• The Journal of Korean Obstetrics and Gynecology
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• v.18 no.4
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• pp.119-135
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• 2005

Purpose : This study was investigated, in order to improve that reliability of disease mechanism diagnosis, which were used for the object diagnosis of Oriental medicine in the dept. of Oriental OB&GY, Oriental Medical hospital of Dong-Eui University, amend to a disease mechanism item of Questionnaires and indexes of disease mechanism, we were thought that the results were more pointed to the condition of appropriate disease mechanism, as a result of put a results of Questionnaires and belongs to indexes of disease mechanism together, we　suggested to call it a　'weight of disease mechanism', because It was possible to　mark a different degrees of indexes of disease mechanism in the same points.　Methods : We analyzed the results of Questionnaires about 3354 outpatients who　had OB&GY disease in the Oriental Medical hospital of Dong-Eui University from April 2000 to March 2004. Results : 1. weight of disease mechanism is 10 score according to disease mechanism score and the standard of reliability. 2. The standard of reliability is same 11 disease mechanism item except stagnated blood, cold syndrome, dry- ness, phlegm. Conclusion : Weight of disease mechanism which show satisfaction the conditions of standard of reliability, appear the results of Questionnaires, against previous study investigated reliability of Questionnaires make it through the standard of reliability.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.910-912
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• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

• The Journal of Korean Medicine
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• v.27 no.2 s.66
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• pp.23-35
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• 2006

Objectives : This study examined the reliability of disease mechanism diagnosis, to evaluate items of questionnaires and inquire about the relationships between disease mechanisms and 'diagnosis program' questionnaires used for the objective diagnosis of Oriental medicine in the department of Oriental OB&GYN, Oriental Medical Hospital of Dongeui University. Method : We analyzed the results of questionnaires from 3504 outpatients of OB&GYN disease at the Oriental Medical Hospital of Dongeui University from April 2000 to April 2005. Results & Conclusions : 1. The research questionnaire had 188 questions, the summary questionnaire 137, and the diagnosis questionnaire 80. 2. The reliability of all questionnaires shows above 90% in deficiency of qi, deficiency of Yin, insufficiency of Yang coldness heat syndrome liver and spleen kidney in all, 8 case disease mechanisms. These are higher in the diagnosis questionnaires than in the research questionnaires and the summary questionnaires, except for kidney disease mechanism. 3. Cronbach a of the questionnaires decreased, especially blood deficiency, phlegm, heat syndrome, and insufficiency of Yang; these 4 case disease mechanisms were lower than 0.6. 4. For degree of correspondence of meeting points, both. the diagnosis and the summary questionnaires were above 80% with the exception of the 2 case disease mechanisms heart and blood deficiency. The meeting points of both the diagnosis and research questionnaires were above 80% in the to case disease mechanisms deficiency of qi blood stasis deficiency of Yim insufficiency of Yang damp dryness liver spleen kidney phlegm. 5. The change in the result values of questionnaires was a decreased level of deficiency of qi heat syndrome phlegm damp kidney and raised level of coldness heart disorder of qi dryness 6. The computation degree of disease mechanism in DSOM(r) D.1.1 was much lower on phlegm deficiency of qi heat syndrome disorder of blood, somewhat lower on insufficiency of Yang and higher on coldness than in the two different questionnaires.

• Bulletin of the Korea Photovoltaic Society
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• v.4 no.2
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• pp.14-24
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• 2018

The potential induced degradation (PID) phenomenon of crystalline silicon photovoltaic (PV) modules has been often found in outdoor PV systems until recently since firstly reported in 2010. Many studies have been conducted about the mechanism and the preventive methods, but systematic diagnosis of the PID has not been applied on-site. This paper focuses on analysis of 5 categories and 10 PID diagnosis methods using the monitoring data, light current-voltage, dark current-voltage, infrared and electroluminescence. We expect to contribute to improvement of power generation through PID diagnosis and troubleshooting in PV plants.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2497-2504
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• 2000

This paper is aimed at defining items of fault, and then constructing rules of fault diagnosis and recovery using INBANCA technology for the purpose of managing the weh server. The fault items of web server consist of the process fault, server overload, network interface fault, configuration and performance fault. Based on these items, the actual fault management is carried out fault referencing. In order to reference the fault, we have formulated the system-level fault diagnosis production rule and the service-level fault diagnosis rule, conjunction with translating management knowledge into active network. Also, adaptive recovery mechanism of web server is applied to defining recovery rule and constructing case library for case-based web server fault recovery. Finally, through the experiment, fault environment and applicability of each proposed production rule and recovering scheme are presented to verify justification of proposed diagnosis rules and recovery mechanism for fault management. An intelligent case-based fault management scheme proposed in this paper can minimize an effort of web master to remove fault incurred web administration and operation.

• The korean journal of orthodontics
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• v.10 no.1
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• pp.95-103
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• 1980

The present paper describes 3 clinical cases in which the orthodontic treatment was effected by the Bioprogressive therapy following the extraction of the upper and lower first premolars. What is most noteworthy in the present treatment is the use of a systems approach to diagnosis and treatment by the application of the visual treatment objective in planning treatment, evaluating anchorage and monitoring results, and the rest being performed routinely by the Bioprogressive mechanism. The result achieved by this method is very favorable and the efficiency of the Bioprogressive therapy is quite satisfactory.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.963-968
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• 2003

The continuous process systems usually consists of various components: driven rollers. idle rolls, load-cell and so on. Even a simple fault in a single component in the line may cause a catastrophic damage on the final products. Therefore it is absolutely necessary to diagnosis the components of the continuous systems. In this paper, an adaptive eccentricity compensation method is presented. And a new diagnosis method for transverse roll shape defects on rolling process is developed. The new method was induced from analyzing the rolling mechanism by using rolling force model, tension model, Hitchcock's equation, and measured delivery thickness of materials etc. Computer simulation results also show that the proposed diagnosis methods is very effective in the diagnosis of 3-D roll shape

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.134-138
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• 2011

Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly.

• Smart Structures and Systems
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• v.25 no.4
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• pp.459-469
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• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.