• 대한기계학회논문집A
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• 제33권2호
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• pp.114-126
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• 2009

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.21-40
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• 2019

This paper presents a comprehensive survey of various artificial intelligence (AI) techniques implemented in cognitive radio engine to improve cognition capability in cognitive radio networks (CRNs). AI enables systems to solve problems by emulating human biological processes such as learning, reasoning, decision making, self-adaptation, self-organization, and self-stability. The use of AI techniques is studied in applications related to the major tasks of cognitive radio including spectrum sensing, spectrum sharing, spectrum mobility, and decision making regarding dynamic spectrum access, resource allocation, parameter adaptation, and optimization problem. The aim is to provide a single source as a survey paper to help researchers better understand the various implementations of AI approaches to different cognitive radio designs, as well as to refer interested readers to the recent AI research works done in CRNs.

• 산업경영시스템학회지
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• 제26권2호
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• pp.1-8
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• 2003

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

• 플랜트 저널
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• 제7권4호
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• pp.67-76
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• 2011

Two main features of the recent building project are the enlargement of the size and the technological complexity. Due to these features, the number or the size of organizations participating in building project is also increasing and the relationships between the organizations are becoming more interdependent than ever. Owing to these features, some delays or errors in the decision-making process in the phase of planning of a building project may influence many subjects related to the project. The most important factor for a successful project is to arbitrate confrontations and build cooperative relationships between parties who have different interests with one another in the project. Efforts were made to find out general features of building project and the features in the phase of planning in order to set a direction of this research. To solve problems in the phase of planning, the concept of 'cooperative design', which means that different features and interests of various organizations should be reflected from the phase of planning, was introduced. By introducing cooperative design in building projects, more rapid and objective decision-makings are possible.

• 대한임베디드공학회논문지
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• 제15권1호
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• pp.25-33
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• 2020

Multipath routing protocols with unmanned aerial vehicles have been proposed to improve reliability in tactical ad hoc networks. Most of existing studies tend to establish the paths with multiple metrics. However, these approaches suffer from link loss and congestion problems according to the network condition because they apply same metric for both ground and air path or employ the simple weight value to combine multiple metrics. To overcome this limitation, in this study, we propose new routing metrics for path over unmanned aerial vehicles and use the multi-criteria decision making (MCDM) method to determine the weight factors between multiple metrics. For the case studies, we extend the ad-hoc on-demand distance vector protocol and propose a strategy for modifying the route discovery and route recovery procedure. The simulation results show that the proposed mechanism is able to achieve high end-to-end reliability and low end-to-end delay in tactical ad hoc networks.

• 한국수자원학회논문집
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• 제45권4호
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• pp.383-391
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• 2012

본 연구는 다기준 의사결정 문제에서 항상 발생하는 가중치와 대안들의 평가치에 대한 불확실성을 최소화하기 위해 민감도 분석을 수행하는 절차를 제시하였다. 제기되는 가중치에 대한 불확실성을 극복하기 위해 일반적으로 순위가 뒤바뀔 수 있는 가장 민감한 평가기준의 결정과 대안의 효과 측정자료의 결정이 있다. 본 연구는 유량확보와 수질개선을 위한 수자원 계획수립을 위해가중합계법을 이용한 문제에 두 경우의 민감도분석을 모두수행하였다. 이 과정에서 결정계수와 민감도 계수를 산정하여 이용하였다. 본 연구에서 제시한 민감도 분석 과정은 향후 수자원 계획 수립에 폭넓게 활용될 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• Journal of Computing Science and Engineering
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• 제2권2호
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• pp.200-219
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• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• 한국환경복원기술학회지
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• 제16권1호
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• pp.171-180
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• 2013

As the second-largest 1st degree of ecological zone in Kangwon Province, Pyeongchang County is expected to play an important role in expanding the protected areas of the Republic of Korea. However, Pyoungchang County is expected to experience an increase in demand for development due to the 2018 Winter Olympics. Problems related to various stakeholders and limited budget will arise regarding the issue of expanding the protected areas. In this study, in order to effectively control these problems, we designed expansion plans for the 1st degree ecological zoning map areas and the observed data of threatened species I and II in Pyoungchang County by using the MARXAN Software. As for the methods, we first set the planning units(PUs) for the spatial analysis. The PUs include boundary length, land cost, land status, etc. Then, we made the input data by controlling the conservation features, BLM(Boundary Length Modifier) and iteration numbers. There are two measures for the establishment of the protected areas, one of which only concerns with the ecological priority, and the other with combining the land cost on forest. The one illustrated shows that the larger patches that include the conservation feature was selected as a candidate of the protected areas. The other one presented shows that inexpensive land cost areas were selected. As this study produces visual results and enables an efficient application of various values in selecting protected areas, we believe that it will be useful to various stakeholders in spatial decision-making process.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.121-130
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• 1999

The objective of this paper is to apply Extended AND-OR Graph (EAOG)-related techniques to extract knowledge from a specific problem-domain and perform analysis in complicated decision making area. Expert systems use expertise about a specific domain as their primary source of solving problems belonging to that domain. However, such expertise is complicated as well as uncertain, because most knowledge is expressed in causal relationships between concepts or variables. Therefore, if expert systems can be used effectively to provide more intelligent support for decision making in complicated specific problems, it should be equipped with real-time inference mechanism. We develop two kinds of EAOG-driven inference mechanisms(1) EAOG-based forward chaining and (2) EAOG-based backward chaining. and The EAOG method processes the following three characteristics. 1. Real-time 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 backward chaining which is mutually exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.