• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1893-1894
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• 2008

Advances in hardware and wireless network technologies have placed us at the doorstep of a new era where small wireless devices will provide access to information anytime, anywhere as well as actively participate in creating smart environments. In this paper, we propose location-aware clustering method in wireless sensor networks. Previous clustering algorithm assumes that all nodes know its own location by GPS. But, it is unrealistic because of GPS module cost and large energy consumption. So, we operate localization ahead of cluster set-up phase. And Considering node density and geographic information, Cluster Heads are elected uniformly. Moreover, communication between CHs is prolonged network lifetime.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.263-269
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• 2012

The ability to characterize feature objects from radar readings is often limited by simply looking at their still frame reflectivity, differential reflectivity and differential phase data. In many cases, time-series study of these objects' reflectivity profile is required to properly characterize features objects of interest. This paper introduces a novel technique to automatically track multiple 3D radar structures in C,S-band in real-time using Doppler radar and profile their characteristic reflectivity distribution in time series. The extraction of reflectivity profile from different radar cluster structures is done in three stages: 1. static frame (zone-linkage) clustering, 2. dynamic frame (evolution-linkage) clustering and 3. characterization of clusters through time series profile of reflectivity distribution. The two clustering schemes proposed here are applied on composite multi-layers CAPPI (Constant Altitude Plan Position Indicator) radar data which covers altitude range of 0.25 to 10 km and an area spanning over hundreds of thousands $km^2$. Discrete numerical simulations show the validity of the proposed technique and that fast and accurate profiling of time series reflectivity distribution for deformable 3D radar structures is achievable.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.253-259
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• 2008

Because a sensor node in wireless sensor networks is battery operated and energy constrained, reducing energy consumption of each node is one of important issues. The clustering technique can make network topology be hierarchical and reduce energy consumption of each sensor node. In this paper, we propose an efficient clustering mechanism considering overlap avoidance in wireless sensor networks. The proposed method consists of three parts. The first is to elect cluster heads considering each node's energy. Then clusters are formed by using signal strength in the second phase. Finally we can reduce the cluster overlap problem derived from two or more clusters. In addition, this paper includes performance evaluation of our algorithm. Simulation results show that network lifetime was extended up to 75 percents than LEACH and overlapped clusters are decreased down to nearly zero percents.

• Macromolecular Research
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• v.10 no.1
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• pp.18-23
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• 2002

We theoretically consider blends of two monodisperse one-end-functionalized homopolymers (denoted by A and B) capable of forming clusters between functional groups (stickers) using weak segregation theory. In this model system resulting molecular architectures via clustering resemble star copolymers having many A- and B-arms. Minimizing the total free energy with respect the cluster distribution, the equilibrium distribution of clusters is obtained and used for RPA (Random Phase Approximation) equations as input. For the case that polymers are functionalized by only one kind of sticker, the phase diagrams show that the associations promote the macrophase separation. When there is strong affinity between stickers belonging to the different polymer species, on the other hand, the phase diagram show a suppression of the macrophase separation at the range of high temperature regime, as well as the phase coexistence between a disordered and a mesoscopic phase at the relatively lower temperatures.

• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.77-98
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• 2003

A web-based information system, that is a dominant type of information systems, suffers from the "web crisis" in development and maintenance of the system. To cope with the problem, a technology of software clustering to web-based application, which is one of web engineering, is strongly needed. In this paper, we propose a Form Clustering Algorithm along with an application example, which are used for internal-system reengineering to web-based information system. A Form Clustering Algorithm focuses on Page-model which is the feature of the web among the various web-based information system's structural model. Specially, we applying distance matrix to navigation model of graph form for easily analyzing, and web log analysis for identifying core function object that have a highly loading. Also, we create web software structure that can be used to maximize reusability and assign hardware effectively through 2-phase clustering step. Form Clustering Algorithm might be used at web-based information system development and maintenance for reusable web component development and hardware assignment, respectively.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• The Journal of Bigdata
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• v.6 no.2
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• pp.29-38
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• 2021

Deviation of route in aviation safety management is a dangerous factor that can lead to serious accidents. In this study, the anomaly score is calculated by classifying the tracks through clustering and calculating the distance from the cluster center. The study was conducted by extracting tracks within 100 km of the airport from the ADS-B track data received for one year. The wake was vectorized using linear interpolation. Latitude, longitude, and altitude 3D coordinates were used. Through PCA, the dimension was reduced to an axis representing more than 90% of the overall data distribution, and k-means clustering, hierarchical clustering, and PAM techniques were applied. The number of clusters was selected using the silhouette measure, and an abnormality score was calculated by calculating the distance from the cluster center. In this study, we compare the number of clusters for each cluster technique, and evaluate the clustering result through the silhouette measure.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.365-367
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• 2012

클러스터링 기법은 반복적인 setup phase와 steady phase의 반복으로 네트워크를 재구성하는 방법을 사용하며, 이 방법으로 일부 노드에 부가되는 부하를 네트워크에 분산하여 네트워크를 장시간 동안 안정적으로 유지시키는 방법을 사용한다. 그러나 이러한 방법의 가장 큰 문제는 setup phase에서 소비되는 에너지가 간과할 만한 수준이 아니라는 데에 있다. 이에 몇 논문은 이러한 반복적인 setup을 제거하여 네트워크 성능 향상을 꾀하기도 하였다. 그러나 setup의 에너지 분산 효과를 고려하면, setup phase의 삭제는 바람직하지 않다. 본 논문에서는 고정 주기를 갖고 발생하는 setup phase의 반복을 네트워크 환경에 맞게 적응적으로 발생시키는 방법을 제안한다.

• Proceedings of the Korean Magnestics Society Conference
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• 2015.05a
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• pp.52-52
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• 2015

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.