• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.131-138
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• 2014

This paper is on a stereo acoustic echo canceller with the blind signal separation for post processing. The convergence speed of the stereo acoustic echo canceller is deteriorated due to mixing two residual signals which are update signals of each echo canceller. To solve this problem, we are to use the blind signal separation(BSS) method separating the mixed signals after the echo cancellers. The blind signal separation method can extracts the source signals by means of the iterative computations with two input signals. We had verified performances of the proposed acoustic echo canceller for stereo through simulations. The results of simulations show that the acoustic echo canceller for stereo using this algorithm operates stably without divergence in the normal state. And, when the speech signals were inputted, this echo canceller achieved about 2dB higher ERLE with the BSS post processing method than without this method. This stereo echo canceller showed the best performance in the case of inputting the real voice signal.

• ETRI Journal
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• v.36 no.3
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• pp.506-509
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• 2014

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by "Joule per bit" metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.3
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• pp.76-82
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• 2010

A laborer always watched a process of production carefully but defective manufactures were inspected after press process. These inspections made a waste of human power and defective manufactures could make a serious damage of press mold. Therefore, AE(Acoustic Emission) system was introduced to prevention of the damage of the press molds, to a real time detection of defective manufactures and to save human power. AE system was introduced to solve this problem which is a detecting defective manufacture on real time and to prevent the damage of the press mold. In this research we get acoustic emission signal in accordance with weight and processing method of press by using AE sensor, Preamplifier, AE board signal board which occurs press processing and it analyzed various signal through using CMD8 software on the time. From the result, we found that the intensity and shape of the signal were changed according to the weight and processing type of the press. By using this special algorithm, it can judge the acoustic signal which occurs from press on real time.

• ETRI Journal
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• v.36 no.4
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• pp.686-689
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• 2014

This paper studies energy-efficiency (EE) power allocation for cognitive radio MIMO-OFDM systems. Our aim is to minimize energy efficiency, measured by "Joule per bit" metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non-convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy-efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.43-50
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• 2016

This paper is on an acoustic echo canceller solving the double-talk problem by using the blind signal separation technology. The acoustic echo canceller may be deteriorated or diverged during the double-talk period. So we use the blind signal separation to detect the double talking by separating the near-end speech signal from the mixed microphone signal. The blind signal separation extracts the near-end signal from dual microphones by the iterative computations using the 2nd order statistical character in the closed reverberation environment. By this method, the acoustic echo canceller operates irrespective of the double-talking. We verified performances of the proposed acoustic echo canceller in the computer simulations. The results show that the acoustic echo canceller with this algorithm detects the double-talk periods well, and then operates stably without diverging of the coefficients after ending the double-talking. The merits are in the simplicity and stability.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.32-40
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• 1994

In this study by using EMAT(electro magnetic acoustic transducer)the artificial slit is installed on 12B-SUS pipe test piece. By making 4 cycle SH-burst (EMA) incidence to 45 .deg. angle, the signal data of pulse, received from EMAT are translated into digital-signal-processing- method SSP(Split Spectrum Processing) and Deconvolution method. The main conclusions obtained are as follows; (1) the signal data received from EMAT are translated with digital signal proc- essing of SSP-method and Deconvolution-method and this method shows exellent results more than Ultrasonic testing method does; (2) noise can well be removed by SSP with signal data, and resolution and S/N ratio are advanced; (3) when used with Ultrasonic wave general stainless steel has proporties of multiscattering and reflection phenomena, but resolution is progressed by using Deconvolution method;and (4) as addition-averaging-processing mumber is increasing, the resolution and S/N ratio are improved and the satisfactory signal is obtained.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.203-208
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• 2010

In this paper, we convert underwater acoustic signal made by sonar system into digital image. We propose the algorithm that detects target candidate and emphasizes information of target introducing image processing technique for the digital image. The process detecting underwater target estimates background noise in underwater acoustic signal changing irregularly, recomposes it. and eliminates background from original image. Therefore, it generates initial target group. Also, it generates weighted map through proceeding doppler information, ensures information for target candidate through filtering using weighted map for image eliminated background noise, and decides the target candidate area in the single frame. In this paper, we verified that proposed algorithm almost had eliminated the noise generated irregularly in underwater acoustic signal made by simulation, targets had been displayed more surely in the image of underwater acoustic signal through filtering and process of target detection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.635-639
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• 2003

When we carry out acoustic holography, step-by-step measurement provides us larger aperture size with same number of microphones. But when we carry out step-by-step measurement, it is blown that sound signal must be stationary. However, when transfer function between input and output signal is time-invariant, we can apply step-by-step measurement to acoustic holography even if sound signal is transient We have to do only some Processing on signals from step-by-step measurements to make the signal data of each step compatiable with other steps. This paper accounts for that processing method.