• 제어로봇시스템학회논문지
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• 제11권12호
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• pp.1083-1087
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• 2005

As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval of multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We model the cerebral cortex and hippocampal neural network in engineering domain, and team content-based feature vectors fast and apply the hippocampal neural network algorithm to compose of optimized feature. And then we present fast and precise retrieval effect when indexing and retrieving.

• Journal of Power Electronics
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• 제11권6호
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1438-1444
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• 2018

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest information. However, microblogs have word limits, and it has there is not enough information to analyze for content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only measures the similarity between the data represented as a vector space model, but also measures the semantic similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering algorithm.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• 방송공학회논문지
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• 제4권2호
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• pp.187-196
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• 1999

본 논문에서는 내용 기반 동영상 검색을 위하여 컬러 정보 및 모션 정보를 사용하는 효율적인 자동 특징 추출 알고리즘을 제안하고, 이를 동영상 검색 시스템에 적용한다. 컬러 정보의 경우 기존의 key-frame단위의 컬러 특징 추출의 한계를 극복하고, 동영상의 컬러 히스토그램 정보와 컬러의 공간분포 정보를 반영할 수 있는 컬러 특징 추출 알고리즘을 제안한다. 또한 MPEG-1 동영상 내의 모션 벡터와 컬러 정보를 조합한 컬러-모션 특징을 추출하여, 기존의 위치 기반 특징 추출 알고리즘의 한계를 극복하였다. 최종적으로 추출된 특징을 이용한 검색 시스템을 구현하여, 제안된 알고리즘의 성능을 평가하였다.

• 한국통신학회논문지
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• 제41권2호
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• pp.178-188
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• 2016

스마트폰의 증가와 PC 성능 향상으로 유무선 통신망에 트래픽이 폭발적으로 증가하고 있다. 여기에는 페이스북, 유투브와 같은 멀티미디어 서비스와 파일 공유가 큰 부분을 차지하고 있다. CDN(Content Delivery Network)은 원거리에 있는 콘텐츠 사업자의 웹 서버에 저장된 콘텐츠를 이용자 근처 CDN 서버에 미리 저장, 콘텐츠 요구 발생 시 최적의 CDN 서버로부터 콘텐츠를 제공하는 콘텐츠 전송 기술이다. 본 논문에서는 콘텐츠 요청 메시지 전달에 Minimum Spanning Tree(MST) 알고리즘을 응용한 SCRP(Shortest Core Routing Path) 알고리즘을 사용해 CDN 서버와 클라이언트의 콘텐츠 전달에 이용되는 전체 트래픽 양을 최적화하였다. 또한 HC_LRU 캐시 알고리즘을 통해 캐시 적중률을 향상시킴으로써 콘텐츠 요청에 대한 평균 응답시간을 단축시켰다. 제안한 SCRP와 HC_LRU 알고리즘을 통해 트래픽을 지역화하고 병목현상을 방지하여 네트워크 자원을 효율적으로 사용하는 확장성 있는 콘텐츠 전송 네트워크 시스템을 구축할 수 있다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.385-386
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• 2020

In this paper, we propose a collaborative filtering recommendation algorithm based on the LDA2Vec topic model. By extracting and analyzing the article's content, calculate their semantic similarity then combine the traditional collaborative filtering algorithm to recommend. This approach may promote the system's recommend accuracy.

• 한국정보과학회논문지:데이타베이스
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• 제36권5호
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• pp.366-373
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• 2009

이 논문은 이미지 데이터베이스를 위한 유사도 순위 매김 알고리즘을 제시한다. 이미지 검색의 문제점 중 하나가 이미지로부터 자동적으로 계산한 하위 레벨 특성과 인간 지각과의 의미 차이이며, 검색시에 이미지 유사도 측정을 위해 많은 알고리즘에서는 민코프스키 측정법($L_p$-norm)을 사용하고 있다. 그러나 민코프스키 측정법은 인간 시각 시스템의 비선형적 특성과 문맥 정보를 반영하지 못한다. 본 알고리즘에서는 인간 지각의 비선형성과 문맥 정보를 반영하는 유사도와 탐색 알고리즘을 통해 이 문제를 해결한다. 본 알고리즘을 필기체 숫자 이미지 데이터베이스에 적용하여 성능의 우수성과 효과를 증명하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.133-151
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• 2019

In order to achieve faster content distribution speed and stronger fault tolerance, a P2P peer can connect to multiple peers in parallel and receive chunks of the data simultaneously. A critical issue in this environment is selecting a set of nodes participating in swarming sessions. Previous related researches only focus on performance metrics, such as downloading time or the round-trip time, but in this paper, we consider a new performance metric which is closely related to the network and propose a peer selection algorithm that produces the set of peers generating optimal worst link stress. We prove that the optimal algorithm is practicable and has the advantages with the experiments on PlanetLab. The algorithm optimizes the congestion level of the bottleneck link. It means the algorithm can maximize the affordable throughput. Second, the network load is well balanced. A balanced network improves the utilization of resources and leads to the fast content distribution. We also notice that if every client follows our algorithm in selecting peers, the probability is high that all sessions could benefit. We expect that the algorithm in this paper can be used complementary to existing methods to derive new and valuable insights in peer-to-peer networking.

• 산경연구논집
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• 제10권11호
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• pp.71-80
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• 2019

Purpose: The purpose of this study is to design the curriculum of Basic College Software Programming to develop creative and logical-thinking. This course is guided by algorithmic thinking and logical thinking that can be solved by computing for problem-solving, and it helps to develop by software through basic programming education. Through the stage of problem analysis, abstraction, algorithm, data structure, and algorithm implementation, the curriculum is designed to help learners experience algorithm problem-solving in various areas to develop diffusion thinking. For Learners aim to achieve the balanced development of divergent and convergent-thinking needed in their creative problem-solving skills. Research design, data and methodology: This study is to design a basic software education for improving algorithm-thinking for non-major. The curriculum designed in this paper is necessary to non-majors students who have completed the 'Creative Thinking and Coding Course' Design Thinking based are targeted. For this, contents were extracted through advanced research analysis at home and abroad, and experts in computer education, computer engineering, SW education, and education were surveyed in the form of quasi-openness. Results: In this study, based on ADD Thinking's algorithm thinking, we divided the unit college majors into five groups so that students of each major could accomplish the goal of "the ability to internalize their own ideas into computing," and extracted and designed different content areas, content elements and sub-components from each group. Through three expert surveys, we established a strategy for characterization by demand analysis and major/textbook category and verified the appropriateness of the design direction to ensure that the subjects and contents of the curriculum are appropriate for each family in order to improve algorithm-thinking. Conclusions: This study helps develop software by enhancing the ability of students who practice various subjects and exercises to explore creative expressions in various areas, such as 'how to think like a computer' that can implement and execute their ideas in computing. And it helps increase the ability to think logical and algorithmic computing based on creative solutions, improving problem-solving ability based on computing thinking and fundamental understanding of computer coding and development of logical thinking ability through programming.