• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.9-16
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• 2016

In this paper, we present a performance evaluation agent based on fuzzy cluster analysis and validity measures. The proposed agent is consists of three modules, fuzzy cluster analyzer, performance evaluation measures, and feature ranking algorithm for feature selection step in CAD system. Feature selection is an important step commonly used to create more accurate system to help human experts. Through this agent, we get the feature ranking on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. Also we design a CAD system incorporating the agent and apply five different feature combinations to the system. Experimental results proposed approach has higher classification accuracy and shows the feasibility as a diagnosis supporting tool.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.166-171
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• 2008

PET of the cerebral metabolic rate of glucose is increasingly used to support the clinical diagnosis in the examination of patients with suspected major neurodegenerative disorders, such as Alzheimer's disease. $^{18}F-FDG$ PET has been reported to have high diagnostic performance, especially, very high sensitivity in the diagnosis and clinical assessment of therapeutic efficacy. According to clinical research data hitherto, $^{18}F-FDG$ PET is expected to be an effective diagnostic tool in early and differential diagnosis of Alzheimer's disease. Since 2004, Medicare covers $^{18}F-FDG$ PET scans for the differential diagnosis of fronto-temporal dementia (FTD) and Alzheimer's disease (AD) under specific requirements; or, its use in a CMS approved practical clinical trial focused on the utility of $^{18}F-FDG$ PET in the diagnosis or treatment of dementing neurodegenerative diseases.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.50-55
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• 2003

This paper presents an intelligent computer system, which can easily diagnose electrical fire causes, without the help of human experts of electrical fires diagnosis. For this system, a database is built with facts and rules driven from real electrical fires, and an intellectual database system which even a beginner can diagnose fire causes has been developed, named as an Electrical Fire Causes Diagnosis System : EFCDS. The database system has adopted, as an inference engine, a mixed reasoning approach which is constituted with the rule-based reasoning and the case-based reasoning. The system for a reasoning model was implemented using Delphi 3, one of program development tools, and Paradox is used as a database building tool. To verify effectiveness and performance of this newly developed diagnosis system, several simulated fire examples were tested and the causes of fire examples were detected effectively by this system. Additional researches will be needed to decide the minimal significant level of the solution and the weighting level of important factors.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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• pp.189-194
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• 2017

An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.224-227
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• 1989

In this paper, The implementation of modular neural net for fault diagnosis in power system is presented. Until now, there have been many researches on expert system for fault diagnosis. On expert system, a lot of time for searching goal is needed. But, neural net processes with high speed, as it has parallel distributed processing structure. So neural net has good performance in on-line fault diagnosis. For fault diagnosis in large power system, the constitution of modular neural net with partition of large power system is presented.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.2
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• pp.99-106
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• 2003

In this paper, an intelligent fault location and diagnosis system is proposed. The proposed system identifies the fault location in two-step procedure. The first step identifies candidates of fault location using an fault distance calculation using an iterative method. The second step is diagnosis the actual fault location in the candidates by comparing the current waveform patterns with the expected operation of the protective devices and considering the interrupted load after the operation protective device. The simulations results in the case study demonstrates a good performance of the proposed fault location and diagnosis system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.215-222
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• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.136-144
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• 2021

Railroad Systems, which are national infrastructure industries, cause unexpected property and human damage if they fail to function while operating. Accordingly, railroad facilities supporting the railroad system are areas where high reliability and safety are required. However, it is time for systematic and scientific maintenance to be taken away from the traditional maintenance methods, as the nation's railroad facilities are now aging seriously. The purpose of this study was to secure the safety and reliability of the aging railroad communication facilities and to improve their performance. The research subjects were selected as a precision diagnosis process for railroad communication facilities, and improvement points were derived through detailed precision diagnosis process analysis. It is deemed that this study can contribute based on securing stability, improving reliability, and continuous improvement of railroad communication facilities should be conducted in the operation of the entire railroad system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.994-999
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• 2008

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Because vibration diagnosis can avoid sudden breakdown of machine and reduce the maintenance costs. In the factory, Air-Compressor which can affect the performance and capacity of output is important machine. Therefore, in this paper, The measuring and analyzing is carried out for air-compressor in order to the factor of resonance and resonance avoidance for air-compressor. The result of diagnosis and solution is discussed in this paper.