• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.97-103
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• 2018

In this paper, the estimation method for the power signal harmonics is proposed by using the time-varying optimal finite impulse response (FIR) filter. To estimate the magnitude and phase-angle of the harmonic components, the time-varying optimal FIR filter is designed for the state space representation of the noisy power signal which the magnitude and phase is considered as a stochastic process. Since the time-varying optimal FIR filter used in the proposed method does not use any priori information of the initial condition and has FIR structure, the proposed method could overcome the demerits of Kalman filter based method such as poor estimation and divergence problem. Due to the FIR structure, the proposed method is more robust against to the model uncertainty than the Kalman filter. Moreover, the proposed method gives more general solution than the time-invariant optimal FIR filter based harmonic estimation method. To verify the performance and robustness of the proposed method, the proposed method is compared with time-varying Kalman filter based method through simulation.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.401-407
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• 2009

In this paper, we propose a novel method for the cross-correlation based double-talk detection (DTD), which employing the Gaussian Mixture Model (GMM) in the frequency domain. The proposed algorithm transforms the cross correlation coefficient used in the time domain into 16 channels in the frequency domain using the discrete fourier transform (DFT). The channels are then selected into seven feature vectors for GMM and we identify three different regions such as far-end, double-talk and near-end speech using the likelihood comparison based on those feature vectors. The presented DTD algorithm detects efficiently the double-talk regions without Voice Activity Detector which has been used in conventional cross correlation based double-talk detection. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional schemes. especially, show the robustness against detection errors resulting from the background noises or echo path change which one of the key issues in practical DTD.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.3
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• pp.162-173
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• 1997

Automatic on-line surface inspection systems have been applied for monitoring a quality of steel strip surfaces. One of the important issues in this application is the performance of on-line defect classifiers. Rule-based classification table methods which are conventionally used for this purpose have been suffered from their low performances. In this work, probabilistic neural networks and the enhanced classification tables which are newly proposed here are applied as alternative on-line classifiers to identify types of surface defects on cold rolled strips. Probabilistic neural networks have shown very excellent performance for classification of surface defects.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.31-37
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• 2021

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2005-2006
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• 2011

Due to the advantages of small sizes, more I/O ports, etc., Ball Grid Array (BGA) has been used in the production of printed circuit board (PCB). However, BGA voids can degrade the performance of the board and cause failure. To automatically detect the voids in X-ray image, a novel blob filter that makes use of the local image gradient magnitude is proposed in this paper. The utilization of the local image gradient magnitude makes the proposed filter invariant to the image brightness, void shape, void position, and component interference. Furthermore, different sizes of box filters are employed to analyze the image in multi-scale, and as a result, the proposed blob filter is robust to void size. Experimental results show that the proposed method can obtain void detection accuracy up to 96.104% while keep low false ratio.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.397-402
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• 2007

In this paper, we apply a support vector machine(SVM) that incorporates an optimized nonlinear decision rule over different sets of feature vectors to improve the performance of statistical model-based voice activity detection(VAD). Conventional method performs VAD through setting up statistical models for each case of speech absence and presence assumption and comparing the geometric mean of the likelihood ratio (LR) for the individual frequency band extracted from input signal with the given threshold. We propose a novel VAD technique based on SVM by treating the LRs computed in each frequency bin as the elements of feature vector to minimize classification error probability instead of the conventional decision rule using geometric mean. As a result of experiments, the performance of SVM-based VAD using the proposed feature has shown better results compared with those of reported VADs in various noise environments.

• The Journal of the Acoustical Society of Korea
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• v.26 no.1
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• pp.16-24
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• 2007

Voice activity detectors (VADs) are important in wireless communication and speech signal processing. In the conventional VAD methods. an expression for the likelihood ratio test (LRT) based on statistical models is derived. Then, speech or noise is decided by comparing the value of the expression with a threshold. We propose a new method with the modified decision rule based on the Gaussian distribution and the uniformly most power (UMP) test. This method requires the distribution of the absolute value of the incoming speech signal. Then we can obtain the final decision through the relation between the Rayleigh distributions. This VAD method can detect speech without a priori signal-to-noise ratio (SNR) which is required in the conventional VAD algorithms. Additionally, in the various VAD performance tests, the proposed VAD method is shown to be more effective than the traditional scheme.

• Journal of Advanced Navigation Technology
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• v.6 no.2
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• pp.138-150
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• 2002

This paper addresses the problem of combining adaptive polarization processing and space-time processing for further performance improvement of radar target detection in clutter and Jammer environments. Since the most straightforward cascade combinations have quite limited performance improvement potentials, we focus on the development of adaptive processing in the joint polarization-space-time domain. Unlike a direct extension of some existing space-time processing algorithms to the joint domain, the processing algorithm developed in this paper does not need a potentially costly polarization filter bank to cover the unknown target polarization parameter. The performance of the new algorithm is derived and evaluated in terms of the probability of detection and the probability of false alarm, and it is compared with other algorithms that do not utilize the polarization information or assume that the target polarization is known.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1115-1123
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• 2011

This paper proposes an image processing algorithms for detection and tracking of aerial vehicles in short-range. Proposed algorithm detects moving objects by using image homography calculated from consecutive video frames and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi-Hypothesis Tracking method(PMHT). This algorithm can perform better than simple color-based detection methods since it can detect moving objects under complex background such as the ground seen during low altitude flight and consider the characteristics of vehicle dynamics. Furthermore, it is effective for the flight test due to the reduction of thresholding sensitivity against external factors. The performance of proposed algorithm is verified by applying to the onboard video obtained by flight test.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.31-38
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• 2006

In this paper, we consider one-step-ahead control of waveform parameters (pulse amplitudes and lengths, and FM sweep rate) as well as detection thresholds for optimal range and range-rate tracking in clutter. The optimal control of the combined parameter set minimizes a tracking performance index under a set of parameter constraints. The performance index includes the probability of track loss and a function of estimation error covariances. The track loss probability and the error covariance are predicted using a hybrid conditional average algorithm The effect of the false alarms and clutter interference is taken into account in the prediction. Tracking performance of the one-step-ahead control is presented for several examples and compared with a control strategy heuristically derived from a finite horizon optimization.