• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.187-196
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• 1997

This paper presents a semi-automatic methodology to synthesize executable digital controller saftware in a multi-loop control system. A digital controller is described by a task graph and end-to-end timing requirements. A task graph denotes the software structure of the controller, and the end-to-end requirements establish timing relationships between external inputs and outputs. Our approach translates the end-to-end requirements into a set of task attributes such as task periods and deadlines using nonlinear optimization techniques. Such attributes are essential for control engineers to implement control programs and schedule them in a control system with limited resources. In current engineering practice, human programmers manually derive those attributes in an ad hoc manner: they often resort to radical over-sampling to safely guarantee the given timing requirements, and thus render the resultant system poorly utilized. After task-specific attributes are derived, the tasks are scheduled on a single CPU and the compiled kernel is synthesized. We illustrate this process with a non-trivial servo motor control system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.148-151
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• 2003

Recently school food service operations are confronted with the wide spread pressures for accountability and the need to increase productivity. This paper is concerned with the make-or-buy decision framework for school food service systems considering the multi-attributes in the decision making. For the purpose of considering the multi-attributes analysis method in decision making for the school foodservice, we developed a make-or-buy decision framework using the multi-attribute analysis method, analytic hierarchy process, AHP method for school food service system. Finally, we developed a systematic and practical solution builder for a three-step decision support system in the view of 1) brainstorming for the idea generation, 2) analytic hierarchy process, AHP as a multi-attribute structure ed analysis method, and 3) aggregation logic model to integrate the results of reviewers. We developed web based program and applied it to a school foodservice problem.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.44-49
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• 2001

This paper is concerned with development of a multi-attribute structured decision model. In this study, we used AHP(analytic hierarchy process) and fuzzy set ranking methodology to overcome the multi-attributes structured decision problems ; such as multi-objective, multi-criterion, and multi-attributes. We proposed a 2-step approach : 1) individual evaluation and 2) integration of individual evaluations. In the first step, we define the performance factors and construct ana]isis structure, and in the second step performance evaluation by individual evaluators, and in second step, the results of individual evaluations are integrate. Also we developed a systematic and practical computer program to solve the problems according to the proposed methods. The proposed approach was known to be effective through a set of sample problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.2
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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 the Korean Society of Clothing and Textiles
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• v.40 no.1
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• pp.12-25
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• 2016

Based on the self-congruity theory, this study investigated how congruity between multi-brand store image and consumers' self-image affect store attributes and consumer responses. A total of 331 questionnaires were used to analyze data. The results of research were: 1) 'Sophistication' as the congruity factor between store image and consumers' self-image affected 'utility', 'atmosphere', and 'design' among store attribute factors. Also, 'sincerity' influenced 'utility' as the store attribute factor. 2) 'Atmosphere' as the store attribute factor positively influenced consumers' emotional responses, and 'utility' and 'design' factors positively influenced consumers' cognitive responses. 3) Consumers' emotional responses had a positive impact on consumers' cognitive responses; in addition, consumers' emotional and cognitive responses had positive impacts on consumers' behavioral responses. 4) A-Land indicated higher scores on 'sophistication', 'atmosphere', and 'design' factors than ABC Mart. ABC Mart had shown higher scores on 'ruggedness' and 'utility' factors than A-Land. This study provides practical implications to develop effective marketing strategies to manage multi-brand stores.

• Journal of the Korean housing association
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• v.25 no.4
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• pp.59-72
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• 2014

This study proposes a data-mining analysis method for examining variable multi-attribute big-data, which is considered to be more applicable in social science using a Correspondence Analysis of variables obtained by AIC model selection. The proposed method was applied on the Seoul Survey from 2005 to 2010 in order to extract interesting rules or patterns on characteristics of single household. The results found as follows. Firstly, this paper illustrated that the proposed method is efficiently able to apply on a big dataset of huge categorical multi attributes variables. Secondly, as a result of Seoul Survey analysis, it has been found that the more dissatisfied with residential environment the higher tendency of residential mobility in single household. Thirdly, it turned out that there are three types of single households based on the characteristics of their demographic characteristics, and it was different from recognition of home and partner of counselling by the three types of single households. Fourthly, this paper extracted eight significant variables with a spatial aggregated dataset which are highly correlated to the ratio of occupancy of single household in 25 Seoul Municipals, and to conclude, it investigated the relation between spatial distribution of single households and their demographic statistics based on the six divided groups obtained by Cluster Analysis.

• Journal of Fashion Business
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• v.22 no.5
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• pp.15-31
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• 2018

The purpose of this study was to classify male consumers and examine their clothing consumption values and the perceived importance of store attributes. Using Internet-based research service, survey data were collected from 651 male consumers aged between 20 and 40. The questionnaire included questions regarding respondents' preference of different store types, clothing consumption values, perceived importance of store attributes. According to the results, the respondents were categorized into 5 distinct groups based on their store type preferences: discount store preference group, multi-channel preference group, store indifferent group, brand store preference group, online store preference group. Multi-channel preference group was the largest among these groups. The five store type preference groups statistically varied in clothing consumption values, i.e., epistemic value, brand/conspicuous value, and economic value. The groups also differed in the importance they placed in the store attributes of: service and product quality, promotion, fashionability, salesperson and store environment, store atmosphere, convenience, and website image. The results of this study have direct implication for retail marketers of fashion companies who are targeting male consumers. Marketers can apply findings of this study in implementing retail strategies for different types of stores.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.419-423
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• 1997

This paper is concerned about the classification of objects together with muti-attributes such as remote sensing image data by using tolerance rough set. To produce more reliable relations from given attributes in the data, we define new similarity measures by using scaling. Our Method will be applied to classify multi-spectral image data.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1187-1192
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• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective 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 Korea Multimedia Society
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• v.7 no.11
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• pp.1505-1514
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• 2004

Sequence matching in time-series databases is an operation that finds the data sequences whose changing patterns are similar to that of a query sequence. Typically, sequence matching hires a multi-dimensional index for its efficient processing. In order to alleviate the dimensionality curse problem of the multi-dimensional index in high-dimensional cases, the previous methods for sequence matching apply the Discrete Fourier Transform(DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes of the multi-dimensional index. This paper first points out the problems in such simple methods taking the firs two or three coefficients, and proposes a novel solution to construct the optimal multi -dimensional index. The proposed method analyzes the characteristics of a target database, and identifies the organizing attributes having the best discrimination power based on the analysis. It also determines the optimal number of organizing attributes for efficient sequence matching by using a cost model. To show the effectiveness of the proposed method, we perform a series of experiments. The results show that the Proposed method outperforms the previous ones significantly.