• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.155-163
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• 2003

The intensity of industrial accidents has been increased while the number of industrial accidents has been decreased in theses days. It represents that we need safety guidelines based on personal attributes as well as physical attributes. This study tries to identify major causative factors for industrial accidents and to suggest an evaluation model based on personal attributes. Specifically, relationship between self-efficacy belief associated with industrial safety and other relative personal attributes has been analyzed. First, FGI(Focus Group Interview) was conducted to identify important personal attributes of industrial accidents. The questionnaire was prepared based on the results of FGI and was applied to identifying personal attributes. The relationship between personal attributes identified in this study and self-efficacy belief was assessed using fuzzy logic and fault tree analysis. The results of the assessment showed that self-efficacy belief was closely associated with industrial accidents. Specifically, workers with accident experience had higher level of self-efficacy beliefs than workers without accident experience.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.483-486
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• 2015

The objective of this study is to develop generalized regression neural networks (GRNN) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using GRNN model. From the performance evaluation and scatter diagrams of GRNN model, GRNN 3 (three input) model produces the best results for both stations. Results obtained indicate that GRNN model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois. These results testify the generation capability of GRNN model and its ability to produce accurate estimates in Illinois.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.62-65
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• 2006

가스하이드레이트의 국내 부존 가능성이 확인됨에 따라 가스하이드레이트 개발기술 및 부존 정보의 확보를 위한 가스하이드레이트 연구개발사업이 추진 중에 있다. 가스하이드레이트 개발사업과 같은 국가주도 연구개발사업의 경우 사업의 타당성 확보와 효율적인 추진을 위해서 사업 및 개발기술의 가치를 객관적으로 평가해야 할 필요성이 높아지고 있다. 본 연구에서는 가스하이드레이트 개발사업의 가치평가를 위한 주요 속성들을 결정하고 속성별 쌍대비교를 통해 중요도를 도출 할 수 있는 Fuzzy AHP 방법론을 사용하여 상대적 가중치를 도출하였다 연구 결과 가스하이드레이트 개발사업 및 기술의 가치는 상위계층으로 직접가치와 유발가치로 나눌 수 있고, 하위계층으로 총 8개의 속성으로 분류 할 수 있는 것으로 분석되었다. 중요도 분석결과 가스하이드레이트를 직접 개발함으로써 얻을 수 있는 개발가치가 사업시행으로 인한 탐사개발 기술의 확보로 유발되는 가치보다 높은 중요도를 가지는 것으로 평가 되었다. 또한 개발가치의 세부항목 중에는 에너지 안보 증진 효과가, 유발가치의 세부항목 중에는 인력양성효과가 가장 중요한 가치평가 요소로 나타났다

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.4 s.27
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• pp.233-239
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• 2006

This paper aims to evaluate the operation efficiency of Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center) using DEA(Data Envelopment Analysis). for this evaluation, this paper use the quantitative data for DEA analysis with two inputs and four outputs and a qualitative data analysis with the use of expert assessment. The tool for integrating heterogeneous data is fuzzy logic model to decision support system. In this paper, therefore, RCC/RSC evaluates the priority for operation efficiency. The result are found as order as Inchon, Mokpo, Jeju, Donghae, Busan, Pohang, Yosu, Sokcho, Tongyeong, Ulsan, Taean, Gunsan RSC.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.711-715
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• 1996

This paper describes an automated computer-aided engineering (CAE) system for micromachines whose size range 10 to 10$^3$${\mu}{\textrm}{m}$. An automatic finite element mesh generation technique, which is based on the fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with one of commercial finite clement (FE) analysis codes, MARC, and one of commercial solid modelers, Designbase. The system allows a geometry model of concern to be a automatically converted to different FE models, depending on physical phenomena to be analyzed, i.e. electrostatic analysis, stress analysis, modal analysis and so on. The FE analysis models are then exported to the FE analysis code, and then analyses are performed. This system is successfully applied to an electrostatic micro actuator.

• Journal of the Korea Society for Simulation
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• v.11 no.3
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• pp.13-22
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• 2002

This paper describes an automated evaluation of electrostatic field for micro motors whose sizes range 10 to 103um. Electric field modeling in micro motors has been generally restricted to in-plane two-dimensional finite element analysis (FEA). In this paper, the actual three-dimensional geometry of the micro motor is considered. An automatic FE mesh generation technique, which is based on the fuzzy knowledge processing and computational geometry techniques, is incorporated in the system, together with one of commercial FE analysis codes and one of commercial solid modelers. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena to be analyzed, electrostatic analysis and stress analysis and so on. The FE models are then exported to the FE analysis code, and then analyses are peformed. Then, analytical analysis and FE analysis about the torque generated by electrostatic micro motor are performed. The starting torque is proportional to $V^2$, the calculated starting torque from the two-dimensional analytical solutions are three times larger than those from the three-dimensional FE solutions.

• Journal of the Korea Safety Management & Science
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• v.4 no.1
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• pp.37-47
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• 2002

Risk analysis is a formal deductive procedure for determining combinations of component failures and human errors that could result in the occurrence of specified undesired events at the system level. This method can be used to analyze the vast majority of industrial system reliability problems. This study deals with the application of knowledge-engineering and a methodology for the assessment ＆ measurement of reliability, availability, maintainability, and safety of industrial systems using FTA(fault tree analysis), A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach (insufficient Information concerning the relative frequencies of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations, The purpose of this study is to describe the knowledge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.203-210
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• 2004

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering. Collaborative filtering recommender systems have been very successful in both information filtering domains and e-commerce domains, and many researchers have presented variations of collaborative filtering to increase its performance. However, the current research on recommendation has paid little attention to the use of time related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest. This paper proposes dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. The results of the evaluation experiment show the proposed model's improvement in making recommendations.