• Knowledge Management Research
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• v.12 no.1
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• pp.39-52
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• 2011

Many LCD manufacturers continue to develop the technologies of LCD manufacturing processes for the reduction of production cost, power consumption and high-resolution. The LTPS (Low Temperature Polycrystalline Silicon) crystallization technology is important for rearranging the internal structure of liquid crystal grain by adding certain energy to amorphous silicon and turning it into poly-silicon in order to manufacture LCD with better performance. We consider 14 existing technologies of LTPS crystallization in the LCD manufacturing and present an intelligent analysis methodology using patent map and AHP (Analytic Hierarchy Process) analysis for determining an optimal LTPS crystallization technology. By using patent map analysis, we easily understand the development process and mega-trend of LTPS crystallization technologies and their relationship. By using AHP analysis, we evaluate 14 LTPS technologies. Through the use of proposed methodology, we determine the Continuous Wave Laser Lateral Crystallization technology as an optimal one.

• Journal of the Korean Society for information Management
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• v.29 no.4
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• pp.123-142
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• 2012

To develop an intelligent search engine to help users retrieve information effectively, various methods, such as Semantic Web, have been used, An effective retrieval method of such methods uses ontology technology. In this paper, we built National R&D ontology after analyzing National R&D Information in NTIS and then implemented National R&D Knowledge Map to represent and retrieve information of the relationship between object and subject (project, human information, organization, research result) in R&D Ontology. In the National R&D Knowledge Map, center-node is the object selected by users, node is subject, subject's sub-node is user's favorite query in National R&D ontology after analyzing the relationship between object and subject. When a user selects sub-node, the system displays the results from inference engine after making query by SPARQL in National R&D ontology.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.501-508
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• 2011

A knowledge map, which has been recently applied in various fields, is discovering characteristics hidden in a large amount of information and showing a tangible output to understand the meaning of the discovery. In this paper, we suggested a knowledge map for research trend analysis based on keyword-relation networks which are constructed by using a database of the domestic journal articles in the computer engineering field from 2000 through 2010. From that knowledge map, we could infer influential changes of a research topic related a specific keyword through examining the change of sizes of the connected components to which the keyword belongs in the keyword-relation networks. In addition, we observed that the size of the largest connected component in the keyword-relation networks is relatively small and groups of high-similarity keyword pairs are clustered in them by comparison with the random networks. This implies that the research field corresponding to the largest connected component is not so huge and many small-scale topics included in it are highly clustered and loosely-connected to each other. our proposed knowledge map can be considered as a approach for the research trend analysis while it is impossible to obtain those results by conventional approaches such as analyzing the frequency of an individual keyword.

• Asian review of World Histories
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• v.4 no.1
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• pp.83-123
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• 2016

We describe a vision of historical analysis at the world scale, through the digital assembly of historical sources into a cloud-based database, where machine-learning techniques can be used to summarize the database into a time-integrated actor-to-actor complex network. Using this time-integrated network as a template, we then apply the method of automatic narratives to discover key actors ('who'), key events ('what'), key periods ('when'), key locations ('where'), key motives ('why'), and key actions ('how') that can be presented as hypotheses to world historians. We show two test cases on how this method works. To accelerate the pace of knowledge discovery and verification, we describe how historians would interact with these automatic narratives through an online, map-based knowledge aggregator that learns how scholars filter information, and eventually takes over this function to free historians from the more important tasks of verification, and stitching together coherent storylines. Ultimately, multiple coherent storylines that are not necessary compatible with each other can be discovered through human-computer interactions by the map-based knowledge aggregator.

• Knowledge Management Research
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• v.10 no.2
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• pp.49-63
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• 2009

In this study, sub domain of flow was investigated on literature survey, and suggested of the measuring method of web-site flow and its simulation analysis. Constructing of measuring method of flow, and using this method what-if analysis was simulated when several condition changed. Using causal map approach to extract knowledge from web-site domain experts and to derives a causal relationship of knowledge. Specially, in our study, describes method of developing and building causal map, and suggests guide line of this method on practical application. This research results show that web-site flow starts "direct searching" or "interesting of special issue(domain)", and when challenges of web-site were accorded with user's skills web-site flow grows. Further, in the web-site, information searching intention results in increase of user's duration time and experience flow to discovery new interesting issues in this process. If user's web-site of interaction is increased, awareness of environment conditions decreased, finally, user's telepresense results in increased web-site flow. This paper contained thai this method make used of measuring flow in the web-site and developing of practical strategy.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.50-56
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• 2011

This paper proposes 3D visualization method of compound knowledge which will be able to identify and search easily compound knowledge objects based the multidimensional relationship. For this, we structurized a compound knowledge with link and node which become the semantic network. and we suggested 3D visualization method using SOM. Also, to arrange compound knowledge from 3D space and to provide the chance of realistic and intuitional information retrieval to the user, we proposed compound knowledge 3D clustering methods using object similarity. Compound knowledge 3D visualization and clustering using SOM will be the optimum method to appear context of compound knowledge and connectivity in space-time.

• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.225-233
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• 2006

This note attempts to present knowledge management strategies for the upcoming 4th elected local government. Despite the series of efforts by the central government of Korea, it seems that local governments and their affiliated organizations have been very slow even understanding the necessities of knowledge management as well as adopting any particular knowledge management system. This study analyzes the evolutionary process of knowledge management policies by the central government and presents knowledge management strategies.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.143-149
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• 2019

The purpose of the study was to examine early childhood teachers' content knowledge on green growth education. The subjects, 45 early childhood teachers, were asked to draw concept maps about early childhood green growth education. Their concept maps were analyzed in terms of superordinate and subordinate concepts by contents and frequencies. The results showed that early childhood teachers used 182 superordinate and 1,292 subordinate concepts in the concept map of green growth education for young children. Although early childhood teachers had a wealth of content knowledge on green growth education as proposed by the Ministry of Education, their knowledge was disproportionate to some areas and sub-areas of green growth education. These results implied the needs of developing teacher education programs for early childhood green growth education.

• Knowledge Management Research
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• v.19 no.2
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• pp.47-68
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• 2018

Business environment is shifting from industrial economy to knowledge based economy. Enterprises go through numerous trials for successful management in changing environment. Along with trial tests, research area has been growing simultaneously. Unlike initial research which focused on basic concepts such as: form of business model and success points. Current research emphasizes on actualization of business that enterprises plan, which brought academic research with perplex form of knowledge structure. On the other hand, there is limitation in understanding business model systematically due to preceding research primarily centered on analyzing definition and case study. In order to analyze knowledge structure, this study utilized social network analysis based on "relationship". For the analysis, 13,412 keywords were extracted from 36years worth of article or research related to business model stored in SCOPUS database. From the analysis, it was shown core research subject was INNOVATION and the number of co-authors has increased due to the academic diversity. Business model research is divided into five sub-categories (E-commerce, SMEs, sustainability, open-source, and e-book). Through cognitive map analysis on each of research characteristics of sub-category, it has shown that E-commerce, SMEs, sustainability, and open-source are core categories.