Pub Date : 2020-07-06DOI: 10.1109/IAI50351.2020.9262203
J. Viola, Y. Chen
In Industry 4.0, the increasing complexity of industrial systems introduces unknown dynamics that affect the performance of manufacturing processes. Thus, Digital Twin appears as a breaking technology to develop virtual representations of any complex system design, analysis, and behavior prediction tasks to enhance the system understanding via enabling capabilities like real-time analytics, or Smart Control Engineering. In this paper, a novel framework is proposed for the design and implementation of Digital Twin applications to the development of Smart Control Engineering. The framework involve the steps of system documentation, Multidomain Simulation, Behavioral Matching, and real-time monitoring, which is applied to develop the Digital Twin for a real-time vision feedback temperature uniformity control. The obtained results show that Digital Twin is a fundamental part of the transformation into Industry 4.0.
{"title":"Digital Twin Enabled Smart Control Engineering as an Industrial AI: A New Framework and Case Study","authors":"J. Viola, Y. Chen","doi":"10.1109/IAI50351.2020.9262203","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262203","url":null,"abstract":"In Industry 4.0, the increasing complexity of industrial systems introduces unknown dynamics that affect the performance of manufacturing processes. Thus, Digital Twin appears as a breaking technology to develop virtual representations of any complex system design, analysis, and behavior prediction tasks to enhance the system understanding via enabling capabilities like real-time analytics, or Smart Control Engineering. In this paper, a novel framework is proposed for the design and implementation of Digital Twin applications to the development of Smart Control Engineering. The framework involve the steps of system documentation, Multidomain Simulation, Behavioral Matching, and real-time monitoring, which is applied to develop the Digital Twin for a real-time vision feedback temperature uniformity control. The obtained results show that Digital Twin is a fundamental part of the transformation into Industry 4.0.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127836331","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}
Pub Date : 2020-01-07DOI: 10.1109/IAI50351.2020.9262173
Xinyu Fan, Faen Zhang, Jiahong Wu, Jingming Guo
As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.
{"title":"Provenance-based Classification Policy based on Encrypted Search","authors":"Xinyu Fan, Faen Zhang, Jiahong Wu, Jingming Guo","doi":"10.1109/IAI50351.2020.9262173","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262173","url":null,"abstract":"As an important type of cloud data, digital provenance is arousing increasing attention on improving system performance. Currently, provenance has been employed to provide cues regarding access control and to estimate data quality. However, provenance itself might also be sensitive information. Therefore, provenance might be encrypted and stored in the Cloud. In this paper, we provide a mechanism to classify cloud documents by searching specific keywords from their encrypted provenance, and we prove our scheme achieves semantic security. In term of application of the proposed techniques, considering that files are classified to store separately in the cloud, in order to facilitate the regulation and security protection for the files, the classification policies can use provenance as conditions to determine the category of a document. Such as the easiest sample policy goes like: the documents have been reviewed twice can be classified as “public accessible”, which can be accessed by the public.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127112905","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}
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