• Journal of Communications and Networks
• /
• v.18 no.2
• /
• pp.210-217
• /
• 2016

In this paper, we propose a low complexity multiple-input multiple-output (MIMO) detection algorithm with adaptive interference mitigation in downlink multiuser MIMO (DL MU-MIMO) systems with quantization error of the channel state information (CSI) feedback. In DL MU-MIMO systems using the imperfect precoding matrix caused by quantization error of the CSI feedback, the station receives the desired signal as well as the residual interference signal. Therefore, a complexMIMO detection algorithm with interference mitigation is required for mitigating the residual interference. To reduce the computational complexity, we propose a MIMO detection algorithm with adaptive interference mitigation. The proposed algorithm adaptively mitigates the residual interference by using the maximum likelihood detection (MLD) error criterion (MEC). We derive a theoretical MEC by using the MLD error condition and a practical MEC by approximating the theoretical MEC. In conclusion, the proposed algorithm adaptively performs interference mitigation when satisfying the practical MEC. Simulation results show that the proposed algorithm reduces the computational complexity and has the same performance, compared to the generalized sphere decoder, which always performs interference mitigation.

• Journal of the Korean Society of Safety
• /
• v.29 no.5
• /
• pp.47-53
• /
• 2014

Despite the automatic fire alarm system, according to the national fire data system of national emergency management agency, the fires account for 40,932 incidents, 2,184 injuries and about 430 billion won in property losses in 2013. Since the conventional automatic fire alarm system has several weaknesses related to electrical signal such as noise, surge, lighting, etc. Most fires are mainly caused by electrical faults, mechanical problem, chemical, carelessness and natural. The electrical faults such as line to ground fault, line to line fault, electrical leakage and arc are one of the major problems in fire. This paper describes the development of a novel real-time fire monitoring system algorithm including fault detection function which puts the existing optic smoke and heat detectors for fire detection with current and voltage sensors in order to utility fault monitoring using high accuracy DAQ measurement system with LabVIEW program. The fire detection and electrical fault monitoring with a proposed a new detection algorithm are implemented under several test. The fire detection and monitoring system operates according to the proposed algorithm well.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.2080-2085
• /
• 2018

There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.

• Journal of applied mathematics & informatics
• /
• v.40 no.1_2
• /
• pp.47-59
• /
• 2022

This paper concerns the problem of simultaneous fault detection and disturbance reject control(SFDDRC) for a class of linear time-invariant system. In the framework of fault detection, residual generators are required to be robust to disturbances existing in the system. Different from most of the existing simultaneous fault and control(SFDC) methods, SFDDRC rejects the influences of disturbances on residual generators by disturbance observer-based control(DOBC). This not only effectively improves the accuracy of fault detection, but also solves the problem that most of the existing SFDC methods require that the disturbance must be bounded. Finally, a numerical example is given to verify the validity of the method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.4
• /
• pp.682-691
• /
• 2017

In this paper, we propose the fusion design methodology of both pedestrian detection and object tracking system realized with the aid of HOG-PCA based RBFNN pattern classifier. The proposed system includes detection and tracking parts. In the detection part, HOG features are extracted from input images for pedestrian detection. Dimension reduction is also dealt with in order to improve detection performance as well as processing speed by using PCA which is known as a typical dimension reduction method. The reduced features can be used as the input of the FCM-based RBFNNs pattern classifier to carry out the pedestrian detection. FCM-based RBFNNs pattern classifier consists of condition, conclusion, and inference parts. FCM clustering algorithm is used as the activation function of hidden layer. In the conclusion part of network, polynomial functions such as constant, linear, quadratic and modified quadratic are regarded as connection weights and their coefficients of polynomial function are estimated by LSE-based learning. In the tracking part, object tracking algorithms such as mean shift(MS) and cam shift(CS) leads to trace one of the pedestrian candidates nominated in the detection part. Finally, INRIA person database is used in order to evaluate the performance of the pedestrian detection of the proposed system while MIT pedestrian video as well as indoor and outdoor videos obtained from IC&CI laboratory in Suwon University are exploited to evaluate the performance of tracking.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.1
• /
• pp.100-106
• /
• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.5
• /
• pp.612-619
• /
• 2013

This paper proposes an early diagnosis technique for the stator-turn fault (STF) in an interior permanent magnet (IPM)-type brushless DC (BLDC) motor using the impedance parameter. We have analyzed the varying characteristics owing to the STF through various experiments and the finite element method (FEM). As a result, we have presented a simple method for fault detection. This technique can be applied without requiring a fast Fourier transform (FFT) and the calculation of the negative-sequence impedance. The fault detection system works on the basis of the comparison the measured impedance with the database impedance. The variations in the characteristics owing to the STF as well as the proposed technique have been verified through the simulation and experiment.

• Journal of international Conference on Electrical Machines and Systems
• /
• v.2 no.1
• /
• pp.23-30
• /
• 2013

This paper proposes a parametric model for inter-turn fault detection in a fault-tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short-circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1541-1542
• /
• 2008

In this paper, we introduce a live wire power transmission line fault detection system using time-frequency domain reflectometry(TFDR). The TFDR is known that is more precise method than the other conventional ones. However, the TFDR is generally adopted only in fault detection for communication cable, and dead line power transmission line. Therefore, this paper suggests a TFDR system with coupler which separates 220V, 60Hz signal and TFDR reference signal for implementation the live wire fault detection system. This approach is verified by circuit simulation.

• Proceedings of the KIEE Conference
• /
• 1999.07e
• /
• pp.2284-2286
• /
• 1999

This paper describes the development of potential detection system in 22.9kV gas insulated switchgear. This system composed with main system and LPS can detect the source voltage by capacitive potential division which is accomplished by inserting signal electrode between main electrode and earthed metal enclosure of the switchgear. The appropriate position of signal electrode is achieved by numerical analysis using finite element method. The developed potential detection system is verified by several tests such as voltage test, swc test and others and by applicaion at site. From the results, it is concluded that potential detection system is very reliable and available to operate the switchgear safely.