• 한국산학기술학회논문지
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• 제17권1호
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• pp.538-543
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• 2016

고품질의 제품과 조업 안전을 확보하기 위해서는 적절한 실시간 공정 감시 및 진단 시스템이 설치되어있는 것이 무엇보다 중요하다. 공정 감시 시스템과 결합된 신뢰도 높은 진단 시스템은 공정에서 발생한 특별한 사건이나 사고의 근본적인 원인과 공정 변수를 알려준다. 본 연구에서는 다변량 통계 분석과 분류기법에 기반한 공정진단 체계를 제시한다. 이 진단시스템은 비선형 데이터 표현과 필터링을 통한 지능적 데이터 표현으로 구성되어 있다. 진단 성능을 평가하기 위해 사례연구를 수행하였으며 다른 방법론과의 결과를 비교하기 위하여 진단 결과와 미래값 추정 방법을 평가하였다. 그 결과 본 연구에서 비교된 진단 방법론들에 비해 신뢰도 높은 진단 결과를 얻을 수 있었다.

• 품질경영학회지
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• 제51권2호
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• pp.223-245
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• 2023

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.511-518
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• 2008

Medical diagnosis systems have been developed to make the knowledge and expertise of human experts more widely available, therefore achieving high-quality diagnosis. In this study, in order to support the diagnosis by the medical diagnosis system, we have preformed medical diagnosis inference three times: first by a neural network with the backpropagation algorithm, secondly by a discriminant analysis with all of the variables, and thirdly by a discriminant analysis with the selected variables. A discussion on comparison of these three methods has been provided.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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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.

• 보건의료산업학회지
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• 제10권4호
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• pp.13-23
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• 2016

Objectives : This research used an exploratory approach to identify factors affecting business strategies due to changes in the healthcare market and customer loyalty factors. Methods : The research model was formulated using antecedents divided into diagnosis quality, employee attitudes, and servicescape. Moreover, differences in the structured model were analyzed according to hospital size. The data were gathered through surveys on clients, who has received care at participating hospitals. From the 200 that were distributed, 150 questionnaires were analyzed, to facilitate analysis of the research model. Results : The effects of diagnosis quality, employee attitudes, and servicescape, on customer loyalty were mediated by trust. We also found the differences between small and large hospitals. Conclusions : Customer loyalty in small hospitals was affected by servicescape, whereas that in large hospitals was affected by diagnosis quality and employee attitudes. The research results could be used to develop strategies to improve customer loyalty.

• 성인간호학회지
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• 제27권2호
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• pp.188-197
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• 2015

Purpose: The purpose of this study was to examine fatigue, sleep disturbances, and quality of life (QOL) among patients with breast cancer receiving radiotherapy. Methods: A cross-sectional, descriptive design was used. Data were collected through questionnaires distributed to 201 breast cancer patients in a hospital. The data were analyzed using SPSS 21.0. Results: The fatigue scores showed significant differences depending on exercise and duration since diagnosis. The sleep disturbance scores showed significant differences depending on duration since diagnosis. QOL scores showed significant differences depending on exercise, duration since diagnosis, and treatment site. Fatigue and sleep disturbances (r=.40, p<.001) showed statistically significant positive correlations, while fatigue and QOL (r=-.55, p<.001), and sleep disturbances and QOL (r=-.45, p<.001) showed statistically significant negative correlations. The multiple regression analysis, which was used to determine the variables influencing on QOL after radiotherapy, resulted in a significant regression model (F=23.88, p<.001), which accounted for approximately 45% of the explanatory power. Fatigue (${\beta}=-.39$, p<.001) and sleep disturbances (${\beta}=-.27$, p<.001) were revealed to adversely affect quality of life. Conclusion: The nursing intervention is necessary to reduce fatigue and sleep disturbance and to promote exercise in order to enhance QOL of patients with breast neoplasm while receiving radiotherapy.

• International Journal of Quality Innovation
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• 제4권1호
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• pp.46-53
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• 2003

Under the strong leadership of Chinese Government to the anti-SARS struggle, the situation has been successfully controlled. Since May 1 of 2003, the Ministry of Health of China published daily the number of newly increased SARS patient of Beijing, the authors analyzed these data using $X_cs$$-R_scs$ cause-selecting control charts of Statistical Diagnosis(SPD) Theory. Data about number of newly increased SARS patient consists of two kinds of variation: random variation and tendency variation of SARS epidemic. It is concluded that SARS epidemic of Beijing was already controlled since May 9 of 2003.

• 품질경영학회지
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• 제24권4호
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• pp.29-43
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• 1996

This paper identifies these data by the data diagnosis in lognormal distribution and presents the method to obtain exact parameter estimates and confidence intervals of regression line. The life-stress relationship uses Arrhenius model and life data generate Class-H insulation complete data by simulation. Also, the method to estimate parameters uses least squares estimation and externally Studentized residuals can be used as test statistics for identifing outliers. And influential cases are identified by Cook's distance. This research is intended to obtain the useful information for the life of products and test method, to save time and costs, and to help optimum accelerated life test plans.

• 벤처창업연구
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• 제3권1호
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• pp.57-72
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• 2008

본 논문에서는 그동안 부분적으로 진행된 발달장애 진단 평가에 관련된 전산처리를 멀티미디어 기법을 응용하여 발달장애 진단 평가분야에 새로운 방법을 제시한다. 발달장애 진단 평가를 위한 멀티미디어 정보는 여러 가지 속성을 지니고 있기 때문에 모든 발달장애 진단 평가 정보에 대한 기술을 사람이 수행해야 할 때는 엄청난 작업량이 수반될 뿐 아니라 동일한 데이터에 대한 기술이 주관에 따라 달라질 수도 있다는 것을 알게 되였다. 특히 발달장애 시스템 구현은 현재의 컴퓨팅 환경에서의 동영상 데이터 처리에 대한 비중의 증가, 텍스트 위주의 데이터에서 시각적인 동영상으로의 데이터 활용의 전이 등 발달장애 데이터가 멀티미디어 환경에 적합한 데이터로의 전이가 필수적이며 사용자 역시 빠른 이해를 위해 시각적 데이터를 선호하기 때문에 본 논문에서는 GUI(Graphics User Interface) 기법을 도입하여 검사 중에 텍스트 명령어는 거의 사용하지 않고도 발달장애 진단 평가를 수행할 수 있게 했다. 특히 발달장애 진단 평가에서 필요한 각종 데이터는 그 속성이 영상, 이미지, 논리연산의 필요성 및 각종 연산이 요구된다. 그래서 본 논문에서는 문제점을 해결하기 위해 편집대상 데이터(Content)에 의해 관련 정보를 검색하는 내용 기반(Content-based)의 검색 기술에 대한 연구를 적용했다.