• Title/Summary/Keyword: cluster detection

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Lifetime Escalation and Clone Detection in Wireless Sensor Networks using Snowball Endurance Algorithm(SBEA)

  • Sathya, V.;Kannan, Dr. S.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.4
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    • pp.1224-1248
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    • 2022
  • In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

Intrusion Detection System based on Cluster (클러스터를 기반으로 한 침입탐지시스템)

  • Yang, Hwan-Seok
    • Journal of Digital Contents Society
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    • v.10 no.3
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    • pp.479-484
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    • 2009
  • Security system of wireless network take on importance as use of wireless network increases. Detection and opposition about that is difficult even if attack happens because MANET is composed of only moving node. And it is difficult that existing security system is applied as it is because of migratory nodes. Therefore, system is protected from malicious attack of intruder in this environment and it has to correspond to attack immediately. In this paper, we propose intrusion detection system using cluster head in order to detect malicious attack and use resources efficiently. we used method that gathering of rules is defined and it judges whether it corresponds or not to detect intrusion more exactly. In order to evaluate performance of proposed method, we used blackhole, message negligence, jamming attack.

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3D Object Detection with Low-Density 4D Imaging Radar PCD Data Clustering and Voxel Feature Extraction for Each Cluster (4D 이미징 레이더의 저밀도 PCD 데이터 군집화와 각 군집에 복셀 특징 추출 기법을 적용한 3D 객체 인식 기법)

  • Cha-Young, Oh;Soon-Jae, Gwon;Hyun-Jung, Jung;Gu-Min, Jeong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.15 no.6
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    • pp.471-476
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    • 2022
  • In this paper, we propose an object detection using a 4D imaging radar, which developed to solve the problems of weak cameras and LiDAR in bad weather. When data are measured and collected through a 4D imaging radar, the density of point cloud data is low compared to LiDAR data. A technique for clustering objects and extracting the features of objects through voxels in the cluster is proposed using the characteristics of wide distances between objects due to low density. Furthermore, we propose an object detection using the extracted features.

Cancer cluster detection using scan statistic (스캔 통계량을 이용한 암 클러스터 탐색)

  • Han, Junhee;Lee, Minjung
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.5
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    • pp.1193-1201
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    • 2016
  • In epidemiology or etiology, we are often interested in identifying areas of elevated risk, so called, hot spot or cluster. Many existing clustering methods only tend to a result if there exists any clustering pattern in study area. Recently, however, lots of newly introduced clustering methods can identify the location, size, and shape of clusters and test if the clusters are statistically significant as well. In this paper, one of most commonly used clustering methods, scan statistic, and its implementation SaTScan software, which is freely available, will be introduced. To exemplify the usage of SaTScan software, we used cancer data from the SEER program of National Cancer Institute of U.S.A.We aimed to help researchers and practitioners, who are interested in spatial cluster detection, using female lung cancer mortality data of the SEER program.

A Cluster modeling using New Convergence properties (새로운 수렴특성을 이용한 클러스터 모델링)

  • Kim, Sung-Suk;Baek, Chan-Soo;Kim, Sung-Soo;Ryu, Joeng-Woong
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.382-384
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    • 2004
  • In this parer, we propose a clustering that perform algorithm using new convergence properties. For detection and optimization of cluster, we use to similarity measure with cumulative probability and to inference the its parameters with MLE. A merits of using the cumulative probability in our method is very effectiveness that robust to noise or unnecessary data for inference the parameters. And we adopt similarity threshold to converge the number of cluster that is enable to past convergence and delete the other influence for this learning algorithm. In the simulation, we show effectiveness of our algorithm for convergence and optimization of cluster in riven data set.

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Improving TCP Performance Over Mobile ad hoc Networks by Exploiting Cluster-Label-based Routing for Backbone Networks

  • Li, Vitaly;Ha, Jae-Yeol;Oh, Hoon;Park, Hong-Seong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.8B
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    • pp.689-698
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    • 2008
  • The performance of a TCP protocol on MANETs has been studied in a numerous researches. One of the significant reasons of TCP performance degradation on MANETs is inability to distinguish between packet losses due to congestion from those caused by nodes mobility and as consequence broken routes. This paper presents the Cluster-Label-based Routing (CLR) protocol that is an attempt to compensate source of TCP problems on MANETs - multi-hop mobile environment. By utilizing Cluster-Label-based mechanism for Backbone, the CLR is able to concentrate on detection and compensation of movement of a destination node. The proposed protocol provides better goodput and delay performance than standardized protocols especially in cases of large network size and/or high mobility rate.

A Wind Turbine Fault Detection Approach Based on Cluster Analysis and Frequent Pattern Mining

  • Elijorde, Frank;Kim, Sungho;Lee, Jaewan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.2
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    • pp.664-677
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    • 2014
  • Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components.

Detection of Variable Stars in the Open Cluster M11 Using Difference Image Analysis Pipeline

  • Lee, Chung-Uk;Koo, Jae-Rim;Kim, Seung-Lee;Lee, Jae-Woo;Park, Byeong-Gon;Han, Cheong-Ho
    • Journal of Astronomy and Space Sciences
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    • v.27 no.4
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    • pp.289-307
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    • 2010
  • We developed a photometric pipeline to be used for a wide field survey. This pipeline employs the difference image analysis (DIA) method appropriate for the photometry of star dense field such as the Galactic bulge. To verify the performance of pipeline, the observed dataset of the open cluster M11 was re-processed. One hundred seventy eight variable stars were newly discovered by analyzing the light curves of which photometric accuracy was improved through the DIA. The total number of variable stars in the M11 observation region is 335, including 157 variable stars discovered by previous studies. We present the catalogue and light curves for the 178 variable stars. This study shows that the photometric pipeline using the DIA is very useful in the detection of variable stars in a cluster.

A Cluster-based Address Allocation Protocol in MANET Environment (MANET 환경에서 클러스터 기반 주소 할당 프로토콜)

  • Cho, Young-Bok;Lee, Sang-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.9A
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    • pp.898-904
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    • 2007
  • I must receive node discernment address for communication between node that participate to network in MANETs(Mobile Ad-hoc Networks). Address is created by node confidence or different node. I achieve address redundancy check (Duplicate Address Detection) to examine whether this address is available unique address. However, this method happens problem that MANETs' extensity drops. This paper can manage by group unit binding transfer nodes to group in MANETs. I suggest method that apply special quality of cluster that exchange subordinate decrease and mobility government official of control message are easy in address assignment protocol minimize time required in redundancy check and solves extensity problem. Method that propose in this paper shows excellent performance according to node number increase than wave and MANETConf [2] through simulation.

Scene Change Detection Using Local Information (지역적 정보를 이용한 장면 전환 검출)

  • Shin, Seong-Yoon;Shin, Kwang-Sung;Lee, Hyun-Chang;Jin, Chan-Yong;Rhee, Yang-Won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.05a
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    • pp.151-152
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    • 2012
  • This paper proposes a Scene Change Detection method using the local decision tree and clustering. The local decision tree detects cluster boundaries wherein local scenes occur, in such a way as to compare time similarity distributions among the difference values between detected scenes and their adjacent frames, and group an unbroken sequence of frames with similarities in difference value into a cluster unit.

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