• Journal of Power Electronics
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• v.18 no.5
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• pp.1448-1457
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• 2018

Many researchers devote themselves to develop model-predictive direct power control (MPDPC) so as to accelerate the response speed of the grid-connected systems, but they are troubled its large computing amount. On the basis of MPDPC, dual MPDPC (DMPDPC) is presented in this paper. The proposed algorithm divides the conventional MPDPC into two steps. In the first step, the optimal sector is obtained, which contains the optimal switching state in three-level converters. In the second step, the optimal switching state in the selected sector is searched to trace reference active and reactive power and balance neutral point voltage. Simulation and experiment results show that the proposed algorithm not only decreases the computational amount remarkably but also improves the steady-state performance. The dynamic response of the DMPDPC is as fast as that of the MPDPC.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.39-51
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• 2017

In October 2016, the NFSA (National Financial Supervisory Authorities) revised the network separation clause of the Regulation on Supervision of Electronic Financial Activities in order to promote the Cloud Computing implementation in the financial sectors. The new regulation, however, limits the Cloud Computing usage to non-critical information and its processing system. Financial institutions that provide customer data analysis and personalized services based on personal data regard current revision as unchanged as before. The implementation of Cloud Computing has greatly contributed to cost reduction, business innovation and is an essential requirement in ever-changing information communication technology environment. To guarantee both security and reliability of the implementation of the Cloud Computing in financial sectors, a considerable amount of research and debate needs to be done. This paper examines current Cloud Computing policies in the Korean financial sector and the challenges associated with it. Finally, the paper identifies policy suggestions based on both European Union and United States' approach as they have successfully introduced Cloud Computing Services for their financial sectors.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.219-228
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• 2019

Thanks to the development of information technology and the vitalization of smart services, the Internet of Things (IoT) technology, in which various smart devices are connected to the network, has been continuously developed. In the legacy IoT architecture, data processing has been centralized based on cloud computing, but there are concerns about a single point of failure, end-to-end transmission delay, and security. To solve these problems, it is necessary to apply decentralized blockchain technology to the IoT. However, it is hard for the IoT devices with limited computing power to mine blocks, which consumes a great amount of computing resources. To overcome this difficulty, this paper proposes an IoT architecture based on the edge computing technology that can apply blockchain technology to IoT devices, which lack computing resources. This paper also presents an operaional procedure of blockchain in the edge computing-based IoT architecture.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.189-200
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• 2004

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of input variables. To overcome this limitation in the conventional PNN, we employed one of three principal soft computing components such as a fuzzy system. As such, a space of input variables is partitioned into several subspaces by the fuzzy system and these subspaces are utilized as new input variables to the PNN architecture. The proposed soft computing technique is achieved by merging the fuzzy system and the PNN into one unified framework. As a result, we can find a workable synergistic environment and the main characteristics of the two modeling techniques are harmonized. Thus, the proposed method alleviates the problems of PNN while providing superb performance. Identification results of the three-input nonlinear static function and nonlinear system with two inputs will be demonstrated to demonstrate the performance of the proposed approach.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.214-224
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• 2023

Fraudulence, cheating, and deception can occur in the commercial real estate (CRE) industry, besides the difficulty in searching for and transferring properties while ensuring the operation is processed through an authoritative source in a trusted manner. Nowadays, real estate transactions use neutral third parties to sell land. Indeed, properties can be sold by the owners or third parties multiple times or without a proper deed. Moreover, third parties request a large amount of money to mediate between the seller and buyer. Methods: We propose a new framework that uses a private blockchain network and predefined BPMN instances to enable the fast and easy recording of deeds and their proprietary transfer management controlled by the government. The blockchain allows for multiple verifications of transactions by permitted parties called peers. It promotes transparency, privacy, trust, and commercial competition. Results: We demonstrated the easy adoption of blockchain for land registration and transfer. The paper presents a prototype of the implemented product that follows the proposed framework. Conclusion: The use of Blockchain-based solutions to resolve the current land registration and transfer issues is promising and will contribute to smart cities and digital governance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.239-250
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• 2013

Context-aware mobile computing has become the primary approach to realize automatic, autonomous, and user-centric computing in the context of largely increasing the amount of mobile devices used that embed available sensors. However, designing an inference engine nonetheless requires the tasks of analyzing contexts, situations that can be inferred, etc. Moreover, a mobile device has limited resources and limited computation capability, which results in recognizing the common sense of its unsuitable environment for processing inference. Hence, we propose context-situation reasoning elements and their formal models in this paper, and we verify the formal models' applicability by applying them to an example. Finally, we design and implement an inference engine that realize the context-situation inference elements in computing environment, and we experiment an example by using the proposed inference engine to verify applicability and reusability of the inference engine.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.119-131
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• 2018

The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.