• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.181-184
• /
• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.303-308
• /
• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• Industrial Engineering and Management Systems
• /
• v.15 no.3
• /
• pp.215-223
• /
• 2016

Design of personnel teams has been studied from diverse perspectives; the most common are the people and systems requirements perspectives. All these point of view are linked, which is the reason why it is necessary to study them simultaneously. Considering this gap, a decision making model is developed based on factors, models, and requirements mentioned in the literature. The model is applied to a real case. The findings indicate that the Personnel Behavior Based Lean model (PBBL) can be converted into a decision making model for the selection of team members. The study is focused not only on the individual candidates' knowledge, skills, and aptitudes, but also on how the model considers the company requirements, conflicts, and the importance of each person to the project.

• Proceedings of the Korean Nuclear Society Conference
• /
• 1996.05a
• /
• pp.340-345
• /
• 1996

Because of limited resources of time and information processing capability during abnormal situation, diagnosis is difficult tasks in nuclear power plant (NPP) operators. Moreover since minimizing of adverse consequences according to process abnormalities is vital for the safety of NPP, introducing of diagnosis support systems have particularly emphasized. However, considerable works to develop effective diagnostic support system are not sufficiently fulfilled because of the complexity of NPP is one of the major problems. To cope with this complexity, a lot of model-based diagnosis support systems have considered and implemented worldwide. In this paper, as a prior step to development of model-based diagnosis support systems, primary side of pressurized water reactor is functionally modeled by multilevel flow modeling (MFM) concept. MFM is suitable for complex system modeling and for diagnosis of abnormalities. Furthermore, knowledge-based diagnosis process, of NPP operator could be supported because this diagnosis strategy can represent operator's one.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.6
• /
• pp.2528-2539
• /
• 2012

In the 21st century, knowledge is an important asset to improve productivity in the business world. In today's rapidly changing business environment, companies have rigorously brought in knowledge management system in order to secure the sustainable competitiveness and effective knowledge management. Knowledge management has now become mandatory, not optional. Today many companies which have built knowledge management on top of information technology and hardware-based infrastructure are having a difficulty in creating organizational culture in knowledge management. Even though companies have invested substantial time and money in knowledge management, the performances have not met their expectations. To solve this problem, software oriented strategy for knowledge management such as education and networking is more needed rather than hardware oriented strategy such as information technology systems. Though the knowledge management framework suggested in this study is exploratory, this study contributes to the clarification of the critical elements that companies which are about to build knowledge management should take in account.

• Journal of Information Science Theory and Practice
• /
• v.9 no.1
• /
• pp.1-23
• /
• 2021

This study introduces novel research using Practice Context Models supported by Knowledge Networks and Percolation Theory with the aim to contribute to knowledge management in Proof-of-Concept (PoC) activities. The authors envision this proposal as a potential instrument to identify network structures based on a percolation (propagation) threshold and to analyze the importance of nodes (e.g., practitioners, practices, competencies, movements, and scenarios) during the percolation of knowledge in PoC activities. After thirty months immersed in the natural PoC habitat, acting as observers and practitioners, and supported by an ethnographic exercise and a designer-research mindset, the authors identified the production of meaning in PoC activities occurring in a hermeneutic circle characterized by the presence of several knowledge networks; thus, discovering the 'natural knowledge' in PoC as a spectrum of cognitive development spread throughout its network, as each node could produce and disseminate certain knowledge that flows and influences other nodes. Therefore, this research presents the use of Practice Context Models 'connected' to Knowledge Networks and Percolation Theory as a potential and feasible proposal to be built using the attribution of values (weights) to the nodes (e.g., practitioners, practices, competencies, movements, scenarios, and also knowledge) in the context of PoC with the aim to allow the players (e.g., PoC practitioners) to have more flexibility in building alliances with other players (new nodes); that is, focusing on those nodes with higher value (focus on quality) in collaboration networks, i.e., alliances (connections) with the aim to contribute to knowledge management in the context of PoC.

• Journal of Intelligence and Information Systems
• /
• v.7 no.2
• /
• pp.95-115
• /
• 2001

We propose classifying the venture firms into four types of knowledge management. For this purpose, we collected questionnaire data from 101 venture firms listed in KOSDAQ, and applied a unsupervised neural network algorithm SOM to obtain four clusters representing knowledge management types-High Tech Type, Organizational Knowledge Type, Information Technology Type, and Beginner Type. Based on the results, we conclude that the venture firms listed in KOSDAQ should first know its own knowledge management type, and then apply appropriate strategies to take advantage of the knowledge management impacts on the competitiveness.

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

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.1
• /
• pp.99-117
• /
• 1999

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine Interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adepts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms using query generalization and specialization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically. four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual Join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.

• Smart Media Journal
• /
• v.8 no.3
• /
• pp.17-22
• /
• 2019

Signature-based intrusion systems use intrusion detection rules for detecting intrusion. However, writing intrusion detection rules is difficult and requires considerable knowledge of various fields. Attackers may modify previous attempts to escape intrusion detection rules. In this paper, we deal with the problem of detecting modified attacks based on previous intrusion detection rules. We show a simple method of reporting approximate occurrences of at least one of the network intrusion detection rules, based on q-grams and the longest increasing subsequences. Experimental results showed that our approach could detect modified attacks, modeled with edit operations.