• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1933-1940
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• 2003

Recently, the importance of plant maintenance(PM) was highly raised to provide efficient plant operation which highly affects the productivity. For this reason, a number of engineering methodologies, such as risk-based inspection(RBI), fitness for service guidelines(FFS), plant lifecycle management(PLM), have been applied to improve the plant operation efficiency. Also, a network-based business operation system, which is called ERP(Enterprise Resource Planning), has been introduced in the field of plant maintenance. However, there was no attempt to connect engineering methodologies to the ERP PM system. In this paper, a knowledge-based information system for the plant operation of steel making company has been proposed. This system which is named as K-VRS(Knowledge-based Virtual Reality System), provides a connection between ERP plant maintenance module and knowledge-based engineering methodologies, and thus, enables network-based highly effective plant maintenance process. The developed system is expected to play a great role for more efficient and safer plant maintenance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of the Korean Society for information Management
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• v.20 no.1
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• pp.341-371
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• 2003

The purpose of the study is to design the ontology-based Prescription Knowledge Management System of Oriental Medicine. The study was done with the premise that the effectiveness of the system can be improved by using ontological abstractional structure based on definition of concept, attribution and relations of words related to the specific area. The system is developed with Protege-2000 by using newly developed KPML(Korean Medicine Prescription Markup Language). The results of the study provide the model of Prescription Knowledge Management System and the possibility of implementing XML-based ontology system to the semantic web environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.79-100
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• 2017

The purpose of this study is to investigate awareness of librarian's job prospects, and to do this a survey was conducted with 502 college students in 14 Departments of Library and Information Science around Korea and 753 librarians in libraries and related agencies. The study results are as follows. First, satisfaction with educational curriculum was higher in students than librarians. Second, both students and librarians regarded workplace based practical training as employment requirements and also evaluated certifications and academic performance as important requirements. Third, both groups asked that information on employment rates be available in a timely manner, and perceived that the librarian's job prospects were not bright. Therefore, in order to improve employment of librarians, it will be necessary to establish a job information system, reorganize the current educational curriculum into a practice-oriented curriculum, and introduce the national curriculum statements (NCS)-based curriculum.

• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.33-42
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• 2015

Based on the Construal Level Theory, this study aims to investigate how information security vaccine users' selection intentions differ from each other according to the selection time of information security vaccine, advertisement message types, and information security knowledge levels. For the foregoing, this study conducted an experiment by applying an experimental design of 2(knowledge level: high/low) ${\times}2$(temporal distances: short distance/long distance) ${\times}2$(advertisement message types: how(concrete)/why(abstract)) on computer security vaccine softwares. As a result, this study confirmed that the selection intentions about information security vaccines differed from each other according to the temporal distance and advertisement message type, and also varied according to the information security knowledge level. In conclusion, this study provides an implication that the consideration of well-timed persuasive message is especially important for the users at the high level of knowledge. Also, this research implies the necessity of development of abstract thinking ability based on temporal distance for the users at the low level of knowledge.

• Journal of Information Science Theory and Practice
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• v.6 no.1
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• pp.18-30
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• 2018

Interest in open data is continuing to grow around the world. In particular, open government data are considered an important element in securing government transparency and creating new industrial values. The South Korean government has enacted legislation on opening public data and provided diversified policy and technical support. However, there are also limitations to effectively utilizing open data in various areas. This paper introduces an administrative district knowledge model to improve the sharing and utilization of open government data, where the data are semantically linked to generate a knowledge graph that connects various data based on administrative districts. The administrative district knowledge model semantically models the legal definition of administrative districts in South Korea, and the administrative district knowledge graph is linked to data that can serve as an administrative basis, such as addresses and postal codes, for potential use in hospitals, schools, and traffic control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4007-4024
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• 2020

It is tremendously important to construct decision trees to use as a tool for knowledge representation from a given decision table. However, the usual algorithms may split the decision table based on each value, which is not efficient for numerical attributes. The methodology of this paper is to split the given decision table into binary groups as like the CART algorithm, that uses binary split to work for both categorical and numerical attributes. The difference is that it uses split for each attribute established by the directed acyclic graph in a dynamic programming fashion whereas, the CART uses binary split among all considered attributes in a greedy fashion. The aim of this paper is to study the effect of binary splits in comparison with each value splits when building the decision trees. Such effect can be studied by comparing the number of nodes, local and global misclassification rate among the constructed decision trees based on three proposed algorithms.

• 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.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.93-104
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• 1997

The major economic health problem of dairy cattle is mastitis which can affect 10 to 50% of cow-quarters. This health problem is difficult for many dairy farmers and health advisors to understand, diagnose and control. Without special laboratory testing, most mastitis is overlooked. Estimates of annual mastitis cast per cow vary from $50 to $200. For the nearly 9 million cows in the United States, annual loss to the dairy industry amounts to over one billion. A knowledge-based decision aid has been developed to evaluate mastitis data retrieved electronically from two of nine U. S. regional dairy records processing centers. Heuristic rules to diagnose herd mastitis problems were collected and incorporated into the system from various domain experts. This system information. It allows users to select mastitis control schemes with various degrees of aggressiveness and teaches commonly accepted mastitis control practices.