• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.162-167
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• 2012

In power quality monitoring, it is very important to detect disturbances (sag, swell, transient, and interruption) accurately. In this paper, a detection method for power quality disturbances by combining the filter bank system and adaptive filter is proposed. To decompose power signal, binary tree structured filter bank system is designed. In the filter bank system, the fundamental filter bank(QMF bank) is used as a module in each decomposing level. An adaptive filter is used to improve the detection accuracy of disturbances for each subband signal. In the adaptive filter, the measure of estimated error change is used to detect singular points of power quality disturbances. Computer simulations were performed on synthetic signals which have disturbances to assess the performance of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.276-280
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• 2016

A fixed rate speech coder based on the filter bank and the non-uniform sampling technique is proposed. The non-uniform sampling is achieved by the detection of inflection points (IPs). A speech block is band passed by the filter bank, and the subband signals are processed by the IP detector, and the detected IP patterns are compared with entries of the IP database. For each subband signal, the address of the closest member of the database and the energy of the IP pattern are transmitted through channel. In the receiver, the decoder recovers the subband signals using the received addresses and the energy information, and reconstructs the speech via the filter bank summation. As results, the coder shows fixed data rate contrary to the existing speech coders based on the non-uniform sampling. Through computer simulation, the usefulness of the proposed technique is confirmed. The signal-to-noise ratio (SNR) performance of the proposed method is comparable to that of the uniform sampled pulse code modulation (PCM) below 20 kbps data rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.179-193
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• 2016

In power quality monitoring, voltage fluctuation is one of the power quality problems, which cause light flickers. To determine the flicker severity, the flicker meter concept was developed in an IEC 61000-4-15 standard. Generally, voltage fluctuations are described as an amplitude modulation(AM). The flicker meter of IEC 61000-4-15 as an international standard for flicker measurement recommends square demodulation method to detect flicker signals from voltage fluctuation signals. This paper suggests a new effective method using filter bank to detect and estimate flicker signals, which do not need square demodulation. For the accurate detection of flicker signals, the filter bank is designed with a full consideration of the spectrum characteristics of voltage fluctuation signals described as AM. The frequency and magnitude of the detected flicker signals are estimated using recursive method. Computer simulations were performed on synthesized signals to prove validity of the proposed method.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.511-520
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• 2003

In room acoustics, reverberation time is an important acoustic parameter. However it is often difficult to measure short reverberation times at low frequencies with a traditional band pass filter bank if the product of filter bandwidth (B) and reverberation time (T) is small. It it well known that the minimum permissible product of bandwidth and reverberation time of the traditional band pass filter is at least 16. This strict requirement makes it difficult to measure short reverberation times of an acoustic room at low frequencies exactly. In order to reduce this strict requirement, in the previous paper, the wavelet filter bank was developed and the minimum permissible product of bandwidth and reverberation time was replaced with 4. In the present paper it is demonstrated how the short reverberation times of an practical room at low frequencies are successfully measured by using the wavelet filter bank and the results are compared with the traditional method using a band past filer bank.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.673-677
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• 2003

In this study, we propose the new filter bank that is adaptive filter bank using neural networks in time domain. Also, we proposed a new filter neuron as neuron with filter window, the structure and algorithm for filter banks. The performance of neural filter banks is shown from two examples. It show characteristics the simple structure and higher speed processing than traditional methods (filter banks in frequency domain, etc.). In many applications, the proposed method will provide the high performance to features detection of signals in time domain.

• ETRI Journal
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• v.25 no.5
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• pp.345-355
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• 2003

In this paper, we propose a method for designing a class of M-channel, causal, stable, perfect reconstruction, infinite impulse response (IIR), and parallel uniform discrete Fourier transform (DFT) filter banks. It is based on a previously proposed structure by Martinez et al. [1] for IIR digital filter design for sampling rate reduction. The proposed filter bank has a modular structure and is therefore very well suited for VLSI implementation. Moreover, the current structure is more efficient in terms of computational complexity than the most general IIR DFT filter bank, and this results in a reduced computational complexity by more than 50% in both the critically sampled and oversampled cases. In the polyphase oversampled DFT filter bank case, we get flexible stop-band attenuation, which is also taken care of in the proposed algorithm.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.930-935
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• 2014

A Gabor filter is a linear filter used for edge detectionas frequency and orientation representations of Gabor filters are similar to those of the human visual system. In this thesis, we propose a pedestrian detection algorithm using a Gabor filter bank. In order to extract the features of the pedestrian, we use various image processing algorithms and data structure algorithms. First, color image segmentation is performed to consider the information of the RGB color space. Second, histogram equalization is performed to enhance the brightness of the input images. Third, convolution is performed between a Gabor filter bank and the enhanced images. Fourth, statistical values are calculated by using the integral image (summed area table) method. The calculated statistical values are used for the feature matrix of the pedestrian area. To evaluate the proposed algorithm, the INRIA pedestrian database and SVM (Support Vector Machine) are used, and we compare the proposed algorithm and the HOG (Histogram of Oriented Gradient) pedestrian detector, presentlyreferred to as the methodology of pedestrian detection algorithm. The experimental results show that the proposed algorithm is more accurate compared to the HOG pedestrian detector.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.279-283
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• 2004

In general, triangular shape filters are used in the filter bank when we extract MFCC feature parameters from the spectrum of the speech signal. A different approach, which uses specific filter shapes in the filter bank that are optimized to the spectrum of training speech data, is proposed by Lee et al. to improve the recognition rate. A principal component analysis method is used to get the optimized filter coefficients. Using a large amount of 4-digit telephone speech database, in this paper, we get the MFCCs based on the PCA-optimized filter bank and compare the recognition performance with conventional MFCCs and direct weighted filter bank based MFCCs. Experimental results have shown that the MFCC based on the PCA-optimized filter bank give slight improvement in recognition rate compared to the conventional MFCCs but fail to achieve better performance than the MFCCs based on the direct weighted filter bank analysis. Experimental results are discussed with our findings.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.140-146
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• 1989

In many situations, radar targets are embedded in a clutter environment and clutter rejection is required. The clutter is unwanted radar echoes and may arise owing to reflections from ground and weather disturbances and statistical properties of the clutter vary with range and azimuth as well as time. That is, adaptive signal processing is required. In this paper, a clutter suppression algorithm based on the clutter whitening filter (WF) and doppler filter bank(DFB) is described which provides improved performance compared with conventional nonadaptive clutter suppression algorithm that is the cascaded moving target indicator (MTI) and (DFB). The clutter whitening filter algorithm is based on the Burg's maximum entropy method.