• The Journal of Korean Institute of Communications and Information Sciences
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• 제41권1호
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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.

• The Korean Journal of Nuclear Medicine Technology
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• 제21권1호
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• pp.29-33
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• 2017

Purpose In this study, we evaluated image by applying normalization factor during 30 days to the PET images. Materials and Methods Normalization factor was acquired during 30 days. We compared with 30 normalization factors. We selected 3 clinical case (PNS study). We applied for normalization factor to PET raw data and evaluated SUV and count (kBq/ml) by drawing ROI to liver and lesion. Results There is no significant difference normalization factor. SUV and count are not different for PET image according to normalization factor. Conclusion We can get a lot of information doing the quality assurance such as performance of sinogram and detector. That's why we need to do quality assurance daily.

• Journal of Internet Computing and Services
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• 제4권3호
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• pp.9-14
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• 2003

The method of vocal tract normalization is a successful method for improving the accuracy of inter-speaker normalization. In this paper, we present an intra-speaker warping factor estimation based on pitch alteration utterance. The feature space distributions of untransformed speech from the pitch alteration utterance of intra-speaker would vary due to the acoustic differences of speech produced by glottis and vocal tract. The variation of utterance is two types: frequency and amplitude variation. The vocal tract normalization is frequency normalization among inter-speaker normalization methods. Therefore, we have to consider amplitude variation, and it may be possible to determine the amplitude warping factor by calculating the inverse ratio of input to reference pitch. k, the recognition results, the error rate is reduced from 0.4% to 2.3% for digit and word decoding.

• Journal of Information Processing Systems
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• 제11권4호
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• pp.573-582
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• 2015

Edit distance metrics are widely used for many applications such as string comparison and spelling error corrections. Hamming distance is a metric for two equal length strings and Damerau-Levenshtein distance is a well-known metrics for making spelling corrections through string-to-string comparison. Previous distance metrics seems to be appropriate for alphabetic languages like English and European languages. However, the conventional edit distance criterion is not the best method for agglutinative languages like Korean. The reason is that two or more letter units make a Korean character, which is called as a syllable. This mechanism of syllable-based word construction in the Korean language causes an edit distance calculation to be inefficient. As such, we have explored a new edit distance method by using consonant normalization and the normalization factor.

• The Journal of the Acoustical Society of Korea
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• 제22권4호
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• pp.299-307
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• 2003

There have been many studies on speaker normalization which aims to minimize the effects of speaker's vocal tract length on the recognition performance of the speaker independent speech recognition system. In this paper, we propose a simple vector quantizer based linear warping speaker normalization method based on the observation that the vector quantizer can be successfully used for speaker verification. For this purpose, we firstly generate an optimal codebook which will be used as the basis of the speaker normalization, and then the warping factor of the unknown speaker will be extracted by comparing the feature vectors and the codebook. Finally, the extracted warping factor is used to linearly warp the Mel scale filter bank adopted in the course of MFCC calculation. To test the performance of the proposed method, a series of recognition experiments are conducted on discrete HMM with thirteen mono-syllabic Korean number utterances. The results showed that about 29% of word error rate can be reduced, and that the proposed warping factor extraction method is useful due to its simplicity compared to other line search warping methods.

• Proceedings of the Korean Institute of Building Construction Conference
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• 한국건축시공학회 2013년도 춘계 학술논문 발표대회
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• pp.142-143
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• 2013

Green building certifications and environmental assessments are extensively implemented and studied to decrease the environmental impact during the life cycle of buildings. However, most of them are not appropriate to assess the environmental performance during the construction phase due to the difference of environmental factors. To develop an environmental index of construction projects, normalization should be conducted to compare the relative impact of each factor. As a first step, this study deduced normalization references of 4 environmental factors : noise, waste, greenhouse gas, and dust.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제29권9C호
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• pp.1227-1234
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• 2004

In this paper, we propose a modified Normalized-BP algorithm by changing the normalization factor according to the reliability of updated messages. Proposed algorithm has almost same decoding complexity as Normalized-BP algorithm and achieves a bit-error probability of $10^4$within 0.02dB away from compared with LLR-BP algorithm.

• The Journal of the Acoustical Society of Korea
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• 제29권1호
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• pp.18-23
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• 2010

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

• The Journal of the Korea Contents Association
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• 제18권7호
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• pp.527-535
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• 2018

This study examines how the implementation of the Normalization policy of public institutions aimed at reducing debt affects accounting conservatism in public corporations. In particular, we analyze the general behavior of accounting conservatism based on debt ratio, and analyze whether the policy has changed this behavior of conservatism. Empirical findings are summarized as following. We show that debt ratios are positively associated with conservatism, consistent with the result for the private corporations. This result means that public corporations increase their conservatism as their debt ratios increase. However, no significant effect is found in this relationship after the implementation of the policy. This finding implies that the implementation of Normalization policy is not a factor that alters the conservative accounting practices of public corporations. This suggests that the recent debt reduction performance of public corporations is irrelevant to conservatism and is the result of the actual process of normalization of management. The results documented in this paper provide an important empirical evidence for evaluating the performance of the government policy at the present time when the debt reduction policy of public institutions is viewed more important than ever.

• The KIPS Transactions:PartB
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• 제12B권4호
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• pp.437-442
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• 2005

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.