: 7

Evaluating Two Semantics for Falsification using an Autonomous Driving Example 以自动驾驶为例评估两种语义的证伪性

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972229

Zahra Ramezani, Nicholas Smallbone, Martin Fabian, K. Åkesson

{"title":"Evaluating Two Semantics for Falsification using an Autonomous Driving Example","authors":"Zahra Ramezani, Nicholas Smallbone, Martin Fabian, K. Åkesson","doi":"10.1109/INDIN41052.2019.8972229","DOIUrl":"https://doi.org/10.1109/INDIN41052.2019.8972229","url":null,"abstract":"We consider the falsification of temporal logic properties as a method to test complex systems, such as autonomous systems. Since these systems are often safety-critical, it is important to assess whether they fulfill given specifications or not. An adaptive cruise controller for an autonomous car is considered where the closed-loop model has unknown parameters and an important problem is to find parameter combinations for which given specification are broken. We assume that the closed-loop system can be simulated with the known given parameters, no other information is available to the testing framework. The specification, such as, the ability to avoid collisions, is expressed using Signal Temporal Logic (STL). In general, systems consist of a large number of parameters, and it is not possible or feasible to explicitly enumerate all combinations of the parameters. Thus, an optimization-based approach is used to guide the search for parameters that might falsify the specification. However, a key challenge is how to select the objective function such that the falsification of the specification, if it can be falsified, can be falsified using as few simulations as possible. For falsification using optimization it is required to have a measure representing the distance to the falsification of the specification. The way the measure is defined results in different objective functions used during optimization. Different measures have been proposed in the literature and in this paper the properties of the Max Semantics (MAX) and the Mean Alternative Robustness Value (MARV) semantics are discussed. After evaluating these two semantics on an adaptive cruise control example, we discuss their strengths and weaknesses to better understand the properties of the two semantics.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126681701","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

Data Preparation for Data Mining in Chemical Plants using Big Data 基于大数据的化工厂数据挖掘的数据准备

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972078

Reuben Borrison, Benjamin Kloepper, Jennifer Mullen

引用次数: 0

Cryptographic Strength and Machine Learning Security for Low Complexity IoT Sensors 低复杂度物联网传感器的加密强度和机器学习安全性

2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Pub Date : 2019-07-01 DOI: 10.1109/INDIN41052.2019.8972053

J. Long, Sam Matsumoto

引用次数: 0