• International Journal of Highway Engineering
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• v.17 no.6
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.562-567
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• 2002

Noise predictive maximum likelihood(NPML) detector embeds noise predictions/ whitening process in branch metric calculation of Viterbi detector and improves the reliability of branch metric computation. Therefore, PRML detector with a noise predictor achieves some performance improvement and has an advantage of low complexity. This paper shows that NP(1221)ML system through noise predictive PR-equalized signal has less complexity and better performance than high order PR(12321)ML system in high density perpendicular magnetic recording. The simulation results are evaluated using (1) random sequence and (2) run length limited (1,7) sequence, and they are applied to linear channel and nonlinear channel with normalized linear density $1.0{\leq}K_p{\leq}3.0$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.336-342
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• 2003

Noise predictive maximum likelihood(NPML) detector embeds noise prediction/whitening process in branch metric calculation of Viterbi detector and improves the reliability of branch metric computation. Therefore, PRML detector with a noise predictor achieves some performance improvement and has an advantage of low complexity. This thesis random sequences are applied to linear channel. In perpendicular magnetic recording density KP=2.5, NP(121)ML and NP(1221)ML detection system which is based on a noise predictive PR-equalized signal are evaluated by the Performance through a computing simulation. Therefore, NPML systems are implemented and are verified by VHDL.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.379-382
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• 2008

In order to reduce the noise quantity in a conversation under the noisy environment, it is necessary for the signal processing system to process adaptively according to the noise quantity in order to enhance the performance. There fore this paper presents a recognition method for noise quantity by linear predictive coefficient using a three layered neural network, which is trained using three kinds of speech that is degraded by various background noises. In the experiment, the average values of the recognition results were 97.6% or more for various noises using Aurora2 database.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.120-126
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• 2009

In order to reduce the noise quantity in a conversation under the noisy environment it is necessary for the signal processing system to process adaptively according to the noise quantity in order to enhance the performance. Therefore this paper presents a recognition method for noise quantity by linear predictive coefficient using a three layered neural network, which is trained using three kinds of speech that is degraded by various background noises. The performance of the proposed method for the noise quantity was evaluated based on the recognition rates for various noises. In the experiment, the average values of the recognition results were 98.4% or more for such noise using Aurora2 database.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.758-763
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• 2008

We introduce a noise-predictive turbo equalization using noise filter in perpendicular magnetic recording(PMR) channel. The noise filter mitigates the colored noise in high-density PMR channel. In this paper, the channel detectors used are SOVA (Soft Output Viterbi Algorithm) and BCJR algorithm which proposed by Bahl et al., and the outer decoder used is LDPC (Low Density Parity Check) code that is implemented by sum-product algorithm. Two kinds of LDPC codes are experimented. One is the 0.5Kbyte (4336,4096) LDPC code with the code rate of 0.94, and the other is 1Kbyte (8432,8192) LDPC code with the code rate of 0.97.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.357-362
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• 2010

This paper proposes the speech and noise recognition system by using a neural network in order to detect the speech and noise sections at each frame. The proposed neural network consists of a layered neural network training by back-propagation algorithm. First, a power spectrum obtained by fast Fourier transform and linear predictive coefficients are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and linear predictive coefficients. Therefore, the proposed neural network can train using clean speech and noise. The performance of the proposed recognition system was evaluated based on the recognition rate using various speeches and white, printer, road, and car noises. In this experiment, the recognition rates were 92% or more for such speech and noise when training data and evaluation data were the different.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.891-897
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• 2003

Partial response maxim likelihood (PRML) is a powerful and indispensable detection scheme for perpendicular magnetic recording channels. The performance of PRML can be improved by incorporating a noise prediction scheme into branch metric computations of Viterbi algorithm (VA). However, the systems constructed by VA have shortcomings in the form of high complexity and cost. In this connection, a new simple detection scheme is proposed by exploiting the minimum run-length parameter d=1 of RLL code. The proposed detection scheme have a slicer instead of Viterbi detector and a noise predictor as a feedback filter. Therefore, to improve BER performance, the proposed detection scheme is extended to dual detection scheme for improving the BER performance. Simulation results show that the proposed scheme has a comparable performance to noise-predictive maximum likelihood (NPML) detector with less complexity when the partial response (PR) target is (1,2,1).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8C
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• pp.569-574
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• 2008

Noise predictive maximum likelihood(NPML) detector embeds noise prediction! whitening process in the branch metric calculation of Viterbi detector and improves the reliability. In this paper, some high-density optical storage channels are examined, and appropriate NPML systems are designed for each channel.