• Spatial Information Research
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• v.11 no.3
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• pp.213-226
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• 2003

With the increasing demand of processing massive geographic data, conventional GISs based on the single processor architecture appear to be problematic. Especially, performing complex GIS operations on the massive geographic data is very time consuming and even impossible. This is due to the processor speed development does not keep up with the data volume to be processed. In the field of GIS, this PC clustering is one of the emerging technology for handling massive geographic data effectively. In this study, a MPI(Message Passing Interface)-based parallel processing approach was conducted to implement the existing raster GIS operations that typically requires massive geographic data sets in order to improve the processing capabilities and performance. Specially for this research, four types of raster CIS operations that Tomlin(1990) has introduced for systematic analysis of raster GIS operation. A data decomposition method was designed and implemented for selected raster GIS operations.

• Convergence Security Journal
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• v.14 no.7
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• pp.53-58
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• 2014

MANET can quickly build a network because it is configured with only the mobile node and it is very popular today due to its various application range. However, MANET should solve vulnerable security problem that dynamic topology, limited resources of each nodes, and wireless communication by the frequent movement of nodes have. In this paper, we propose a domain-based distributed cooperative intrusion detection techniques that can perform accurate intrusion detection by reducing overhead. In the proposed intrusion detection techniques, the local detection and global detection is performed after network is divided into certain size. The local detection performs on all the nodes to detect abnormal behavior of the nodes and the global detection performs signature-based attack detection on gateway node. Signature DB managed by the gateway node accomplishes periodic update by configuring neighboring gateway node and honeynet and maintains the reliability of nodes in the domain by the trust management module. The excellent performance is confirmed through comparative experiments of a multi-layer cluster technique and proposed technique in order to confirm intrusion detection performance of the proposed technique.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.10
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• pp.1173-1179
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• 2012

In this paper, collision avoidance beamforming(CA-BF) technology is proposed to mitigate inter-cell interference in cooperative wireless communications system with limited feedback. Each acess terminal(AT) selects both the best BF weight vector for a serving base transceiver station(BTS) and the most interfering BF weight vectors of interfering BTSs within a cluster, and sends it back to a cluster scheduler. At the cluster scheduler, a set of transmit BF weights of BTSs and the corresponding scheduled ATs are jointly determined to avoid collision among beams formed by BTSs within a cluster, which enhances system throughput by mitigating inter-cell interference. It is shown that the proposed CA-BF outperforms existing non-coordinated BF schemes in terms of the average system throughput.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.1-14
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• 2006

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

• 전자공학회논문지 IE
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• v.47 no.3
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• pp.41-46
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• 2010

WSN, or Wireless Sensor Network, consists of a multitude of inexpensive micro-sensors. Because the batteries in sensor nodes can not be replaced once they are deployed, the life of a WSN is absolutely determined by the batteries. So, energy efficiency of a network is a critical factor for long-life operation. LEACH protocol which divides WSN into two groups is a typical routing protocol based on the clustering scheme for the efficient use of limited energy. It is composed of round units which are separated into set-up and steady state. In this paper we propose a power saving scheme to minimize set-up phase itself and to involve a data comparison algorithm. We evaluate the performance of the proposed scheme in comparison with original LEACH protocol. Simulation results validate our scheme has better performance in terms of the number of alive nodes as time evolves and average energy dissipated.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.78-87
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• 2006

In this paper. we propose an effective mask estimation scheme for missing-feature reconstruction in order to achieve robust speech recognition under unknown noise environments. In the previous work. colored noise is used for training the mask classifer, which is generated from the entire frequency Partitioned signals. However it gives a limited performance under the restricted number of training database. To reflect the spectral events of more various background noise and improve the performance simultaneously. a new Bayesian classifier for mask estimation is proposed, which works independent of other frequency bands. In the proposed method, we employ the colored noise which is obtained by combining colored noises generated from each frequency band in order to reflect more various noise environments and mitigate the 'sparse' database problem. Combined with the cluster-based missing-feature reconstruction. the performance of the proposed method is evaluated on a task of noisy speech recognition. The results show that the proposed method has improved performance compared to the Previous method under white noise. car noise and background music conditions.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.91-107
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• 2006

To handling the extreme situation that must manage positional information of a very large volume, at least millions of moving objects. A cluster-based sealable distributed computing system architecture, called the GALIS which consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone, was proposed. In this paper, we proposed a valid time management and time-zone shifting scheme, which are essential in realizing the long-term location data subsystem of GALIS, but missed in our previous prototype development. We explain how to manage valid time of moving objects to avoid ambiguity of location information. We also describe time-zone shifting algorithm with three variations, such as Real Time-Time Zone Shifting, Batch-Time Zone Shifting, Table Partitioned Batch-Time Zone Shifting, Through experiments related with query processing time and CPU utilization, we show the efficiency of the proposed time-zone shifting schemes.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.2
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• pp.87-93
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• 1989

Squared-error Pattern Clustering algorithm used in unsupervised pattern recognition and image processing application demands substantial processing time for operation of feature vector matrix. So, this paper propose the fast squared-error Pattern Clustering Processor using the Residue Number System which have been the nature of parallel processing and pipeline. The proposed Squared-error Pattern Clustering Processor illustrate satisfiable error rate for Cluster number which can be divide meaningful region and about 200 times faster than 80287 coprocessor from experiments result of image segmentation. In this result, it is useful to real-time processing application for large data.