• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.661-667
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• 2010

This paper presents a failure diagnosis and accommodation strategy which is capable of tolerating faulty actuators of a submersible vehicle. The proposed method is mainly based on a sliding mode control technique. The primary ideas include a performance index to describe the effectiveness of actuators, and a controller reconfiguration strategy using the actuator effectiveness index. The actuator effectiveness proposed in this work is defined as the relationship between the sliding surface and the controlled system behavior. The resulting actuator effectiveness is then used in reconfiguring the controller in order to counteract for the deteriorated control performance in the presence of a faulty actuator. The effectiveness of the proposed method is demonstrated by means of numerical simulations with a submersible vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• The Journal of Pediatrics of Korean Medicine
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• v.20 no.2
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• pp.147-175
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• 2006

Objective : This study is to investigate the method for assesment and diagnosis of ADHD, especially focusing on behavior rating scales. Methods : We searched the recent date of the publication and paper in ADHD. Results : For Assesment and Diagnosis of ADHD, various method such as interview with parents, child and teacher, behavior observation, behavior rating scales and neuropsychological test are used. The structured interview consists of the restrictive questions and response, and then have diagnostic algorithm, consequently can be used by untrained clinicians. Of the structured interview, standardization of K-SADS in Korean version is finished. Behavior rating scales, the form of parent, teacher and self-report questionnaires, are used as diagnosis and treatment evaluation of ADHD. Behavior rating scales consist of both ADHD-specific scales and broad-band scales designed to screen for various symptoms (including ADHD symptoms). ADHD-specific scales are useful in differential diagnosis, discrimination of subtype, treatment evaluation, However, broad-band scales are useful in preliminary examination. The neuropsychological tests can evaluate attention deficit and effect of attention deficit on cognitive function and academic performance. The neuropsychological tests also used in diagnosis and treatment evaluation of ADHD. Conclusion : For Assesment and Diagnosis of ADHD, various method are used, especially behavior rating scales are both useful and simple tool for diagnosis and treatment evaluation.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.152-158
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• 2004

The passive filter is economic and efficient in suppressing harmonics but it may cause resonance problems and its performance is constantly dependent on power system impedance or working conditions of loads. This paper presents the DSP (Digital Signal Processor)-based control system, which automatically controls the passive filter in order to solve these problems. The control system can automatically control the passive filter according to working conditions of loads and measured harmonics, reactive power, power factor and so on. Experimental results in the power system using the 100HP DC motor drive are presented in order to verify the performance of the control system.

• Transactions of The Korea Fluid Power Systems Society
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• v.3 no.1
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• pp.7-14
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• 2006

In this paper, an in-process diagnosis method for performance of position control servo system was studied, which was based upon null bias, slew-rate ratio and delay time measurement. Slew-rate ratio and delay time were analyzed by theoretical analysis, computer simulation and experiment. As a result of these analysis, when spool of servovalve was weared, slew-rate ratio was decreased and delay time was increased.

• The Journal of Korean Medicine
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• v.41 no.3
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• pp.1-8
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• 2020

Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

• Journal of the Korean Society for Railway
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• v.9 no.5 s.36
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• pp.612-617
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• 2006

As the on-board electric devices determine the performances of vehicles, production of reliable devices is important. To keep the reliability of devices constant, management of performance evaluation of the on-board devices is crucial. Because temperature has a serious effect on failures of the components of the devices, its measurement is the first step for good management. In this study, we described performance characteristics of domestic auxiliary block developed through G7 project. We measured the performances of auxiliary block during test running by the developed on-line measurement system. After we save the input real-time data from each signal of Korean High Speed Train through the network line, we can acquire necessary information through post-processing program. We verify the motor block characteristics of Korean High Speed Train by this system.

• Tuberculosis and Respiratory Diseases
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• v.82 no.4
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• pp.319-327
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• 2019

Background: Radial probe endobronchial ultrasound (R-EBUS), is effective for tissue diagnosis of lung lesions. We evaluated the diagnostic performance of R-EBUS both a guide-sheath and fluoroscopy and identified factors associated with accurate diagnosis. The feasibility of molecular and genetic testing, using specimens obtained by R-EBUS, was also investigated. Methods: The study retrospectively reviewed 211 patients undergoing R-EBUS without a guide-sheath and fluoroscopy, June 2016-May 2017. After excluding 27 patients of which the target lesion was not reached, 184 were finally included. Multivariate logistic regression was used, to identify factors associated with accurate diagnosis. Results: Among 184 patients, R-EBUS-guided biopsy diagnosed malignancy in 109 patients (59%). The remaining 75 patients (41%) with non-malignant results underwent additional work-ups, and 34 were diagnosed with malignancy. Based on final diagnosis, diagnostic accuracy was 80% (136/170), and sensitivity and specificity for malignancy were 76% (109/143) and 100% (27/27), respectively. In multivariate analysis, peripheral location (adjusted odds ratio [aOR], 3.925; 95% confidence interval [CI], 1.203-12.811; p=0.023), and central position of the probe (aOR, 2.435; 95% CI, 1.424-7.013; p=0.035), were associated with accurate diagnosis of malignancy. Molecular and genetic analyses were successful, in all but one case, with inadequate specimens. Conclusion: R-EBUS-guided biopsy without equipment, is effective for tissue diagnosis. Peripheral location and central position of the radial probe, were crucial for accurate diagnosis. Performance of molecular and genetic testing, using samples obtained by R-EBUS, was satisfactory.