• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.374-376
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• 2001

In this paper, we developed the fault diagnostic expert system of silicone-immersed transformer using dissolved gas analysis. The knowledge base module consists of the knowledge using the rule: if Then . The inference engine uses the fuzzy rule for the management of uncertainty of the boundary and rule and derivate the Belief and Plausibility of the normality and fault using Dempster-Shafer theory. The expert system is connected to the database and it can manages the history of gas-data of the transformer.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.859-861
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• 1996

This paper presents the novel fault diagnostic expert system based on dissolved gas analysis(DGA) techniques in power transformer. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set concept. The uncertainty of rules are handled by fuzzy measures. Trend analysis through the monthly increment of key gas and DGA analysis are combined by the Dempster-Shafer theory, and the state of transformer and confidence factor are yielded by using this combined analysis. To verify the effectiveness of the proposed diagnosis technique, the expert system has been tested by using KEPCO's transformer gas records.

• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.1
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• pp.22-27
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• 2021

This study conducted a questionnaire for students of Pusan National University Graduate School of Korean Medicine who practiced using the Oriental Medicine Diagnosis System (ODS). From the questionnaire, this study investigated current state of application and perception of AI in Korean Medicine and explored the direction of ODS improvement and utilization. The survey questions consisted of six questions examining the satisfaction of the diagnostic expert system, five questions evaluating the availability of the diagnostic expert system, and six questions to predict the impact of AI on the Korean medicine community. The survey analysis showed high satisfaction with practice using ODS. On the other hand, the possibility of using ODS, especially in clinical use, was evaluated as relatively low compared to the satisfaction of the practice. Therefore, the overall impact of AI on the Korean medical community is not expected to be large. Although there are difficulties in standardization of clinical data due to the academic characteristics of Korean medicine, it is necessary to continue attempts to apply AI. By actively introducing educational tools using the latest AI techniques to the diagnosis experience and doctor-patient role in a practice, students will be able to increase their satisfaction with their practice and respond appropriately to the state-of-the-art medical environment.

• 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 Physiology & Pathology in Korean Medicine
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• v.23 no.4
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.288-291
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• 2003

For the purpose of building the more efficient knowledge learning system, it is very important to make a good structure of the knowledge system first of all. The well designed knowledge system can make the stored knowledge to be easily accessed for knowledge acquisition and extraction. Expert knowledge can also play a good role for controlling. Accordingly, in this paper we propose the Hierarchical modular system with expert knowledge gating mechanism. This system consists of the mechanisms for knowledge acquisition, constructing the associative memory, knowledge inference and extraction according to the expert knowledge gating mechanism. We applied this system to the medical diagnostic area for classifying Virus(coxackie virus, echovirus, cold), Rhinitis(Nonallergic, allergic) and tested with symptom data

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.131-134
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• 1990

A back-propagation neural network based system for a fault diagnosis of a chemical process is developed. Training data are acquired from FCD(Fault-Consequence Digraph) model. To improve the resolution of a diagnosis, the system is decomposed into 6 subsystems and the training data are composed of 0, 1 and intermediate values. The feasibility of this approach is tested through case studies in a real plant, a naphtha furnace, which has been used to develop a knowledge based expert system, OASYS (Operation Aiding expert SYStem).

• Journal of Sasang Constitutional Medicine
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• v.29 no.3
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• pp.224-231
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• 2017

Objectives Several researches have been done to develop instruments or questionnaire for diagnosis of sasang constitution. In this study, we developed a user-friendly web system to enhance the utilization of KS-15. Methods The KS-15 Web application was constructed by considering the responsive web design and easy survey answer. This system is designed only to authorized users for security purposes, and provides two modes, simple mode and expert mode, depending on the purpose of using the system. A simple mode do not keep user information and survey answer in the database. An expert mode support management of patients, diagnosis of sasang constitution and statistical functions. Results & Conclusions The developed KS-15 system can be operated from any smart device's web browser. In order to use information in clinic field, it was developed so that it can be accessed only by authorized users. It can be divided into an account which can use only simple mode and an account which can use expert mode by using a difference in access authority. These functions can enhance the applicability of sasang constitution in real life such as clinical or education.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.149-157
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• 2014

Diabetes is a chronic disease that requires continuous medical care and patient-self management education to prevent acute complications and reduce the risk of long-term complications. The worldwide prevalence and incidence of diabetes mellitus are reached epidemic proportions in most populations. Early detection of diabetes could help to prevent its onset by taking appropriate preventive measures and managing lifestyle. The major objective of this research is to develop an automated decision support system for detection of diabetes using mixture of experts model. The performance of the classification algorithms was compared on the Pima Indians diabetes dataset. The result of this study demonstrated that the mixture of expert model achieved diagnostic accuracies were higher than the other automated diagnostic systems.

• Tribology and Lubricants
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• v.17 no.2
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• pp.153-161
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• 2001

The development of techniques in diagnosing the state of the system is one of the essential tools in establishing the automation and unmanned manufacturing system for the realization of CIM/FMS in the fields. In this paper, we developed various diagnostic schemes for the journal bearing supported rotor system. Up to now, vibration of the shaft, measurement of the displacement and the temperature have been used for diagnostic tools, however, the statistical features only could not differentiate the state from states. Thus, we identified the sensor data f3r the steady state in the signal processing and then applied the fuzzy c-mean technology to cope with the nonlinear characteristics of the system. This will, in return, establish a possible diagnostic system for the rotor system in the fields.