• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.535-541
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• 2005

It has been suggested that the endoscopic color of intramucosal gastric carcinoma is correlated with mucosal vascularity within the carcinomatous tissue. The development of electronic endoscopy has made it possible to quantitatively measure the mucosal hemoglobin volume, using a hemoglobin index. The aim of this study was to make a software program to calculate the hemoglobin index (IHb) and then investigate whether the mucosal IHb determined from the electronic endoscopic data is a useful marker for evaluating the color of intramucosal gastric carcinoma, in particular with regard to its value for discriminating between the histologic type. The mean values of IHb for the carcinoma (IHb-C) and the mean values of IHb for the surrounding non-cancerous mucosa ( IHb-N) were calculated in 75 intestinal-type and 34 diffuse-type gastric carcinomas. Then, we analyzed the ratio of the IHb-C to IHb-N. The mean IHb-C/IHb-N ratio in the intestinal-type carcinoma group was higher than that in the diffuse-type carcinoma group ($1.28{\pm}0.19$ vs. $0.81{\pm}0.18$, respectively, p<0.001). When the cut-off point of the C/N ratio was set at 1.00, the accuracy rate, the sensitivity, the specificity, and the positive and negative predictive values of a C/N ratio below 1.00 for the differential diagnosis of diffuse-type carcinoma from intestinal-type carcinoma were $94.5\%$, $94.1\%$, $94.7\%$, $88.9\%$ and $97.3\%$, respectively. IHb is useful for quantitative measurement of the endoscopic color in intramucosal gastric carcinoma and the IHb-C/IHb-N ratio would be helpful in distinguishing diffuse-type carcinoma from intestinal -type carcinoma.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.226-230
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• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.49-55
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• 2012

Asynchronous counters, where the counter value is changed not by a synchronizing clock but by outer inputs, are used in various modern digital systems such as spaceborne electronics. In this paper, we propose a scheme of fault tolerance for asynchronous counters with critical races caused by total ionizing dose (TID) in space. As a typical design flaw of asynchronous digital circuits, critical races cause an asynchronous circuit to show non-deterministic behavior, i.e., the next stable state of a state transition is not a fixed value but may be any value of a state set. Using the corrective control scheme for asynchronous sequential machines, this paper provides an existence condition and design procedure for a state feedback controller that can invalidate the effect of critical races. We implement the proposed control system in VHDL code and conduct experiments to demonstrate that the proposed control system can overcome critical races.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.244-251
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• 2014

In this study, we give a detailed account of normal and abnormal state of GIS(Gas Insulated Switch-gear) using the preventive diagnostic technology. And it is based on the analysis and diagnosis for storing data of GIS by intelligent GIS monitoring system. The wave shape of GIS sound is similar to noise and is systematically generated by discharge and its corona sound. Therefore, in this paper, to classify normal and abnormal GIS sound. We could discriminate between normal and abnormal case using level crossing rate(LCR) and spectrogram energy rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.227-237
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• 2003

Conventional studies of Computer Assisted Instruction(CAI) and Intelligent Tutoring System(ITS) have been general patterns to solve problems, so to solve specialized problems, the learner which has the attitude of passiveness should to solve problems including unnecessary processing to the need of the system. Consequently, those are not support the process of creativity and individual problems for the learner to solve the fractional number operations as this study. This study is the design and implementation of ITS on the fractional number addition and subtraction for the supplementary student. Our system can diagnosis mistakes of learning and guide the student to know their errors of learning process automatically And our system assist the learners to study with self-initiative learning, replacement their lacking of learning and control the process of fractional addition and subtraction operation with creativity according to their level. We showed that this system had improved problems of lacking care to supplementary student result in are not enough teachers involved their school and that the learner had achieved the higher learning effect according to the improved self-initiative learning causing this system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.65-72
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• 2010

For developing navigation system of total hip arthroplasty (THA) and evaluating hip joint kinematics, 3-D pose position of the femur and acetabulum in the pelvic coordinate system has been quantified. The pelvic coordinate system is determined by manually indicating pelvic landmarks in multidetector-row computed tomography (MDCT) images. It includes intra- and inter-observer variability, and may result in a variability of THA operation or diagnosis. To reduce the variability of pelvic coordinate system determination, this paper proposes an automated method in MDCT images. The proposed method determines pelvic coordinate system automatically by detecting pelvic landmarks on anterior pelvic plane (APP) from MDCT images. The method calibrates pelvic pose by using silhouette images to suppress the affect of pelvic pose change. As a result of comparing with manual determination, the proposed method determined the coordinate system with a mean displacement of $2.6\;{\pm}\;1.6$ mm and a mean angle error of $0.78\;{\pm}\;0.34$ deg on 5 THA subjects. For changes of pelvic pose position within 10 deg, standard deviation of displacement was 3.7 mm, and of pose was 1.28 deg. We confirmed the proposed method was robust for pelvic pose changes.