• Journal of Families and Better Life
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• v.20 no.4
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• pp.113-126
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• 2002

This study focused on defining and applying the diagnose scales to the household life in context with the family life planning based on the systems approach. In this study the household life consisted in 4 life subareas, i.e. time use, nonhuman resources(housing and durable goods), household financial and communication/problem solving competence of family members. Data were collected from 1200 full-time housewives who live in Seoul, Kyungki, Chungbuk, Jeonnam and Jeonbuk, Kyungnam and Kyungbuk and have at least 1 child in school age. The results show that the 4 areas of household life are in the level under the diagnose scale totally. The results of this study contribute to the systematic family life planning and the Problem solving of general household life. And the scales that are investigated through this study can be used the self family life diagnose Program.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.2
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• pp.320-327
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• 2006

This study was written in order to help understanding of listening diagnosis to vocal sound and speech. The purpose of listening diagnosis is that we know states of essence(精), Qi(氣) and spirit(神). Vocal sound and speech are made by Qi and spirit. Vocal sound originates from the center of the abdominal region(丹田) and comes out through vocal organs, for example lung, larynx, nose, tongue, tooth, lip and so on. Speech is expressed by vocal sound and spirit. They are controled by the Five Vital organs(五臟). Various changes of vocal sound and speech observe the rules of yinyang. For example, if we consider patient likes to say or not, we can diagnose heat and coldness of illness. If we consider he speaks loudly or quietly, we can diagnose weak and severe of illness. If we consider he speaks clearly or thick, we can diagnose inside and outside of illness. If we consider he speaks damp or dry, we can diagnose yin and yang of illness. If we consider change of voice, we can diagnose new and old illness. Symptoms of changes of five voices, five sounds, dumbness and huskiness are due to abnormal vocal sound, and symptoms of changes of mad talk, mumble, sleep talking and so on are due to abnormal speech.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.191-193
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• 2002

Insulation aging diagnosis system provides early warning in regard to electrical equipment defects. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. For solving this problem, many researchers proposed a method that diagnose power plant by using partial discharge. In this paper, we design the intelligent sensor to diagnose insulation degradation state that uses a Microprocessor and Al. Proposed sensor has MCU that is used to diagnose insulation degradation and communicate with main IDD system. And we use a fuzzy model to diagnose insulation degradation.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.33-39
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• 2015

Purpose The head of infants under 24 months old who has Craniosynostosis grows extraordinarily that makes head shape unusual. To diagnose the Craniosynostosis, surgeon has to inspect computed tomography(CT) images of the patient in person. It's very time consuming process. Moreover, without a surgeon, it's difficult to diagnose the Craniosynostosis. Therefore, we developed technique which detects Craniosynostosis automatically from the CT volume. Materials and Methods At first, rotation correction is performed to the 3D CT volume for detection of the Craniosynostosis. Then, cranial area is extracted using the iterative thresholding method we proposed. Lastly, we diagnose Craniosynostosis by analyzing centroid relationships of clusters of cranial bone which was divided by cranial suture. Results Using this automatical cranial detection technique, we can diagnose Craniosynostosis correctly. The proposed method resulted in 100% sensitivity and 90% specificity. The method perfectly diagnosed abnormal patients. Conclusion By plugging-in the software on CT machine, it will be able to warn the possibility of Craniosynostosis. It is expected that early treatment of Craniosynostosis would be possible with our proposed algorithm.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.8 no.2
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• pp.103-111
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• 2022

Idiopathic pulmonary fibrosis (IPF) is a progressive disease caused by some risk factors, including smoking, viral infection, toxic substances, and radiation, that decline lung function of fresh oxygen and blood delivery throughout the body. Patients with pulmonary fibrosis have suffered from breathing and cough and the average survival rate is only 3 years after diagnosis. Therefore, it is significant to diagnose IPF and start treatment in enough time. Usually, lung biopsy is available to diagnose localized pulmonary fibrotic sites directly. However, it is insufficient to visualize whole lung tissue, and also it has a risk of infection for patients. In the clinic, medical imaging systems can diagnose pulmonary fibrosis non-invasively without infection. In this review, we introduce current medical imaging systems used to diagnose pulmonary fibrosis, including CT, MRI, and nuclear medicine. Further, we introduce several molecular imaging probes targeting specific biomarkers which are expressed in pulmonary fibrosis. Through this paper, it is expected that it would be helpful to understand the latest knowledge and research trends on pulmonary fibrosis diagnostic imaging.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.109-114
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• 1993

It is well known that neural networks can be used to diagnose multiple faults to some limited extent. In this work we present a Multiple Fault Diagnosis Method (MFDM) via neural network which can effectively diagnose multiple faults. To diagnose multiple fault, the proposed method finds the maximum value in the output nodes of the neural network and decreases the node value by changing the hidden node values. This method can find the other faults by computing again with the changed hidden node values. The effectiveness of this method is explored through a neural-network-based fault diagnosis case study of a fluidized catalytic cracking unit (FCCU).

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.97-101
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• 2016

This paper presents a CART(Classification and Regression Tree)-based model to diagnose mental fatigue using speech technology. The parameters used in the model are the significant speech parameters highly correlated to the fatigue and questionnaire responses obtained before and after imposing the fatigue. It is shown from the experiments that the proposed model achieves classification accuracies of 96.67% and 98.33% using the speech parameters and questionnaire responses, respectively. This implies that the proposed model can be used as a tool to diagnose the mental fatigue, and that speech technology is useful to diagnose the fatigue.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.5
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• pp.117-127
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• 1992

In this work, the possibility to diagnose valve lashes of an automotive diesel engine via cylinder head vibration/noise analysis is studied. First of all the measurement signals and conditions are selected after considering which signals and conditions are most suitable to diagonse valve lashes. Both accelerometer and microphone are used to measure cylinder head accelerations and acoustic pressure due to valve impact on cylinder head. The signals are measured in both cranking and engine firing conditions. Finally, it was found that acceleration signal obtained in engine operating condition is the most reliable signal to diagnose the valve lash condition. The valve closing angle and the peak acceleration due to valve close are chosen to analyze the valve lash condition. The measured cylinder head acceleration signals are statistically tested to derive information which are useful to judge the valve lash. In conclusion, it was found that the developed technique can be one of feasible methods to diagnose the valve conditions while the engine is in operation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1882-1888
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• 2010

It is widely known that ESA(Electric Signature Analysis) method is very useful one for fault diagnosis of an induction motor. Online fault diagnosis system of induction motors using LabVIEW is proposed to detect the fault of broken rotor bars and shorted turns in stator. This system is not model-based system of induction motor but LabVIEW-based fault diagnosis system using FFT spectrum of stator current in faulty motor without estimating of motor parameters. FFT of stator current in faulty induction motor is measured and compared with various reference fault data in data base to diagnose the fault. This paper is focused on to predict and diagnose of the health state of induction motors in steady state. Also, it can be given to motor operator and maintenance team in order to enhance an availability and maintainability of induction motors. Experimental results are demonstrated that the proposed system is very useful to diagnose the fault and to implement the predictive maintenance of induction motors.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.109-114
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• 2008

The fractal dimension aims to quantitatively define the irregular characteristics of the shape in nature. It can be useful in describing morphological characteristics of various wear particles. This paper was undertaken to diagnose failure condition for sliding members in lubrication using fractal dimension. The experiments were undertaken to analyze the shape of wear particles and to diagnose failure condition for sliding members in lubrication using the image processing and the fractal parameters. It was possible to diagnose wear mechanism, friction, and damage state of machines through analysis of shape characteristics for wear particle in driven condition using fractal parameters.