• Electric Engineers Magazine
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• v.185 no.1
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• pp.22-30
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• 1998

전력계통 가운데 중요한 역할을 담당하고 있는 대용량 전력용변압기는 효율이 높고 그 특성이 매우 안정되어 있는 설비이나 절연유의 여과와 같은 보수작업에도 불구하고 점진적인 열화과정이 누적진행되며 결국에는 교체를 필요로 하게 된다. 이러한 교첵 적절한 시기에 이루어지지 않는 경우 불시의 고장현상 또는 사고가 발생하게 되어 전력계통뿐 아니라 사회적 경제적으로도 큰 문제를 야기한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.740-742
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• 2003

오랜 기간동안 메타데이터를 기반으로 한 데이터통합에 대한 많은 연구들이 진행되어 왔다. 그러나 기존 방법론들은 전역 뷰 또는 전역 스키마와 같은 초기 가이드라인을 구축하는데 많은 비용이 요구 된다는 단점이 있다. 이는 기존 연구들이 해당 도메인 특성들을 간과했기 때문이다. 예를 들어 과학 데이터의 경우 일반사용자들은 생물의 이름이나 모양 등과 같은 단순정보에 관심을 갖는 반면 과학자나 전문가들은 보다 상세하고 전문적인 데이터에 관심을 갖는다. 추가적으로 모든 데이터에 대한 초기 표준 가이드라인을 구축하는 것은 현실적으로 많은 어려움이 따른다. 본 논문에서는 이러한 도메인 특성을 고려하여 점진적인 통합방법론(LOG : Localization-based Global metadata registry)을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.175-177
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• 2002

데이터베이스를 통합하기 위한 많은 연구들이 진행되어 왔지만, 통합하고자 하는 모든 데이터들을 고려함으로써 초기 비용과 시간에 대한 오버헤드로 인해 비효율적이며 현실적으로 불가능한 경우가 발생하게 된다. 이 논문에서는 이러한 문제점을 개선하여 점진적인 통합을 위한 개념적인 통합 방법론을 제안하고, 제안된 방법론을 한국과학기술정보연구원에서 보유하고 있는 데이터베이스 통합에 적용하기 위한 통합 메타 모델을 설계한다. 또한 다른 기관 또는 다른 포맷들과의 상호운용성을 향상시키기 위하여 해당 분야의 국제 표준 또는 사실 표준들을 고려하여 통합 메타 모델을 설계하였다.

• The KIPS Transactions:PartA
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• v.17A no.2
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• pp.73-80
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• 2010

As the power of hardware has improved, there have been numerous researches in processing concurrently using multitasking method. The incremental evaluation is the evaluation method of reevaluating only affected parts instead of reevaluating overall program when the program has been changed. It is necessary to do more studies that improve the efficiency of concurrent incremental evaluation to do multitasking using multi-threading of Java not to do in parallel using multiprocessor. In this paper, the dependency in the dependency chart is based on the attribute that describes the real value of the variable that directly affects the semantics, thereby doing efficient evaluation. So using the dependency, this paper presents the concurrent incremental evaluation algorithm for Java Languages and proves its correctness, analyzing the efficiency of concurrent incremental evaluation by the simulation.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.157-164
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• 2005

본 연구는 해양환경을 파악하는 데에 가장 기초적인 자료라고 할 수 있는 수심자료를 GIS 데이터로 구축하여 1979년부터 2004년까지의 금강하구의 수심변화를 정량적으로 분석하였다. 금강하구는 시간이 흐를수록 수심이 깊어지는 지역보다는 얕아지는 지역이 점진적으로 늘어가고 있으며, 금강하구둑의 수문 폐쇄, 남 북측 도류제 및 북방파제 축조, 서측호안 매립공사 등이 완공되거나 진행되어진 1990년대 중반 이후에는 수심이 얕아지는 면적이 증가함과 동시에 얕아지는 정도가 보다 급진적으로 진행되어 2004년에는 하구둑 전면과 대죽사주는 저조시에 해수면 위로 노출되는 해저면의 높이가 4m이상에 이르는 부분이 확산되었다. 금강하구 내 수심변화특성은 지역적으로 다소 차이를 보이므로 5개의 세부지역으로 구분하여 살펴보았다. 하구둑-군산내항, 군산내항-군산외항, 개야수로, 대죽사주 지역은 현시점에 가까워질수록 수심이 얕아지는 경향이, 도류제 지역은 수심이 깊어지는 경향이 뚜렷이 나타났으며, 서측호안 지역은 시기별로 얕아지는 경향과 깊어지는 경향이 교차되며 나타났다. 하구둑-군산내항, 군산내항-군산외항, 개야수로, 대죽사주 지역에서 꾸준한 준설공사가 있었음에도 불구하고 수심이 얕아지고 있다는 것은 퇴적작용이 매우 우세하게 진행되고 있음을 시사한다.

• Journal of Plant Biology
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• v.35 no.1
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• pp.53-60
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• 1992

This study has been carried out to investigate the ultrastructural changes in the associated with the disintegration of the storage materials in endosperm cell of ginseng (Panax ginseng C.A. Meyer) seed during after-ripening with light and electron microscope. The protein body of endosperm cells near the umbiliform layer showed various degenerative patterns, and so electron density of proteinaceous matrix was gradualJy decreased during afterripening. These results indicate that the decomposition of endosperm was already initiated during after-ripening. As the degeneration of endosperm was more progressed after the dehiscence of seed, non-decomposed part of protein body appeared amorphously with high electron density. Decomposed protein bodies were vacuolized with the loss of their matrix and gradually expanded by fusion. Also, spherosomes were gradually dissolved with the lowered electron density during the degeneration of endosperm. The vesicles of dictyosomes near the cell wall are observed in endosperm contacting with umbiliform layer and are fused with plasma membrane. Umbiliform layer which was the complex of the decomposed remnants of lysis and materials has strong stainability for toluidine blue and basic fuchsin.

• Journal of the Korea Computer Graphics Society
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• v.16 no.3
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• pp.41-48
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• 2010

Photon mapping is a traditional global illumination method using many photons emitted from the light source for photo-realistic rendering. However, this method needs a lot of resources to perform tracing of millions of photons. Progressive photon mapping solves this problem. Typical progressive photon mapping performs ray tracing at first to find the hit points on diffuse surface of objects. Next, light source repeatedly emits a small number of photons in photon tracing pass, and power of photons in each sphere that has a fixed radius with the hit points in the center is accumulated. This method requires less resources than previous photon mapping, but it spends much time for gathering enough photons since each of photons progresses through a random direction and rendering high quality image. To improve the method, we propose photon probing that calculates variance of photons in the sphere and controls radius of sphere. In addition, we apply cone filter in radiance estimation step for reducing aliasing at the edges in result image.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.39-48
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• 2020

Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.