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수요자 맞춤형 스마트설계 플랫폼 구축을 위한 비즈니스 모델 관점의 사용자 수요인식 조사 및 분석
이민규(Mingyu Lee),이성곤(Seongkon Lee),이욱현(Wookhyun Lee),구기관(Kikwan Koo),한경진(Kyungjin Han),유재경(Jaekyung Yoo),박성준(Sungjoon Park),이유경(Youkyoung Lee) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
Discovering users demands is essential for successfully developing a service or product. Users perception and demand play a critical role in developing the Smart Design Platform that is a platform service helping thermal energy-intensive industrial facilities to more efficiently and easily design their products. This study investigates a process and analysis of perception and demands from the two viewpoints: technology providers and consumers who are targeted users of the Smart Design Platform. A preliminary online survey gained a total of 630 potential users’ feedbacks. It will demonstrate sufficient demands for the Smart Design Platform. For an advanced analysis of users’ demands, additional interviews will be conducted with technology providers and consumers.
Android malware dataset construction methodology to minimize bias–variance tradeoff
Shinho Lee,Wookhyun Jung,Wonrak Lee,Hyung Geun Oh,Eui Tak Kim 한국통신학회 2022 ICT Express Vol.8 No.3
Recently, research on Android malware categorization and detection is increasingly directed toward proposing different learned models based on various features of Android apps and machine learning algorithms. For the implementation of such modeling, properly constructing a dataset is no less important than selecting a suitable algorithm. The present study examines dataset construction using Dexofuzzy and proposes methods to determine the degree of bias and variance in the process and minimize the noise in sample set labeling where there is a possibility that even the same samples can be differently labeled. The method proposed in the present study goes beyond existing dataset construction methods relying on label data provided by AV vendors to include an effective approach to construct new types of datasets built on unified labels combined with opcode morphology. Based on newly constructed datasets, a flexible dataset, which allows overfitting and underfitting to be considered, was obtained via N-Gram and M-Partial Matching. This flexible dataset was then subjected to clustering, and the resultant clustering performance was evaluated.