• 정보처리학회논문지B
• /
• 제9B권3호
• /
• pp.319-326
• /
• 2002

본 논문에서는 위성영상을 처리하기 위한 무감독분류 기법인 군집분류 시스템을 설계하고 구현하였다. 구현된 시스템은 새로운 위성영상 포맷과 군집분류 기법의 지원이 용이하고, 확장성 있는 시스템의 설계를 위하여 팩토리 패턴과 전략적 패턴 등 다양한 디자인 패턴을 적용하였다. 군집분류 시스템은 순차군집분류 기법, K-Means 군집분류 기법, ISODATA 기법, Fuzzy C-Means군집분류 기법을 설계, 구현하였으며 Landsat TM 위성영상을 분류기의 입력영상으로 실험하였다. 그 결과 군집분류 기법은 사전지식이 없는 위성영상의 분류를 위한 표본영역의 추출작업과 위성영상의 실시간 분류에 효과적인 사용이 가능함을 보였으며, 재사용성 및 확장성이 우수한 시스템을 개발하였다.

• International Journal of Contents
• /
• 제2권3호
• /
• pp.11-17
• /
• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• 한국해양공학회:학술대회논문집
• /
• 한국해양공학회 2001년도 춘계학술대회 논문집
• /
• pp.38-42
• /
• 2001

An engineering database system is presented for ship hull form and hasic design. Web i, nd clustering systems are adopted to develope the system. It is appeared that the system is efficient and useful for integrated ship design.

• 한국콘텐츠학회논문지
• /
• 제2권1호
• /
• pp.68-76
• /
• 2002

클러스터링은 부품 분류의 대표적인 방법인데, 클래스나 모듈의 응집도와 결합도를 이용한 기존의 클러스터링 방법은 클래스간의 관계에 중점을 둔 디자인 패턴을 기존의 클러스터링 방법을 이용하는 것은 효과적일 수 있다. 본 논문에서는 디자인 패턴을 분류하기 위해 패턴 구조의 특성을 가지고 분류하였다. 그리고 클러스터링에 의한 분류는 패싯 분류에 의한 방법보다 높은 정확도를 보여주었다. 따라서 자동화된 분류방법인 클러스터링 알고리즘을 사용하여 디자인 패턴을 분류하는 것이 효과적이라 할 수 있다. 디자인 패턴의 분류는 검색 시 유사한 패턴들이 같은 카테고리에 저장이 되므로 유사 패턴을 비교하여 사용할 수 있으며, 패턴 클러스터링에 의해 분류되고, 패턴의 링크정보를 이용하여 저장하므로 저장소를 효율적으로 관리할 수 있다.

• 대한산업공학회지
• /
• 제27권1호
• /
• pp.47-53
• /
• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• 동력기계공학회지
• /
• 제20권4호
• /
• pp.32-37
• /
• 2016

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.109-118
• /
• 2023

Energy awareness is an essential design flaw in wireless sensor network. Clustering is the most highly regarded energy-efficient technique that offers various benefits such as energy efficiency and network lifetime. Clusters create hierarchical WSNs that introduce the efficient use of limited sensor node resources and thus enhance the life of the network. The goal of this paper is to provide an analysis of the various energy efficient clustering algorithms. Analysis is based on the energy efficiency and network lifetime. This review paper provides an analysis of different energy-efficient clustering algorithms for WSNs.

• 산업경영시스템학회지
• /
• 제37권3호
• /
• pp.89-98
• /
• 2014

Platform-based product family design is recognized as an effective method to satisfy the mass customization which is a current market trend. In order to design platform-based product family successfully, it is the key work to define a good product platform, which is to identify the common modules that will be shared among the product family. In this paper the clustering analysis using dendrogram is proposed to capture the common modules of the platform. The clustering variables regarding both marketing and engineering sides are derived from the view point of top-down product development. A case study of a cordless drill/drive product family is presented to illustrate the feasibility and validity of the overall procedure developed in this research.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2005년도 춘계 학술대회 발표논문집
• /
• pp.87-90
• /
• 2005

Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

• Journal of Information Processing Systems
• /
• 제15권4호
• /
• pp.986-1016
• /
• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.