• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.61-74
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• 2011

The author analyses the challenging activities of Japanese libraries in this decade by launching two keywords; "product-sharing" and "outcome generation." "Product-sharing" means that libraries share knowledge, skills, and records which are produced as the result of the services or in the process of activities. And "outcome generation" means that libraries generate any efficiency or effectiveness through their services to users. Using these concepts, reported are the current situation and aspects of Japanese libraries which try to make various contributions to the society; research and learning of the people, and education and training for professional librarians, and so on. In the analysis, the author shows some examples of "product-sharing" at first, including the records of reference transaction and the multi-functioned online public access catalogue. Especially, focused is on the various possibility and adoptability of the Collaborative Reference Database System of the National Diet Library of Japan. This system is one of digital reference service in Japan, and the database of reference transaction records is expected to be useful for research and academic studyies as knowledge-base of professional librarians. And the system is also expected to be a platform for LIS education and professional development in the e-learning environment. Secondly, as the examples of "outcome generation", explained are the problem-solving-type activities, and provision of the collection about books on struggling against disease and illness. A few examples of outcome in the problem-solving-type activities are these; increase of sales in the services for shop managers, business persons, and entrepreneurs, contribution to affluent daily life by providing the local information services to residents and neighbourhoods, and etc. And for both the patients with serious cases and their family or those who nurse them, books about other persons' notes or memorandum are the greatest support, and sometime healing. The author discuss the 'raison d'etre' of these activities focusing on public libraries in Japan.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.69-75
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• 2011

This paper presents a model of e-learning based on ubiquitous computing configuration. First of all, we survey the advanced e-learning systems for foreign and domestic universities. Next we propose the optimal e-learning model based on ubiquitous computing configuration. The proposed e-learning model as following. we propose the e-learning system's hardware and software configurations, that are server and networking systems. Also, we construct the proposed e-learning systems's services. There are attendance and absence service, class management service, common knowledge service, score processing service, facilities management service, personal management service, personal authorization issue management service, campus guide service, lecture-hall management service. Then we propose the laboratory equipment management service, experimental materials management service etc. The proposed model of e-learning based on ubiquitous computing configuration will be able to contribute to the next generation university educational paradigm.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• Journal of KIBIM
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• v.14 no.3
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• pp.22-30
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• 2024

Recently, LLM (Large Language Model), a rapidly developing generative AI technology, is receiving much attention in the smart construction field. This study proposes a methodology for implementing an knowledge expert system by linking BIM (Building Information Modeling), which supports data hub functions in the smart construction domain with LLM. In order to effectively utilize LLM in a BIM expert system, excessive model learning costs, BIM big data processing, and hallucination problems must be solved. This study proposes an LLM-based BIM expert system architecture that considers these problems. This study focuses on the RAG (Retrieval-Augmented Generation) document generation method and search algorithm for effective BIM data retrieval, with the goal of implementing an LLM-based BIM expert system within a small GPU resource. For performance comparison and analysis, a prototype of the designed system is developed, and implications to be considered when developing an LLM-based BIM expert system are derived.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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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.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• Knowledge Management Research
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• v.17 no.2
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• pp.27-50
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• 2016

Korean manufacturing industry have recently faced the catch-up of China in the mass commodity product, such as automotive, display, and smart phone in terms of market as well as technology. Accordingly, discussion on the importance of achieving catch-up in complex product systems (CoPS) has been increasing as a new innovation engine for the industry. In order to achieve successful catch-up of CoPS, we explored emerging technologies of CoPS, which are featured by the characteristics of radical novelty, relatively fast growth and self-sustaining, through the study of emerging technologies of gas turbine for power generation. We found that emerging technologies of the gas turbine are technologies for combustion nozzle and composition of electrical machine for increasing power efficiency, washing technology for particulate matter, cast and material processing technology for enhancing durability from fatigue, cooling technologies from extremely high temperature, interconnection operation technology between renewable energy and the gas turbine for flexibility in power generation, and big data technology for remote monitoring and diagnosis of the gas turbine. We also found that those emerging technologies resulted in technological progress of the gas turbine by converging with other conventional technologies in the gas turbine. It indicates that emerging technologies in CoPS can be appeared on various technological knowledge fields and have complementary relationship with conventional technologies for technology progress of CoPS. It also implies that latecomers need to pursue integrated learning that includes emerging technologies as well as conventional technologies rather than independent learning related to emerging technologies for successful catch-up of CoPS. Our findings provide an important initial theoretical ground for investigating the emerging technologies and their characteristics in CoPS as well as recognizing knowledge management strategy for successful catch-up of latecomers. Our findings also contribute to the policy development of the CoPS from the perspective of innovation strategy and knowledge management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1673-1681
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• 2007

The u-Learning just begins to emerge as the next-generation knowledge-based business. Since it has a great potential to become a high value-added industry, there is much attention paid in this field. In this work, we first summarized the concept of the u-Learning where the architecture of various u-learning areas has been identified. Then we investigated the current status and problems of the u-Learning industry. Through the SWOT analysis, we have extracted the political strategies that will be essential for the rapid industrialization of u-Learning which will, in turn, contribute much to enhance the competitiveness of national economy.