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PDF Estimation 과 Dempster-Shafer theory 를 이용한 레이저용접 결함검출 기법
오록규(Rocku Oh),김덕영(DuckYoung Kim) (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.2
Although laser welding has many advantages such as fast processing time and single-sided access, the requirement of tight part-to-part gap control has been a main obstacle to maintaining the quality of laser welding. Traditionally, various stochastic anomaly detection methods have been developed for on-line weld defect detection, so that physical signals during the welding process can be monitored and classified. In order to improve the accuracy of weld defect detection, in this research paper, plasma intensity, weld pool temperature and back reflection signals are monitored, and their nominal trends are estimated by PDF estimation methods. We then aggregate these information, based on Dempster-Shafer theory. The performance of the proposed method is compared to the commercially available solutions of PRECITEC’s LWM<SUP>TM</SUP> and Hotelling’s T² method that are widely used in the literature. The proposed method reveals better performance in terms of Type I and Type II errors.
LaserWel: 레이저용접 모니터링 및 용접불량 분석 시스템
오록규(Rocku Oh),박종일(Jong Il Park),김덕영(Duck Young Kim) (사)한국CDE학회 2016 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2016 No.동계
Remote laser welding is an emerging joining technology to meet the increasing demand of corrosion resistance, fast, non-contacted and single sided joining for automotive body-in-white assemblies. This paper presents a developed laser welding monitoring system, LaserWel, characterized by sensor fusion-based fault detection and analysis using rich information from multiple sensors and easy-to-use graphical interface that is an essential feature for industrial usage. The system consists mainly of two photodiode sensors with signal amplifiers, optical filters, a data acquisition system, and a monitoring/analysis software.
Text-based industry classification by Autoencoder
Kyounghun Bae,Daejin Kim,Rocku Oh 한국재무학회 2018 한국재무학회 학술대회 Vol.2018 No.05
Industry classification has been one of the crucial issues in financial analysis. However, classical industry classification systems have several limitations. Several studies have been progressed to overcome the limitations by using the text information that firms use to describe their business process and products. In this paper, we propose an industry classification methodology based on their business descriptions by reducing high dimensions using autoencoder to avoid a high dimensionality problem in vector space. The main contribution of this paper is first, we overcome the limitation of cosine similarity measure where the word vector is large and highly sparse by reducing the dimension of word vector utilizing the autoencoder. Second, we are able to visualize the relative industry relation of the firms based on the lower dimensional information extracted from the business description text. The relative location can also describe the industry-level relationship as well as the position of individual firms which were originally involved in conflicting assignment problem in terms of the classical classification scheme.