• Journal of Korean Society of Environmental Engineers
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• v.22 no.4
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• pp.699-712
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• 2000

Juam lake is a major water resource for the industrial and agricultural activities as well as the resident life of Kwangju and Chonnam regions. However, the water quality of the lake is getting worse due to a large quantity of pollutant inflowing to the lake. Thus, the strategy for achieving the water quality goal of the lake should be developed as soon as possible. When there are various alternatives that can be used as the strategy, several criteria based on the achievement degree of water quality goal, the applicability of technique and social environment, and the reasonableness of the cost required are made to evaluate and rank the alternatives. However, it is difficult to make a decision when there are multiple criteria and conflicting objectives and specifically the estimated values of criteria contain elements of uncertainty. The uncertainty stems from the lack of available information, the randomness of future situation, and the incomplete knowledge of expert. As the degree of uncertainty is higher, the decision becomes more difficult. In this study, a fuzzy decision-making method is presented to assist decision makers in evaluating various alternatives under uncertainty. The method allows decision makers to characterize the associated uncertainty by applying fuzzy theory and incorporate the uncertainty directly into the decision making process for selecting the "best" alternative so decisions can be made that are more appropriate and realistic than those made without taking uncertainty in account.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.7-17
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• 2009

The precise analysis of exercise data for designing an effective rehabilitation system is very important as a feedback for planing the next exercising step. Many subjective and reliable research outcomes that were obtained by analysis and evaluation for the human motor ability by various methods of biomechanical experiments have been introduced. Most of them include quantitative analysis based on basic statistical methods, which are not practical enough for application to real clinical problems. In this situation, data mining technology can be a promising approach for clinical decision support system by discovering meaningful hidden rules and patterns from large volume of data obtained from the problem domain. In this research, in order to find relational rules between posture training type and muscle activation pattern, we investigated an application of the WAR(Weishted Association Rule) to the biomechanical data obtained mainly for evaluation of postural control ability. The discovered rules can be used as a quantitative prior knowledge for expert's decision making for rehabilitation plan. The discovered rules can be used as a more qualitative and useful priori knowledge for the rehabilitation and clinical expert's decision-making, and as a index for planning an optimal rehabilitation exercise model for a patient.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3785-3792
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• 2013

This study aimed to discover to what extent ethical issues are considered in the reimbursement decision process based on health technology assessment (HTA) in Korea, especially for oncology medications. Public summary documents (PSDs) published by the Health Insurance Review and Assessment Service (HIRA) were analyzed for empirical and normative factors. For external comparison, PSDs presented by corresponding institutions of Australia and the United Kingdom were employed. Furthermore, the opinions of eight expert oncologists were obtained regarding the accountability of the evidence in PSDs. Among 7 oncology drugs, there were differences in the final decisions and empirical factors considered, such as selected comparators and interpretation of evidence between the PSDs from the three institutions. From an ethical viewpoint, the following matters were deficient in the HTA decision-making process for oncology drugs: clear and reasonable standards; identifying and evaluating ethical values; and public accountability for reasonableness about decisions and due process.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.61-68
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• 2001

A rotor system is composed of a rotating shaft with supporting bearings. The rotor system is widely used in every rotating machinery such as the turbine generator and the high precision machine tools. A negligible error or malfunction in the rotor, however, can cause a catastrophic failure in the system then result in the environmental and economic disasters. A diagnosis of the rotor system is important in preventing these kinds of failures and disasters. Up to now, many researchers have devoted in the development of diagnosing tools for the system. The basic principles behind the tools are to retrieve the data through the sensors for a specific state of the system and then to identify the specific state through the heuristic methods such as neural network, fuzzy logic, and decision matrix. The proper usage of the heuristic methods will enhance the performance of the diagnostic procedure when together used with the statistical signal processing. In this paper, the methodologies in using the above 3 heuristic methods for the diagnostics of the rotor system are established and also tested and validated for the data retrieved from the rolling element bearing and journal bearing supported system.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.7-17
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• 2018

Purpose - The purpose of this paper is to improve last mile delivery capability and ensure customers' satisfaction by approaching an analytic hierarchy process(AHP) and identifying criteria framework to determine locations of last mile delivery centre(LMDC). Research design, data, and methodology - Traffic congestion and emission policy in cities are barriers of last mile delivery in dense areas. The urban consolidation centre(UCC) cannot increase last mile delivery efficiency in dense cities because of their space and traffic limitation. In this paper, we develop a case to improve last mile delivery efficiency and to ensure customers' satisfaction by concentrating on LMDC. In addition, AHP has been applied to identify criteria framework and determine LMDC locations. The weighted priorities are derived from parcel delivery industry experts and have been calculated using Expert Choice software. Results - The framework criteria have assisted decision makers to place LMDC in a dense area to enhance customer's satisfaction with last mile delivery service. Conclusions - AHP has provided ranking framework criteria of LMDC potential for parcel delivery industry. The LMDC helps by improving last mile delivery efficiency to final destination amids conditions of CO₂ emissions, traffic congestion, and pollution problems. It especially concerns delivery service activities when delivering parcels to customers rather than UCC.

• Journal of the Korean Society of Systems Engineering
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• v.9 no.2
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• pp.1-6
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• 2013

Developing Build-Own (or Operate)-Transfer (BOT) nuclear power project carrying large capital in the long term requires initially well-made multi-decision which it prevents sorts of risks from unexpected situation of target countries. In order to analyze the feasibility of project country, the Analytic Hierarchy Process is adopted. Firstly, the factors influencing the success of BOT nuclear power project in overseas countries were investigated through the literature survey for the country risk and were evaluated by expert interview for estimating comparative weight through pairwise comparison between such factors. Finally, it is developed comparative database of alternate countries with respect to each factor. This analytic method enables the developer to select and focus on the country which has preferable circumstance so that it enhances the efficiency of the project promotion. Also, it enables the developer to quantify the qualitative factors so that it diversifies the project success strategy and policy for the target country.

• Spatial Information Research
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• v.14 no.2 s.37
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• pp.177-189
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• 2006

In the past several years there has been relying on USA fur the resource of global positioning data in the pluralistic GNSS environment(Galileo, Post GPS, Quasi-Zenith Satellite System). For this reason, we should make a decision which strategy for research is carried out. According to a diversity of resource for obtaining positioning data, well organized research support at government level for efficient using is required. Specially, it is very Important that subcommittee for research organized with National GIS Promotion Committee as a leader make counterplan strategy. We have obtained an conclusion that we can make a good decision by establishing Spatial Information Expert Group and selection of essential element of technology.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1507-1518
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• 1999

본 논문은 제어대상체의 지식을 이용하여 적절한 의사결정을 내리거나 또는 지속적으로 변화하는 주변환경에 적응해 나갈 수 있는 지능시스템 설계를 위한 내배엽성 모델링 방법론을 제시한다. 이러한 지능적 내배엽성 시스템은 의사결정 모델, 지식기반의 내부모델, 그리고 내부모델의 구축모델 등을 기반으로 달성될 수 있다. 학습기능의 모델링을 위하여 수정된 귀납추론 방법과 적응형 전문가 시스템 방법이 제안되었다. 제시된 방법론은 지능적 학습 및 의사결정 기능을 갖춘 지능적 카드경기자 모델링의 예를 통하여 그 가능성을 검증하였다. Abstract This paper presents an endomorphic modeling methodology for designing intelligent systems that can determine by itself using its knowledge of the world and adapt itself to continuously changing circumstances. We have developed such an intelligent endomorphic system by integrating the decision making component and knowledge based internal model with internal model construction model. Learning capabilities are established using the modified inductive reasoning and adaptive expert system techniques we developed. Proposed methodology has been successfully applied to a design of intelligent card game players capable of supporting the intelligent learning and decision making.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.242-250
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• 1999

The objective of this paper is to propose an Extended AND-OR Graph (EAOG)-driven inferential simulation mechanism with which decision makers engaged in electronic commerce (EC) can effectively deal with complicated decision making problem. In the field of traditional expect systems research, AND-OR Graph approach cannot be effectively used in the EC problems in which real-time problem-solving property should be highly required. In this sense, we propose the EAOG inference mechanism for EC problem-solving in which heurisric knowledge necessary for intelligent EC problem-solving can be represented in a form of matrix. The EAOG method possesses the following three characteristics. 1. Realtime inference: The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation.2. Matrix operation: All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient.3. Bi-directional inference: Traditional inference method of expert systems is based on either forward chaining or based on either and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.We have proved the validity of our approach with several propositions and an illustrative EC example.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.