2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)最新文献

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Pitfalls of Machine Learning Methods in Smart Grids: A Legal Perspective 智能电网中机器学习方法的缺陷:法律视角

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-11-01 DOI: 10.1109/ISCSIC54682.2021.00053

A. Antonov, Tobias Häring, T. Korõtko, A. Rosin, T. Kerikmäe, H. Biechl

{"title":"Pitfalls of Machine Learning Methods in Smart Grids: A Legal Perspective","authors":"A. Antonov, Tobias Häring, T. Korõtko, A. Rosin, T. Kerikmäe, H. Biechl","doi":"10.1109/ISCSIC54682.2021.00053","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00053","url":null,"abstract":"The widespread implementation of smart meters (SM) and the deployment of the advanced metering infrastructure (AMI) provide large amounts of fine-grained data on prosumers. Machine learning (ML) algorithms are used in different techniques, e.g. non-intrusive load monitoring (NILM), to extract useful information from collected data. However, the use of ML algorithms to gain insight on prosumer behavior and characteristics raises not only numerous technical but also legal concerns. This paper maps electricity prosumer concerns towards the AMI and its ML based analytical tools in terms of data protection, privacy and cybersecurity and conducts a legal analysis of the identified prosumer concerns within the context of the EU regulatory frameworks. By mapping the concerns referred to in the technical literature, the main aim of the paper is to provide a legal perspective on those concerns. The output of this paper is a visual tool in form of a table, meant to guide prosumers, utility, technology and energy service providers. It shows the areas that need increased attention when dealing with specific prosumer concerns as identified in the technical literature.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128665360","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

Automated Enterprise Architecture Model Mining 自动化企业架构模型挖掘

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2021-08-15 DOI: 10.1109/ISCSIC54682.2021.00044

Peter Hillmann, Erik Heiland, A. Karcher

{"title":"Automated Enterprise Architecture Model Mining","authors":"Peter Hillmann, Erik Heiland, A. Karcher","doi":"10.1109/ISCSIC54682.2021.00044","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00044","url":null,"abstract":"Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organization to get a clear view of their business, application, technical and physical layer. This modeling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organization. Based on this, the new approach generates EA models with the desired views points. Furthermore, a rule and knowledge-based reasoning is used to obtain a holistic overview. This offers a strategic decision support from business structure over process design up to planning the appropriate support technology. Therefore, it forms the base for organizations to act in an agile way. The modeling can be performed in different modeling languages, including ArchiMate and the Nato Architecture Framework (NAF). The designed approach is already evaluated on a small company with multiple services and an infrastructure with several nodes.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117057854","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

Traffic Flow Forecast of Road Networks With Recurrent Neural Networks 基于递归神经网络的道路交通流预测

2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) Pub Date : 2020-06-08 DOI: 10.1109/ISCSIC54682.2021.00018

Ralf Rüther, Andreas Klos, Marius Rosenbaum, W. Schiffmann

{"title":"Traffic Flow Forecast of Road Networks With Recurrent Neural Networks","authors":"Ralf Rüther, Andreas Klos, Marius Rosenbaum, W. Schiffmann","doi":"10.1109/ISCSIC54682.2021.00018","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00018","url":null,"abstract":"The interest in developing smart cities has increased dramatically in recent years. In this context an intelligent transportation system depicts a major topic. The forecast of traffic flow is indispensable for an efficient intelligent transportation system. The traffic flow forecast is a difficult task, due to its stochastic and non linear nature. Besides classical statistical methods, neural networks are a promising possibility to predict future traffic flow. In our work, this prediction is performed with various recurrent neural networks. These are trained on measurements of induction loops, which are placed in intersections of the city Hagen. We utilized data from beginning of January to the end of July in 2018. Each model incorporates sequences of the measured traffic flow from all sensors and predicts the future traffic flow for each sensor simultaneously. A variety of model architectures, forecast horizons and input data were investigated. Most often the vector output model with gated recurrent units achieved the smallest error on the test set over all considered prediction scenarios. Due to the small amount of data, generalization of the trained models is limited.","PeriodicalId":431036,"journal":{"name":"2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127196821","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

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