• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1669-1678
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• 2002

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. In this paper, we proposed symmetric by using symmetry region searching in the reduced image to reduce computation time of symmetry region searching. It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction legion.

• Journal of Internet Computing and Services
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• v.5 no.1
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• pp.59-66
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• 2004

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. in this paper, it proposed symmetric by using symmetry region searching and Wavelet Transform to reduce computation time of symmetry region searching, and It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction region.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.39-45
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• 2010

In this paper we propose a new fast peak searching method using the gradient and present simulation results. The proposed method is a solution to the problem that finds the peak(maximum) of the unimodal function on a finite interval with minimum searching steps. Its main application is the auto-focus in the mobile phone. We propose the three steps to find the peak; periodic search, gradient-based search and detail search. In simulation we generated the Gaussian functions with white noise and have the result of about 8 searching steps and 1.04 errors on average.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.304-309
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• 2002

We propose the computation reduction method of real root method that is mainly used in the CELP (Code Excited Linear Prediction) vocoder. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSP. However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. In this paper, to reduce the computation time of real root, we compare the real root method with two methods. In first method, we use the mal scale of searching frequency region that is linear below 1 kHz and logarithmic above. In second method, The searching frequency region and searching interval are ordered by each coefficient's distribution. In order to compare real root method with proposed methods, we measured the following two. First, we compared the position of transformed LSP (Line Spectrum Pairs) parameters in the proposed methods with these of real root method. Second, we measured how long computation time is reduced. The experimental results of both methods that the searching time was reduced by about 47% in average without the change of LSP parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.31-39
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• 1995

This study reflected sensitivity cognition in using color and evaluated the performance in task regarded GUI environment. Largely, three experiments were conducted. First, the searching time in text environment was compared with the searching time in color environment. Second, to know relationship of hue, saturation, lightness which are factors of color, and searching time, the searching time was measured using two-way ANOVA with interaction with three independent variables: hue, saturation, and distance. Third, sensitivity cognition about color was investigated and the performance of searching task was analyzed in the environment designed by color regarded sensitivity cognition. According to statistical results, the average searching time was decreased about 50.31% in color environment. The searching time was significant among the difference of hue and saturation. For the factor of color, the more the ratio of green and red was increased, the more searching time was decreased. The more the ratio of gray was increased, the more searching time was increased. And the searching time was developed in the environment designed by color regarded sensitivity. The purpose of this study is the presentation of sensitivity realization method and verification in the reflection and application of sensitivity to the industrial environment and design.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.163-178
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• 1999

Conventional shortest path searching a1gorithms are based on the partial searching method such as Dijsktra, Moore etc. The a1gorithms are effective to find a shortest path in mini-modal condition of a network. On the other hand, in multi-modal case they do not find a shortest path or calculate a shortest cost without network expansion. To copy with the problem, called Searching Area Problem (SAP), a global searching method is developed in this paper with Genetic Algorithm. From the results of two examples, we found that the a1gorithm is useful to solving SAP without network expansion.

• Journal of the Korean Society for Library and Information Science
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• v.26
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• pp.213-233
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• 1994

This study measured whether subject descriptor searching and citation searching retrieve different documents for conceptual queries and methodological queries in natural science, engineering and social science. The retrieval effectiveness of two search methods was measured using as criteria, total number of documents retrieved, total number of relevant documents, overlapping and unique documents and precision ratio. The search subject was water resources and the databases used were Selected Water Resources Abstracts (SWRA) and SCISEARCH. Data were collected for 21 doctoral students working on their dissertations in the three fields of water resources. Principal findings included: 1) subject searching and citation searching each retrieved substantially equal number of documents; 2) total number of relevant documents for conceptual queries was larger than that for methodological queries, while there was a large variation among the three fields; 3) the average overlap was quite small, while citation searching yielded more unique documents than subject searching; 4) for conceptual queries, citation searching yielded a higher precision ratio than subject searching, while subject searching obtained a slightly higher precision ratio than citation searching for methodological queries ; and 5) citation searching was effective for both specific queries and broad queries if seed articles are well chosen, while subject searching only worked well for broad queries. It was further found that: 1) citation searching is not a subsidiary but a substantial retrieval method in water resources; 2) SWRA is effective for queries for engineering and SCISEARCH is appropriate for queries for natural science, while neither SWRA nor SCISEARCH work well for queries for social science; and 3) characteristics of queries affect retrieval results more than the characteristics of documents or the coverage of databases.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.5-16
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• 2016

Generally, methods using transfer techniques, like the transfer matrix method and the transfer stiffness coefficient method, find natural frequencies using the sign change of frequency determinants in searching frequency region. However, these methods may omit some natural frequencies when the initial frequency interval is large. The Sylvester-transfer stiffness coefficient method ("S-TSCM") can always obtain all natural frequencies in the searching frequency region even though the initial frequency interval is large. Because the S-TSCM obtain natural frequencies using the number of natural frequencies existing under a searching frequency. In this paper, the algorithm for the free vibration analysis of axisymmetric conical shells was formulated with S-TSCM. The effectiveness of S-TSCM was verified by comparing numerical results of S-TSCM with those of other methods when analyzing free vibration in two computational models: a truncated conical shell and a complete (not truncated) conical shell.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.650-656
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• 1999

In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.