• Membrane and Water Treatment
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• 제8권2호
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• pp.137-147
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• 2017

A water treatment utility in South Korea operates a large system of pressurized hollow fiber membrane (PHFM) modules. The optimal selection of membrane module for the full scale plant was critical issue and carried out using Risk-based Life Cycle Cost (RbLCC) analysis based on the historical data of operation and maintenance. The RbLCC analysis was used in the process of decision-making for replacing aged modules. The initial purchasing cost and the value at risk during operation were considered together. The failure of modules occurs stochastically depending on the physical deterioration with usage over time. The life span of module was used as a factor for the failure of Poisson's probability model, which was used to obtain the probability of failure during the operation. The RbLCC was calculated by combining the initial cost and the value at risk without its warranty term. Additionally, the properties of membrane were considered to select the optimum product. Results showed that the module's life span in the system was ten years (120 month) with safety factor. The optimum product was selected from six candidates membrane for a full scale water treatment facility. This method could be used to make the optimum and rational decision for the operation of membrane water purification facility.

• 한국의류학회지
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• 제37권4호
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• pp.526-539
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• 2013

This study analyzed the effects of internet fashion consumer's anticipated regret and the selection difficulty on decision making delay and purchase switching intention. The survey was conducted in 2012 on internet fashion consumers in their 20s to 40s from May 1 to June 30; subsequently, 487 responses were used for the data analysis. The anticipated regret of internet fashion consumers was composed of product, service, social psychology, and function-related anticipated regret. The selection difficulty of internet fashion consumers was composed of process, information, and experience-related selection difficulty. There are significant differences in anticipated regret, selection difficulty, decision making delay, and purchase switching intention by gender. The anticipated regret (product, service, and social psychology-related anticipated regret) and selection difficulty effected decision making delay. In addition, the anticipated regret for product and selection difficulty by process or information influenced purchase switching intention. The results of this study provide useful information on the success and efficient operation of internet shopping malls.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.226-236
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• 2021

The purpose of this study is to analyze the type of latent profile for general high school students' subject selection criteria and to identify the characteristics of the latent class. The survey data of 1072 general high school students (male; 648, female; 424) in G city, Jeollabuk-do and the scale composed of 8 sub-factors: 'SAT orientation', 'academic achievement', 'ability orientation', 'pursuit of interest', 'teacher orientation', 'career development', 'others' recommendation', and 'subject availability' were used for latent profile analysis and cross-analysis between potential layers. As a result of the analysis, high school students' perceptions of subject selection were classified into four latent profiles. The four groups were named 'High Perception Type', 'Low Perception Type', 'Self-Directed Type', and 'Stability-Oriented Type' according to their types. It was found that there was a difference between the latent classes in the importance and performance level of the subject selection criteria. These results can help identify the subject selection tendencies of latent groups in the operation of the 2015 revised curriculum and the 2025 high school credit system that emphasizes the student-centered course selection curriculum and they can also provide customized course selection guidance considering individual differences.

• Structural Monitoring and Maintenance
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• 제4권1호
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• pp.85-105
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• 2017

Monitoring is the most important part of the construction and operation of the embankment dams. Applied instruments in these dams should be determined based on dam requirements and specifications. Instruments selection considered as one of the most important steps of monitoring plan. Competent instruments selection for dams is very important, as inappropriate selection causes irreparable loss in critical condition. Lack of a systematic method for determining instruments has been considered as a problem for creating an efficient selection. Nowadays, decision making methods have been used widely in different sciences for optimal determination and selection. In this study, the Multi-Attribute Decision Making is applied by considering 9 criteria and categorisation of 8 groups of geotechnical instruments. Therefore, the Analytic Hierarchy Process and Multi-Criteria Optimisation and Compromise Solution methods are employed in order to determine the attributes' importance weights and to prioritise of instruments for embankment dams, respectively. This framework was applied for a rock fill with clay core dam. The results indicated that group decision making optimizes the selection and prioritisation of monitoring instruments for embankment dams, and selected instruments are reliable based on the dam specifications.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2007년도 정기학술발표대회 논문집
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• pp.41-47
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• 2007

최적 발주방식을 선정하는 것은 해당 사업의 성공적 수행을 위한 것이며, 이는 곧 사업의 목표를 달성시키는 것이다. 또한 사업특성에 적합한 발주방식의 선정과 사업의 성과는 밀접한 연관성을 갖는다. 이러한 취지에서 재정경제부는 2007년 10월 국가계약법시행령 개정을 통해 "기술제안입찰", "설계공모${\cdot}$기술제안입찰"이라는 발주제도를 새로이 도입하였다. 다양해진 발주제도를 효율적으로 운영할 수 있도록 하기 위해 본 연구는 공공건설사업의 특성을 정량적, 정성적으로 평가하여 해당 사업에 가장 적합한 발주방식을 선정할 수 있도록 하는 발주방식 선정모델을 제시하였다. 그리고 발주방식 성과평가 모델을 함께 제시하여 발주방식 선정 및 운영의 결과를 후속 사업의 피드백 할 수 있도록 하였다. 이와 같은 표준적인 모델은 공공발주기관의 의사결정을 지원하는 도구로 활용될 수 있을 것이다.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• 전자공학회논문지A
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• 제31A권1호
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• pp.84-92
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• 1994

In this paper, we present a novel scheduling algorithm using the weighted interval graph. An interval graph is constructed, where an interval is a time frame of each operation. And for each operation type, we look for the maximum clique of the interval graph: the number of nodes of the maximum clique represents the number of operation that are executed concurrently. In order to minimize resource cost. we select the operation type to reduce the number of nodes of a maximum clique. For the selected operation type, an operation selected by selection rule is moved to decrease the number of nodes of a maximum clique. A selected operation among unscheduled operations is moved repeatly and assigned to a control step consequently. The proposed algorithm is applied to the pipeline and the nonpipeline data path synthesis. The experiment for examples shows the efficiency of the proposed scheduling algorithm.

• 한국정밀공학회지
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• 제9권3호
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• pp.53-60
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• 1992

This paper deals with developing an Expert system for tool selection using knowledge base system approach, and its application. For the sake of building of knowledge base, the information from process through sensor, tool handbook and interview with expert are referrenced and managed. The system developed shows good application flexibility in providing the actual cutting process with the selection of tool(insert, holder) and cutting conditions(feed, speed, rake type, and so on), is found as a useful system for real-time machining process. The Expert system for tool selection is written in TURBO PROLOG ver. 2.0 for inference engine capability, and can be run in interactive mode for user friendliness. In order to apply the system developed in actual cutting process, more parameters should be considered and scrutinized, and the system should be further extended in modular basis.

• 정보과학회지
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• 제5권2호
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• pp.41-48
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• 1987

어느 기관이 컴퓨터 씨스템을 처음 도입하고자 할때, 어떤 씨스템이 그 기관의 고유 업무를 수행하는데 가장 적합한지를 결정하는 일은 무엇보다도 중요 하다. 이와 같은 씨스템 능력 측정의 주요한 목적은 임대료와 컴퓨터 씨스템 구 성이 비슷한 경우 능력 측정을 하고자 하는 기관 고유 업무의 각 기종에 대한 상 대적인 thruput을 알아 보는데 있다.

• 지능정보연구
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• 제4권1호
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• pp.1-10
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• 1998

Process planning is a very complex task and requires the dynamic informatioon of shop foor and market situations. Process plan selection is one of the main problems in the process planning. In this paper, we propose a new process plan selection model considering operation flexibility for the computer aided process planing. The model is formulated as a 0-1 integer programming considering realistic shop factors such as production volume, machining time, machine capacity, transportation time and capacity of tractors such as production volume, machining time, machine capacity, transportation time capacity of transfer device. The objective of the model is to minimize the sum of the processing and transportation time for all parts. A genetic algorithm a, pp.oach is developed to solve the model. The efficiency of the proposed a, pp.oach is verified with numerical examples.