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조대희(Daehee Cho),이지수(Jisoo Lee),이강원(Kangwon Lee) 한국SCM학회 2015 한국SCM학회지 Vol.15 No.1
Although value stream mapping is a well known methodology to implement lean manufacturing, cases of applying this methodology to Korean enterprises are rarely reported. This paper deals with a case of applying value streaming mapping for implementing lean manufacturing in a small enterprise which produces components for TV assembly. Using information collected in a current shop flow, a current value stream map was drawn and kaizen burst was identified. Then a future value stream map was developed and various kaizen events were applied to implement a lean manufacturing. As a result of this implementation, the KPI’s were drastically improved.
조대희(DaeHee Cho),임경환(Kyeonghwan Lim),조성제(Seong-je Cho),한상철(Sangchul Han),황영섭(Young-sup Hwang) Korean Institute of Information Scientists and Eng 2016 정보과학회논문지 Vol.43 No.12
Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.