• 한국산학기술학회논문지
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• 제13권4호
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• pp.1863-1871
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• 2012

기존의 OSS의 활용은 운영체제나 DBMS와 같은 시스템 애플리케이션의 사용이 주를 이루었다. 그러나 현재 많은 기업에서 시스템 소프트웨어가 아닌 응용 애플리케이션 중심으로 OSS를 활용하려고 한다. 그러나 OSS를 활용한 응용 애플리케이션 개발을 위해서는 기반이 되는 OSS의 선정이 무엇보다 중요하다. 기존의 OSS 평가 연구의 범위는 전체 OSS 품질을 포함하고 있다. 그러므로 OSS 선정에 대한 평가 연구는 미흡하다. 또한 평가의 결과는 정량적인 측정보다 정성적인 측정에 기반하고 있다. 본 연구에서는 기존의 OSS 평가지표들 중 선정을 위한 지표들만을 도출하고 이를 기반으로 프로젝트 특징에 따라 정량적인 평가가 가능한 평가지표를 제안한다. 제안하는 평가지표는 OSS 커뮤니티 내의 정보만을 가지고 평가할 수 있는 초기평가지표와 정량적인 측정이 가능한 상세평가지표로 구분하여 제안한다. 이를 통해 OSS 선정 시 정량적인 점수와 지표를 통해 객관적인 근거를 제공한다.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2001년도 춘계학술대회
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• pp.235-239
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• 2001

The purpose of this study is to develop an insurance system for product quality liability(PQL) by reviewing some legal issues concerning the product liability It is concluded that the purpose and the function of PQL insurance have to be considered with robust experience data for the life of product, quality system of the company, size of company, the number and amount of products produced by a company and the type of company etc. And this article reviews some problems of policy including the possibility of anti-selection and reverse selection.

• 기술혁신학회지
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• 제1권3호
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• pp.326-337
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• 1998

This study aims to propose the model of functional specialization for university and the selection of research university in Korea. This study propose that we diversify universities into three categories ; research university, educational university, and technical university. Considering the current research capability and future research prospect of Korean universities, this study found that 8 universities could be classified as research university. However, in light of a balanced regional growth of research system two more universities could be designated as research university.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.653-655
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• 2011

EPCglobal proposed a Q-algorithm, which is used for selecting a slot-count in the next query round. However, it is impossible to allocate an optimized slot-count because the original Q-algorithm did not define an optimized weight C value. In this paper, we propose an adaptive Q-algorithm, in which we differentiate the weight values with respect to collision and empty slots. The weight values are defined with the identification time as well as the collision probability.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (2)
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• pp.460-462
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• 2003

본 논문에서는 모바일 환경에서. 사용자의 상황정보와 개인적 선호도 정보를 고려하여 사용자에게 적합한 영화를 선택. 추천하고 선택된 영화의 요약(Summary)을 서비스 할 수 있는 시스템을 제안한다. 제안된 시스템은 사용자가 이동하는 상황에 따라 변하는 위치, 시간 정보와 개인적 선호도인 영화장르 정보에 기반한 영화선택 서비스를 제공하고, 선택된 영화 콘텐츠의 요약을 MPEG-7 메타데이터로 기술하고, 이를 이용해 요약을 효과적으로 소모할 수 있게 한다. 제안된 시스템을 통해, 모바일 환경 기반 영화 선택 및 서비스 시스템(Mobile Environment-Based Movie Selection and Summary Service System)을 실현하고, 그 효용성을 입증하였다.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 대한기계학회논문집
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• 제17권9호
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• pp.2264-2270
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• 1993

This paper describes the development of a tool management system of milling operations in CIM environment. The system consists of modules for tool room management, tool purchasing, tool magazine management and tool selection. The tool selection is interactively performed by the aid of the graphic icons of milling cutter. The so-called UNICODE tool coding system is also developed to unify different kinds of codes from different tool manufacturers. The system runs on IBM PC AT.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권5호
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• pp.283-289
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• 2003

This paper proposes an expert system which can determine automatically the shunting routes corresponding to the given shunting works by considering totally the train operating environments in the station. The expert system proposes the multiple shunting routes with priority of selection based on heuristic search strategy. Accordingly, system operator can select a shunting route with the safety and efficiency among the those shunting routes. The expert system consists of a main inference engine and a sub inference engine. The main inference engine determines the shunting routes with selection priority using the segment routes obtained from the sub inference engine. The heuristic rules are extracted from operating knowledges of the veteran route operator and station topology. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique. And, the validity of the builted expert system is proved by a test case for the model station.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 춘계학술대회 논문집
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• pp.459-460
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• 2008