• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.165-170
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• 2014

Speech enhancement techniques that remove surrounding noise are stressed to preprocessor of speech recognition. Among the various speech enhancement techniques, Codebook-based Speech Enhancement (CBSE) operates efficiently in non-stationary noise environments. But, CBSE has some problems that inaccurate gains can be estimated if mismatch occur between input noisy signal and trained speech/noise codevectors. In this paper, the Normalized Weighting Factor (NWF) is calculated by long-term noise estimation algorithm based on Signal-to-Noise Ratio, compensated to the conventional inaccurate gains. The proposed CBSE shows better performance than conventional CBSE.

• Phonetics and Speech Sciences
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• v.3 no.2
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• pp.65-70
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• 2011

Recently, audio-visual speech recognition (AVSR) has been studied to cope with noise problems in speech recognition. In this paper we propose a novel method of deciding weighting factors for audio-visual information fusion. We adopt the particle swarm optimization (PSO) to weighting factor determination. The AVSR experiments show that PSO-based normalized confidence measures (NCM) improve the rejection performance of mis-recognized words by 33%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.17-24
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• 1996

In this paper, we present a robust performance improvement method for the NLCF(normalized left coprime factor) uncertain structure using loop shaping and the structure singular value. For this, we select weighting functions for a loop shaping considering condition numer, and transform the NLCF uncertain structure into the 4-block structure. However, we can't get a good performance on account of the restriction of weighting functions. To cope with this, we motivate the use of structured singular vlaue in the robust performance improvement procedure. After all, the robust performance improvement can be obtained by a factor W$_{a}$ and a scaling factor of D-K iteration.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.153-159
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• 2004

A control-structure combined optimal design problem is discussed taking a 3-D truss structure as a design object. We use descriptor forms for a controlled object and a generalized plant because the structural parameters appear naturally in these forms. We consider not only minimum weight design problem for structure system, but also suppression problem of the effect of disturbances for control system as the purpose of the design. By minimizing the linear sum of the normalized structural objective function and control objective function, it is possible to make optimal design by which the balance of the structural weight and the control performance is taken.

• MALSORI
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• no.47
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• pp.131-140
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• 2003

We propose a new method of generating client models for HMM based text-dependent speaker verification system with only a small amount of training data. To make a client model, statistical methods such as segmental K-means algorithm are widely used, but they do not guarantee the quality or reliability of a model when only limited data are avaliable. In this paper, we propose a blind speech segmentation based on level building DTW algorithm as an alternative method to make a client model with limited data. In addition, considering the fact that voiced sounds have much more speaker-specific information than unvoiced sounds and energy of the former is higher than that of the latter, we also propose a new score evaluation method using the observation probability raised to the power of weighting factor estimated from the normalized log energy. Our experiment shows that the proposed methods are superior to conventional HMM based speaker verification system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.2
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• pp.82-87
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• 1985

The performance comparison method against ground clutter, when the trasnfer function of MTI filter or integrator is given, is considered for the MTI signal processors using a constant PRF. The MTI signal processors are modelled as the transversal filters, and the ground clutter power density spectrum as Gaussian type and the performance of the MTI signal processors are compared by calculating the MTI improvent factors. The MTI imrpovement factors versus normalized spectral width is depicted as examples for the MTI filters, the integrator using Hanning weighting function and the cascading of the two.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.4
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• pp.539-553
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• 2004

Up to the present time, many methods to estimate emissions from a particular diesel engines have wholly depended on the quantity of diesel fuel consumed. Then, the recommended emission factors were normalized by fuel consumption, and further total activity was estimated by the total fuel consumed. One of main purposes in the study is newly to develop emission factors for the railroad diesel rolling stock (RDRS) and to estimate a total amount of major gaseous pollutants from the RDRS in Korea. Prior to develop a Korean mode emission factor. the emission factor from the USEPA was simply applied for comparative studies. When applying the USEPA emission factors, total exhaust emissions from the RDRS in Korea were estimated by 28,117tons of NOx, 2,832.3tons of CO, and 1,237.5tons of HC, etc in 2001. In this study, a emission factor for the RDRS, so called the KoRail mode (the Korean Railroad mode) has been developed on the basis of analyzing the driving pattern of the Gyeongbu-Line especially for the line-haul mode. Explicitly to make the site specific emission factors, many uncertainty problems concerning weighting factors for each power mode, limited emission test, incomplete data for RDRS, and other important input parameters were extensively examined. Total exhaust emissions by KoRail mode in Korea were estimated by 10,960tons of NOx, and 4,622tons of CO, and so on in the year of 2001. The emissions estimated by the USEPA mode were 2.6 times higher for NOx, and 1.6 times lower for CO than those by the KoRail mode. As a conclusion, based on the emission calculated from both the USEPA mode and the KoRail mode, the RDRS is considered as one of the significant mobile sources for major gaseous pollutants and thus management plans an(1 control strategies for the RDRS must be established to improve air quality near future in Korea.