• Journal of Information Technology Applications and Management
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• 제16권3호
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• pp.29-43
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• 2009

The advancement of emerging technologies can create more national value, and this motivates many nations to invest their resources in the emerging technologies. However, due to limited financial and human resources, even a wealthy nation cannot afford to randomly invest its resource in all profitable technologies. Therefore, cautious appraisal and prioritization of the competitive technologies should be conducted first, and then concentrated investment should be done for only the selected technologies. In this study, we propose a quantitative criterion for prioritizing the targeted electronic device technologies. The prioritization scheme devised in this study consists of a growth layer, a profitability layer, a vitality layer, and an influence layer. The proposed model forecasts the most promising technologies by applying the revised version of the Analytic Hierarchy Process (AHP). We performed empirical experiments on 12 emerging electronic device technologies to analyze the practical applicability of our study. The experimental data was obtained from 70 experts in high-tech industry as a part of the 2004 Prioritization and Selection project that was carried out in South Korea. As a result, the proposed scheme was able to present the most promising areas for investment in the field of electronic device technology.

• 지능정보연구
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• 제4권1호
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• 한국통신학회논문지
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• 제28권12B호
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• pp.1091-1099
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• 2003

국가정보원에서 방화벽(Firewall)의 인증을 하고 있고, 여기에서 K4 등급을 받은 방화벽이 모든 공공기관에 설치되고 있다. Solaris를 운영체제로 하는 K4 방화벽의 기능에서 패킷필터링과 NAT, 프락시 및 인증서비스 기능 등에 관해 기능 설정 전과 기능 설정 후의 성능을 비교 평가한다. 그리고 기존 32비트 체제 방화벽성능에 비해 최근 인증을 받고 있는 64비트 체제의 Solaris 방화벽을 비교 평가하여. 32비트에 비해 64비트 체제의 방화벽이 2배 이상 성능 개선이 나타남을 평가한다. 그리고, 결론에서 K4 방화벽 및 대한민국 방화벽의 연구 및 개발에 방향을 제시하여 세계에서 경쟁력 있는 시스템으로 도움이 되고자 한다.

• 지능정보연구
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• 제9권3호
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• pp.1-21
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• 2003

전자상거래의 확산에 따른 환경변화 속에서 다품종소량 생산하는 주문제조업체들이 다양한 고객들의 주문에 대응하고, 동적으로 변화하는 내 외적 기업환경 속에서 경쟁력을 제고시키기 위해 본 연구에서는 멀티에이전트 기반 협상지원시스템을 개발하였다. 이 시스템은 동적으로 변하는 환경과 고객들의 주문에 대응하고, 유연한 시스템구조를 이루는데 있어 새로운 패러다임으로 부각되고 있는 에이전트 기술을 사용하였다. 또한, 에이전트간의 협업을 통해 문제를 해결할 수 있는 멀티에이전트 기술을 사용하였다 본 연구에서 제시한 멀티에이전트 기반 협상지원시스템은 주문제조업체에서 가장 중요한 거래활동인 협상의 자동화를 통해 주문에서부터 생산에 이르는 일련의 모든 거래활동을 자동화하는 것을 목적으로 한다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Computers and Concrete
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• 제27권5호
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• 한국IT서비스학회지
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• 제20권5호
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• pp.105-118
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• 2021

As we enter the era of the 4th industrial revolution, information and communication technologies, including artificial intelligence and big data, are converging throughout society. Especially, as the importance of the social foundation of hyper-connection grows, the social influence of IoT, a network of connecting objects, people, and various entities, is also gradually expanding. In addition, as a pandemic, COVID-19, continues, interests in untact-oriented technology and service development are growing more than ever, and each company is trying to establish a core competency strategy to gain an edge in competition in the changing society. This study is a case study centered on Dawon DNS, a company that provides an IoT-based AI smart plug platform. Dawon DNS is broadening its services while developing products by applying advanced technologies, and this study is aiming to investigate the core competencies of the business evolution process. The obtained result of this study will provide implications for companies to become more competitive by suggesting the attitudes and strategies that startups should have during the transforming business environment.

• 전자통신동향분석
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• 제37권4호
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• pp.70-80
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• 2022

With the rapid growth in media service platforms providing broadcast programs or content services, content production has become more important and competitive. As a strategy to meet the diverse needs of global consumers for a variety of content and to retain them as long-term repeat customers, global over-the-top service providers are increasing not only the number of content productions but also their production efficiency. Moreover, a considerable amount of scene composition in the flow of content production work appears to be combined with rendering technology from a game engine and converted to object-based computer programming, thereby enhancing the creativity, diversity, quality, and efficiency of content production. This study examines the latest technology trends in content production such as virtual studio technology, which has emerged as the center of content production, the use cases in various fields of artificial intelligence, and the metadata standards for content search or scene composition. This study also examines the possibility of using object-based computer programming as one of the future candidate technologies for content production.

• 동아시아경상학회지
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• 제10권2호
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• pp.105-113
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• 2022

Purpose - An increase in automation has been as a result of digital technologies. The data will be instrumental in the determination of the services that are more necessary so that more resources can be allocated for them. The purpose of the current research is to investigate how big data utilization will help increase the profitability in the industry 4.0 era. Research design, Data, and methodology - The present research has conducted the comprehensive literature content analysis. Quantitative approaches allow respondents to decide, but qualitative methods allow them to offer more information. In the next step, respondents are given data collection equipment, and information is collected. Result - The According to qualitative literature analysis, there are five ways in which big data utilization will help increase the profitability in the industry 4.0 era. The five solutions are (1) Better Customer Insight, (2) Increased Market Intelligence, (3) Smarter Recommendations and Audience Targeting, (4) Data-driven innovation, (5) Improved Business Operations. Conclusion - Modern companies have been seeking a competitive advantage so that they can have the edge over other companies in the same industries providing the same services and products. Big data is that technology that businesses have always wanted for an extended period of time to revolutionize their operations, making their businesses more profitable.

• KEPCO Journal on Electric Power and Energy
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• 제7권2호
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.