• 제어로봇시스템학회논문지
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• 제8권8호
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• pp.691-697
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• 2002

Myocardial ischemia is a disorder of cardiac function caused by insuficient blood flow to the muscle tissue of the heart. We can diagnose myocardial ischemia by observing the change of ST-segment, but this change is temporary. Our primary purpose is to detect the temporary change of the 57-segment automatically In the signal processing, the wavelet transform decomposes the ECG(electrocardiogram) signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily. Amplitude comparison method is adopted to detect QRS complex. Reducing the effect of noise to the minimum, we grouped ECG by 5 data and compared the amplitude of maximum value. To recognize the ECG .signal pattern, we adopted the polynomial approximation partially and statistical method. The polynomial approximation makes possible to compare some ECG signal with different frequency and sampling period. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. After removing the distorted ECG by calculating the difference between the orignal ECG and the approximated ECG for polynomial, we compared the approximated ECG pattern with the database, and we detected and classified abnormality of ECG.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• 대한전기학회논문지:전력기술부문A
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• 제54권5호
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• pp.217-225
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• 2005

In a power system, an out-of-step condition causes a variety of risk such as serious damage to system elements, tripping of loads and generators, mal-operation of relays, etc. Therefore, it is very important to detect the out-of-step condition and take a proper measure. Several out-of-step detection methods have been employed in relays until now. Most common method used for an out-of-step detection is based on the transition time through the blocking impedance area in R-X diagram. Also, the R-R dot out-of-step relay, the out-of-step prediction method and the adaptive out-of-step relay using the equal area criterion (EAC) and Global Positioning Satellite (GPS) technology have been developed. This paper presents the out-of-step detection algorithm using the time variation of the complex power. The complex power is calculated and the mechanical power of the generator is estimated by using the electrical power, and then the out-of-step detection algorithm which is based on the complex power and the estimated mechanical power, is presented. This algorithm may detect the instant when the generator angle passes the Unstable Equilibrium Point (UEP). The proposed out-of-step algorithm is verified and tested by using Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP) MODELS.

• 한국신뢰성학회지:신뢰성응용연구
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• 제12권2호
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• pp.105-119
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• 2012

In this paper, we have proposed a methodology which can make effective test for system integration of complex guided missile system. System integration test play a significant role in the development of weapon system, providing the means to detect and isolate faults on first linkage between sub-systems. Integration tests for domestic weapon system has executed not a technology-intensive method based on tool but labor-intensive method based on experience. Higher cost, longer period, and more resource are required to execute system integration test for complex guided missile system comparing with past weapon systems, because recently weapon systems have more complex and more networked functions. Because the proposed design method for system integration test decreases number of test case, it lead to a decrease of cost, period, and resource for integration test of weapon system. The proposed configuration for system integration test will ensure reliability through detection and isolation of fault on linkage between sub-systems.

• 한국해양공학회지
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• 제27권3호
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• pp.8-14
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• 2013

Although there are various factors that threaten the security of ships, one of the most harmful is corrosion. It is not easy to find corroding areas and the status of corrosion, even though corrosion causes serious problems such as submergence and marine pollution as a result of leaking oil and polluted water. To monitor the corrosion of ships, non-destructive inspection, weight loss coupons, electrical resistance, linear polarization resistance, zero resistance ammeter, and electrochemical impedance spectroscopy have been developed. However, these methods require much time to detect corrosion, and most are not appropriate for real time monitoring. Coating, sacrificial anode, and impressed current cathodic protection (ICCP) methods have been developed to control corrosion. The ICCP and sacrificial anode methods are the most popular ways to prevent ship corrosion. However, ICCP is only appropriate for the outside of a ship and cannot be used for complex structures such as ballast tanks because these are composed of many separate chambers. Sacrificial anodes have to be replaced periodically. This paper proposes an integrated corrosion monitoring and control system (ICMCS) that can detect corrosion in real time and is appropriate for complex structures such as ballast tanks. Because the system uses titanium for an anode, exhausted anodes do not need to be replaced.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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• pp.113-116
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• 2000

In this paper presents the portable wireless ECG data detection and diagnosis system based on discreet wavelet transform. An algorithm based on wavelet transform suitable for real time implementation has been developed in order to detect ECG characteristics. In particular, QRS complex, S and T waves may be distinguished form noise, baseline drift or artifacts. Proposed telemetry system that a transmitting media using radio frequency(RF) for the middle range measurement of the physiological signals and receiving media using optical for electromagnetic interference problem. A standard hi-directional serial communication interface between the telemetry system and a personal computer or laptop, allows read-time controlling, diagnosing and monitoring of system. A portable telemetry system within a size. of 65${\times}$125${\times}$45mm consists of three parts: a digital signal processing part for physiological signal detect or diagnose, RF transmitter for data transfer and a optical receiver for command receive. Advantages of proposed telemetry system is wireless middle range(50m) FM transmission, reduce electromagnetic interference to a minimum. which enables a comfortable diagnosis system at home.

• 한국기계가공학회지
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• 제18권12호
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.313-315
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• 2005

In a power system, an out-of-step condition causes a variety of risk such as serious damage to system elements, tripping of loads and generators, mal-operation of relays, etc. Therefore, it is very important to detect the out-of-step condition and take a proper measure. Several out-of-step detection methods have been employed in relays until now Mo,;t common method used for an out-of-step detection is based on the transition time through the blocking impedance area in R-X diagram. Also, the R-R dot out-of- step relay, the out-of-step prediction method and the adaptive out-of-step relay using the equal area criterion (EAC) and Global Positioning Satellite (GPS) technology have been developed. This paper presents the out-of-step detection algorithm using the time variation of the complex power. The complex power is calculated and the mechanical power of the generator is estimated by using the electrical power, and then the out-of-step detection algorithm, which is based on the complex Power and the estimated mechanical power, is presented. This algorithm, may detect the instant when the generator angle passes the Unstable Equilibrium Point (UEP). The proposed out-of-step algorithm is verified and tested by using Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP) MODELS.

• 대한의용생체공학회:의공학회지
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• 제18권3호
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• pp.315-323
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• 1997

Driving is a complex vigilance task that includes improper lookout, excessive speed and inattention. The primary objective of this research is to detect driver drowsiness so that the driver can be alerted to an impending traffic accident in performance. We developed the automatic detection and warning system during drowsy driving. A drowsiness detection system must be able to monitor driver status and detect the detrimental changes of a driver performance. Eyeblink has been found to be a reliable factor of drowsiness detection in earlier studies. As an additional parameter, we also considered the yawning which often occurs in a low vigilance state and predicts the drowsy state. We used a computer vision method to extract the eyeblink and yawning in the face image sequences. When the drowsy state was detected, the driver was refreshed by alarming device and menthol scent generator after deciding the warning level by fuzzy logic. For the evaluation of our system, we measured the physiological parameters such as EOG and EEG. The results indicated that it is possible to detect and alert the driver drowsiness temporarily or continuously by using our system.