• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.135-146
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• 1991

This paper shows the infinite horizon model of Partially Observable Markov Decision Process with lagged information. The lagged information is uncertain delayed observation of the process under control. Even though the optimal policy of the model exists, finding the optimal policy is very time consuming. Thus, the aim of this study is to find an .eplison.-optimal stationary policy minimizing the expected discounted total cost of the model. .EPSILON.- optimal policy is found by using a modified version of the well known policy iteration algorithm. The modification focuses to the value determination routine of the algorithm. Some properties of the approximation functions for the expected discounted cost of a stationary policy are presented. The expected discounted cost of a stationary policy is approximated based on these properties. A numerical example is also shown.

• Journal of the Korea Safety Management & Science
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• v.4 no.3
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• pp.141-156
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• 2002

This article deals with the multiple alternative proposal of Venture Business policy. when Decision makers meet a very complex and important business to take a good choice. It might not be easy that we make a decision and accept the decision as an exact result of analysis at a complication and uncertain situation. This is the reason why the original research use Statistical Survey method and Visual Decision Making Process(VDMP) to improve decision analysis method. Therefore, Our research suggests that the VDMP utilized in the strategic decision making situation as a group decision adding tool, can be applied in the development of a process vision and implementation plan. as a result, researcher describe step by step the process of VDMP

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.133-144
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• 2001

This article deals with the multiple alternative proposal of Strategy. when Decision makers meet a very complex and important problems to take a good choice. It might not be easy that we make a decision and accept the decision as an exact result of analysis at a complication and uncertain situation. Although the decision under unpredictable state is many existence and each field is classified to support it. he can not provide exact estimations and be able to specify a result and forecasting. This is the reason why the original research use Statistical Survey method and Visual Decision Making Process(VDMP) to improve decision analysis method. Therefore, Our research suggests that the VDMP utilized in the strategic decision making situation as a group decision adding tool, can be applied in the development of a process vision and implementation plan. as a result, researcher describe step by step the process of VDMP.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.430-432
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• 1993

In this paper, an algorithm for the fault diagnosis using simple FCM(Fuzzy Cognitive Map) is proposed FCMs which store uncertain causal knowledges are fuzzy signed graphs with feedback. The algorithm allows searching the origin of fault and the ways of propagating the abnormality throughout the process simply and has following characteristics. First, it can distinguish the cause of soft failure which can degenerate the process as well as hard failure. Second, it is proper for the processes which have difficulties to establish the exact quantative model. Finally, it has short amputation time in comparison with the fault tree or the other AI methods. The applicability of the proposed algorithm for the fault diagonosis to a tank or pipeline system is demonstrated

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.419-426
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• 2023

Assembly tasks are difficult to fully automate due to uncertain errors occurring in unstructured environments. When assembling parts such as electrical connectors, advances in grasping and assembling technology have made it possible for the robot to assemble the connectors without the aid of humans. However, some parts with tight assembly tolerances should be assembled by humans. Therefore, task sharing with human-robot interaction is emerging as an alternative. The goal of this concept is to achieve shared autonomy, which reduces the efforts of humans when carrying out repetitive tasks. In this study, a task-sharing robotic system for assembly process has been proposed to achieve shared autonomy. This system consists of two parts, one for robotic grasping and assembly, and the other for monitoring the process for robot-human task sharing. Experimental results show that robots and humans share tasks efficiently while performing assembly tasks successfully.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.43-63
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• 2013

Remanufacturing is a process of recovering end-of-life products into serviceable parts for producing new products. Due to the limited supply of recovery cores to remanufacture, a remanufacturing firm also needs to produce or procure new parts for fulfilling the demand. This paper is targeted for solving the problem of determining the optimal amount of newly produced and remanufacturing parts, which is called production and remanufacturing planning (PRP) problem, under uncertain supply of recovery cores. The new production mitigates the risk of insufficient core supply while it takes more costs than the remanufacturing. The PRP model in this paper also considers disassemble-to-order (DTO) environment, in which multiple kinds of parts are remanufactured from multiple products on order of the parts. Whereas existing studies presents only heuristic solutions for DTO remanufacturing, this paper provides an exact solution for this problem and analytical sensitivity of the involved cost parameters, adopting multi-dimensional newsvendor modeling and stochastic linear programming techniques. The result shows that production and remanufacturing plans for multiple products are mutually dependent, and a change of cost parameters involved in only one part is propagated to all other parts.

• Proceedings of the Technology Innovation Conference
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• 2001.06a
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• pp.117-130
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• 2001

R&D is the core competence of an enterprise. Furthermore, R&D requires huge capital investment and has very risky characteristics. Therefore, to be successful in R&D process, several approaches of engineering economics are used prior to decision-making. Until now, typical approaches of engineering economics such as NPV(net present value) or DCF(Discounted cash flow) have been used. But, they cannot properly capture managerial flexibility to adapt and revise later decisions in response to unexpected market development. In a constantly changing and always uncertain marketplace, managerial operation flexibility and strategic adaptability have become vital in order to successfully capitalize on favorable future investment opportunities and limit losses from adverse market development. For the alternatives of conventional static decision-making approaches, new concept of using real options is introduced. Real option theory is based on financial option's characteristics and checks every revision interval whether situation have changed favorable to decision maker or not. In advantageous situation, the decision maker has only to go on. In contrast, with unfavorable situation, he abandons the investment immediately. In this aspect, real option model is more suitable in very uncertain and dynamic business environment in that it can provide the opportunity to cope with flexibility. This paper suggests efficient and effective R&D investment strategy by using real options model. In addition, this paper compares financial options and real options.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.119-126
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• 2012

Recently, the breakdown of online banking servers and the leakage of customer information give rise to much concern about the security of information systems in financial and banking companies in Korea. The enforcement of security for information system becomes much more important issue than earlier. However, the security reinforcement of information system is restricted by a budget. In addition, the activities' cost to secure information system from threatening are under uncertain circumstances and should be established by a human decision maker who is basically uncertain and vague. Thus, making the budget for information system is exposed to any extent of the risk for these reasons. First, we introduce brief fuzzy set theory and fuzzy AHP (Analytic Hierarchy Process) methodology. Then, the cost elements that comprise yearly budget are presented and the priorities among the cost elements are calculated by fuzzy AHP. The cost elements that are exposed to risk are evaluated from the both perspectives of the risk impact and risk occurrence possibility which are expressed as linguistic terms. To get information on the risk profiles-pessimistic, most likely, and optimistic-for each cost element, the evaluation is accomplished and the result is presented. At last, the budget ranges-minimum, mode, maximum-for each cost element are estimated with the consideration of the risk profiles.

• Bulletin of the Korean Space Science Society
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• 2010.04a
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• pp.27.5-28
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• 2010

The unexpected observation condition or insufficient measurement modeling can lead to uncertain measurement errors. The uncertain measurement error of orbit determination problem typically consists of noise, bias and drift. It must be removed by using a proper estimation process for better orbit accuracy. The estimation of noise and drift is not easy because of their random or unpredictable variation. On the other hand, bias is a constant difference between the mean of the measured values and the true value, so it can be simply removed. In this study, precision orbit determination with SLR observations considering range bias estimation is presented. The Yonsei Laser-ranging Precision Orbit Determination System (YLPODS) and SLR NP (Normal Point) observations of CHAMP satellite are used for this work. The SLR residual test is performed to estimate the range bias of each arc. The result shows that we can get better orbit accuracy through range bias estimation.