• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.185-191
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• 2006

As development of information technology, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. They experience that constructing information system help knowledge management. Now, we focus on engineering knowledge. Because engineering data contains experts' experience and know-how in its own, engineering knowledge is a treasure house of knowledge. Korean shipyards are leader of world shipbuilding industry. They have accumulated a store of knowledges and data. But, they don't have data minning tool to utilize accumulated data. This paper treats development of data minning tools for the utilization of shipbuilding knowledge based on genetic programming (GP).

• Journal of the Society of Naval Architects of Korea
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• v.43 no.6 s.150
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• pp.700-706
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• 2006

As development of information technology, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. They experience that constructing information system help knowledge management. Now, we focus on engineering knowledge. Because engineering data contains experts' experience and know-how in its own, engineering knowledge is a treasure house of knowledge. Korean shipyards are leader of world shipbuilding industry. They have accumulated a store of knowledge and data. But, they don't have data mining tool to utilize accumulated data. This paper treats development of data mining tools for the utilization of shipbuilding knowledge based on genetic programming(GP).

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.19-25
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• 2005

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.298-302
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• 2006

As the age of information technology is coming, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. we focused on data mining system by using genetic programming. But, we don't have enough data to perform the learning process of genetic programming. We have to reduce input parameter(s) or increase number of learning or training data. In order to do this, the enhanced data mining system by using GP combined with SOM(Self organizing map) is adopted in this paper. We can reduce the number of learning data by adopting SOM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1865-1872
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• 2013

The social network services are grown rapidly through dissemination of smart devices. Therefore, increasing the data exponentially because many people use web services. Using these big data, it will be needed study of providing customized knowledge. So in this paper, we had collected data of 40 people for implementation of knowledge service using big data during one month. Based on these data, we had inferred information of location and moving type, and evaluated accuracy. Through that we had studied personal experience knowledge evaluation model for knowledge service.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.197-206
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• 1995

In structural design procedure, The procedure of member selection manages complex data relationship and reflects structural expert's knowledge. It is a difficult problem to construct an effective system with the conventional l programming technique. Knowledge_based s!'stem is a software system capable of supporting the explicit representation of expert's knowledge in member selection process through member data and reasoning mechanisms. This study describes useful methodology for structuring knowledge and representing relation between member data and knowledge. And this study shows the application of this member for member selection in the steel structural design.

• Korean Journal of Computational Design and Engineering
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• v.5 no.1
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• pp.1-11
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• 2000

The purpose of Computer Aided Design is to design good products as quickly as possible. To do this, it is important to reuse the old designs and build on them. Most engineering designers, therefore. hale studied methods for reusing old designs, and the results of their study hale been applied to new designs Unfortunately, this only involves reuse of geometric data without design knowledge. If they can design products with the geometric data, including design knowledge, of the old designed and share them through the Internet, design efficiency will be improved. In other words, one way to improve designer's productivity is to share design information that includes knowledge and scattered data across design offices. This paper introduces a shared design environment with distributed knowledge-base. A product design environment based on distributed knowledge-base is proposed, which integrates the CORBA, OLE and WWW as the network architecture, a 3D CAD system, and an expert system shell. A design example of the lens barrel of a projection television is implemented to verify the idea.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.13-23
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• 2016

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization's current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model.