• Proceedings of the IEEK Conference
• /
• 2003.07c
• /
• pp.2775-2778
• /
• 2003

Neural network-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.127.1-127
• /
• 2001

In this paper we propose an input/output model based fault diagnosis method to detect and isolate single faults in the robot arm control system. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation, When a change in the system occurs, the errors between the system output and the estimated output cross a predetermined threshold, and once a fault in the system is detected, and in this zone the estimated parameters are transferred to the fault classifier by ART2(adaptive resonance theory 2) neural network for fault isolation. Since ART2 neural network is an unsupervised neural network fault classifier does not require the knowledge of all possible faults to isolate the faults occurred in the system. Simulations are carried out to evaluate the performance of the proposed ...

• Proceedings of the KSME Conference
• /
• 2001.11b
• /
• pp.89-95
• /
• 2001

On-site diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for the purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.12 no.sup1
• /
• pp.77-84
• /
• 2009

Pediatric dysphagia comes from disturbances in swallowing process, which has 'preparatory phase', 'oral phase', 'pharyngeal phase', and 'esophageal phase', and mainly the causes are neuro-muscular discoor-dination. It is necessary to recognize clinical manifestation if they have accompanied organic disorder and diagnose accurately. Videofluoroscopic study evaluation is a valuable method to find out abnormal swallowing mechanism at each phases. Treatment should be diagnosis specific, and multidisciplinary team approach is desirable. We can use various behavioral techniques to facilitate normal swallowing mechanism including conditioning of oral and pharyngeal structures, bolus manipulation, postural compensation, and adaptive feeding utensils. Important point is that the diagnosis and treatment for pediatric dysphagia should not be delayed because children are under development.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.319-322
• /
• 1997

This paper presents a CAD (Computer-Aided Diagnosis) system or detection of breast cancer, which is composed of personal computer, X-ray film scanner, high resolution display and application softwares. There are three major algorithms implemented in the application software. The irst algorithm is the adaptive enhancement of the digitized X-ray mammograms based on the first derivative and the local statistics. The second one is to detect the clustered microcalcifications by using the statistical texture analysis, and the third one is the classification of the clustered microcalcifications as malignant or benign by using the shape analysis. These algorithms were verified by real experiments.

• Journal of Biomedical Engineering Research
• /
• v.12 no.4
• /
• pp.277-284
• /
• 1991

This study describes the ambulatory ECG monitoring system for the remote autom atic diagnosis. System: tlardware is based on one chip microcomputer(80c31) and its peripherals which consists of A/D, EPROM, RAM, LCD display and two preamplifiers, Power circuits, control logic circuits. A/D converted data were differentiated and low pass filtered. The detection of QRS complex and R point were accomplished by software algorithm based on adaptive threshold computed on low pass fi:leered signal. Rhythm analysis is performed by RR interval and average RR interval. The performance of QRS detection algorithm is evaluated by using MIT/BIH data base. Using this system, the trends of the arrythmia during the long term could be saved and displayed.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2016.01a
• /
• pp.19-20
• /
• 2016

본 논문에서는 서포트 벡터 머신 (SVM)의 분류 성능에 영향을 주는 인수인 C와 ${\sigma}$ 값을 적응적으로 최적화할 수 있는 적응적 자가튜닝 SVM을 이용한 베어링의 상태 진단 방법을 제안한다. SVM의 각 인수의 변화에 따른 베어링 상태 진단의 성능 변화 패턴을 분석하여 적합한 인수를 적응적으로 찾을 수 있는 방법을 제안하고, 제안한 방법의 우수성을 검증하기 위해 실제 베어링 신호를 이용하여 기존방법인 격자탐색과의 성능을 비교하였다.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.303-326
• /
• 2023

Mathematics is a discipline with a strong systemic structure, and learning deficits in previous stages have a great influence on the next stages of learning. Therefore, it is necessary to frequently check whether students have learned well and to provide immediate feedback, and for this purpose, intelligent tutoring system(ITS) can be used in math education. For this reason, it is necessary to reveal how the intelligent tutoring system is effective in personalized adaptive learning. The purpose of this study is to investigate the functions and applications of intelligent tutoring system for personalized adaptive learning in mathematics. To achieve this goal, literature reviews and surveys with students were applied to derive implications. Based on the literature reviews, the functions of intelligent tutoring system for personalized adaptive learning were derived. They can be broadly divided into diagnosis and evaluation, analysis and prediction, and feedback and content delivery. The learning and lesson plans were designed by them and it was applied to fifth graders in elementary school for about three months. As a result of this study, intelligent tutoring system was mostly supporting personalized adaptive learning in mathematics in several ways. Also, the researcher suggested that more sophisticated materials and technologies should be developed for effective personalized adaptive learning in mathematics by using intelligent tutoring system.

• Journal of Korea Multimedia Society
• /
• v.13 no.7
• /
• pp.973-982
• /
• 2010

In this paper, we proposed the single-channel adaptive noise canceller for the enhancement of heart sound (HS) in the auscultation signal. In case of either normal or emergency, a HS diagnosis is difficult due to the various signal source in the chest. Therefore, the HS enhancement is necessary. The conventional active noise canceller(ANC) has two channel, main signal and reference signal. For signal channel, the reference signal in ANC was generated by the proposed HS analyser and BS-Gate based on the characteristic of HS. This reference signal is suitable to the ANC condition. Experimental data were acquisited from MP36, SS30L in BIOPAC Inc., By the experiment, we confirmed that the proposed single-channel ANC was efficient for HS enhancement. And by the comparison with active linear enhancement, it was validate that the proposed ANC is not affected by the variation of a heartbeat.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11B
• /
• pp.1289-1295
• /
• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.