• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.1
• /
• pp.111-117
• /
• 2011

In this paper, proposed client virtualization technology to minimize degradation of the local computing environment, efficient and qualified users in the area of virtual functions needed to enable the user to provide important information in the local computing environment protection and performance, stability and continuity was important to keep. As well as the local computing environment from malicious code attacks such as methods for protecting virtualized domain also can not be overlooked as a major problem area in a virtualized, virtualized data through the encryption of user-space security, maximized. In addition, through virtualization using local computing resources efficiently while still a local computing system separate from the computing resources to a single user can get the same effect.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.3972-3988
• /
• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2222-2232
• /
• 1999

This paper proposed a Systolic Arrays architecture for computing edge detection on images. It is very difficult to be processed images to real time because of operations of local operators. Local operators for computing edge detection are to be used in many image processing tasks, involve replacing each pixel in an image with a value computed within a local neighborhood of that pixel. Computing such operators at the video rate requires a computing power which is not provided by conventional computer. Through computationally expensive, it is highly regular. Thus, this paper presents a systolic arrays for tasks such as edge detection and laplacian, which are defined in terms of local operators.

• Structural Engineering and Mechanics
• /
• v.23 no.4
• /
• pp.325-337
• /
• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• ETRI Journal
• /
• v.42 no.5
• /
• pp.658-668
• /
• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.3E
• /
• pp.37-43
• /
• 1999

In this paper, we present a new technique for the optimal local decomposition of convex structuring elements on a hexagonal grid, which are used as templates for morphological image processing. Each basis structuring element in a local decomposition is a local convex structuring element, which can be contained in hexagonal window centered at the origin. Generally, local decomposition of a structuring element results in great savings in the processing time for computing morphological operations. First, we define a convex structuring element on a hexagonal grid and formulate the necessary and sufficient conditions to decompose a convex structuring element into the set of basis convex structuring elements. Further, a cost function was defined to represent the amount of computation or execution time required for performing dilations on different computing environments and by different implementation methods. Then the decomposition condition and the cost function are applied to find the optimal local decomposition of convex structuring elements, which guarantees the minimal amount of computation for morphological operation. Simulation shows that optimal local decomposition results in great reduction in the amount of computation for morphological operations. Our technique is general and flexible since different cost functions could be used to achieve optimal local decomposition for different computing environments and implementation methods.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.100-106
• /
• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.4
• /
• pp.281-285
• /
• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• The Journal of the Korea Contents Association
• /
• v.10 no.7
• /
• pp.81-87
• /
• 2010

This paper proposes a G-BLAST(BLAST using Grid Computing) system, an integrated software package for BLAST searches operated in heterogeneous distributed environment. G-BLAST employed 'database splicing' method to improve the performance of BLAST searches using exists computing resources. G-BLAST is a basic local alignment search tool of DNA Sequence using grid computing in heterogeneous distributed environment. The G-BLAST improved the existing BLAST search performance in gene sequence analysis. Also G-BLAST implemented the pipeline and data management method for users to easily manage and analyze the BLAST search results. The proposed G-BLAST system has been confirmed the speed and efficiency of BLAST search performance in heterogeneous distributed computing.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.224-226
• /
• 2005

Grid computing enables the fundamental computing shift from a localized resource computing model to a fully-distributed virtual organization with shared resources. In the grid computing environment, grid users usually get access rights by mapping their credential to local account. The mapped total account is temporally belongs to grid user. So, data on the secondary storage, which is produced by grid operation, can increase the load of system administration or can issue grid user's privacy. In this paper, we design a data management system for grid user to cover these problems. This system implements template account mechanism and manages local grid data.