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김광섭 ( Gwang Sub Kim ),이병룡 ( Byung Yong Lee ) 한국품질경영학회 2007 품질경영학회지 Vol.35 No.3
CSR (Corporate Social Responsibility) is the subject which has been discussed for a long time, but real preparations of the corporations begin recently. International organizations and global corporation councils have announced their own guidelines about CSR The corporations have to establish responsible departments in their organizations and begin to publish reports which deals with the social responsibility. GRI (Global Reporting Initiative) have launched international guideline for corporate reporting and ISO has progressed making international standard for CSR. This paper is a study about understanding the international regulations tendency of CSR and the trend of Corporate Reporting for their sustainable development of the organizations.
진동의 주파수분석을 통한 결함 식별 - 회전기계를 중심으로-
정윤성,왕지남,김광섭,Jeong, Yoon-Seong,Wang, Gi-Nam,Kim, Gwang-Sub 한국정밀공학회 1995 한국정밀공학회지 Vol.12 No.11
This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.
최봉욱 ( Bong Wook Choi ),김광섭 ( Gwang Sub Kim ) 대한설비관리학회 2002 대한설비관리학회지 Vol.7 No.3
N/A The Forecasting system has two steps which are outliers detection using neural network and the deteced outliers replace using interpolation. Therefore the effects of outliers are removed, the one-step ahead prediction neural network is constructed by using the filtered series. The two step prediction neural network is also trained by using the previous predicted values. Experimental results indicated that the algorithm show much better than the neural network method. And in conclusion, we can get more accurated forecasting data by using the proposed method.