• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.11-27
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• 2010

Nowadays, ultrasound Doppler imaging is widely used in assessing cardiovascular functions in the human body. However, a major drawback of ultrasonic Doppler methods is that they can provide information on blood flow velocity along the ultrasound beam propagation direction only. Thus, the blood flow velocity is estimated differently depending on the angle between the ultrasound beam and the flow direction. In order to overcome this limitation, there have been many researches devoted to estimating both axial and lateral velocities. The purpose of this article is to survey various two-dimensional velocity estimation methods in the context of Doppler imaging. Some velocity vector estimation methods can also be applied to determine tissue motion as required in elastography. The discussion is mainly concerned with the case of estimating a two-dimensional in-plane velocity vector involving the axial and lateral directions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.109-114
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• 2011

In this paper, we apply a context awareness based on Gaussian mixture model (GMM) to a sound reinforcement for hearing impaired. In our approach, the harmful sound amplified through the sound reinforcement algorithm according to context awareness based on GMM which is constructed as Mel-frequency cepstral coefficients (MFCC) feature vector from sound data. According to the experimental results, the proposed approach is found to be effective in the various acoustic environments.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.365-368
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• 2005

We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.714-718
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• 2007

The high-throughput sequencing of a lot of genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable attention in recent years since exact promoter prediction can give a clue to the elucidation of overall genetic networks. In this study, applications of support vector machine(SVM) to promoter prediction are explored to show a right approaches to discriminate between promoter and non-promoter regions by means of SVM. The results of various experiments show that encoding method, encoding region and learning data constitution can play an important role in the performance of SVM.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.100-103
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• 2008

In this paper, we propose H.264 Encoder integrating Intra Prediction, Deblocking filter, Context-Based Adaptive Variable Length Coding, and Motion Estimation encoder module. This designed module can be operated in 440 cycle for one-macroblock. To verify the Encoder architecture, we developed the reference C from JM 9.4 and verified the our developed hardware using test vector generated by reference C. The designed circuit can be operated in 166MHz clock system, and has 1800k gate counts using Charterd 0.18um process including SRAM memory. Manufactured chip has the size of $6{\times}6mm$ and 208 pins package.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.108-111
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• 2008

In this paper, we propose an advanced hardware architecture for the CAVLC entropy encoder engine for real time Full HD video compression. Since there are 384 data coefficients which are sum of 376 AC coefficient and 8 DC coefficient per one macroblock, 384 coefficient have to be processed per one macroblock in worst case for real time processing. We propose an novel architecture which includes parallel architecture and pipeline processing, and reduction "0" in AC/DC coefficient table. To verify the proposed architecture, we develop the reference C for CAVLC and verified the designed circuit with the test vector from reference C code.

• Journal of Information Science Theory and Practice
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• v.8 no.2
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• pp.6-17
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• 2020

Word similarity is often measured to enhance system performance in the information retrieval field and other related areas. This paper reports on an experimental comparison of values for word similarity measures that were computed based on 50 intentionally selected words from a Reuters corpus. There were three targets, including (1) co-occurrence-based similarity measures (for which a co-occurrence frequency is counted as the number of documents or sentences), (2) context-based distributional similarity measures obtained from a latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), and Word2Vec algorithm, and (3) similarity measures computed from the tf-idf weights of each word according to a vector space model (VSM). Here, a Pearson correlation coefficient for a pair of VSM-based similarity measures and co-occurrence-based similarity measures according to the number of documents was highest. Group-average agglomerative hierarchical clustering was also applied to similarity matrices computed by individual measures. An evaluation of the cluster sets according to an answer set revealed that VSM- and LDA-based similarity measures performed best.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.237-240
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• 2005

This paper propose an novel CAVLC architcture for H.264 and designed the CAVLC module which can be used in AMBA based design. This designed module can be operated in 420 cycle for one-macroblock and support both long-start code method using Annex B.1 and RTP. To verify the CAVLC architecture, we developed the reference C from JM8.5 and verified the our developed hardware using test vector generated by reference C. The designed circuit can be operated in 54MHz clock system, and has 14096 gate counts using Hynix 0.35 um TLM process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2647-2654
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• 2009

In this paper, we propose H.264 Encoder integrating Intra Prediction, Deblocking Filter, Context-Based Adaptive Variable Length Coding, and Motion Estimation encoder module. This designed module can be operated in 440 cycle for one-macroblock. To verify the Encoder architecture, we developed the reference C from JM 9.4 and verified the our developed hardware using test vector generated by reference C. The designed circuit can be operated in 166MHz clock system, and has 1800K gate counts using Charterd 0.18 um process including SRAM memory. Manufactured chip has the size of $6{\times}6mm$ and 208 pins package.