• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.219-222
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• 2011

There are number of methods that track the movement of an object or the change of state, such as Kalman filter, particle filter, dynamic clustering, and so on. Amongst these method, dynamic clustering method is an useful way to track cluster across multiple data frames and analyze their trend. In this paper we suggest the similarity-based dynamic clustering method, and verifies it's performance by simulation. Proposed dynamic clustering method is how to determine the same clusters for each continuative frame. The same clusters have similar characteristics across adjacent frames. The change pattern of cluster's characteristics in each time frame is throughly studied. Clusters in each time frames are matched against each others to see their similarity. Mamdani fuzzy model is used to determine similarity based matching algorithm. The proposed algorithm is applied to radar reflectivity data over time domain. We were able to observe time dependent characteristic of the clusters.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.84-95
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• 2008

Wireless sensor networks have recently emerged as a platform for several applications. By deploying wireless sensor nodes and constructing a sensor network, we can remotely obtain information about the behavior, conditions, and positions of objects in a region. Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable to prolong the lifetime of a sensor network as long as possible. In this paper, we propose a novel clustering algorithm that distributes the energy consumption of a cluster head. First, we analyze the energy consumption if cluster heads and divide each cluster into a collection layer and a transmission layer according to their roles. Then, we elect a cluster head for each layer to distribute the energy consumption of single cluster head. In order to show the superiority of our clustering algorithm, we compare it with the existing clustering algorithm in terms of the lifetime of the sensor network. As a result, our experimental results show that the proposed clustering algorithm achieves about $10%{\sim}40%$ performance improvements over the existing clustering algorithms.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.783-790
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• 2009

The necessity of wireless sensor networks is increasing in the recent years. So many researches are studied in wireless sensor networks. The clustering algorithm provides an effective way to prolong the lifetime of the wireless sensor networks. The one-hop routing of LEACH algorithm is an inefficient way in the energy consumption of cluster-head, because it transmits a data to the BS(Base Station) with one-hop. On the other hand, other clustering algorithms transmit data to the BS with multi-hop, because the multi-hop transmission is an effective way. But the multi-hop routing of other clustering algorithms which transmits data to BS with multi-hop have a data bottleneck state problem. The unequal clustering algorithm solved a data bottleneck state problem by increasing the routing path. Most of the unequal clustering algorithms partition the nodes into clusters of unequal size, and clusters closer to the BS have small-size the those farther away from the BS. However, the energy consumption of cluster-head in unequal clustering algorithm is more increased than other clustering algorithms. In the thesis, I propose an energy efficient unequal clustering algorithm which decreases the energy consumption of cluster-head and solves the data bottleneck state problem. The basic idea is divided a three part. First of all I provide that the election of appropriate cluster-head. Next, I offer that the decision of cluster-size which consider the distance from the BS, the energy state of node and the number of neighborhood node. Finally, I provide that the election of assistant node which the transmit function substituted for cluster-head. As a result, the energy consumption of cluster-head is minimized, and the energy consumption of total network is minimized.

• ICROS
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• v.1 no.3
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• pp.101-108
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• 1995

이 글에서는 퍼지제어기의 기본 구성에 대해 간단히 다루고 모델에 근거해 다음 제어상태를 예견해 내는 제어기법인 모델참조 적응을 기반으로 한 적응 퍼지제어에 대해서, 그리고 신경회로망을 이용한 퍼지제어기의 파라미터의 조정과 클러스터링을 통해서 퍼지규칙을 예측하는 적응 퍼지제어기에 대해서 살펴보았다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.91-97
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• 2022

Pipes transport and supply fuel in various categories; however, corrosion occurs because of the external environment, impurities are mixed in the fuel, and substances leak to the outside, which can lead to serious accidents. Therefore, in this study, inspection equipment using a laser scanner was manufactured to classify conditions according to the degree of corrosion of the outer wall of the pipe, and the corrosion height and maximum value of the pipe were obtained from the surface information. Using the k-means method, it was classified into four states, and the standard of the average height and maximum height of corrosion for each state was derived.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.11-14
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• 2008

웹 2.0 환경에서 인터넷 사용자가 생성하는 정보는 폭발적인 규모로 증가하고 있다. 또한 UCC 등의 사용자 참여 서비스 및 VOD, IPTV 등의 대용량 서비스가 본격화 되고 있다. 그러나 이러한 데이타 전송량 증가 속도를 네트워크 전송 설비의 증설이 따라가지 못하고 있는 실정이다. 이를 극복하기 위해 P2P 기술을 이용하고 있지만 대부분의 P2P 기술들은 실제 물리적인 네트워크 상태를 고려하지 않고 응용 계층만을 고려하기 때문에 데이타 전송의 비효율이 발생하게 된다. 게다가 이러한 비효율을 해결하기 위한 방안들은 분산형 Pure P2P 시스템이나 구조적 P2P 시스템에 대한 연구가 대부분이고 비구조적 중앙 집중형에 대한 연구는 없는 실정이다. 본 논문에서는 물리적인 네트워크 정보와 그래프 클러스터링 기법을 적용한 계층적 클러스터링 방법을 이용하여 실제 기업에서 운영하는 중앙 집중형 P2P 시스템에서 성능을 향상 시킬 수 있는 기법을 제안한다. 그리고 이를 통해 기존의 기법들이 가지고 있는 과도한 메시지 교환, 고정된 랜드마크의 유지 등의 문제점을 보완하여 대규모 데이타의 효율적인 전송을 가능케 하는 실제적인 P2P 환경에 적합한 오버레이 네트워크 모듈을 구현하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6C
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• pp.624-633
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• 2003

We propose a novel method of selecting key VOPs from MPEG-4 compressed domain without fully decoding the compressed data. Approximated shapes of VOPs are obtained from the shape coding mode and then VOPs are clustered by shape similarity to generate key VOPs. The proposed method reduces the computation time of shape approximation, compared with Erol's method. Nevertheless, the resulting VOPs have a good summarizing capability of a video sequence. NMHD (normalized mean Hausdorff distance) values are 2-means clustered to generate key VOPs. In the video search, the MHD of a query VOP from key VOPs are computed and the VOP with the lowest distance is returned. Tests on standard MPEG-4 test sequences show that the computational complexity is very low. Recursive clustering proved to be very effective for generating suitable key VOPs.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.39-44
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• 2021

In this paper, we propose a method of disease diagnosis system that can diagnose the health status of household pets for the people who lack veterinary knowledge. The proposed diagnosis system holds 50 different kinds of diseases with the symptoms for each of them as a database to provide results from symptom input. Each disease database has its own symptom codes for a disease, and by using the disease database, FCM clustering technique is applied to disease which outputs membership degree to determine diseases close to the input symptom as a pet diagnosis result. The implementation results of the proposed pet diagnosis system were obtained by the number of selected symptoms and the possibility values of the diseases that have the selected symptoms being sorted in descending order to derive top 3 diseases closest to the pet's symptom.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.221-226
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• 2019

The characteristic of the beta wave among the EEG waves corresponds to the stress area of human perception. The over-bandwidth of the stress is extracted by analyzing the beta-wave correlation between the low-bandwidth and high-bandwidth. We present a KMeans clustering analysis model for unsupervised machine learning to construct an analytical model for analyzing and extracting the beta-wave correlation. The proposed model classifies the beta wave region into clusters of similar regions and identifies anomalous waveforms in the corresponding clustering category. The abnormal group of waveform clusters and the normal category leaving region are discriminated from the stress risk group. Using this model, it is possible to discriminate the degree of stress of the cognitive state through the EEG waveform, and it is possible to manage and apply the cognitive state of the individual.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.