Quantum mechanics-based deep learning framework considering near-zero variance data
- Authors
- 비전임; 이현수
- Issue Date
- Apr-2024
- Publisher
- SPRINGER
- Keywords
- Quantum mechanics; Low-variance data; Near-zero variance; Deep learning; Ordinary differential equations
- Citation
- APPLIED INTELLIGENCE, v.54, no.8, pp 6515 - 6528
- Pages
- 14
- Journal Title
- APPLIED INTELLIGENCE
- Volume
- 54
- Number
- 8
- Start Page
- 6515
- End Page
- 6528
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28741
- DOI
- 10.1007/s10489-024-05465-3
- ISSN
- 0924-669X
1573-7497
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - School of Industrial Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.