• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.651-657
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• 1998

In this study, an intelligent scheduling with hybrid architecture, which integrates expert system and neural network, is proposed. Neural network is trained with the data acquired from simulation model of FMC to obtain the knowledge about the relationship between the state of the FMC and its best dispatching rule. Expert system controls the scheduling of FMC by integrating the output of neural network, the states of FMS, and user input. By applying the hybrid system to a scheduling problem, the human knowledge on scheduling and the generation of non-logical knowledge by machine teaming, can be processed in one scheduler. The computer simulation shows that comparing with MST(Minimum Slack Time), there is a little increment in tardness, 5% growth in flow time. And at breakdown, tardness is not increased by expert system comparing with EDD(Earliest Due Date).

• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.5-23
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• 2014

This study reports the first step in the Classification-based Search and Knowledge Discovery (CSKD) project, which aims to combine information organization and retrieval approaches for building digital library applications. In this study, we explored the generation and application of a faceted vocabulary as a potential mechanism to enhance knowledge discovery. The faceted vocabulary construction process revealed some heuristics that can be refined in follow-up studies to further automate the creation of faceted classification structure, while our concept search application demonstrated the utility and potential of integrating classification-based approach with retrieval-based approach. Integration of text- and classification-based methods as outlined in this paper combines the strengths of two vastly different approaches to information discovery by constructing and utilizing a flexible information organization scheme from an existing classification structure.

• Korean Management Science Review
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• v.23 no.1
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• pp.87-113
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• 2006

This paper describes a comprehensive approach to examine how technological innovation contributes to the renewal of the firm's competences through its dynamic and reciprocal relationship with R&D and product commercialization. Three theories of technology and innovation (R&D and technological knowledge concept, product-process concept, technological interdependence concept) are used to relate technology and innovation to strategic management. Based on those theories, this paper attempts to identify dynamic relationship between product innovation and process innovation by system dynamics, by investigating the aspect of the dynamic changes of the closed feedback circulation structure in which R&D investments drive technological knowledge accumulation, and such knowledge accumulation actualizes product innovation and process innovation, subsequently resulting in the increase of productivity, customer satisfaction, profit generation, and re-investment on R&D from the created profits. This provides the ability to assess the advantages and disadvantages of different technological innovation strategies and commitments, and the opportunity to explore equilibrium point and suggest a generalized technological innovation model under different industry environment parameters and time-strategies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.74-82
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• 2015

Two key challenges in statistical relational learning are uncertainty and complexity. Standard frameworks for handling uncertainty are probability and first-order logic respectively. A Markov logic network (MLN) is a first-order knowledge base with weights attached to each formula and is suitable for classification of dataset which have variables correlated with each other. But we need domain knowledge to construct first-order logics and a computational complexity problem arises when calculating weights of first-order logics. To overcome these problems we suggest a method to generate first-order logics and learn weights using association analysis in this study.

• The Korean Journal of Zoology
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• v.26 no.4
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• pp.295-296
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• 1983

Further research needs in environmental carcinogenesis are much to be desired. Currently, the methodology for identification, quantitation, and assay of some environmental carcinogens is available, but there remains a pressing need for additional fundamental knowledge of the carcinogenic process at the molecular level. The best hope for the control of cancer lies in prevention and chemotherapy, and this in turn depends upon the generation of basic information not yet available. The history of medical science reveals that quantum advances in control and prevention of disease have been direct consequences of research applications derived from pools of existing knowledge. It is hoped that the major efforts on specific problems of eivironmental carcinogenesis and substantial support of research to obtain new knowledge will prove to be equally rewarding.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.479-481
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• 2023

Generative Adversarial Networks (GANs) have facilitated substantial improvement in single-image super-resolution (SR) by enabling the generation of photo-realistic images. However, the high memory requirements of GAN-based SRs (mainly generators) lead to reduced performance and increased energy consumption, making it difficult to implement them onto resource-constricted devices. In this study, we propose an efficient and compressed architecture for the SR-GAN (generator) model using the model compression technique Knowledge Distillation. Our approach involves the transmission of knowledge from a heavy network to a lightweight one, which reduces the storage requirement of the model by 58% with also an increase in their performance. Experimental results on various benchmarks indicate that our proposed compressed model enhances performance with an increase in PSNR, SSIM, and image quality respectively for x4 super-resolution tasks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.223-223
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• 1998

• Journal of the Korean Society for Precision Engineering
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• v.20 no.9
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• pp.27-31
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• 2003

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.188.1-188.1
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• 2010

This paper describes the effort for the development of an actual wind simulation method on the wind turbine performance evaluation. It should be emphasized that the deep knowledge on real wind field is a key factor for both the design of a wind turbine and the performance evaluation. With this reason, there had been several simulation attempts to accurately match with the actual wind data. With an existing wind generation algorithm is under consideration, this study introduces several more new concepts including Van der Hoven spectrum being implemented in different methodology. Also this paper will compare the result from the wind simulations by using the basic formula with that by using MATLAB and SIMULINK previously developed. In addition, like the existing wind generation algorithm, random process for actual wind field simulation and white noise are incorporated to closely produce the actual wind field models.