• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.2
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• pp.203-214
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• 1998

The purpose of this study is to classify the upper body of women into several kinds of somatotypes, using the method of Surgical Tape and making their shells. The subjects are 53 females 30 to 39 years-old. Fifty-three anthropometric data are measured per shell of bodysurface; six somatotype factors are obtained through principal component analysis and orthogonal rotation by the method of Varimax, Somatotype of women's upper body is achieved by cluster analysis, using the standardized factor score as an independent variable and the FASTCLUS of SAS by Kmeans. The results are as follows: 1. The number of the factors which explain the somatotype is six and those factors comprise 76.12 percent of total variance. Factor 1: related to the size of shape in the front of upper body Factor S: related to the size of shape in the back of upper body Factor 3: related to the type of the upper chest over the chest circumference line Factor 4: related to the length of·the upper body Factor 5: related to the part of the neck Factor 6: related to the type of the lower chest under the chest circumference line 2. Cluster analysis results in classification of upper body into five clusters. Cluster L: the length is the largest and the circumference is small. The part of waist is the largest and widest among surface areas. Cluster 2: Slender body line from chest to waist is characteristic. The length is longer. The part of upper and lower chest is larger among surface areas. Cluster S: the circumference is the smallest and armhole is small. The length and surface area are small. Cluster 4: the circumference and armhole is the largest. The length is the smallest. Cluster 5: the circumference is average and the length is a little long. The body line(silhouette) from chest to waist is curved slightly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3439-3457
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• 2014

Clustering is an effective method for improving the performance of large scale mobile ad hoc networks. However, when the moving speed is very fast, the topology changes quickly, which leads to frequent cluster topology updates. The drastically increasing control overheads severely threaten the throughput of the network. SCBCS (Signal Characteristic Based Cluster Scheme) is proposed as a method to potentially reduce the control overheads caused by cluster formation and maintenance in aeronautical ad hoc networks. Each node periodically broadcasts Hello packets. The Hello packets can be replaced by data packets, which preserve bandwidth. The characteristics of the received packets, such as the Doppler shift and the power of two successive Hello packets, help to calculate the relative speed and direction of motion. Then, the link connection lifetime is estimated by the relative speed and direction of motion. In the clustering formation procedure, the node with the longest estimated link connection time to its one-hop neighbors is chosen as the cluster head. In the cluster maintenance procedure, re-affiliation and re-clustering schemes are designed to keep the clusters more stable. The re-clustering phenomenon is reduced by limiting the ripple effect. Simulations have shown that SCBCS prolongs the link connection lifetime and the cluster lifetime, which can reduce the topology update overheads in highly dynamic aeronautical ad hoc networks.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.21-30
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• 2004

In Ad-Hoc network, the cluster structure enables effective use of multiple channels, reduces the number of control messages, and increase the scalability of network, Also, it is employed for reducing the number of broadcast messages in an Ad-Hoc network. With the consideration of these advantages, it is desirable that a cluster structure keeps a few clusters in the network, Generally, the cluster formation scheme based on connectivity yields fewer clusters than the other schemes. However, the connectivity based scheme may yield even more clusters than the other schemes according to the network topology. In this paper, a cluster formation scheme dividing the cluster formation into two phases is proposed. In the first phase, the lowest connectivity host in neighborhood initiates the cluster formation. Then, an adjustment procedure for affiliating a lot of the lowest connectivity hosts is employed. In the second phase, the hosts which were not affiliated to the first phase clusters are grouped into one or more clusters through criterions of connectivity and host ID. As a result, the proposed scheme yields a fewer clusters compared with existing other schemes in fully distributed method. The simulation results proves that our scheme is better than LIDCP(3) and HCCP(3).

• Journal of KIISE:Information Networking
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• v.31 no.1
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• pp.62-69
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• 2004

The cluster structure reduces the number of retransmission messages, when a broadcast to all hosts in ad hoc network is needed. A cluster maintenance scheme is employed to preserve this advantage from time to time. However, most of the cluster maintenance schemes require additional control messages for cluster reformation as well as control messages for acquiring neighbor information. This mitigates the advantages of employing cluster structure in ad hoc network. In this paper, a cluster maintenance scheme which forces only clusterheads to broadcast control messages during hello time is proposed. When the cluster reformation is needed, the proposed scheme employs a strategy to reduce the control messages to a minimum. In these processes, the proposed scheme doesn't violate the definition of 2-cluster and produces the clusters in fully distributed method. The simulation results prove that our scheme is better than LCC(1).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.87-91
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• 2019

WSN is a wirelessly configured network of sensor nodes with limited power such as batteries. If the sensor node's battery is exhausted, the node is no longer available. Therefore, if the network is to be used for a long time, energy consumption should be minimized. There are many Wireless Sensor Network Protocols to improve energy efficiency, including Cluster-based and chain-based Protocols. Cluster-based Protocols elect Cluster Heads and divide sensor field into Clusters. The Cluster Head collects the data in the Cluster and transmits it to the Base Station. In the case of nodes elected as Cluster Heads, there is a problem of energy consumption. The chain-based Protocol links sensor nodes in a chain and finally transmits all data to the Base Station. In this paper, we intend to increase the network lifetime by using a chain to reduce the energy consumption of the Cluster Head in the Cluster-based Protocol, LEACH Protocol.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.1-6
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• 2019

Multicast is an efficient method for real-time transmission in many multimedia applications. It is important to recover lost packets and to manage multicast groups according to the network status in order to improve the reliability of multicast transmissions. In this paper, we propose a method that can efficiently recover lost packets in a large multicast group. In the proposed method, we create a recovery cluster (RC) using a multicast domain (MD) for recovery of lost packets. In the conventional methods, clusters send a request message for lost packets to the senders in order to recover the packets lost from many multicast applications. This increases packet delay time and overhead because of the feedback messages and retransmitted packets. In the proposed method, we improve these problems using the RC, which consists of many MDs (which have overlay multicast senders), and many cluster heads (CHs). We divide the message into blocks, and divide each block into many segments for packet recovery using the CHs. When packet loss occurs, all CHs share the segment information and recover the lost segments at the same time. Simulation results show that the proposed method could improve the packet recovery ratio by about 50% compared to the conventional methods.

• Knowledge Management Research
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• v.13 no.5
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• pp.43-64
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• 2012

Emerging technologies have significant implications in establishing competitive advantages and are characterized by continuous rapid development. Efficient benchmarking is more and more important in the development of emerging technologies. Similar input level and importance are two necessary criteria need to be considered for emerging technology's benchmarking. In this study, we proposed a sequential use of self-organizing map(SOM), data envelopment analysis(DEA) and analytical hierarchy process(AHP) method for the stepwise benchmarking of emerging technology. The proposed method uses two-level SOM to cluster the emerging technologies with similar required input levels together, then, in each cluster, uses DEA-BCC model to evaluate the efficiencies of the emerging technologies and do tier analysis to form tiers. On each tier, AHP rating method is used to calculate each emerging technology's importance priority. The optimal benchmarking path of each cluster is established by connecting the emerging technologies with the highest importance priority. In order to validate the proposed method, we apply it to a case of biotechnology. The result shows the proposed method can overcome difficulties in benchmarking, select suitable benchmarking targets and make the benchmarking process more efficient and reasonable.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.27-33
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• 2016

This paper proposes to increase the node energy efficiency, which rapidly drops during the transmission of L-TEEN (Layered Threshold sensitive Energy Efficient sensor Network protocol), using the method of DL-TEEN (Dual-hop Layered TEEN). By introducing dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission was introduce. By introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was introduces. In the proposed DL-TEEN, the energy consumption of cluster head for remote transmission reduces and increases the energy efficiency of sensor node by reducing the transmission distance and simplifying the transmission routine for short-range transmission. As compared the general L-TEEN, it was adapted to a wider sensor field.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.