• Journal of Digital Convergence
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• v.19 no.10
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• pp.143-149
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• 2021

As the need to establish a big data system for rapid provision of big data and efficient management of resources has emerged due to rapid entry into the hyper-connected intelligence information society, public institutions are pushing to establish a big data system. Therefore, this study analyzed and combined the success factors of big data-related studies and the specific aspects of big data in public institutions based on the measurement of environmental factors for establishing an integrated information system for higher education institutions. In addition, 19 measurement items reflecting big data characteristics were derived from big data experts using brainstorming and Delphi methods, and a plan to successfully apply them to public institutions that want to build big data systems was proposed. We hope that this research results will be used as a foundation for the successful establishment of big data systems in public institutions.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.1-9
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• 2019

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.22-35
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• 2019

Utilizing fit theory in strategic human resource management, this study examines the impact of two HR practices on newcomer turnover rates. While there is a growing body of research identifying the linkage between high-performance work systems(HPWS) and improved organizational performance through higer employee commitment, little research addresses how specific mechanisms among the systems deliver different performances to organizations. Using management by objective(MBO) and job rotation in HPWS, we found a strong main effect of each practice-it decreases newcomer turnover rates. This main effect becomes stronger when one practice combined with another, showing synergy exists. Linking HPWS research to turnover, this study provides insightful evidence of interactions between MBO and job rotation for organizational performance.

• Management & Information Systems Review
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• v.37 no.4
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• pp.93-108
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• 2018

The service industry needs face-to-face customer service, which suggests the need for education training for workers. In other words, it is meaningful to look at whether education training for workers has a positive impact on the organization's performance. The success through education training will be due to the combined role of various factors. In this regard, the study focuses on how the attitude of workers in education training can play a role rather than just verifying the effectiveness of education training alone. As a result of the study, it was found that an employee's attitude toward education training, which was considered necessary to produce more effective results, was a regulator to further improve the educational satisfaction of training. After all, the more positive workers are about education training, the better workers' job performance can be. Therefore, promoting employee's education attitude before education training needs to be a priority, and further studies may call for more in-depth examination of the role of education training attitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2049-2068
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• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.592-606
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• 2021

This study examines the impact of knowledge management strategies and knowledge management determinants for each type of knowledge management of a company on knowledge activities, and because each company's situation and conditions are different, it is necessary to approach each knowledge management type. The analysis was conducted using the SPSS 18.0 program targeting 81 companies that have introduced knowledge management systems or knowledge management by categorizing them into technology-oriented and combined types. The result is that Initial types are based on changes in organizational structure and active adoption of information technology, and the Organizational knowledge centers use management systems (CEO support, performance compensation system, education and training system) and infrastructure building and information technology to maximize individual knowledge. The Information technology-oriented types rely more on information technology such as infrastructure building and information technology use rather than organizational knowledge. and the Combination type is understood as an educational and training system and decentralized organizational structure for the overall expansion of the organization. Through this study, effective and significant strategies, knowledge management determinants, and knowledge activities for each type were presented.

• Journal of Korea Trade
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• v.24 no.5
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• pp.87-105
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• 2020

Purpose - The purpose of the study is to propose a theoretical framework of cross-border e-commerce ecosystems based on blockchain technology. The ecosystem includes five systems, namely, crossborder supply chain intelligent system, cross-border logistics system, cross-border payment system, cross-border product quality traceability system and cross-border customs supervision system. Design/methodology - This study firstly derived the main improvement factors for the new ecosystem based on blockchain through prior research and expert interviews on cross-border e-commerce. Then explored the use of virtue of decentralization, anti-counterfeiting traceability, consensus mechanism, smart contract and other means of the core technology of blockchain to overcome the bottleneck of cross-border e-commerce development among Korea, China, and Japan. Finally, proposed valuable implications in both theoretical and practical perspectives. Findings - As a result, we combined with the problems existing in cross-border e-commerce among Korea, China and Japan, this paper proposes a solution based on blockchain. On this basis, it constructs a cross-border e-commerce ecosystem among these three countries, including five systems. In addition, we discuss the main problems existing in the current blockchain, such as low transaction concurrency, security loopholes, and inconsistent standards, the corresponding countermeasures are proposed from the technical level, security level and industry standards. Originality/value - This study is the first to apply the blockchain technology to solve the cross-border e-commerce problems in Korea, China and Japan, which is of pioneering significance in both literature and practice. Block chain technology is in the ascendency. This study provides technical solutions for promoting the development of cross-border e-commerce import and export trade between Korea, China and Japan.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.617-624
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• 2022

The small electric vehicles equipped with V2X(vehicle to everything) systems may provide more information and function to the existing navigation system of the vehicle. The key components of V2X technology include V2V (vehicle to vehicle), V2N(vehicle to network) and V2I (vehicle to infrastructure). This study is to design and implementation of VI type E-PTO which is interfaced with external equipments, the work designs the components of E-PTO such as DC/DC converter, DC/AC converter, battery bidirectional charging system etc. Also, it implements the devices and control systems for driving. The test results of VI type E-PTO components showed allowable 10% requirements of transient voltage variation rate and recovery time within 100ms for start/stop and normal operation.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.318-323
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• 2022

Barium titanate (BaTiO₃) is considered to be a beneficial ceramic material for multilayer ceramic capacitor (MLCC) applications because of its high dielectric constant and low dielectric loss. Numerous attempts have been made to improve the physical properties of BaTiO₃ in response to recent market trends by employing multicomponent alloying strategies. However, owing to its significant number of atomic combinations and unpredictable physical properties, finding a traditional experimental approach to develop multicomponent systems is difficult; the development of such systems is also time-consuming. In this study, 168 new structures were fabricated using special quasi-random structures (SQSs) of Ba_1-xCa_xTi_1-yZryO₃, and 1680 physical properties were extracted from first-principles calculations. In addition, we built an integrated database to manage the computational results, and will provide big data solutions by performing data analysis combined with AI modeling. We believe that our research will enable the global materials market to realize digital transformation through datalization and intelligence of the material development process.

• Smart Structures and Systems
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• v.31 no.4
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• pp.393-407
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• 2023

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.