• Journal of Ship and Ocean Technology
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• v.12 no.1
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• pp.28-34
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• 2008

The purpose of this paper is to define the concept of KAD/KAM, furthermore is to establish my own idea on the knowledge related works in engineering domain for a next decade ahead. KAD/KAM is represented as "Knowledge Everywhere" based on the concept of ubiquitous computing in engineering domain. At the beginning of the paper, the definition of KAD/KAM is described. And the related technologies to realize KAD/KAM, such as augmented reality, ontology, data mining, and knowledge management, are introduced. The concept of KAD/KAM is still immature. But this will be a new paradigm to change entire engineering environment in near future.

• ETRI Journal
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• v.42 no.2
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• pp.239-246
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• 2020

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.

• The Journal of Society for e-Business Studies
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• v.5 no.1
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• pp.1-18
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• 2000

As the networks (i.e., intranet and internet) proliferate all over the world, it is inevitable to move some (or all) of the enterprise activities into virtual spaces. Differently from business data, product data have complex semantics and thus are not properly exchanged among different application programs. Even though some neutral formats of product data have been developed by standard organizations, translating them among various application programs still needs the comprehensive understanding of the complex semantics. Recently, it is widely recognized that capturing more knowledge is the next step In overcome the current difficulties on sharing product data. In this paper, we present Web-based knowledge management that facilitates seamless sharing of product data among various application programs in virtual enterprises. Three types of knowledge are managed by the knowledge management system - metadata, ontology, and mapping. In this environment, we consider both business applications (e.g., ERP, SCM, and EDI System) and engineering applications (e.g., CAD and CAM system).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.1-7
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• 2005

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute's value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough Set theory and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Korean Journal of Computational Design and Engineering
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• v.11 no.2
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• pp.88-96
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• 2006

This paper introduces an approach to ontology-based framework for knowledge management in a product development domain. The participants in a product life cycle want to share the product knowledge without any heterogeneity. However, previous knowledge management systems do not have any conceptual specifications of their knowledge. We suggest the three levels of knowledge framework. First level is an axiom, which specifies the semantics of concepts and relations. Second level is a product development knowledge map. It defines the common domain knowledge which domain experts agree with. Third level is a specialized knowledge for domain, which includes three knowledge types; expert knowledge, engineering function and data-analysis-based knowledge. We propose an ontology-based knowledge framework based on the three levels of knowledge. The framework has a uniform representation; first order logic to increase integrity of the framework. We implement the framework using prolog and test example queries to show the effectiveness of the framework.

• Journal of Navigation and Port Research
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• v.27 no.6
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• pp.683-691
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• 2003

This exploratory study aims to investigate issues that, according to the Nonaka's theoretical model, are believed to hold significant ramifications on the effectiveness of creating and sharing organizational knowledge among distributed workers. These include changes in accessibility of knowledge with different levels of implicity, and the choice of communication media as a knowledge management channel. Related data were gathered from distributed-workers in Japan through interviews and a survey questionnaire. Data analysis revealed changes in the dynamics of internal and external interactivity, in the accessibility of necessary knowledge, and in the reliance on electronic media for knowledge exchange. The findings' implications are discussed from the perspective of knowledge creation ana sharing, and further suggestions have been made for the direction of future research efforts.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.221-232
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• 2021

In recent years, with the existence of huge amounts of data stored in huge databases, the need for developing accurate tools for analyzing data and extracting information and knowledge from the huge and multi-source databases have been increased. Hence, new and modern techniques have emerged that will contribute to the development of all other sciences. Knowledge discovery techniques are among these technologies, one popular technique of knowledge discovery techniques is data mining which aims to knowledge discovery from huge amounts of data. Such modern technologies of knowledge discovery will contribute to the development of all other fields. Data mining is important, interesting technique, and has many different and varied algorithms; Therefore, this paper aims to present overview of data mining, and clarify the most important of those algorithms and their uses.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.