• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.455-461
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• 2011

In object tracking using the block mating algorithm, it is not proper to use a fixed matching block to track an object of which size may be various and can be changed at any time. This paper defines an adaptive matching block for the dynamic environment and proposes a block matching algorithm for it. The matching block is composed of a main-block of $10{\times}10$ pixels and 8 sub-blocks of $6{\times}6$ pixels in a wide area of $42{\times}42$ pixels, the main-block located its center is used as an object block, and the sub-blocks located its boundary are used as candidates for the object block. The proposed algorithm extracts the object blocks from the sub-blocks by using their motion vectors for 10 previous frames and performs the block matching with the main block and them. The experiments for perform estimation show that the proposed algorithm extracts just valid object blocks from the matching block and keeps an object having free movement in image center area.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.21-26
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• 2010

This paper proposed an efficient algorithm to reduce the amount of calculation for Scalability Extension which takes a great deal of the operational time in H.264/AVC. This algorithm decides a search range according to the direction of predicted motion vector, and then performs an adaptive spiral search for the candidates with JM(Joint Model) FME(Fast Motion Estimation) which employs the rate-distortion optimization(RDO) method. Experimental results by applying the proposed method to various video sequences showed that the process time was decreased up to 80% comparing to the previous prediction methods. The degradation of video Quality was only from 0.05dB to 0.19dB and the compression ratio decreased as small as 0.58% in average. Therefore, we are sure that the proposed method is an efficient method for the fast inter prediction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1902-1911
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• 1999

In this paper we present a content-based image retrieval algorithm using the visual feature vectors which describe the spatial characteristics of objects. The proposed technique uses the Gaussian mixture model(GMM) to represent multi-colored objects and the expectation maximization(EM) algorithm is employed to estimate the maximum likelihood(ML) parameters of the model. After image segmentation is performed based on GMM, the shape and color features are extracted from each object using Fourier descriptors and color histograms, respectively. Image retrieval consists of two steps: first, the shape-based query is carried out to find the candidate images whose objects have the similar shapes with the query image and second, the color-based query is followed. The experimental results show that the proposed algorithm is effective in image retrieving by using the spatial and visual features of segmented objects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2478-2484
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• 2010

In this paper, real-time detection methods for Panorama system Key-Points offers. A recent study in PANORAMA system real-time area navigation or DVR to apply such research has recently been actively. The detection of the Key-Point is the most important elements that make up a Panorama system. Not affected by contrast, scale, Orientation must be detected Key-Point. Existing research methods are difficult to use in real-time Because it takes a lot of computation time. Therefore, this paper propose BLOG(BitRate Laplacian Of Gaussian)method for faster time Key-Point Detecting and Through various experiments to detect the Speed, Computation, detection performance is compared against.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.204-207
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• 2009

수소를 기반으로 하는 신재생에너지의 생산, 관리 및 이용 시스템을 연계하여 운전하는 Green City 실증단지 구현을 위한 국내 도서 선정은 지자체별 이해관계와 지역발전계획에 따라 첨예한 논란을 수반할 소지가 있다. 입지 선정 절차상에서 예비후보지 선정과정이 형식적이거나 입지선정기준이 없어 입지선정을 위한 평가항목, 평가기준, 항목별 배점 등이 분석자의 의도에 따라 조정될 수 있는 문제점이 있다. 또한 각 지역에 대한 입지선정을 위한 기준이 있다 하더라도 추상적이거나 객관적이지 않은 기준이 많아 입지선정에 직접 적용하기 어렵고, 자연환경, 사회경제, 법제, 입지조건, 부하특성, 지자체 호응 등 인자들의 평가의 형평성이 결여될 수 있다. 이를 해결하기 위해 의사결정도구로 이용되는 계층분석법(AHP)을 입지선정절차에 적용하였다. 객관적인 정보가 제공 가능한 평가항목을 설정하고, 관련 전문가들의 설문조사를 통하여 주관적인 중요도 결과를 취합하였다. 이 결과를 계층분석법을 활용하여 평가항목별 가치를 측정하여 가중치를 부여하였고, Green City 실증단지 구현을 위한 후보도서의 순위를 제시하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.1-8
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• 2006

This paper presents a fast full search algorithm for motion estimation applicable to multiple reference images. The proposed method is an extended version of the rate constrained successive elimination algorithm (RSEA) for multiple reference frame applications. We will show that motion estimation for the reference images temporally preceding the first reference image can be less intensive in computation compared with that for the first reference image. for computational reduction, we will drive a new condition to lead the smaller number of candidate blocks for the best matched block. Simulation results explain that our method reduces computation complexity although it has the same quality as RSEA.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.625-632
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• 2009

In these days mobile handsets have come to be used at almost every user. The performance improvement of mobile devices and networks have made this trend possible. As a great variety of mobile applications are published, the necessity of running large-scale mobile applications becomes greater than before. To accomplish this, the existing researchers have developed mobile cluster computing libraries like Mobile-JPVM. In this paper, we implement a compute-intensive Animated GIF generating application and its cell phone viewer software using Mobile-JPVM library. We find out by the real execution of our softwares on the KTF handsets that they can sufficiently run on cellular phones. Our Animated GIF generator and its viewer are going to be commercially used for the mobile fashion advertisement systems.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.21-24
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• 2001

본 논문은 신경 회로망을 이용하여, 전화망 대역의 음성, 즉, narrowband 음성에서 wideband 음성을 복원하고자 했다. BP 알고리즘을 사용하는 기존의 신경 회로망의 경우에는 음성과 같이 복잡하고 크기가 큰 훈련데이터에 대해서는 훈련이 제대로 되지 않는 단점이 있다. 그러므로 븐 논문에서는 이를 해결하기 위해 입력으로 들어온 LPC 켑스트럼 벡터를 k-means 알고리즘을 이용하여 미리 정한 개수의 cluster로 나눈 다음, 각각의 cluster에 대해 독립적인 신경 회로망을 적용했다 이로 인해 각각의 신경 회로망은 제한되고 서로 상관관계가 많은 음성들만 훈련하면 되므로, 기존의 신경 회로망에서 생기는 훈련의 정체를 개선할 수 있었다. 또 clustering 과정에서 생기는 오류를 보완하기 위해 후보신경 로망들의 출력에 fuzzy 개념을 적용해서 최종 출력을 내도록 했다 실험 결과에서, 제안한 알고리즘은 기존의 codebook mapping 알고리즘보다 스펙트럼 거리척도에 의한 비교 및 주관적인 음질 평가 양쪽에서 개선된 성능을 보였다.