• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.135-140
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• 1999

This paper describes a affine transform coding which is reducing long time to take on image encoding by using scaling method and limited search area technique. For evaluating its performance, the proposed algorithm compare with Jacquin's method using traditional affine transform coding methods. Simulation results, the proposed algorithm considerably reduces encoding time by using scaling method and limited search area method. Also, the proposed algorithm provides much shorter encoding time with a little degradation of the decoded image quality than Jacquin's method.

• Journal of Korean Medical classics
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• v.25 no.4
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• pp.105-113
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• 2012

Objectives : Acquiring information from symptoms is one of the important method to gain clinically available information in korean medicine. Therefore, up to now, study of symptom terms was frequently implemented in promotion of various information project. In data extraction methods using symptom information from DB, information search using synonym and method using ontology is studied and utilized. However, considering concept of symptom has essential information of appeared body area and phenomenon we think that extending synonym and ontology relationship in symptom terms can be useful for search and set to this study. Methods : We collect terms relevant to human body area and structure described in Donguibogam. Synonymous relationship between collected terms is organized. Relationship between collected terms is build to human-body-knowledge table which has form of Concept+Relation+Concept. Type of relationship is limited on a range of expressing content about parts of human body. Result & Conclusion : Search condition is generated automatically using relationship of the upper area in knowledge table contents. Information of next and previous acupuncture point, upper and lower acupuncture point, left and right acupuncture point can be searched using information of acupuncture point location, order, relative position in area, direction in knowledge table contents.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.99-106
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• 1998

This Paper proposes a novel fractal image method for image communication to shorten time to take on fractal encoding by using limited search area method and scaling method First, the original image is contracted respectively by half and by quarter with the scaling method. And then, the corresponding domain block of the quarter-sized image which is most similar with one range block of the half-sized image is searched within the limited area in order to reduce the encoding time extremely As the result of the evaluation, the proposed algorithm provided much shorter encoding time and better compression ratio with a little degradation of the decoded image quality than Jacquin's method.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.64-69
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• 2000

This paper proposes a novel fractal coding method for fast encoding and high compression to shorten time to take on fractal encoding by using limited search area. First. the original image is contracted respectively by half and by quarter with the scaling method and bit-plane method. And then, the corresponding domain block of the quarter-sized image which is most similar with one range block of the half-sized image is searched within the limited area in order to reduce the encoding time extremely. As the result of the evaluation, the proposed algorithm provided much shorter encoding time and better compression ratio with a little degradation of the decoded image qualify than Jacquin's method.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.559-566
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• 2012

In this paper, we propose a fast fractal image coding algorithm to shorten long time to take on fractal image encoding. For its performance evaluation, the algorithm compares with other traditional fractal coding methods. In the traditional fractal image coding methods, an original image is contracted by a factor in order to make the corresponding image to be compared with. Then, the whole area of the contracted image is searched in order to find the fixed point of contractive transformation of the original image corresponding to the contracted image. It needs a lot of searching time on encoding. However, the proposed algorithm considerably reduces encoding time by using scaling method and limited search area method. On comparison of the proposed algorithm with Jacquin's method, the proposed algorithm is dozens of times as fast as that of Jacquin's method on encoding time with a little degradation of the decoded image quality and a little increase of the compression rate. Therefore, it is found that the proposed algorithm largely improves the performance in the aspect of encoding time when compared with other fractal image coding methods.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.705-717
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• 2001

In this paper, we propose a rapid lane detection method to extract a preceding vehicle from sequential images captured by a single monocular CCD camera. We detect positions of lanes for an individual image within the limited area that would not be hidden and thereby compute the slopes of the detected lanes. Then we find a search area where vehicles would exist and extract the position of the preceding vehicle within the area with edge component by applying a structured method. To verify the effects of the proposed method, we capture the road images with a notebook PC and a CCD camera for PC and present the results such as processing time for lane detection, accuracy and vehicles detection against the images.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.24-29
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• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1057-1065
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• 1998

This paper describes a new fractal image coding algorithm to shorten time to take on fractal image encoding by using limited search area method and scaling method. First, the original image is contracted respectively by half and by quarter with the scaling method. And then, the corresponding domain block of the quarter-sized image which is most similar with one range block of the half-sized image is searched within the limited area in order to reduce the encoding time extremely. This searched block is used in encoding. Also, we propose an algorithm provided much shorter encoding time and better compression ratio with a little degradation of the decoded image quality than Jacquin's method.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.824-830
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• 2010

It is not easy job to find a underwater target using sonar system in the ASW operations. Many researchers have tried to solve anti-submarine search problem aiming to maximize the probability of detection under limited searching conditions. The classical 'Search Theory' deals with search allocation problem and search path problem. In both problems, the main issue is to prioritize the searching cells in a searching area. The number of possible searching path that is combination of the consecutive searching cells increases rapidly by exponential function in the case that the number of searching cells or searchers increases. The more searching path we consider, the longer time we calculate. In this study, an effective algorithm that can maximize the probability of detection in shorter computation time is presented. We show the presented algorithm is quicker method than previous algorithms to solve search problem through the comparison of the CPU computation time.