• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.12-19
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• 2000

In the development of a production process scheduling system a collaboration method must be defined for the cooperation among submodules within the system. The blackboard architecture is exploited for solving the collaboration problem, which is one of the problem solving architecture that belongs to the distributed artificial intelligence. The dynamic states of the problem solving processes are presented in the hierarchically constructed shared working memory called as a blackboard. The communication for the collaboration is done through the blackboard. The problem solving steps are contained in the global controller, one of a component that consists the blackboard architecture, as knowledge. The global controller activates proper submodules based on the knowledge. By applying the blackboard architecture the collaboration problem among submodules in the grating production process scheduling system (GPSS) has been solved as well as the system became adaptable to the future modifications and expansions.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.315-323
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• 2001

The concept of vertical partitioning has been discussed so far in an objective of improving the performance of query execution and system throughput. It can be applied to the areas where the match between data and queries affects performance, which includes partitioning of individual files in centralized environments, data distribution in distributed databases, dividing data among different levels of memory hierarchies, and so on. In general, a vertical partitioning algorithm should support n-ary partitioning as well as a globally optimal solution for the generation of all meaningful fragments. Most previous methods, however, have some limitations to support both of them efficiently. Because the vertical partitioning problem basically includes the fuzziness property, the proper management is required for the fuzziness problem. In this paper we propose an efficient vertical $\alpha$-partitioning algorithm which is based on the fuzzy theory. The method can not only generate all meaningful fragments but also support n-ary partitioning without any complex mathematical computations.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.8
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• pp.421-431
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• 2004

This paper presents adaptive execution techniques that determine whether parallelized loops are executed in parallel or sequentially in order to maximize performance. The adaptation and performance estimation algorithms are implemented in a compiler preprocessor. The preprocessor inserts code that automatically determines at compile-time or at run-time the way the parallelized loops are executed. Using a set of standard numerical applications written in Fortran77 and running them with our techniques on a distributed shared memory multiprocessor machine (SGI Origin2000), we obtain the performance of our techniques, on average, 26%, 20%, 16%, and 10% faster than the original parallel program on 32, 16, 8, and 4 processors, respectively. One of the applications runs even more than twice faster than its original parallel version on 32 processors.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.445-455
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• 2016

A goal of the Real-time railway safety supervision system is to improve the safety oversight efficiency and to prevent accidents by integrating existing distributed monitoring systems, train, signal, power and facilities. So, the system require better performance regarding real-time processing based on big data. The disk-based database that is used in existing railway control systems has a problem with real-time processing; memory-based databases haves a limitation in terms of big-data processing; and time series databases haves a limitation in terms of real-time processing. So, we need a new database architecture for simultaneous real-time processing based on big data. In this study, we review the existing railway monitoring systems and propose a new database architecture for a real-time railway safety supervision system.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3327-3334
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• 2021

Present electric grids are advanced to integrate smart grids, distributed resources, high-speed sensing and control, and other advanced metering technologies. Cybersecurity is one of the challenges of the smart grid and nuclear plant digital system. It affects the advanced metering infrastructure (AMI), for grid data communication and controls the information in real-time. The research article is emphasized solving the nuclear and smart grid hardware security issues with the integration of field programmable gate array (FPGA), and implementing the latest Time Authenticated Cryptographic Identity Transmission (TACIT) cryptographic algorithm in the chip. The cryptographic-based encryption and decryption approach can be used for a smart grid distribution system embedding with FPGA hardware. The chip design is carried in Xilinx ISE 14.7 and synthesized on Virtex-5 FPGA hardware. The state of the art of work is that the algorithm is implemented on FPGA hardware that provides the scalable design with different key sizes, and its integration enhances the grid hardware security and switching. It has been reported by similar state-of-the-art approaches, that the algorithm was limited in software, not implemented in a hardware chip. The main finding of the research work is that the design predicts the utilization of hardware parameters such as slices, LUTs, flip-flops, memory, input/output blocks, and timing information for Virtex-5 FPGA synthesis before the chip fabrication. The information is extracted for 8-bit to 128-bit key and grid data with initial parameters. TACIT security chip supports 400 MHz frequency for 128-bit key. The research work is an effort to provide the solution for the industries working towards embedded hardware security for the smart grid, power plants, and nuclear applications.

• Korean Journal of Environmental Agriculture
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• v.40 no.1
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• pp.1-12
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• 2021

BACKGROUND: G protein-coupled receptors (GPCRs) are widely distributed in various organisms. Insect GPCRs shown as in vertebrate GPCRs are membrane receptors that coordinate or involve in various physiological processes such as learning/memory, development, locomotion, circadian rhythm, reproduction, etc. This study aimed to identify GPCRs expressed in fat body and compare the expression pattern of GPCRs in different temperature conditions. METHODS AND RESULTS: To identify GPCRs genes and compare their expression in different temperature conditions, total RNAs of fat body in Plutella xylostella larva were extracted and the transcriptomes have been analyzed via next generation sequencing method. From the fat body transcriptomes, genes that belong to GPCR Family A, B, and F were identified such as opsin, gonadotropin-releasing hormone receptor, neuropeptide F (NPF) receptor, muthuselah (Mth), diuretic hormone receptor, frizzled, etc. Under low temperature, expressions of GPCRs such as C-C chemokine receptor (CCR), opsin, prolactin-releasing peptide receptor, substance K receptor, Mth-like receptor, diuretic hormone receptor, frizzled and stan were higher than those at 25℃. They are involved in immunity, feeding, movement, odorant recognition, diuresis, and development. In contrast to the control (25℃), at high temperature GPCRs including CCR, gonadotropin-releasing hormone receptor, moody, NPF receptor, neuropeptide B1 receptor, frizzled and stan revealed higher expression whose biological functions are related to immunity, blood-brain barrier formation, feeding, learning, and reproduction. CONCLUSION: Transcriptome of fat body can provide understanding the pools of GPCRs. Identifications of fat body GPCRs may contribute to develop new targets for the control of insect pests.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.147-156
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• 2023

Electronic voting machines (EVMs) are replacing research ballots due to the errors involved in the manual counting process and the lengthy time required to count the votes. Even though these digital recording electronic systems are advancements, they are vulnerable to tampering and electoral fraud. The suspected vulnerabilities in EVMs are the possibility of tampering with the EVM's memory chip or replacing it with a fake one, their simplicity, which allows them to be tampered with without requiring much skill, and the possibility of double voting. The vote data is shared among all network devices, and peer-to-peer verification is performed to ensure the vote data's authenticity. To successfully tamper with the system, all of the data stored in the nodes must be changed. This improves the proposed system's efficiency and dependability. Elections and voting are fundamental components of a democratic system. Various attempts have been made to make modern elections more flexible by utilizing digital technologies. The fundamental characteristics of free and fair elections are intractability, immutability, transparency, and the privacy of the actors involved. This corresponds to a few of the many characteristics of blockchain-like decentralized ownership, such as chain immutability, anonymity, and distributed ledger. This working research attempts to conduct a comparative analysis of various blockchain technologies in development and propose a 'Blockchain-based Electronic Voting System' solution by weighing these technologies based on the need for the proposed solution. The primary goal of this research is to present a robust blockchain-based election mechanism that is not only reliable but also adaptable to current needs.

• The Korean Journal of Archival Studies
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• no.32
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• pp.3-44
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• 2012

The purpose of the study is to explore the strategies to build participatory digital archives for documenting localities. Following the introduction of the chapter one, the chapter two deals with categorizing participation types of persons and organizations for documenting localities, analysing characteristics and benefits of each type, and listing up the requirements of participatory archives based on literature reviews. The chapter three focuses on the analyses of digital archives especially based on the participation of organizations such as collecting institutions and community archives in USA, Canada and UK. The cases of participatory archives are divided into two types; i) digital archives based on archival collections of institutions such as libraries, archives, and museums, ii) digital archives mainly based on various community archives. Online Archives California(OAC) and Calisphere of University of California, MemoryBC of British Columbia of Canada, and People's Collection Wales of UK as the first type cases, and Connecting Histories of Birmingham, 'Community Archives Wales(CAW), Cambridgeshire Community Archive Network(CCAN), Norfolk Community Archives Network(NORCAN) as the second type cases are selected for comparative analyses. All these cases can be considered as archival portals since they cover collections from various organizations. This study then evaluates how these digital archives fulfill the requirements of participatory archives such as : i) integrated search of archives that are to be distributed, ii) participation of individuals and organizations, and iii) providing broader contextual information and representation of context as well as contents of archives. Lastly the final chapter suggests the implications for building participatory archives in Korean local areas based on following aspects : host organizations and implementation strategy, networks of collection institutions and community archives, preserving and reorganizing contextual information, selection and appraisal, and participation of records users and creators.