• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.146-152
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• 2010

In this paper, we implemented the intelligent healthcare system self-diagnosis that can achieve self-diagnosis by measured bio-signal(blood pressure, blood sugar, body fat monitor) after the recognize a user to access using RFID. The implemented healthcare self-diagnosis intelligent system is consist of kiosk structure that is RFID reader, bio-signal measuring instrument(hemadynamometer, glucometer, body fat monitor), computer for a part of database server and printer for print the result of self-diagnosis. It can achieve self-diagnosis of a user after compare and analyze the measured data and information of a user from database. The implemented system can make simple self-diagnosis even if not take a physical examination at hospital and apply to company, school, etc.

• Nuclear Engineering and Technology
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• v.32 no.3
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• pp.261-273
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• 2000

During an extensive review made as part of the Integrated Diagnosis System project of the Maiden Reactor Project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS is an integrated on-line diagnosis system that encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used at the Maiden Man-Machine Laboratory HAMMLAB of the OECD Maiden Reactor Project. The performance of MOAS, developed in G2 real-time expert system shell for the high-pressure preheaters of the NORS process in the HAMMLAB, was tested against a variety of transient scenarios, including failures of the control valves and sensors, and tube leakage of the preheaters. These tests showed that MOAS successfully carried out its intended functions, i.e., quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.

• Convergence Security Journal
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• v.9 no.2
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• pp.1-6
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• 2009

Most diagnosis algorithms have been done using the characteristic of t-diagnosable system based on PMC model. But as parallel systems grow fast, more faulty units occur in the system. Previous researches are done on the assumption of small number of faulty units in the system. There have been little studies on the system where number of faulty units exceed t. In this study, we assume the number of faulty units exceed t and there exist small number of nodes where the correctness of diagnosis can't be decided, then we propose an algorithm which increase the maximum number of faulty units in diagnosis system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of Sasang Constitutional Medicine
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• v.24 no.1
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• pp.13-20
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• 2012

1. Objectives : It is very important to classify people into Sasang constitution correctly in SCM. There have been many researches for this and several tools have been developed for diagnosis of Sasang constitution. In our study, we introduce a new web application for Integrated Sasang Constitutional Diagnosis (ISCD) ${\beta}$-version and algorithm on the base of face, body shape, voice and questionnaire. 2. Development : The web application of ISCD ${\beta}$-version was designed to be used easily for subject, staffs, and oriental medical doctors. For this purpose, we developed a web-application of Integrated Sasang Constitutional Diagnosis ${\beta}$-version using mysql database, tomcat web system, JSP, JAVA, and C++ languages. 3. Current State : The ISCD ${\beta}$-version could be accessed at http://210.218.196.115/SDT/login.jsp. The ISCD ${\beta}$-version consisted of 3 parts, for staffs, subject and oriental medical doctors. The system has been managed since February 2011. Currently 7 oriental hospitals have used the system and 1,439 subjects have been diagnosed by the system. 4. Conclusion and future work : Although many researchers have tried to develop a system or an algorithm for diagnosis of subject's constitution, we could have not used the system based on objective information of human body type, characters, symptoms. In this study, we describe a web application of objective diagnosis algorithm as ISCD ${\beta}$-version. This system may help an oriental medical doctors to make a decision of Sasang constitutional diagnosis easily and correctly.

• Journal of Convergence Korean Medicine
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• v.6 no.1
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• pp.37-42
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• 2024

Objectives: This study aims to evaluate the clinical validity of the system by conducting a clinical study to assess the diagnostic agreement between the system and Korean medical doctors. Methods: This study was conducted from September 7, 2023, to December 7, 2023, across five Korean medicine institutions, involving 100 adult participants aged 20-64 who consented to participate. Participants first entered their symptoms into a web-based program, which utilized an AI-based algorithm to diagnose 36 types of pattern differentiation. Subsequently, Korean medical doctors conducted face-to-face diagnoses using the same 36 types. The diagnostic agreement between the system and the doctors' diagnoses was analyzed using descriptive statistical analysis, and the results were expressed as a percentage agreement. Results: Analysis of the diagnostic data from 100 participants revealed that the web-based diagnosis support system identified an average of 7.76±0.79 patterns per patient, while Korean medical doctors identified an average of 7.99±0.10 patterns per patient. The diagnostic agreement between the system and the doctors showed an average of 7.08±1.08 patterns per patient, with an overall diagnostic agreement rate of 88.57±13.31%. Conclusion: This study developed a web-based diagnosis support system for traditional Korean medicine and evaluated its clinical validity by assessing diagnostic agreement. Comparing the diagnoses of the system with those of Korean medical doctors for 100 patients, the system showed an approximately 89% agreement rate with the clinical diagnoses. The system holds potential for aiding Korean medical doctors in pattern differentiation diagnosis in clinical practice.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.5
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• pp.160-168
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• 2011

As importance of the field of maintenance and management come to the fore because of collapses of the Seongsu bridge and the Sampoong department store, "Special Act for the Safety Control of Public Structures" was established in 1995 and the major maintenance and management system began taking effect "Detailed inspection and Precision safety diagnosis". However, a technical standard of "Detailed inspection and Precision safety diagnosis" was low because its history was not long, and also the results of research were not enough so anxiety for "Insufficient Detailed inspection and Precision safety diagnosis" was continuously left. While its evaluation system introduced in 2002, the ratio of "Insufficient Detailed inspection and Precision safety diagnosis" has been getting lower. However, according to the evaluation result after carrying out "Detailed inspection and Precision safety diagnosis" recently, it seems difficult to become lower for the ratio of "Insufficient Detailed inspection and Precision safety diagnosis" in future. Therefore, it is considered of questionary survey of the concerned organization and the mechanism side in connection with "Insufficient Detailed inspection and Precision safety diagnosis". So it is arranged the fundamental problems caused by an "Insufficient Detailed inspection and Precision safety diagnosis" that is to show the improving direction of the existing evaluation system in a based on this.

• Proceedings of the KIEE Conference
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• 2001.11a
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• pp.224-226
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• 2001

In this paper, a novel high-voltage motor monitoring system (HVMMS) is proposed. This system monitors the insulation condition of the stator winding by on-line measurements of partial discharge (PD). Sensor, EMC (Epoxy-Mica Coupler) is used for PD measurement PD signals are continuously measured and digitized with a peak-hold A/D converter to build the database of the high-voltage motor's insulation condition. Also, this system can communicate with the central monitoring system via RS-485. This helps more efficient operation and maintenance of the generator.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.279-284
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• 2007

Nowadays, due to development of automatic control devices and various sensors, one operator can freely handle several remote plants and processes. Automatic diagnosis and warning systems have been adopted in various fields, in order to prepare an operator's absence for patrolling plants. In this paper, a Bayesian networks based on-line diagnosis system is proposed for a wastewater treatment process. Especially, the suggested system is included learning structure, which can continuosly update conditional probabilities in the networks. To evaluate performance of proposed model, we made a lab-scale five-stage step-feed enhanced biological phosphorous removal process plant and applied on-line diagnosis system to this plant in the summer.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.