• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.6
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• pp.58-67
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• 2003

Many adult diseases(cerebral apoplexy, athymiait, etc.) result from hypertension, blood circulation disturbance and increment of HBP. In early diagnosis of these diseases, MRI, X-ray and PET have been used rather aim for treatment than prevention of a disease. Since, cerebral apoplexy and athymiait have been caused to the regular/irregular persons, it is very important to measure HBP which has connection with cerebral blood low state. HBP has more diagnosis elements than that of BP. So, we can diagnose accurate hypertension by measuring of HBP. But, existing sphygmomanometers and automatic BP monitors can not measure HBF, and can not execute complex function(measuring of BP/HBP, blood flow improvement). The purpose of this paper is to develop the system and algorithm which can measure BP/HBP for accurate diagnosis. Also, we extracted diagnosis factors by the correlativity analysis of BP/HBP. The maximum pressure of HBP corresponds to 62% that of BP, the minimum pressure of HBP corresponds to 46% that of BP. Therefore, we developed the multi function automatic blood pressure monitor which can measure BP/HBP and improve cerebral blood flow state.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.3
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• pp.252-272
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• 2017

Fuel cell systems have to satisfy acceptable operating reliability, sufficient lifetime and price to enter the market in competition with existing products. Fuel cells are made up of complex element technologies and various problems related to the failure of the components can affect the reliability and safety of the system. This problem can be overcome by introducing a monitoring and supervisory control system in addition to automatic control to detect the failure of the fuel cell quickly and properly diagnose the performance degradation. For the fault detection and diagnosis of polymer electrolyte fuel cells, the model based method using the theoretical superposition value and the non-model based method of checking the signal tendency or the converted signal characteristic can be applied. The methods analyzed in this paper can contribute to the development of integrated monitoring and control technology for the whole system as well as the stack.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.457-466
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• 2021

This paper describes the design of model-based fault diagnosis software to apply to the propulsion system in tracked amphibious assault vehicle which consists of an engine, a transmission, a cooling system, and two waterjets. This software includes specific functions to detect the failures regarding sensor malfunctions, mechanical malfunctions, control errors, and communication errors. This software generates the proper malfunction codes which are classified as the warning and caution. In order to validate the fault diagnosis software, the manual and automatic test are performed using the test program with 32 test cases. Test results show that the designed fault diagnosis software is reliable and effective for applying to the propulsion system.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1217-1226
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• 1989

This paper describes a design of hardware system for real time automatic diagnosis of ECG arrhythmia based on pipeline processor consisting of three microcomputer. ECG data is acquisited by 12 bit A/D converter with hardware QRS triggered detector. Four diagnostic parameters-heart rate, morpholigy, axis, and ST segment-are used for the classification and the diagnosis of arrhythmia. The functions of the main CPU were distributed and processed with three microcomputers. Therefore the effective data process and the real time process using microcomputer can be obtained. The interconnection structure consisting of two common memory unit is designed to decrease the delay time caused by data transfer between processors and be which the delay time can be taken 1% of one clock period.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.89-96
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• 2020

Solar lamp systems may not operate normally in the event of some system or controller failure due to internal or external factors, in which case secondary problems occur, which may cost the system recovery. Thus, when these errors occur, a technology is needed to recover to the state it was in before the failure occurred and to enable re-execution. This paper designs and implements a system that can recover the state of the system to the state prior to the time of the error by using the Watchdog Timer within the controller if a software error has occurred inside the system, and it also proposes a technology to reset and re-execution the system through a separate reset circuit in the event of hardware failure. The proposed system provides stable operation, maintenance cost reduction and reliability of the solar lamp system by enabling the system to operate semi-permanently without external support by utilizing the automatic recovery and automatic reset function for errors that occur in the operation of the solar lamp system. In addition, it can be applied to maintain the system's constancy by utilizing the self-operation, diagnosis and recovery functions required in various high reliability applications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.350-354
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• 1995

The Tension Leveller of Cold Rolling Mill In POSCO performs levelling the strip in high speed line. But minor variations in operating condition of driving machines such as motor, gear box, and support bearings, a small gap-variation of supporter and strip slip by poor roll revolutions can cause serious problems in the quality of strip. In this study, firstly, A condition monitoring standard for each sensor is made through with the detail analysis of vibration and strip slip. Secondly, An automatic monitoring and diagnosing system was developed to monitor the condition of Tension Leveller, and diagnose the cause of abnormal condition. Finally, A diagnosing algorithm for abnormal condition and man-machine interface (MMI) for easy operation are developed.

• Journal of Sasang Constitutional Medicine
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• v.17 no.2
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• pp.46-51
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• 2005

1. Objectives The purpose of this study is to objectify the diagnosis of Sasang Constitution. Using the methods of this study, it will improve to classificate Sasang Constitution. 2. Methods 1) Automatic feature extraction of human frontal faces for Sasang Constitution classification. 2) Color feature extraction of human frontal faces (1)Erosion filtering (skin-white, the other-black) (2) Median median 3. Results and Conclusions Observing a person's shape has been the major method for Sasang Constitution classification, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for Sasang Constitution classification. For this, in this paper, firstly, the signal processing techniques are applied to automatic feature extraction of human frontal faces for Sasang Constitution classification. The experiment is conducted to verify the effectiveness of the proposed system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 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.