• Journal of information and communication convergence engineering
• /
• 제6권2호
• /
• pp.182-186
• /
• 2008

RFID systems generate huge amount of data quickly. The data are associated with the locations and the timestamps and the containment relationships. It is requires to assure efficient queries and updates for product tracking and monitoring. We propose a clustering technique for fast query processing. Our study presents the state charts of temporal event flow and proposes the dependence trees with data association and uses them to cluster the linked events. Our experimental evaluation show the power of proposing clustering technique based on dependence tree.

• Journal of Information Technology Applications and Management
• /
• 제23권2호
• /
• pp.143-152
• /
• 2016

We can organize, manage, search, and process the documents efficiently by a document clustering. In general, the documents are clustered in a high dimensional feature space because the documents consist of many terms. In this paper, we propose a new method to cluster the documents efficiently in a low dimensional feature space by finding the core concepts from a domain ontology corresponding to the particular area documents. The experiment shows that our clustering method has a good performance.

• Nuclear Engineering and Technology
• /
• 제49권6호
• /
• pp.1211-1218
• /
• 2017

Monte Carlo neutron transport codes generally use the method of successive generations to converge the fission source distribution to-and then maintain it at-the fundamental mode. Recently, a phenomenon called "clustering" has been noted, which produces fission distributions that are very far from the fundamental mode. In this study, a mathematical model of clustering in Monte Carlo has been developed. The model draws on previous work for continuous-time birth-death processes, as well as methods from the field of population genetics.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2006년도 학술대회 1부
• /
• pp.711-716
• /
• 2006

Since the introduction of digital camera to the mass market, the number of digital photos owned by an individual is growing at an alarming rate. This phenomenon naturally leads to the issues of difficulties while searching and browsing in the personal digital photo archive. Traditional approach typically involves content-based image retrieval using computer vision algorithms. However, due to the performance limitations of these algorithms, at least on the casual digital photos taken by non-professional photographers, more recent approaches are centered on time-based clustering algorithms, analyzing the shot times of photos. These time-based clustering algorithms are based on the insight that when these photos are clustered according to the shot-time similarity, we have "event clusters" that will help the user browse through her photo archive. It is also reported that one of the remaining problems with the time-based approach is that people perceive events in different scales. In this paper, we present an adaptive time-based clustering algorithm that exploits the usage history of digital photos in order to infer the user's preference on the event granularity. Experiments show significant performance improvements in the clustering accuracy.

• 한국군사과학기술학회지
• /
• 제26권5호
• /
• pp.384-391
• /
• 2023

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.

• 한국산학기술학회논문지
• /
• 제19권11호
• /
• pp.758-764
• /
• 2018

특허(patent), 학술 논문(scholarly paper)과 연구 보고서(research report)와 같은 디지털 문서(digital document)에는 주제(topic)를 요약하는 저자 키워드(author keyword)가 있다. 서로 다른 문서가 동일한 키워드를 공유하고 있다면 두 문서가 동일한 주제의 내용을 기술하고 있을 가능성이 매우 높다. 문서 군집화(document clustering)는 비슷한 주제를 가지는 문서들을 비지도 학습 방법(unsupervised learning)을 이용하여 같은 군집으로 그룹(group)화 하는 것이다. 문서 군집화는 다양한 분석에 이용되지만 대용량의 문서 데이터에 적용하기 위해서는 많은 계산량이 필요함으로 쉽지 않다. 이러한 경우, 문서의 내용을 이용하는 것보다 문서의 키워드를 이용하여 군집화하면 더욱 효율적으로 대용량의 데이터를 연결할 수 있다. 기존의 상향식 군집화 방법(bottom-up hierarchical clustering)은 대용량의 키워드 군집화(keyword clustering)를 수행하는데 있어서 많은 시간이 필요하다는 문제점이 있다. 본 논문에서는 정보검색(information retrieval)에서 널리 사용되는 역인덱스(inverted-index) 구조를 상향식 군집화에 적용한 효율적인 군집화 방법을 제안하고, 제안 방법을 대용량의 키워드 데이터에 적용하였으며, 그 결과를 분석하였다.

• Journal of information and communication convergence engineering
• /
• 제15권1호
• /
• pp.49-52
• /
• 2017

Ganglion cysts are benign soft tissues usually encountered in the wrist. In this paper, we propose a method to extract a ganglion cyst region from ultrasonography images by using image segmentation. The proposed method using the possibilistic c-means (PCM) clustering method is applicable to ganglion cyst extraction. The methods considered in this thesis are fuzzy stretching, median filter, PCM clustering, and connected component labeling. Fuzzy stretching performs well on ultrasonography images and improves the original image. Median filter reduces the speckle noise without decreasing the image sharpness. PCM clustering is used for categorizing pixels into the given cluster centers. Connected component labeling is used for labeling the objects in an image and extracting the cyst region. Further, PCM clustering is more robust in the case of noisy data, and the proposed method can extract a ganglion cyst area with an accuracy of 80% (16 out of 20 images).

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2006년도 춘계 학술대회 발표논문집
• /
• pp.41-44
• /
• 2006

The purpose of state tying is to reduce the number of models and to use relatively reliable output probability distributions. There are two approaches: one is top down clustering and the other is bottom up clustering. For seen data, the performance of bottom up approach is better than that of top down approach. In this paper, we propose a new clustering technique that can enhance the undertrained triphone clustering performance. The basic idea is to tie unreliable triphones before clustering. An unreliable triphone is the one that appears in the training data too infrequently to train the model accurately. We propose to use monophone distance to preprocess these unreliable triphones. It has been shown in a pilot experiment that the proposed method reduces the error rate significantly.

• 한국항행학회논문지
• /
• 제14권2호
• /
• pp.239-246
• /
• 2010

본 논문은 비음수 행렬 분해와 퍼지 관계를 이용한 새로운 문서군집 방법을 제안한다. 제안된 방법은 비음수 행렬 분해된 의미특징을 이용하여 군집 레이블과 군집의 대표 용어들을 선택함으로서 문서군집의 내부구조를 더 잘 표현할 수 있으며, 퍼지 관계 값을 이용한 군집은 문서군집에 유사하지 않은 문서를 더 잘 구분함으로써 문서군집의 성능을 높일 수 있다. 실험결과 제안방법을 적용한 문서군집방법이 다른 문서군집 방법에 비하여 좋은 성능을 보인다.

• 센서학회지
• /
• 제19권5호
• /
• pp.342-348
• /
• 2010

The wireless sensor networks have been extensively researched. One of the issues in wireless sensor networks is a developing energy-efficient clustering protocol. Clustering algorithm provides an effective way to extend the lifetime of a wireless sensor networks. In this paper, we proposed an energy efficient clustering scheme by adjusting group size. In sensor network, the power consumption in data transmission between sensor nodes is strongly influenced by the distance of two nodes. And cluster size, that is the number of cluster member nodes, is also effected on energy consumption. Therefore we proposed the clustering scheme for high energy efficiency of entire sensor network by controlling cluster size according to the distance between cluster header and sink.