• Journal of the Korean Society for information Management
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• v.8 no.2
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• pp.32-73
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• 1991

The purpose of this study is to find important personal characteristics that affect search process and outcome, and to formulate causal models about searching behavior, by examining the channels and the magnitude of the factors identified. The study was designed to conduct a quasi-experiment with 67 student subjects. A total of 29 elements concerned with aptitude, personality, formal education, effectiveness of online training, search process, and search outcome are measured and reduced to 9 variables. 12 hypotheses were tested statistically and path analysis was done to investigate causal relationship among variables. Finally 5 models were formulated.

• Journal of Family Resource Management and Policy Review
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• v.6 no.1
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• pp.149-165
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• 2002

This study is to understand consumer's information search activity and satisfaction. When they buy the elderly related goods through internet market and to get some ideas for silver industry on internet shopping. The 382 subjects by online banner formatted Questionnaires were analyzed by frequency, percentage, standard deviation, Person's relation and regression analysis by SPSS PC program. The major findings are summarized as follows. (1) The most respondents were young and well-educated. In terms of psychological variables, the degree of the consumer's perception for internet usefulness and using capability were relatively high. (2) Information search amount of the group who have experienced purchasing elderly related goods through internet shopping and had low perception of internet risk is higher than other group. (3) The variables influenced mostly on consumer satisfaction were the age, the sex, the purchasing experience from Internet shopping, the Internet using capacity and the perception of internee usefulness as well as of the perception of internet risk.

• Korean Journal of Social Welfare
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• v.43
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• pp.299-327
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• 2000

Although economic crisis is allaying in Korea, the more effective unempoyment policies are requried in this present. So in this paper, we analyze the effects of relevant factors, especially job search behaviors of the unempyoed on reemployment and look for implications to the improvement of unemployment policies. Major findings are as follows: First, we find that job search behaviors, especially the effectiveness of job search activity and job search attitude are significantly different between the unemployed and the re-employed. Second, we find that the variables of job search behaviors - the effectiveness of job search activity (number of job offers), job search attitude (reservation wage), positive use of job search methods - significantly affect the re-employment of the unemployed from logistic regression analysis results. These findings' implications are as follows: First, the approach based on search theory may be useful in finding out determinants of re-employment. Second, the effects of job search behaviors on the reemployment and their implications should be actively accepted to policy makers in order to improve the effectiveness of un-employment policies. It meams that the effects of job search behaviors must be carefully considered in making or restructuring unemployment policies and their administrations.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.255-265
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• 2017

Objective: This study aims to survey visual search performance models to assess and predict individual's visual tasks in everyday life and industrial sites. Background: Visual search is one of the most frequently performed and critical activities in everyday life and works. Visual search performance models are needed when designing or assessing the visual tasks. Method: This study was mainly based on survey of literatures related to ergonomics relevant journals and web surfing. In the survey, the keywords of visual search, visual search performance, visual search model, etc. were used. Results: On the basis of the purposes, developing methods and results of the models, this study categorized visual search performance models into six groups: probability-based models, SATO models, visual lobe-based models, computer vision models, neutral network-based models and detection time models. Major models by the categories were presented with their advantages and disadvantages. More models adopted the accuracy among two factors of accuracy and speed characterizing visual tasks as dependent variables. Conclusion: This study reviewed and summarized various visual search performance models. Application: The results would be used as a reference or tool when assessing the visual tasks.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.53-57
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• 2003

Optimum design of most structural system requires that design variables are regarded as discrete quantities. This paper presents the use of Genetic Algorithm for determining the optimum design for truss with discrete variables. Genetic Algorithm are know as heuristic search algorithms, and are effective global search methods for discrete optimization. In this paper, Elitism and the method of conferring penalty parameters in the design variables, in order to achieve improved fitness in the reproduction process, is used in the Genetic Algorithm. A 10-Bar plane truss and a 25-Bar space truss are used for discrete optimization. These structures are designed for stress and displacement constraints, but buckling is not considered. In particular, we obtain continuous solution using Genetic Algorithms for a 10-bar truss, compared with other results. The effectiveness of Genetic Algorithms for global optimization is demonstrated through two truss examples.

• Korean journal of food and cookery science
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• v.32 no.6
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• pp.754-761
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• 2016

Purpose: This study was investigated the effects of culinary and foodservice major selection motive and work value on the major satisfaction, career search efficacy and career exploration behavior of college students. Methods: Survey comprising total 53 questions including 8 for major selection motive, 8 for work value, 6 for major satisfaction, 20 for career search efficacy, 5 for career exploration was distributed to 400 college students majoring in culinary or foodservice. Frequency analysis, factor analysis, correlation analysis, and multiple regression were conducted using SPSS Statistics (ver. 21.0). Organization of concept and analyses of the relationship between variables based on the advanced study and database is essential. Research model based on the theory and analysis of the extracted data was established; and subsequently, the proof relations between the variables were analysed. Results: The study results indicated that, only intrinsic work values among personal motivation and work values were significant. This is because personal motivation and intrinsic work values are related to subjects' aptitudes, interests, talents, and future professions when students choose their majors. Therefore, interest, abilities, achievement, high understanding, and satisfactory schooling are important factors. Conclusion: This study has newly expanded the research on career search behavior to include psychological factors and cognitive and attitudinal factors. We also determined competitive operations that enhance students' major satisfaction, career search efficiency, and career search behavior.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.111-127
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• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• Advances in Energy Research
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• v.4 no.4
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.41-53
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• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.639-647
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• 2020

In the vibration target localization algorithms based on time difference of arrival (TDOA), Fang algorithm is often used in practice because of its simple calculation. However, when the delay estimation error is large, the localization equation of Fang algorithm has no solution. In order to solve this problem, one dimensional search location algorithm based on TDOA is proposed in this paper. The concept of search is introduced in the algorithm. The distance d1 between any single sensor and the vibration target is considered as a search variable. The vibration target location is searched by changing the value of d1 in the two-dimensional plane. The experiment results show that the proposed algorithm is superior to traditional methods in localization accuracy.