An Intelligent Signal Processing Data Denoising Method for Control Systems Protection in the Industrial Internet of Things
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Guangjie | - |
dc.contributor.author | Tu, Juntao | - |
dc.contributor.author | Liu, Li | - |
dc.contributor.author | Martinez-Garcia, Miguel | - |
dc.contributor.author | Choi, Chang | - |
dc.date.accessioned | 2022-01-13T02:40:53Z | - |
dc.date.available | 2022-01-13T02:40:53Z | - |
dc.date.created | 2022-01-13 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83244 | - |
dc.description.abstract | The development of the industrial Internet of Things paradigm brings forth the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems (i.e., artificial intelligence algorithms) eliciting optimal decisions in real time. Ensuring the accuracy and reliability of the intelligent wireless sensing and control system pipeline is crucial toward achieving this goal. Nevertheless, the presence of noise in actual wireless transmission processes considerably affects the quality of the sensed data. Typically, noise and anomalies present in the data are very difficult to distinguish from each other. Conventional anomaly-detection techniques generate many error reports, which cause the control systems to issue incorrect responses that hinder the industrial production. In this article, a novel solution is proposed to denoise data while simultaneously preserving the actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts in computing a noise score. The trust level of each data point is then calculated through a correlation measure to purge spurious data. Extensive experiments on real datasets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency-facilitating the normal operation of the monitored control systems. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.title | An Intelligent Signal Processing Data Denoising Method for Control Systems Protection in the Industrial Internet of Things | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000739636900054 | - |
dc.identifier.doi | 10.1109/TII.2021.3096970 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.18, no.4, pp.2684 - 2692 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85110852811 | - |
dc.citation.endPage | 2692 | - |
dc.citation.startPage | 2684 | - |
dc.citation.title | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.volume | 18 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Choi, Chang | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Industrial Internet of Things | - |
dc.subject.keywordAuthor | Noise reduction | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Control systems | - |
dc.subject.keywordAuthor | Noise measurement | - |
dc.subject.keywordAuthor | Informatics | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | denoising | - |
dc.subject.keywordAuthor | fuzzy systems | - |
dc.subject.keywordAuthor | industrial Internet of Things (IIoT) | - |
dc.subject.keywordAuthor | intelligent signal processing | - |
dc.subject.keywordPlus | WIRELESS SENSOR NETWORKS | - |
dc.subject.keywordPlus | INTEGRITY ATTACKS | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
dc.subject.keywordPlus | IOT | - |
dc.subject.keywordPlus | ANOMALIES | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.