• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.832-835
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• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self. Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2015년도 춘계 종합학술대회 논문집
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• pp.3-4
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• 2015

A self-organizing map (SOM) is a good tool to visualize applied data in the form of a feature map. With the help of such functions, a disaster risk diagnosis based on the residential house structure is tried in this study. According to some computer simulations with actual residential data, it is found that overall tendencies in the developed feature map are acceptable. Then, it is concluded that the proposed method is an effective means to estimate disaster risk appropriately.

• 조명전기설비학회논문지
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• 제27권10호
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• pp.69-74
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• 2013

There is needed the triple pyroelectric Infrared flame detector to really recognize problem, for the prevention and early suppression of fire. This system recognizes the characteristics of fire sources in various type and is communicated the message to the operators. Therefore, the prevention and early suppression of fire is available. Especially this paper focuss on development of the self diagnosis type flame detector for preventing malfunction comparing of basic and detected values.

• 한국산학기술학회논문지
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• 제14권1호
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• pp.358-362
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• 2013

본 논문을 통해 연구된 자가 진단 기능을 갖는 RFID 리더 platform은 자동 상태 인식 기능 및 보고 기능이 탑재되어 있고, 자동 업그레이드 기능과 복수 리더 환경에서 주파수 운영 정책을 사용하기 때문에 앞으로 시스템의 전체적인 운영 환경이 점점 대형화 되고, 멀티 환경에서의 활용도에서 많으며, 국내 RFID 시장 뿐 아니라 세계시장에서 많이 발생하는 요구를 충족할 수 있는 기술이다.

• 한국정보통신학회논문지
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• 제11권11호
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• pp.2150-2157
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• 2007

본 논문에서는 개인의 건강 상태를 일련의 과정에 따라 스스로 파악하여 전문 의료 관리에 대한 접근 방향의 결정을 돕고 전문의가 쉽게 새로운 질병 및 증상을 학습 할 수 있도록 하는 자가 질병 진단 시스템을 제안하였다. 제안된 자가 진단은 보건 복지부에 제출된 #한국인이 부담을 가지는 질병# 관련 보고서와 의료 콘텐츠 #Engel Pharm#을 참조하여 선정한 60가지의 질병과 각 질병에 대한 대표 증상 161가지를 이용하여 질병을 도출한다. 개선된 ART2 학습 알고리즘을 적용하여 질병 종류를 군집화하고 각 질병의 증상에 관련된 질의 결과를 입력 벡터로 제시하여 사용자의 건강 상태를 진단함으로써 자신의 건강에 대한 정보를 제공한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• 한국학교ㆍ지역보건교육학회지
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• 제12권3호
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• pp.1-15
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• 2011

Objectives: We describe the development of a smoking and drinking prevention program for adolescents, using intervention mapping. Methods: The study sample consisted of 1,000 high school second-grade students from 6 high schools in Seoul. The PRECEDE model was applied for the needs assessment. We carried out a social diagnosis by assessing the factors such as the quality of life, happiness level, and satisfaction with school life; an epidemiological diagnosis on the perceived health status, stress levels, and priority of health issues; a behavioral diagnosis on the smoking and drinking rate and the intention to smoke and drink; and an educational diagnosis on knowledge, beliefs, attitudes, self-efficacy, outcome expectations, social norms and life skills. Results: The development process included a needs assessment, identifying factors that influence smoking and drinking among adolescents. Intention, knowledge, perceived norms, perceived benefit, perceived cost, perceived susceptibility, self-efficacy, and life skills were identified as determinants. Three performance objectives were formulated to describe what an individual needs to do in order to avoid smoking and drinking. Subsequently, we constructed an intervention matrix by crossing the performance objectives with the selected determinants. Each cell describes the learning objectives of the smoking and drinking prevention program. The program used methods from the transtheoretical model, such as consciousness raising, outcome expectations, self-reevaluation, self-liberation, counterconditioning, environmental reevaluation, and stimulus control. The program deals with the effects of smoking and drinking, self-improvement, decision making, understanding advertisements, communication skills, social relationships, and assertiveness. Conclusions: By using the process of intervention mapping, the program developer was able to ensure a systematical incorporation of empirical and new data and theories to guide the intervention design. Programs targeting other health-related behavior and other methods or strategies can also be developed using this intervention mapping process.

• 한국보건간호학회지
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• 제29권3호
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• pp.582-593
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• 2015

Purpose: This study was conducted to examine the effectiveness of a self-management program on patients with thyroid cancer, particularly during the time of waiting for surgery after cancer diagnosis. Psychological distress, biological responses of immune cell counts, and quality of life were the variables of this study. Methods: One group pre-post test design was used with the nature of a pilot study. Ten newly diagnosed thyroid cancer patients were recruited through physicians' referrals. After drop out of 4 participants, final data were collected from six participants. Small group technique, a one and half hour-session per week for one month (total 4 sessions, 6 hours) was used. Relaxation techniques, meditation training, and strategies to reduce distress were provided by researchers. Standardized questionnaires and an established bio-assay were used for collection of data. Results: Participants showed significant lowering of psychological distress (p<.05) and improvement in global quality of life (p<.05). Biological responses of immune cell counts did not show statistical significance. Conclusion: The self-management program may reduce psychological distress and improve quality of life of patients with thyroid cancer between the time of diagnosis and surgery. The self-management program would be a valuable approach for patients with an unexpected cancer diagnosis to prepare for their disease experience in a community setting.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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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.