• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.201-206
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• 1994

The linear search problem is concerned with finding a hiden target on the real line R. The position of the target governed by some probability distribution. It is desired to find the target in the least expected search time. This problem has been formulated as an optimization problem by a number of authors without making use of Markov Decision Process (MDP) theory. It is the aim of the paper to give a (MDP) formulation to the search problem which we feel is both natural and easy to follow.

• Korean Management Science Review
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• v.12 no.3
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• pp.177-185
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• 1995

This paper presents a Tabu Search method to minimize a number of tardy jobs in the nonidentical parallel machine scheduling. The Tabu Search method employs a restricted neighborhood for the reduction of computation time. In this paper, we use two different types of method for a single machine scheduling. One is Moore's algorithm and the other is insertion method. We discuss computational experiments on more than 1000 test problems.

• ETRI Journal
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• v.43 no.6
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• pp.1013-1023
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• 2021

In this study, we propose a practical path planning method that combines the A^* search algorithm and minimum snap trajectory generation. The A^* search algorithm determines a set of waypoints to avoid collisions with surrounding obstacles from a starting to a destination point. Only essential waypoints (waypoints necessary to generate smooth trajectories) are extracted from the waypoints determined by the A^* search algorithm, and an appropriate time between two adjacent waypoints is allocated. The waypoints so determined are connected by a smooth minimum snap trajectory, a dynamically executable trajectory for the quadrotor. If the generated trajectory is invalid, we methodically determine when intermediate waypoints are needed and how to insert the points to modify the trajectory. We verified the performance of the proposed method by various simulation experiments and a real-world experiment in a forested outdoor environment.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.1
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• pp.128-143
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• 2014

Purpose: Effective literature searching is essential to support evidence-based nursing. The aim of this study was to present our recent systematic search experience to identify guidelines in PubMed for prevention of catheter-associated urinary tract infections. Methods: Five approaches to the literature search via PubMed were employed. The searches were restricted to items published from 1980 to 2010, for patients admitted to hospital, and in the English language. The search results were compared using the number of records and relevant items, and the sensitivity and precision of each search strategy. Results: The individual approaches retrieved 19-141 of records and 3-6 of relevant items. Sensitivity ranged from 37.5% to 75.0% with the highest values for simple searches and a search combining MeSH terms and free textwords with a methodological search filter. Precision varied from 4.3% to 21.7% and the highest precision was found for MeSH terms with limits feature. Conclusion: The simple search in PubMed is an appropriate way for nurses in a busy clinical practice to search the literature for evidence. However, several approaches using MeSH terms, free textwords, limits feature or methodological search filters are also required to have more efficient and better informed search results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1392-1400
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• 1999

In this paper, we propose a two-stage fast full search algorithm for block motion estimation that produces the same performance to that of full search algorithm (FSA) but with remarkable computation reduction. The proposed algorithm uses the search region subsampling and the difference of adjacent pixels in the current block. In the first stage, we subsample the search region by a factor of 9, and then calculate mean absolute error (MAE) at the subsampled search points. And in the second stage, we reduce the search points that need block matching process by using the lower bound of MAE value at each search Point. We Set the lower bound of MAE value for each search point from the MAE values which are calculated at the first stage and the difference of adjacent pixels in the current block. The experimental results show that we can reduce the computational complexity considerably without any degradation of picture quality.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.1-14
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• 2015

In this paper, we investigate the relationship between Internet search trends and stock market. Under the assumption that investors may use Internet search engine to obtain information for companies of their interests before taking actual investment actions, the relationship between the changes on Internet search volume and the fluctuation of trading volume as well as stock price of a company is analyzed with actual market data. A search trend investment strategy that reflects the changes on Internet search volume is applied to large enterprises' group and to small and medium enterprises' (SMEs) group, and the correlation between profit rate and trading volume is analyzed for each company group. Our search trend investment strategy has outperformed average stock market returns in both KOSPI and KOSDAQ markets during the seven-year study period (2007~2013). It is also shown that search trend investment strategy is more effective to SMEs than to large enterprises. The relationship between changes on Internet search volume and stock trading volume is stronger at SMEs than at large enterprises.

• Korea Journal of Hospital Management
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• v.18 no.2
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• pp.57-80
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• 2013

The purpose of this study is to examine the information search behavior of medical consumers visiting a hospital, and investigate the consumption values of medical consumers, classified according to the information search behavior, and characteristics of online health and disease information. This study also tried to identify the factors affecting medical consumers classified according to information search behavior, and gain an extensive understanding of medical consumers'consumption values and online information use. The analysis results of this study are as follows: First, the consumption values of medical consumers were classified into a total of 7 factors, i.e. future-positive value, family-oriented value, sustainablehealth value, rational-progressive value, social-cooperative value, socialachiever value and hedonistic-individual value. Next, the characteristics of medical consumers'online information search behavior were classified into three types of consumer groups, i.e. the limited information-oriented consumer group, the practical information-oriented consumer group and the passionate information-oriented consumer group. Second, the analysis of the differences among the three groups classified according to the characteristics of the information search behavior in terms of consumption values, use of online information sources, utility and the intention to reuse online information showed that all the differences were statistically significant. The passionate information-oriented consumer group showed the highest scores in the sustainable-health value, the social-achiever value and the hedonisticindividual value. Third, the factors affecting medical consumers, classified into three groups according to the characteristics of the information search behavior, were found to be socio-demographic variables like consumers' age and occupation, consumers'consumption values, use of online information sources, and utility of online information. This study tried to understand what values medical consumers have according to the differences in the information search behavior by examining the consumption values of medical consumers according to the information search behavior. The significance of this study lies in the fact that consumption values are instrumental in understanding medical consumers by identifying the fundamental motives and desires of consumers' behavior.

• The Journal of Korean Medicine
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• v.30 no.4
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• pp.47-58
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• 2009

Objectives: Several domestic search systems for research projects providing capabilities of searching and managing the research information based on metadata have been completed, although they cannot search projects for specific domains such as the Oriental Medicine field. Therefore, in this paper we propose an effective method for collecting and refining information of research projects in the oriental medicine field. We also design and implement a search management system based on the collected information. Methods: We chose three search elements and defined the collecting process for research projects. The collected information was refined to enhance the quality of results. Then, the search management system was implemented by Adobe Flex. Results: We were able to collect 1,688 research projects in the oriental medicine field. We also implemented the search management system, which provides the functionalities of search and management such as input, update, delete, spreadsheet import/export of R&D projects, user management and access control. Conclusions: The collected information and the search management system will be used in a project analysis system which will be constructed in the future. The project analysis system could help to analyze trends in oriental medicine research as well as construct a technical roadmap.