Quantum mechanics-based deep learning framework considering near-zero variance data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 비전임 | - |
dc.contributor.author | 이현수 | - |
dc.date.accessioned | 2024-06-14T09:00:15Z | - |
dc.date.available | 2024-06-14T09:00:15Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 0924-669X | - |
dc.identifier.issn | 1573-7497 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28741 | - |
dc.description.abstract | With the development of automation technology, big data is collected during operation processes, and among various machine learning analysis techniques using such data, deep neural network (DNN) has high analysis performance. However, most industrial data has low-variance or near-zero variance data from the refined processes in the collected data itself. This reduces deep learning analysis performance, which is affected by data quality. To overcome this, in this study, the weight learning pattern of an applied DNN is modeled as a stochastic differential equation (SDE) based on quantum mechanics. Through the drift and diffuse terms of quantum mechanics, the patterns of the DNN and data are quickly acquired, and the data with near-zero variance is effectively analyzed simultaneously. To demonstrate the superiority of the proposed framework, DNN analysis was performed using data with near-zero variance issues, and it was proved that the proposed framework is effective in processing near-zero variance data compared with other existing algorithms. | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | Quantum mechanics-based deep learning framework considering near-zero variance data | - |
dc.type | Article | - |
dc.publisher.location | 네델란드 | - |
dc.identifier.doi | 10.1007/s10489-024-05465-3 | - |
dc.identifier.scopusid | 2-s2.0-85193218252 | - |
dc.identifier.wosid | 001226679800002 | - |
dc.identifier.bibliographicCitation | APPLIED INTELLIGENCE, v.54, no.8, pp 6515 - 6528 | - |
dc.citation.title | APPLIED INTELLIGENCE | - |
dc.citation.volume | 54 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 6515 | - |
dc.citation.endPage | 6528 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | UNCERTAINTY | - |
dc.subject.keywordAuthor | Quantum mechanics | - |
dc.subject.keywordAuthor | Low-variance data | - |
dc.subject.keywordAuthor | Near-zero variance | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Ordinary differential equations | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh 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.