• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Phonetics and Speech Sciences
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• v.9 no.4
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• pp.43-50
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• 2017

These days voice models of speech recognition software are sophisticated enough to process the natural speech of people without any previous training. However, not much research has reported on the use of speech recognition tools in the field of pronunciation education. This paper examined Google speech recognition of a short English paragraph produced by Korean college students in clear and casual speech styles in order to diagnose and resolve students' pronunciation problems. Thirty three Korean college students participated in the recording of the English paragraph. The Google soundwriter was employed to collect data on the word recognition rates of the paragraph. Results showed that the total word recognition rate was 73% with a standard deviation of 11.5%. The word recognition rate of clear speech was around 77.3% while that of casual speech amounted to 68.7%. The reasons for the low recognition rate of casual speech were attributed to both individual pronunciation errors and the software itself as shown in its fricative recognition. Various distributions of unrecognized words were observed depending on each participant and proficiency groups. From the results, the author concludes that the speech recognition software is useful to diagnose each individual or group's pronunciation problems. Further studies on progressive improvements of learners' erroneous pronunciations would be desirable.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.11
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• pp.562-568
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• 2001

In this paper, we described the export system to monitor and diagnose 765KV power apparatus. To develop this expert system, we studied the knowledge bases and data bases for 765KV transformer and GIS. In order to make the reliable inference of knowledge base and the good MMI(Man Machine Interface), the data bases were consisted of the tables of power apparatus information, limit level value, measured input data, inference result and diagnosis result. The knowledge base had various rules to infer the conditions of transformer and GIS. We applied both the forward chaining and backward chaining methods to these rules of system for good inferences. This paper describes the applied methods for expert system. Also, this developed system was tested with dissolved gas analyzing result and the result was shown.

• Journal of Sasang Constitutional Medicine
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• v.24 no.3
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• pp.42-49
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• 2012

Objectives This study aimed to assessing the performance of Sasang constitutional analysis Tool (SCAT) for Chinese as one of method to diagnose Sasang Constitution and evaluating the possibility of applying SCAT to foreign people. Methods This cross-sectional study was approved by IRB (Independent Review Board) at J Hospital in Yanbian. Informed consent to take part in the study was obtained from each of the participants. The performance of SCAT was assessed through Kappa coefficient and the concordance rate between SCAT and expert diagnosis. Results The concordance rate between SCAT and expert was 61.1% in total and the Kappa was 0.408. When the constitutional probability increased, the concordance rate and Kappa showed a increasing tendency. The concordance rate of Chosun race was 62.1% and others 60.7%. Conclusions SCAT, as Sasang constitutional diagnostic supporting system, may help the expert to diagnose Sasang constitution in Yanbian region.

• Nuclear Engineering and Technology
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• v.17 no.4
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• pp.302-316
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• 1985

Small break LOCA caused by a stuck-open PORV is one of the important contributors to nuclear power plant risk. This paper deals with the design of a pressurizer surveillance system using microcomputer to prevent the malfunction of system and has assessed the effect of this improvement through Probabilistic Risk Assessment (PRA) method. Micro-computer diagnoses the malfunction of system by a process checking method and performs automatically backup action related to each malfunction. Owing to this improvement, we can correctly diagnose “Spurious Opening”, “Fail to Reclose” and “Small break LOCA” which are difficult for operator to diagnose quickly and correctly and reduce the probability of a human error by an automatic backup action.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.1 no.1
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• pp.13-19
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• 2007

In this study, diagnostic algorithm was suggested to diagnose quantitatively the degree of the stress urinary incontinence. The bio-signal measurement system was developed to measure the contraction pressure of the pelvic floor muscle and diagnostic parameters were drawn out by analyzing the contraction pressure data. Statistical evaluations were done to classify the diagnositc parameters by order that relationship is high. The diagnostic algorithm that was able to diagnose degree of the urinary incontinence as quantitatively was realized from the high relationship parameters.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.184-189
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• 1999

In this paper, we develope a diagnosis device for ZnO arrester by detecting the leakage current in service. To diagnose the deterioration of ZnO arrester, the device detects the total leakage current which flows between an arrester and ground, and analyzes the resistive current(third harmonic current) which is an indicator of deterioration of ZnO arrester. We use the optical cable which can transfer a detected data without a noise, also use a microprocessor for a data storage, processing, and trend analysis. Experiment are executed to verify its performance in laboratory and the results show that the diagnosis device exactly detects the total leakage current and the resistive current, so it can diagnose the deterioration of ZnO arrester. Also the leakage current of ZnO arrester is detected using the developed diagnostic device in field, these results are presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.897-902
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• 2018

The purpose of this study is to develop a bioinformatic diagnostic system that diagnoses the patient 's health condition by using the output waveform generated by applying the impulse voltage of 13Hz to 7 body parts based on DFM (Diagnose Fure Funktionelle Medizine) theory. It is expected that the data acquired by the diagnostic system will be served as a device for diagnosing the status of the body organ as well as the mesenchymal tissue through inductive reasoning based on artificial intelligence. In this paper, we will limit the system to acquire and manage bio-signals.

• The Journal of the Korean dental association
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• v.54 no.9
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• pp.684-691
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• 2016

The odontogenic cutaneous fistula in facial area is uncommon but, well defined disease. It is difficult to diagnose from the dental origin of cutaneous sinus tract. Most patients may visit to the dermatologists or general hospital without cause of disease. They usually be treated by repeated surgical excisions, biopsies, and antibiotic medications, but suffered from recurrences. We studied odontogenic cutaneous fistula through retrospective study in Jeju Special Self-Governing Province between 1 January 2009 and 12 December 2015. There were 3 males, 5 females from 14 to 78 years old with an average age of 50.4 years old. Only 2 patients felt the toothache, others didn't detect it. They suffered from recurrences and repeated treatments for 3 to 11 months with an average period of 7.9 months. They visited average 2.8 hospitals before a precise diagnosis in a dental clinic. All cases were fully healed endodontic treatment or extraction of origin teeth without recurrences. In conclusion, the cause of cutaneous fistula in facial area can be odontogenic. If dentists or doctors diagnose a patient with cutaneous fistula on face, they should check dental problems or take x-ray views for precise diagnosis. It could be helpful for differential diagnosis.