• Title/Summary/Keyword: cluster method

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A Study of FPGA Algorithm for consider the Power Consumption (소모전력을 위한 FPGA 알고리즘에 관한 연구)

  • Youn, Choong-Mo;Kim, Jae-Jin
    • Journal of Digital Contents Society
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    • v.13 no.1
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    • pp.37-41
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    • 2012
  • In this paper, we proposed FPGA algorithm for consider the power consumption. Proposed algorithm generated a feasible cluster by circuit partition considering the CLB condition within FPGA. Separated the feasible cluster reduced power consumption using glitch removal method. Glitch removal appled delay buffer insertion method by signal process within the feasible cluster. Also, removal glitch between the feasible clusters by signal process for circuit. The experiments results show reduction in the power consumption by 7.14% comparing with that of [9].

Parallel Process System and its Application to Steam Generator Structural Analysis

  • Chang Yoon-Suk;Ko Han-Ok;Choi Jae-Boong;Kim Young-Jin
    • Journal of Mechanical Science and Technology
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    • v.19 no.11
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    • pp.2007-2015
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    • 2005
  • A large-scale analysis to evaluate complex material and structural behaviors is one of interesting topic in diverse engineering and scientific fields. Also, the utilization of massively parallel processors has been a recent trend of high performance computing. The objective of this paper is to introduce a parallel process system which consists of general purpose finite element analysis solver as well as parallelized PC cluster. The later was constructed using eight processing elements and the former was developed adopting both hierarchical domain decomposition method and balancing domain decomposition method. Then, to verify the efficiency of the established system, it was applied for structural analysis of steam generator in nuclear power plant. Since the prototypal evaluation results agreed well to the corresponding reference solutions it is believed that, after reinforcement of PC cluster by increasing number of processing elements, the promising parallel process system can be utilized as a useful tool for advanced structural integrity evaluation.

An Adaptive Clustering Algorithm of Wireless Sensor Networks for Energy Efficiency (에너지 효율을 위한 무선센서 네트워크의 적응형 클러스터링 알고리즘)

  • Cho, Young-bok;Lee, Sang-ho;Woo, Sung-Hee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.1
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    • pp.99-106
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    • 2017
  • In the WSN environment, the sensor node is selected as the cluster header and consumes a lot of energy. Therefore, we proposed a method to automatically select a cluster algorithm using the sensor field environment that can improve the reliability of the whole network by applying an energy efficient clustering algorithm based on already deployed sensor field. Experimental results show that FDN is extended about 3 times by using the proposed algorithm. In addition, the network energy is extended by up to 30% compared to the conventional method, thereby improving the reliability of the sensor network.

Availability Analysis of (n,k) Cluster Web Server System using Software Rejuvenation Method over Switchover ((n,k) 클러스터 웹서버 시스템의 작업전이를 고려한 소프트웨어 재활기법의 가용도 분석)

  • 강창훈
    • Journal of the Korea Computer Industry Society
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    • v.3 no.2
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    • pp.227-234
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    • 2002
  • An cluster web server system, one has the problem that does the low availability occured by the high chance of the server failures and it is not easy to provide high performance and availability occuring software aging. In this paper, running cluster web sewers consists of n primary servers and k backup servers, propose software rejuvenation model on performance and switchover time. Based on the various system operational parameters, we calculate to evaluate the rejuvenation policy such steady-state probabilities, availability, and downtime cost. And we validate the solutions of mathematical model by experiments based on various operation parameters and fud that the software rejuvenation method can be adopted as prventive fault tolerant technique for stability of system.

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A weight-based cluster head replacement algorithm in the Internet of Things (사물인터넷에서 가중치 기반 클러스터 헤드 교체 알고리즘)

  • Kim, Jeong-Won
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.11
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    • pp.91-96
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    • 2014
  • Since the sensors of Internet of Things (IOT) collect various data, the lifetime of sensor network is very important and the data should be aggregated efficiently. The contiguous collection by the certain sensors occurs an excessive battery consumption and successive transmission of same value of data should be avoided. To solve these things, we propose an weight-based cluster head replacement method that divides whole network into several grids and cluster head is selected by remaining energy, density of alive sensors and location of sensor. The aim of algorithm maximizes the lifetime of network. Our simulation results shows that the proposed method is very simple as well as balances energy consumption.

Cluster and information entropy analysis of acoustic emission during rock failure process

  • Zhang, Zhenghu;Hu, Lihua;Liu, Tiexin;Zheng, Hongchun;Tang, Chun'an
    • Geomechanics and Engineering
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    • v.25 no.2
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    • pp.135-142
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    • 2021
  • This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

Categorical Data Clustering Analysis Using Association-based Dissimilarity (연관성 기반 비유사성을 활용한 범주형 자료 군집분석)

  • Lee, Changki;Jung, Uk
    • Journal of Korean Society for Quality Management
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    • v.47 no.2
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    • pp.271-281
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    • 2019
  • Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.

Single Pixel Compressive Camera for Fast Video Acquisition using Spatial Cluster Regularization

  • Peng, Yang;Liu, Yu;Lu, Kuiyan;Zhang, Maojun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.11
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    • pp.5481-5495
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    • 2018
  • Single pixel imaging technology has developed for years, however the video acquisition on the single pixel camera is not a well-studied problem in computer vision. This work proposes a new scheme for single pixel camera to acquire video data and a new regularization for robust signal recovery algorithm. The method establishes a single pixel video compressive sensing scheme to reconstruct the video clips in spatial domain by recovering the difference of the consecutive frames. Different from traditional data acquisition method works in transform domain, the proposed scheme reconstructs the video frames directly in spatial domain. At the same time, a new regularization called spatial cluster is introduced to improve the performance of signal reconstruction. The regularization derives from the observation that the nonzero coefficients often tend to be clustered in the difference of the consecutive video frames. We implement an experiment platform to illustrate the effectiveness of the proposed algorithm. Numerous experiments show the well performance of video acquisition and frame reconstruction on single pixel camera.

Content_based Load Balancing Technique In Web Server Cluster (웹 서버 클러스터에서 내용 기반으로한 부하 분산 기법)

  • Myung, Won-Shig;Jang, Tea-Mu
    • The KIPS Transactions:PartA
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    • v.10A no.6
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    • pp.729-736
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    • 2003
  • With the rapid growth of the Internet, popular Web sites are visited so frequently that these cannot be constructed with a single server or mirror site of high performance. The rapid increase of Internet uses and uses raised the problems of overweighted transmission traffic and difficult load balancing. To solve these, various schemes of server clustering have been surveyed. Especially, in order to fully utilize the performance of computer systems in a cluster, a good scheduling method that distributes user requests evenly to servers in required. In this paper, we propose a new method for reducing the service latency. In our method, each Web server in the cluster has different content. This helps to reduce the complexity of load balancing algorithm and the service latency The Web server that received a request from the load balancer responds to the client directly without passing through the load balancer. Simulation studies show that our method performs better than other traditional methods. In terms of response time, our method shows shorter latency than RR (Round Robin) and LC (Least Connection) by about 16%, 14% respectively.

The Application of an HMM-based Clustering Method to Speaker Independent Word Recognition (HMM을 기본으로한 집단화 방법의 불특정화자 단어 인식에 응용)

  • Lim, H.;Park, S.-Y.;Park, M.-W.
    • The Journal of the Acoustical Society of Korea
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    • v.14 no.5
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    • pp.5-10
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    • 1995
  • In this paper we present a clustering procedure based on the use of HMM in order to get multiple statistical models which can well absorb the variants of each speaker with different ways of saying words. The HMM-clustered models obtained from the developed technique are applied to the speaker independent isolated word recognition. The HMM clustering method splits off all observation sequences with poor likelihood scores which fall below threshold from the training set and create a new model out of the observation sequences in the new cluster. Clustering is iterated by classifying each observation sequence as belonging to the cluster whose model has the maximum likelihood score. If any clutter has changed from the previous iteration the model in that cluster is reestimated by using the Baum-Welch reestimation procedure. Therefore, this method is more efficient than the conventional template-based clustering technique due to the integration capability of the clustering procedure and the parameter estimation. Experimental data show that the HMM-based clustering procedure leads to $1.43\%$ performance improvements over the conventional template-based clustering method and $2.08\%$ improvements over the single HMM method for the case of recognition of the isolated korean digits.

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