• Journal of the Korea Safety Management & Science
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• v.9 no.6
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• pp.205-214
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• 2007

Nowadays the customer is classified with external customers and the inside customers. Which are not only end users who consume products but also all people who contribute to their earnings through the management activity of the enterprise. Furthermore, the fact that the external customer satisfaction index and inside customer satisfaction index are closely related is supported by many researches. It is interpreted if the inside customer satisfaction is not improved, achievement of the external customer satisfaction cannot be easy. In this paper, First, we will deduce the inefficient index through DEA model in each department after setting up the weight of items of inside customer satisfaction and measuring them. Second, as well as research entire models about improvement methods of inside customer satisfaction getting improvement methods for reaching a goal in the minimum amount of efforts.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.960-967
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• 2002

SPC와 EPC의 통합은 연속공정산업에서 성공적으로 사용되고 있다고 널리 인식되어 있다. 그러나, 이산적인 부품 제조공정의 모니터링과 연속 생산공정 모니터링 양쪽 모두를 포함하는 하이브리드(hybrid)산업에서의 이들 기법을 적용하기 위해서는 샘플링비율, 고장감지, 실시간 보정과 공정통제 등에 관한 연구가 필요하다. 본 연구에서는 SPC와 EPCD의 차이를 비교분석하고, 하이브리드 산업에서 SPC와 EPC를 성공적으로 통합하여 제품공정품질관리에 적용하는 사례연구를 제시하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.153-157
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• 2005

The injury accidents in fishing vessels account for $67.2\%$ of all marine injury casualties$(1997\~2001)$ and is on an increasing trend every year. Also, it is remarkable for the injury accidents to be basically caused by human errors. This study aims to investigate the human error of injury accidents in fishing vessels and presents the injury preventing program in them. Human errors were analysed by the methods such as SHELL & Reason Hybrid Model, GEMS Model adopted by International Maritime Organization(IMO). Based on the analysis, the following propositions were made to reduce the fishing vessels accidents by human errors : improvement of hazard awareness and quality of personnel, establishment of safety management system, and enforcement of vessels inspection.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• Korean Management Science Review
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• v.13 no.1
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• pp.13-27
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• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Journal of Korean Society of Transportation
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• v.24 no.6 s.92
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• pp.89-102
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• 2006

The purpose of this paper is to develop a dynamic OD estimating model to overcome the limitation of depicting teal situations in dynamic simulation models based on static OD trip. To estimate dynamic OD matrix we used the hybrid discrete choice model(called the 'Demand Simulation Model'), which combines travel departure time with travel mode and travel path. Using this Demand Simulation Model, we deduced that the traveler chooses the departure time and mode simultaneously, and then choose his/her travel path over the given situation In this paper. we developed a hybrid simulation model by joining a demand simulation model and the supply simulation model (called LiCROSIM-P) which was Previously developed. We simulated the hybrid simulation model for dependent/independent networks which have two origins and one destination. The simulation results showed that AGtt(Average gap expected travel time and simulated travel time) did not converge, but average schedule delay gap converged to a stable state in transportation network consisted of multiple origins and destinations, multiple paths, freeways and some intersections controlled by signal. We present that the hybrid simulation model can estimate dynamic OD and analyze the effectiveness by changing the attributes or the traveler and networks. Thus, the hybrid simulation model can analyze the effectiveness that reflects changing departure times, travel modes and travel paths by demand management Policy, changing network facilities, traffic information supplies. and so on.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.539-544
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• 2012

Most of the man-made systems can be modeled as a hybrid system which consists of both the high-level and the low-level component model. High level model is responsible for decision-making and the low-level one takes control of the mechanical component parts. Since the two models requires different interpretation method according to their type, analysis of a hybrid system becomes a difficult job. For the Analysis of the high-level model, methods for discrete event system models such as FSM can be used. On the contrary, numerical analysis techniques are required for the low-level continuous-time system model. Since it becomes a difficult thing for a modeller specifies and develops both the two-level models altogether, we propose an efficient hybrid simulation method which employs a game physics engine that has been widely and successfully used in the area of game industry.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.31-46
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• 2002

The objective of this paper is to develop an evaluation model for pilot informatization projects. There are relatively many researches on evaluation models for information systems. These previous researches, however, lack of comprehensive view of informatization Projects and pilot systems. We apply and test a hybrid evaluation model for to measure the success of pilot informatization projects. A user satisfaction model has been used and hypotheses are developed to find relationships of evaluation factors. The hypotheses have been tested with 51 user surveys. The result of this research can give an insight for the evaluation of pilot informatization projects.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.