• ETRI Journal
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• v.29 no.3
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• pp.300-304
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• 2007

This paper proposes a new data-driven method for high-pass approaches, which suppresses slow-varying noise components. Conventional high-pass approaches are based on the idea of decorrelating the feature vector sequence, and are trying for adaptability to various conditions. The proposed method is based on temporal local decorrelation using the information-maximization theory for each utterance. This is performed on an utterance-by-utterance basis, which provides an adaptive channel normalization filter for each condition. The performance of the proposed method is evaluated by isolated-word recognition experiments with channel distortion. Experimental results show that the proposed method yields outstanding improvement for channel-distorted speech recognition.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.21-30
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• 1987

Length normalization by variable frame size is proposed as a novel approach to length normalization to solve the problem that the length variation of spoken word results in a lowing of recognition accuracy. This method has the advantage of curtailment of recognition time in the recognition stage because it can reduce the number of frames constructing a word compared with length normalization by a fixed frame size. In this paper, variable frame length normalization is applied to multisection vector quantization and the efficiency of this method is estimated in the view of recognition time and accuracy through practical recognition experiments.

• YAKHAK HOEJI
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• v.38 no.4
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• pp.366-371
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• 1994

To improve the identification power of TLC/HPTLC the in situ reflectance spectra obtained directly from plates with commercial scanner are used. The spectrum normalization should be carried out prior to comparing and searching the spectra from library for the identification of compounds. Because the reflectance does not obey the Lambert-Beer's law, there arise some problems in normalization. These problems could be solved to some extent by normalizing the spectra with regression methods. The spectra are manipulated with the regression function of a curve obtained from the correlation plot. When the parabola was used as the manipulating function, the spectra were identified with the accuracy of 97% and this result was better than that of conventionally used the point and area normalization method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.77-79
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• 2016

In this paper, we consider hierarchical modulation (HM), which has been widely exploited in digital broadcasting systems. In HM, each independent data stream is mapped to the modulation symbol with different transmission power and normalization factors of conventional M-QAM cannot be used. In this paper, we derive the method and formula for exact normalization factor of three-level hierarchical 64QAM.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.103-111
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• 2004

The method of speaker normalization has been known as the successful method for improving the accuracy of speech recognition at speaker independent speech recognition system. A frequency warping approach is widely used method based on maximum likelihood for speaker normalization. This paper propose a new power spectrum warping approach to making improvement of speaker normalization better than a frequency warping. Th power spectrum warping uses Mel-frequency cepstrum analysis(MFCC) and is a simple mechanism to performing speaker normalization by modifying the power spectrum of Mel filter bank in MFCC. Also, this paper propose the hybrid VTN combined the Power spectrum warping and a frequency warping. Experiment of this paper did a comparative analysis about the recognition performance of the SKKU PBW DB applied each speaker normalization approach on baseline system. The experiment results have shown that a frequency warping is 2.06%, the power spectrum is 3.06%, and hybrid VTN is 4.07% word error rate reduction as of word recognition performance of baseline system.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.193-195
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• 2009

In this paper, we propose an effective compression method for electrocardiogram(ECG) signals. 1-D ECG signals are reconstructed to 2-D ECG data by period and complexity sorting schemes with image compression techniques to Increase inter and intra-beat correlation. The proposed method added block division and mean-period normalization techniques on top of conventional 2-D data ECG compression methods. JPEG 2000 is chosen for compression of 2-D ECG data. Standard MIT-BIH arrhythmia database is used for evaluation and experiment. The results show that the proposed method outperforms compared to the most recent literature especially in case of high compression rate.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.262-267
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• 2004

The unloading compliance method is the most commonly used method to evaluate the fracture resistance characteristics of a material. In dynamic loading condition, the direct current potential drop(DCPD) method has been used because the unloading compliance method can not be applied due to the discontinuity of loading. However, even in the dynamic test using DCPD method, there is a problem that the voltage drops sharply on the initiation of crack. For the reason metioned above, the normalization method was suggested on ASTM E 1820 which is revised recently, as a new method to evaluate the dynamic fracture resistance characteristic. The nomalization method can be used to obtain a fracture resistance curve directly from a load-load line displacement. In this study, we obtained two fracture resistance curves from static test of welding part of nuclear piping both by unloading compliance and nomalization method. The two curves were almost same each other, so the adaptability of the nomalization method has been proved. We conducted a dynamic fracture resistance test for the same material. The fracture resistance curve from the dynamic test was obtained by normalization method and compared to that of the static test result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8C
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• pp.801-805
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• 2006

A normalization approach to coefficient adaptation in the escalator(ESC) filter structure that conventionally employs least mean square(LMS) algorithm is introduced. Using Taylor's expansion of the local error signal, a normalized form of the ESC-LMS algorithm is derived. Compared with the computational complexity of the conventional ESC-LMS algorithm employs input power estimation for time-varying convergence coefficient using a single-pole low-pass filter, the computational complexity of the proposed method can be reduced by 50% without performance degradation.

• Nuclear Engineering and Technology
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• v.38 no.7
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• pp.639-656
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• 2006

In order to apply the leak before break (LBB) concept to nuclear piping systems, the dynamic strain aging effect of low carbon steel materials has to be taken into account, in compliance with the requirements of the Korean Standard Review Guide (KSRG) 3.6.3-1. For this goal, J-R tests are needed for a range of various temperatures and loading rates, including dynamic loading conditions. In the dynamic loading J-R test, the unloading compliance method can not be applied to measure the crack growth and direct current potential drop (DCPD) method; this method also has a problem defining the crack initiation point. The normalization method is known as a very useful method to determine the J-R curve under dynamic loading because it does not need additional equipment or complicated loading sequences such as electric current or unloading. This method was accepted by the American Society for Testing and Materials (ASTM) as a standard test method E1820 A15 in 2001. However, it has not yet been clearly verified yet if the normalization method is sufficiently reliable to be applied to LBB. In this study, the basic background of the J-integral, LBB and dynamic loading J-R test are explained, and the current status for dynamic loading J-R test methods are reviewed from the view point of LBB for nuclear piping. In particular, the theoretical and historical background of the normalization method which has received attention recently, is summarized. Recent studies for this method are introduced and future works are suggested that may improve the reliability of LBB for nuclear piping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.