• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.405-406
• /
• 2006

Recently, the flat display modules such as plasma or TFT-LCD employ thin crystallized panels which are normally weak to high level transient mechanical energy inputs. As a result, anti-shock performance is one of the most important design specifications for TFT-LCD modules. However, most of large display module designs are generated based on engineers own experiences. Also, a large-scale analysis to evaluate complex material and structural behaviors is one of interesting topic in diverse engineering and scientific fields. The utilization of massively parallel processors has also 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 parallel processing system is constructed using thirty-two processing elements and the finite element program is developed by adopting hierarchical domain decomposition method. In order to verify the efficiency of the established system, an impact analysis on thin and complex sub-parts of flat display modules is performed. The evaluation results showed a good agreement with the corresponding reference solutions, and thus, the parallel process system seems to be a useful tool fur the complex structural analysis such as IT related products.

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

• The Journal of the Korea Contents Association
• /
• v.12 no.2
• /
• pp.30-39
• /
• 2012

In these day, some of IT venders already were going to cloud computing market, as well they are going to expand their territory for the cloud computing market through that based on their hardware and software technology, making collaboration between hardware and software vender. Distributed file system is very mainly technology for the cloud computing that must be protect performance and safety for high levels service requests as well data store. This paper introduced distributed file system for cloud computing and how to use this theory such as memory database, Hadoop file system, high availability database system. now In the market, this paper define a very large distributed processing architect as a reference by kind of distributed file systems through using technology in cloud computing market.

• Journal of Intelligence and Information Systems
• /
• v.8 no.2
• /
• pp.177-187
• /
• 2002

Spatial data mining is a process to discover interesting relationships and characteristics those exist implicitly in a spatial database. Many spatial clustering algorithms have been developed. But, there are few approaches that focus simultaneously on clustering spatial data and assigning weight to non-spatial attributes of objects. In this paper, we propose a new spatial clustering algorithm, called DBSCAN-W, which is an extension of the existing density-based clustering algorithm DBSCAN. DBSCAN algorithm considers only the location of objects for clustering objects, whereas DBSCAN-W considers not only the location of each object but also its non-spatial attributes relevant to a given application. In DBSCAN-W, each datum has a region represented as a circle of various radius, where the radius means the degree of the importance of the object in the application. We showed that DBSCAN-W is effective in generating clusters reflecting the users requirements through experiments.

• Journal of the Korean Institute of Educational Facilities
• /
• v.24 no.4
• /
• pp.15-24
• /
• 2017

This paper aims to investigate in what extent subject-centered clusters are different from one another in terms of message system, which is composed of curriculum, pedagogy and evaluation. For this, Bernstein's pedagogic transmission code(i.e., classification and framing) and school typology(i.e., open-type or close-type) have been explored, and then applied into Shimin Junior School, Japan, in order to find out substantial characteristics between subject-centered clusters. In this case study, VGA(visibility graph analysis), as one of syntactical methodologies in space syntax theory, has been used to measure to what degree they are actually different. Throughout in-depth investigation of spatial configurations, it can be said that the square of clusters is strongly connected and integrated very well, so that it acts as an anchor place for school life within a cluster. However, it works in different ways according to message systems. In the subjects like Japanese and Science whose message system are characterized by strong classification and strong framing, integration values are relatively low, and this means that it is hard to expect cross-referencing activities through the subject squares. On the contrary, the subject of Social Studies defined by weak classification and weak framing shows the highest mean integration values, and this can be expected that there are inter-changeable learning activities in the square.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.23 no.3
• /
• pp.220-226
• /
• 2003

This paper presents the result of an investigation on the application of the acoustic emission method to the monitoring of fatigue crack initiation, growth and track location in welded joints. Fatigue test was carried out for a typical fillet welded joint of ship structure. AE parameter such as ring down count was analyzed in time domain and crack locations were examined by source location and cluster option which is one of the functions of AE signal processor The usability of AE mettled was confirmed for the detection of the initiation and location of through crack.

• Journal of Korea Multimedia Society
• /
• v.16 no.3
• /
• pp.302-308
• /
• 2013

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.1
• /
• pp.43-50
• /
• 2010

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many applications in their original or modified forms. However, FCM's noise sensitivity problem and PCM's overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate the problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we proposed a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a well-known technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), can take advantage of regularization and further reduce the effect of noise. Experimental results are given and show that the proposed method is better than the existing methods in noisy conditions.

• Journal of KIISE:Databases
• /
• v.29 no.5
• /
• pp.367-380
• /
• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• The KIPS Transactions:PartD
• /
• v.13D no.5 s.108
• /
• pp.633-640
• /
• 2006

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set would make the clustering process extremely costly. In this paper, we propose an enhanced Density-Grid based Clustering algorithm for Large spatial database by setting a default number of intervals and removing the outliers effectively with the help of a proper measurement to identify areas of high density in the input data space. We use a density threshold DT to recognize dense cells before neighbor dense cells are combined to form clusters. When proposed algorithm is performed on large dataset, a proper granularity of each dimension in data space and a density threshold for recognizing dense areas can improve the performance of this algorithm. We combine grid-based and density-based methods together to not only increase the efficiency but also find clusters with arbitrary shape. Synthetic datasets are used for experimental evaluation which shows that proposed method has high performance and accuracy in the experiments.