• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• The Korean Nurse
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• v.33 no.3
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• pp.79-91
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• 1994

The purpose of this study was to identify the meaningful nursing diagnosis in maternity unit and to suggest formally the basal data to the nursing service with scientific approach. The subject for this paper were 64 patients who admitted to Chung Ang University Hospital, Located in Seoul, from Mar. 10, to July 21, 1993. The results were as follows: 1. The number of nursing diagnosis from 64 patients were 892 and average number of nursing diagnosis per patient was 13.9. 2. Applying the division of nursing diagnosis to Roy's Adaptation Model, determined nursing diagnosis from the 64 patients were 621 (69.6%) in physiological adaptation mode and (Comfort, altered r/t), (Injury, potential for r/t), (Infection, potential for r/t), (Bowel elimination, altered patterns r/t), (Breathing pattern, ineffective r/t), (Nutrition, altered r/t less than body requirement) in order, and 139 (15.6%) in role function mode, (Self care deficit r/t), (Knowledge deficit r/t), (Mobility, impaired physical r/t) in order, 122 (13.7%) in interdependence adaptation mode, (Anxiety r/t), (Family Process, altered r/t) in order, 10(1.1%) in self concept adaptation mode, (Powerlessness r/t), (Grieving, dysfunctional r/t) in order. 3. Nursing diagnosis in maternity unit by the medical diagnosis, the average hospital dates were 3.8 days in normal delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 64.6%, (Self care deficit r/t) 13.6% in order, and the average hospital dates were 9.6 days in cesarean section delivery and majority of used nursing diagnosis, (Comfort, altered r/t) 51.6%, (Self care deficit r/t) 15.2%, (Infection, potential for r/t) 9.9%, (Injury, potential "for r/t) 8.1%, (Anxiety r/t) 5.0%, (Mobility, impaired physical r/t) 3.3% in order, and the average hospital dates were 15.8days in preterm labor and majority of used nursing diagnosis, (Comfort, altered r/ t), (Anxiety r/t), (Injury, potential for r/t) in order, and the average short-term hospital dates were 2.5days, long-term hospital dates were 11.5days in gynecologic diseases and majority of used nursing diagnosis, (Comfort, altered r/t). (Self care deficit r/t), (Injury, potential for r/t), (Infection, potential for r/t) in order.

• Journal of Physiology & Pathology in Korean Medicine
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• v.24 no.6
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• pp.1111-1116
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• 2010

We in this paper design and implement a traditional korean medicine diagnosis system based on basic ontology. If doctors put the symptoms or tongues or pulses of a patient in the diagnosis system, they can be recommended for the diagnosis results. To support the doctors decision, the diagnosis system make the inference based on the basic ontology and compute the similarity between symptoms of patient and those of ontology. The diagnosis systems also provide the learning mechanism about diagnosis results which save the results in the ontology and reuse them in the next diagnosis. Thus, doctors can share their knowledge for the diagnosis by exchanging their ontology each other. In future, we will expand the knowledge of the basic ontology continuously so that doctors can get the more accurate diagnosis results. We also implement the prescription function and integrate it to the diagnosis system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.125-131
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• 2016

Tongue image segmentation is critical for automation of the tongue diagnosis system. However, most image segmentation methods for tongue diagnosis systems in oriental medicine have been proposed as user-based manual types or semi-automatic types. This study proposed a new method for tongue image segmentation, which is the most important image processing stage for complete automation of the tongue diagnosis system in oriental medicine. The proposed method improved the conventional snake algorithm, by making improvement on the internal energy function so that, as the points move outward reversely, the snake energy function is minimized, by using the image characteristics of tongue images. To calculate external energy, hierarchical spatial filtering is applied to ensure resistance against noise. Also, The proposed method was tested by using sample images and actual images, and showed more robustness against the background noise than the conventional snake algorithm. And, when one selected point was moved by the improved snake algorithm, energy values at the starting, middle, and end points were analyzed, and showed robustness that does not fall in the local minima.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.496-507
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• 2001

An expert system for the diagnosis and indication of hypertension is implemented through HTML-based backward inference. HTML-based backward inference is performed using the hypertext function of HTML, and many HTML files, which are hyperlinked to each other based on the backward rules, should be prepared beforehand. The development and maintenance of the HTML files are conducted automatically using the decision graph. Still, the drawing and input of the decision graph is a time consuming and tedious job if it is done manually. So, automatic generator of the decision graph for the diagnosis and indication of hypertension was implemented. The HTML-based backward inference ensures accessibility, multimedia facilities, fast response, stability, easiness, and platform independency of the expert system. So, this research reveals that HTML-based inference approach can be used for many Web-based intelligent site with fast and stable performance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.677-682
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• 1997

Vibration diagnosis of steam turbine generator is essential for safe operation. For a fast few decades, several data base systems for diagnosis of steam turbine generators have been developed and proved useful. However, there still remains a problem in using data base systems such that they require an expert engineer who has a deep insight or knowledg into the system. Moreover,such data base systems can not give any information if the input is not completely fit with data base. This paper presents an effective method for vibration diagnosis of steam turbine generators using fuzzy inference. The proposed method includes also a strategy to overcome the drawback of data base system such that one cannot obtain any information when the input is insufficient or not exact. A computer program is written to realize the entire procedure for the diagnosis. Three realistic problems are dealt with to show the effectiveness of the proposed method.

• Journal of Power Electronics
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• v.16 no.2
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• pp.695-703
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• 2016

A fuel cell power conditioning system for online diagnosis and load leveling under the condition of varying load is developed in this study. The proposed system comprises a unidirectional boost converter and a bidirectional buck-boost converter with a battery. The system operates in two different modes. In normal mode, the bidirectional converter is utilized for load leveling; in diagnostic mode, it is utilized to control load voltage while the boost converter generates perturbation current to implement the online diagnosis function through in-situ electrochemical impedance spectroscopy (EIS). The proposed method can perform EIS for a fuel cell under varying-load conditions with no influence on the load. The validity and feasibility of the proposed system are verified by experiments, and the design procedure of the proposed system is detailed.