• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.678-690
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• 2014

The current networks do not disclose information about a management domain due to scalability, manageability and commercial reasons. Therefore, it is very hard to calculate an optimal path to the destination. Also, due to poor information sharing, if an error occurs in the intermediate path, it is very difficult to re-search the path and find the best path. Hence, to manage each domain more efficiently, an architecture with top-level path computation node which can obtain information of separate nodes are highly needed This study aims to investigate a federation of a united network around NRN(National Research Network) that could allow resource sharing between countries and also independent resource management for each country. Considering first the aspects that can be accessed from the perspective of a national research network, ICE(Information Control Element) and GFO(Global Federation Organizer)-based architecture is designed as a top-level path computation element to support traffic engineering and applied to the multi-domain network. Then, the federation for the independent management of resources and resource information sharing among national research networks have been examined.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.189-196
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• 1994

This paper describes a parallel computing algorithm in Gauss elimination of Jacobian matrix to large-scale power system. The structure of Jacobian matrix becomes different according to ordering method of buses. In sequential computation buses are ordered to minimize the number of fill-in in the triangulation of the Jacobian matrix. The proposed method develops the parallelism in the Gauss elimination by using ND(nested dissection) ordering. In this procedure the level structure of the power system network is transformed to be long and narrow by using end buses which results in balance of computing load among processes and maximization of parallel computation. Each processor uses the sequential computation method to preserve the sqarsity of matrix.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.111-118
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• 2021

Banking systems are sensitive to data privacy since users' data, if not well protected, may be used to perform fake transactions. Blockchains, public and private, are frequently used in such systems thanks to their efficiency and high security. Public blockchains fail to fully protect users' data, despite their power in the accuracy of the transactions. The private blockchain is better used to protect the privacy of the sensitive data. They are not open and they apply authorization to login into the blockchain. However, they have a lower security compared to public blockchain. We propose in this paper a hybrid public-private architecture that profits from network virtualization. The main novelty of this proposal is the use of network virtualization that helps to reduce the complexity and efficiency of the computations. Simulations have been conducted to evaluate the performance of the proposed solution. Findings prove the efficiency of the scheme in reducing complexity and enhancing data privacy by guarantee high security. The contribution conducted by this proposal is that the results are verified by the centralized controller that ensures a correct validation of the resulted blockchains. In addition, computation complexity is to be reduced by profiting from the cooperation performed by the virtual agents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4849-4864
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• 2019

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.769-776
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• 2000

RP(Rapid Prototyping) technology is gaining its popularity in building a prototype in all industries. SLS(Slective Laser Sintering) is one of RP technologies, which is focused on tooling processes as well as three dimension solid model. There are several factors, the length and the cross-sectional area of a part, that have an effect on build setup in SLS process. In this paper, the computation on geometrical relationship is used to slice STL file and to estimate these factors. Based on these values, the build setup parameters such as the heating temperature, the laser power, and the powder cartridge feed rate are determined by neural network approaches. The test results show that the computation time is saved and the neural network approach is able to apply to get the optimal parameters of build process within an acceptable error rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.1024-1034
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• 1998

Most public cryptosystem widely used in communication network are based on the exponentiation-arithmetic. But, cryptosystem has to use bigger and bigger key parameter to attain an adequate level of security. This situation increases both computation and time delay. Montgomery, yang and Kawamura presented a method by using the pre-computation, intermediately computing and table look-up on modular reduction. Coster, Brickel and Lee persented also a method by using the pre-computation on exponentiation. This paper propose to reduce computation of exponentiation with spare prime. This method is to enhance computation efficiency in cryptosystem used discrete logarithms.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.271-276
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• 2002

The test statistic in ANOVA tests has a single or doubly noncentral F distribution and the noncentral F distribution is applied to the calculation of the power functions of tests of general linear hypotheses. Although various approximations of noncentral F distribution are suggested, they are troublesome to compute. In this paper, the calculation of noncentral F distribution is applied to the neural network theory, to solve the computation problem. The neural network consists of the multi-layer perceptron structure and learning process has the algorithm of the backpropagation. Using fables and figs, comparisons are made between the results obtained by neural network theory and the Patnaik's values. Regarding of accuracy and calculation, the results by neural network are efficient than the Patnaik's values.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.18
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• pp.7645-7652
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• 2014

Neuroblastoma (NB), the most common extracranial solid tumor, accounts for 10% of childhood cancer. To date, scientists have gained quite a lot of knowledge about microRNAs (miRNAs) and their genes in NB. Discovering inner regulation networks, however, still presents problems. Our study was focused on determining differentially-expressed miRNAs, their target genes and transcription factors (TFs) which exert profound influence on the pathogenesis of NB. Here we constructed three regulatory networks: differentially-expressed, related and global. We compared and analyzed the differences between the three networks to distinguish key pathways and significant nodes. Certain pathways demonstrated specific features. The differentially-expressed network consists of already identified differentially-expressed genes, miRNAs and their host genes. With this network, we can clearly see how pathways of differentially expressed genes, differentially expressed miRNAs and TFs affect on the progression of NB. MYCN, for example, which is a mutated gene of NB, is targeted by hsa-miR-29a and hsa-miR-34a, and regulates another eight differentially-expressed miRNAs that target genes VEGFA, BCL2, REL2 and so on. Further related genes and miRNAs were obtained to construct the related network and it was observed that a miRNA and its target gene exhibit special features. Hsa-miR-34a, for example, targets gene MYC, which regulates hsa-miR-34a in turn. This forms a self-adaption association. TFs like MYC and PTEN having six types of adjacent nodes and other classes of TFs investigated really can help to demonstrate that TFs affect pathways through expressions of significant miRNAs involved in the pathogenesis of NB. The present study providing comprehensive data partially reveals the mechanism of NB and should facilitate future studies to gain more significant and related data results for NB.

• ETRI Journal
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• v.25 no.6
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• pp.475-488
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• 2003

In this paper, we propose an efficient and simple fair queuing algorithm, called new starting potential fair queuing (NSPFQ), which has O(1) complexity for virtual time computation and also has good delay and fairness properties. NSPFQ introduces a simpler virtual time recalibration method as it follows a rate-proportional property. The NSPFQ algorithm recalibrates the system virtual time to the minimum virtual start time among all possible virtual start times for head-of-line packets in backlogged sessions. Through analysis and simulation, we show that the proposed algorithm has good delay and fairness properties. We also propose a hardware implementation framework for the scheduling algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.