• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Journal of Oral Medicine and Pain
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• v.47 no.2
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• 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.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.8
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• pp.30-38
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• 2011

This paper proposes a new intelligent diagnosis equipment for the partial discharge, which keeps deteriorating the insulating materials inside electric machineries, ultimately leading to electrical breakdown. In order to simulate experimentally the partial discharge inside the electric machinery, the tip-to-plate, the sphere-to-plate, the sphere-to-sphere and the plate-to-plate electrodes are used respectively, of which the gaps are 1[mm], 3[mm] or 5[mm] and the applied voltages are 3[kV], 5[kV] or 7[kV]. Ceramic coupler sensor and FIR digital filter are used to measure the partial discharge and the artificial neural network is used for the deterioration diagnosis of the electric machinery. The microprocessor of PD diagnosis equipment is DSP (TMS320C6713) with FPGA (Cyclone II). The results of the real-time and on-line experiments performed with the developed equipment are also explained.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.12
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• pp.578-584
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• 2005

This paper describes development of management software for transformers based on artificial intelligent analysis technology of dissolved gases in oil. Fault interpretation using the artificial intelligent analysis is performed by the artificial neural network and a rule based on the analysis of dissolved gases. The used gases are acetylene($C_{2}H_{2}$), hydrogen($H_2$), ethylene($C_{2}H_{4}$), methane($CH_4$), ethane($C_{2}H_{6}$), carbon monoxide(CO) and carbon dioxide($CO_2$). This software is mainly composed of gases input, fault's causes, expected fault's phenomena in detail, the decision on maintenance as well as report and gas trend windows. It is indicated that this is very powerful software for the efficient management of oil-immersed transformers using data analysis of gas components.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.87-90
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• 2004

This paper describes development of power transformer maintenance system using intelligent dissolved gases in oil analysis. The used gases are acetylene(C2H2), hydrogen(H2), ethylene(C2H4), methane(CH4), ethane(C2H6), carbon monoxide(CO) and carbon dioxide(CO2). The rule and neural network based gas analysis methods are used for artificial intelligent diagnosis. It is indicated that this program is efficient for diagnosis of oil immersed transformers diagnosis from application of gas analysis data of serviced transformer which has local overheating

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.130-133
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• 2006

본 연구에서는 진단분야에서의 시스템의 성능을 향상시키고 최적의 해를 찾고자 사례기반추론과 인공 신경망을 혼합한 시스템을 제안한다. 사례기반추론은 과거의 사례(경험)를 통해 현재의 제시된 문제를 해결하는 추론방식으로, 지식이 획득이 덜 복잡하고, 정형화되기 어려운 규칙이나 문제영역이 불분명한 분야에 효율적으로 활용되었다. 그러나 사례의 양이 방대해야 효율적인 추론을 할 수 있으며, 검색된 시간 또한 지연되는 단점이 있다. 이러한 문제를 보완하고자 본 논문에서는 인공 신경망의 학습을 통해 저장된 ANN Library를 생성하여, 사례기반추론에서의 부적절한 해를 유추하는 것을 방지하고, 효율적이고 신뢰성이 높은 해를 유추해 내는데 목적이 있다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.945-948
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• 2005

Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering a considerable market opportunity. The focus of this paper is on the development of a platform of fault diagnosis system integrating with personal digital assistant (PDA). An improvement of induction machine rotor fault diagnosis based on AI algorithms approach is presented. This network system consists of two parts; condition monitoring and fault diagnosis by using Artificial Intelligence algorithm. LabVIEW allows easy interaction between acquisition instrumentation and operators. Also it can easily integrate AI algorithm. This paper presents a development environment fur intelligent application for PDA. The introduced configuration is a LabVIEW application in PDA module toolkit which is LabVIEW software.