• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2255-2258
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• 1998

Method of diagnosis in oriental medicine, the unbalance of the physiological function of the five viscers and six bowels of the human body is determined from time immemorial with the condition of blood circulation which is performed through blood vessels by the vitality of the heart. In oriental medicine, treatment is largely attempted by adjusting this unbalance. The analysis of pulse wave, which mainly measures the changes in blood flows, is to evaluate the shapes of a pulse wave rather than the quantitative changes like rates and strength of the pulse. This paper presents the development of Hardware System and Pulse Diagnosis Algorithm for automatic diagnosis of the pulse wave. This system makes the precise diagnosis and the objective recording possible.

• 전기학회논문지
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• 제56권8호
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• pp.1351-1359
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• 2007

In this paper, we have studied the power quality(PQ) diagnosis system with the two methods for PQ diagnosis. One to Apply a regulation value in compliance with mathematics calculation, and the other Automatic identification using Neural network algorithm. Neural network algorithm is used for an automatic diagnosis of the PQ. The regulation proposed by IEEE 1159 Working group is applied for the precision of the diagnosis. In order to divide accurate segmentation, the algorithm for a computer training used the back propagation out of several neural network algorithms. We have configured the proto-type sample by using Labview and a programmed Neural Networks Algorithm using with C. And arbitrary electric Signal generated by OMICRON Company's CMC 256-6 for an efficiency test.

• 대한의용생체공학회:의공학회지
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• 제30권2호
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• pp.153-161
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• 2009

Mammogram is one of the important techniques for mass detection, which is the early diagnosis stage of a breast cancer. Especially, the CAD(Computer Aided Diagnosis) using mammogram improves the working performance of radiologists as it offers an effective mass detection. There are two types of CAD systems using mammogram; automatic and semi-automatic CAD systems. However, the automatic segmentation is limited in performance due to the difficulty of obtaining an accurate segmentation since mass occurs in the dense areas of the breast tissue and has smoother boundaries. Semi-automatic CAD systems overcome these limitations, however, they also have problems including high FP (False Positive) rate and a large amount of training data required for training a classifier. The proposed system which overcomes the aforementioned problems to detect mass is composed of the suspected area selection, the level set segmentation and SVM (Support Vector Machine) classification. To assess the efficacy of the system, 60 test images from the FFDM (Full-Field Digital Mammography) are analyzed and compared with the previous semi-automatic system, which uses the ANN classifier. The experimental results of the proposed system indicate higher accuracy of detecting mass in comparison to the previous systems.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.201-210
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• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• 대한의용생체공학회:의공학회지
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• 제3권2호
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• pp.83-94
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• 1982

This paper presents a practical approach to develop computer algorithm for automatic diagnosis electrocardiogram(ECG) based on a decision table techniques.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.103-108
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• 2008

Automatic control makes the air-handling unit go into operation and determines the functions of high-efficient and energy-saving machines. Yamatake, an automatic control system manufacturer, have expanded fault detection and diagnosis, and data volumes so as to achieve higher technology in control by developing a sensor which makes field data visible, an actuator and Intelligent Conponent. This study, thus, focuses on applications for saving energy with Intelligent Conponent and goes in for easing global warming by creating future field data-based applications.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 춘계학술대회
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• pp.13-16
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm represents more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it expresses the complicated medical problems simply and clearly, those are not solved by the mathematical methods. The Decision-Tables have very simple structure and so it is very easy to correct or expand the system by adding or correcting some rules.

• 대한의용생체공학회:의공학회지
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• 제11권1호
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• pp.171-178
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm provides more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it can express clearly the complicated medical problems those are not solved by the mathematical methods. The Decision-Tables have very simple structure. Therefore, it is very easy to correct or expand the system by adding or correcting some rules.

• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.133-143
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• 2023

In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3192-3194
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• 2000

Many I/o cards such as AOCs, DICs, DOCs and ROCs are used to deal with I&C instruments of control panel in full-scope power plant simulator. To help the maintenance of I/O cards, an I/o card fault diagnosis system is implemented in this paper. The implemented fault diagnosis system has the automatic fault diagnosis function and manual card test function for fault diagnosis. Finally, the test result using I/O cards shows the validity of the implemented fault diagnosis system.