• International Journal of CAD/CAM
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• 제5권1호
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• pp.1-10
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• 2005

A new approach based on vector field clustering for tool path optimization of 5-axis CNC machining is presented in this paper. The strategy of the approach is to produce an efficient tool path with respect to the optimal cutting direction vector field. The optimal cutting direction maximizes the machining strip width. We use the normalized cut clustering technique to partition the vector field into clusters. The spiral and the zigzag patterns are then applied to generate tool path on the clusters. The iso-scallop method is used for calculating the tool path. Finally, our numerical examples and real cutting experiment show that the tool path generated by the proposed method is more efficient than the tool path generated by the traditional iso-parametric method.

• 한국경영과학회지
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• 제41권2호
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• pp.17-33
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• 2016

Clustering is one of the leading methods to analyze big data and is used in many different fields. This study deals with Clustering Validity Index (CVI) to verify the effectiveness of clustering results. We compare the performance of CVIs with business applications of various field. In this study, the used CVIs for comparing performance are DU, CH, DB, SVDU, SVCH, and SVDB. The first three CVIs are well-known ones in the existing research and the last three CVIs are based on support vector data description. It has been verified with outstanding performance and qualified as the application ability of CVIs based on support vector data description.

• 정보처리학회논문지A
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• 제16A권6호
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• pp.411-418
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• 2009

퍼지 클러스터링 기반 벡터 양자화 알고리즘은 퍼지 클러스터링 분석이 벡터 양자화 프로세스 초기단계에서 초기화에 덜 민감하게 하기 때 문에 데이터 압축 분야에서 널리 사용되어 왔다. 하지만, 퍼지 클러스터링 처리는 훈련 벡터 공간에 포함된 불확실한 양적 공식의 복잡한 프레 임워크 때문에 상당한 계산량이 요구된다. 이러한 상당한 계산량 부하를 극복하기위해 본 논문은 4,096 프로세싱 엘리먼트로 구성된 어레이 아 키텍처를 이용하여 퍼지 벡터 양자화 알고리즘의 병렬 구현을 제안한다. 제안하는 병렬 구현은 4,096 프로세싱 엘리먼트를 이용하여 클러스터 링 프로세스 동안 효과적인 벡터 할당 정책을 적용함으로써 계산적으로 효율적인 솔루션을 제공한다. 모의실험 결과, 제안한 병렬 구현은 기존 의 다른 어레이 아키텍처를 이용한 구현보다 성능 및 효율 측면에서 상당한 향상을 보였다. 또한동일한 130nm 기술에서 제안한 병렬 구현은 오늘날의 ARM이나 TI DSP 프로세서를 이용한 구현과 비교하여 약 1000배의 성능 향상 및 100배의 에너지 효율 향상을 보였다. 이 결과들은 향상된 성능 및 에너지효율에서 제안한 병렬 구현의 잠재가능성을 입증한다.

• International journal of advanced smart convergence
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• 제5권2호
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• pp.53-58
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• 2016

With the growing use of big data and data mining, it serves to understand how such techniques can be used to understand various relationships in the healthcare field. This study uses hierarchical methods of data analysis to explore similarities in hospitalization across several New York state counties. The study utilized methods of measuring crowding distance of data for age-specific hospitalization period. Crowding distance is defined as the longest distance, or least similarity, between urban cities. It is expected that the city of Clinton have the greatest distance, while Albany the other cities are closer because they are connected by the shortest distance to each step. Similarities were stronger across hospital stays categorized by age. Hierarchical clustering can be applied to predict the similarity of data across the 10 cities of hospitalization with the measurement of crowding distance. In order to enhance the performance of hierarchical clustering, comparison can be made across congestion distance when crowding distance is applied first through the application of converting text to an attribute vector. Measurements of similarity between two objects are dependent on the measurement method used in clustering but is distinguished from the similarity of the distance; where the smaller the distance value the more similar two things are to one other. By applying this specific technique, it is found that the distance between crowding is reduced consistently in relationship to similarity between the data increases to enhance the performance of the experiments through the application of special techniques. Furthermore, through the similarity by city hospitalization period, when the construction of hospital wards in cities, by referring to results of experiments, or predict possible will land to the extent of the size of the hospital facilities hospital stay is expected to be useful in efficiently managing the patient in a similar area.

• Journal of Information Science Theory and Practice
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• 제8권2호
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• pp.6-17
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• 2020

Word similarity is often measured to enhance system performance in the information retrieval field and other related areas. This paper reports on an experimental comparison of values for word similarity measures that were computed based on 50 intentionally selected words from a Reuters corpus. There were three targets, including (1) co-occurrence-based similarity measures (for which a co-occurrence frequency is counted as the number of documents or sentences), (2) context-based distributional similarity measures obtained from a latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), and Word2Vec algorithm, and (3) similarity measures computed from the tf-idf weights of each word according to a vector space model (VSM). Here, a Pearson correlation coefficient for a pair of VSM-based similarity measures and co-occurrence-based similarity measures according to the number of documents was highest. Group-average agglomerative hierarchical clustering was also applied to similarity matrices computed by individual measures. An evaluation of the cluster sets according to an answer set revealed that VSM- and LDA-based similarity measures performed best.

• Journal of Advanced Research in Ocean Engineering
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• 제2권4호
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (B)
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• pp.193-195
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• 2006

As multimedia information increases sharply, In image retrieval field the method that can analyze image data quickly and exactly is required. In the case of image data, because each data includes a lot of informations, between accuracy and speed of retrieval become trade-off. To solve these problem, feature vector extracting process that use Genetic Algorithm for implementing prompt and correct image clustering system in case of retrieval of mass image data is proposed. After extracting color and texture features, the representative feature vector among these features is extracted by using Genetic Algorithm.

• 응용통계연구
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• 제34권3호
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• pp.389-399
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• 2021

최근 자연어 처리 문제에서의 단어 임베딩은 아주 큰 주목을 받고 있는 연구 주제이며 스킵그램은 성공적인 단어 임베딩 기법 중 하나이다. 주변단어들 정보를 이용해서 단어들의 의미를 학습하여 단어 임베딩 벡터를 할당하며 텍스트 자료를 효과적으로 분석할 수 있게 한다. 그러나 벡터 공간 모델의 한계로 인해 기본적인 단어 임베딩 방법들은 모든 단어가 하나의 의미를 가지고 있다는 것을 가정한다. 다의어, 즉 하나 이상의 의미를 가진 단어가 실생활에서 존재 하기 때문에 Neelakantan 등 (2014)은 군집분석 기법을 이용하여 다의어의 여러 의미들에 해당하는 의미 임베딩 벡터를 찾기 위해 MSSG (multi-sense skip-gram)를 제안했다. 본 논문에서는 MSSG의 통계적 성능을 개선시킬 수 있는 변형된 MSSG 방법을 제안한다. 먼저, 가중치를 활용한 가중문맥 벡터를 제안한다. 나아가, 군집의 수, 즉 다의어의 의미 수를 자료에서 자동적으로 추정해주는 x-means 방법을 활용한 알고리즘을 제안한다. 본 논문에서 수행한 실증자료를 기반한 모의실험에서 제안한 방법은 기존 방법에 비해 우수한 성능을 보여주었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• 한국측량학회지
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• 제22권3호
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• pp.303-309
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• 2004

영상의 밝기값과 텍스쳐 모두를 사용하여 대상물의 경계를 보다 정확하게 추출할 수 있는 적응적 베이즈 영상 분할기법을 C 프로그래밍 언어로 개발하였다. 사전확률밀도함수를 추정하기 위하여 깁스 분포 모델을 적용하였고, 조건확률밀도함수를 추정하기 위하여 퍼지 C-군집화 기법을 도입하였다. 추정된 두 확률밀도함수로부터 최대 사후주변확률이 산출되었고, 이를 시뮬레이션영상에 적용하여 99% 이상의 신뢰도를 획득하였다. 또한 개발된 알고리즘을 1963년 미 정찰위성사진을 이용하여 제작한 남극 정사영상에 적용하여 남극 전체 해안선에 대하여 최대 300미터 정확도를 갖는 벡터지도를 제작하였다.