• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.665-672
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• 2021

Humanity in the 21st century is ushering in an era of practical use of AI. Until now, even though the industrial structure has been advanced, mankind has seen that the abstraction of knowledge production is only their own domain, but they have doubts about that belief. Therefore, this paper tried to examine the identity of modern humanity from the perspective of the result of knowledge accumulated from the past. These discussions were summarized and presented in a historical model called "Changes in the way of accumulating knowledge step by step" starting from the emergence of the earth and mankind. The first stage of this analytical model is the "accumulation of DNA knowledge" until the emergence of human intelligence on Earth. The second stage is the process of "accumulating civilized knowledge" by human biological intelligence, which has become capable of producing knowledge on its own. It is currently classified into three stages and it is considered that it is entering the stage of "accumulating mechanical knowledge" using AI technology. This paper proposes human history as such a step-by-step knowledge accumulation model and describes related discussions.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Journal of Korea Port Economic Association
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• v.28 no.3
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• pp.91-110
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• 2012

In the modern management literature, knowledge has been recognized as a new strategic resource enabling a firm to create its competitiveness. Shipping companies under fierce competitive structure need to pay attentions to the utility of knowledge management. A shipping firm may develop its unique service capability by classifying, sharing, and transferring the data, information and knowledge obtained from both inside and outside its global service network. The current study attempts to analyze influential relationships between liner shipping firms' knowledge management and service capabilities. In order to achieve this goal, this study designed a knowledge chain model measuring shipping companies' knowledge management levels and tested its validity and reliability based on a total of 80 replied questionnaires from national and foreign liners. The empirical result presents that supportive and primary activities composing a knowledge chain could exert significant positive influences on the enforcement of shipping service capabilities. This research shows that the utility of knowledge management is adoptable in the maritime industry, and recommends that shipping firms should recognize strategic importance of knowledge and actively pursue knowledge management at an entire firm level.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.1-13
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• 1994

Deep reasoning procedures are model-based, inferring single or multiple causes and/or timing relations from the knowledge of behavior of component models and their causal structure. The overall goal of this paper is to develop an automated deep reasoning methodology that exploits deep knowledge of structure and behavior of a system. We have proceeded by building a software environment that uses such knowledge to reason from advanced symbolic simulation techniques introduced by Chi and Zeigler. Such reasoning system has been implemented and tested on several examples in the domain of performance evaluation, and event-based control.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.800-806
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• 1999

This paper proposes IIKBAG(Intelligent Interlocking Knowledge Base Generator) which can build automatically the interlocking knowledge base utilized as the real-time interlocking strategy of the electronic interlocking system in order to enhance it's reliability and expansion. The IIKBAG consists of the inference engine and the knowledge base. The former has an auto-learning function which searches all the train routes for the given station model based on heuristic search technique while dynamically searching the model, and then generates automatically the interlocking patterns obtained from the interlocking relations of signal facilities on the routes. The latter is designed as the structure which the real-time expert system embedded on IS(Interlocking System) can use directly in order to enhances the reliability and accuracy. The IIKBAG is implemented in C computer language for the purpose of the build and interface of the station structure database. And, a typical station model is simulated to prove the validity of the proposed IIKBAG.

• ETRI Journal
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• v.29 no.4
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• pp.527-529
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• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.

• Journal of the Korean Society for information Management
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• v.15 no.3
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• pp.133-150
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• 1998

For the purpose of new‘organizational knowledge centric knowledge management’, this paper proposes the knowledge creating organizational memory which shows the knowledge creation in organization according to the dialectical circulation between the domain knowledge and the task knowledge, based on the Yin Yang theory. This paper defines two kinds of organizational knowledge such as the domain knowledge and task knowledge and designs them in the pursuit of its lifecycle. Knowledge creating organizational memory is designed to three knowledge components that circulate through the domain knowledge and the task knowledge according to the object-oriented methodology. Organizational knowledge is designed into the graphical structure of ( i ) knowledge ( ⅱ ) relation between knowledge objects and ( ⅲ ) degree of relation, which receive the legacy of organizational knowledge such as data schema, process model and knowledge base. This design of organizational knowledge can be applied to CBR(Case Based Reasoning), one of knowledge mining tools to create new organizational knowledge.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.137-154
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• 2000

This paper suggests a balanced scorecard (BSC) framework for measuring and evaluating the performance of knowledge workers in professional service firms(PSFs) which are typical knowldege-based organizations. As a strategic learning system, the balanced scorecard allows business leaders to drive and modify their business strategies based on the balanced measurement of key performance indicators(KPIs), which are basically divided into four domains such as financial achievement, customer orientation, internal business process, and innovation and learning. Conducting a focused case study on performance evaluation of knowledge workers from a balanced viewpoint, we could evaluate their competency and potential in more comprehensive manner. We also employ the analytic hierarchy process (AHP) approach for derive relative weights of key performance indicators and link it to a spreadsheet model for rating the individual performance of knowledge workers in a systematic way.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.61-71
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• 2022

Organizations pursue innovation to improve performance and gain competitive advantage, and knowledge workers represent an integral part of creating knowledge and helping organizations in their innovation efforts. The current paper seeks to examine the impact of the work environment on knowledge workers' creativity. Building on The Investment Theory of Creativity, The Componential Theory of Creativity, the Job Demand-Resource model, and the Resource Based View, we develop and test a model suggesting a relationship between work environment-social support, sufficient resources, organizational freedom, and organizational regulations-and the quantity and quality of the creative performance of knowledge workers. Using a sample of 167 engineers in Saudi Arabia, an emerging but wealthy country with huge innovation inspirations, the results of our Ordinary Least Squares (OLS) regression analysis indicate that all four elements of the work environment included in our study positively impact the quantity and quality of knowledge workers' creative performance. Our paper provides important contributions to the literature on the work environment, creativity, and knowledge management, with an emphasis on creativity in developing countries. Our study highlights the importance of creating a supportive and encouraging work environment for knowledge workers to foster their creativity. The study offers several theoretical and managerial implications, along with suggestions for future research.