• Computers and Concrete
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• v.10 no.5
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• pp.469-487
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• 2012

This paper presents a study on the behaviour of the joints of segmental concrete bridges with external prestressing, focusing on the structural response of dry non-epoxied joints with shear keys. A Finite Element joint model to study such structures is validated modelling eight concrete panel tests. The most important feature of this model is that it has been validated with experimental tests on concrete panels which were specifically designed to fail in shear. Interface elements are used to reproduce the non linear behaviour of the joint and parameters deduced from the tests are used to define the constitutive law of these elements. This joint model is of great importance because it will permit the development of a structural model that faithfully reproduces the behaviour of these structures under combined flexure and shear and the study of its global behaviour after the opening of the joints. Interesting conclusions about the behaviour of the dry joints, about the contribution of the different mechanisms transferring shear (friction and cohesion) and about the shear stress distribution in the joint have been reached.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.401-408
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• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• Journal of Auto-vehicle Safety Association
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• v.4 no.2
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• pp.11-16
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• 2012

In 2014 Flex-PLIfor the pedestrian protection will be applied to NCAP test. The most significant feature of Flex-PLI is constructed with segmental bone cores for the femur and tibia regions. So it can be more reproducible by representing pedestrian injuries such as knee ligament and tibia injury during the pedestrian crash against vehicle. In this paper, Analyzed the characteristics of Flex-PLI through the structural analysis and the test results by using Flex-PLI for our compact vehicles. Finally countermeasures into compact vehicle were proposed to fulfill the injury criteria of Flex-PLI.

• MALSORI
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• no.49
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• pp.159-174
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• 2004

The development of the durational component of prosody modelling was overviewed and discussed in text-to-speech conversion of spoken English and Korean, showing the strengths and weaknesses of each approach. The possibility of integrating linguistic feature effects into the duration modelling of TTS systems was also investigated. This paper claims that current approaches to language timing synthesis still require an understanding of how segmental duration is affected by context. Three modelling approaches were discussed: sequential rule systems, Classification and Regression Tree (CART) models and Sums-of-Products (SoP) models. The CART and SoP models show good performance results in predicting segment duration in English, while it is not the case in the SoP modelling of spoken Korean.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.323-326
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• 2007

The various studies of combining neural network and hidden Markov models within a single system are done with expectations that it may potentially combine the advantages of both systems. With the influence of these studies, tandem approach was presented to use neural network as the classifier and hidden Markov models as the decoder. In this paper, we applied the trend information of segmental features to tandem architecture and used posterior probabilities, which are the output of neural network, as inputs of recognition system. The experiments are performed on Aurora2 database to examine the potentiality of the trend feature based tandem architecture. The proposed method shows the better results than the baseline system on very low SNR environments.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.17-20
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• 2001

본 논문에서는 분절 특징 HMM(SFHMM)의 매개변수를 줄이는 방법을 제안한다 SFHMM이 HMM보다 우수한 성능을 보이더라도, SFHMM의 매개 변수 수는 HMM보다 많기 때문에 매개 변수 수를 줄이는 방법에 대한 연구가 필요하다. 일반적으로 궤적(trajectory)은 경향(trend) 정보와 위치(location) 정보로 분리될 수 있다. 경향은 분절 특징의 변이를 나타내며, SFHMM 변수의 많은 부분을 담당하기 때문에, 경향 정보를 공유할 수 있다면 SFHMM의 매개 변수 수는 감소될 수 있을 것이다. 제안된 방법은 궤적의 경향 정보를 양자화(quantization)에 의하여 공유한다. 제안된 방법의 성능을 살펴보기 위하여 영어 데이터베이스인 TIMIT 자료를 사용하여 실험하였다. 실험 결과 제안된 방법의 성능은 기존 연구와 거의 유사하나, 궤적의 다양한 정보를 이용한다면 궤적 정보의 공유에 의하여 매개 변수를 줄일 수 있을 것으로 보인다.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1996.04a
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• pp.254-254
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• 1996

Eosinophils are known to be important effector cells in pathogenesis of asthma. The elucidation of mechanism by which eosinophil survival is regulated in vivo at sites of inflammation is critical tn our understanding of asthma pathogenesis. The maintenance of these cells at site of inflammation depends upon tile balance between its tendency to undergo apoptosis and tile local eosinophil-viability enhancing activity, Qualitative and quantative phenotypic differences have been observed between bronchoalveolar lavage (BAL) and peripheral blood (PB) eosinophils (EOS). We hypothesize that BAL EOS Possess altered functional feature compared to PB EOS. BAL and PB EOS were obtained from ragweed allergic asthmatics after segmental antigen challenge (SAC) at 24 hour or one week, and purified over percoll and CDl6 negative selection. Cells were cultured in duplicate in RPMI, 15% FCS and 1% penicillin/streptomycin without exogenous cytokines. Eosinophil purity and viability was >92%. BAL. EOS viability was 69${\pm}$4.4% versus 39${\pm}$1.6% for PB EOS (p<0.005) at 48 hour time point, and this difference was maintained through day 5 (32${\pm}$7.6% vs. 3.0${\pm}$ 1.4%, p<0.05), Among BAL EOS, those harvested one week after SAC appeared to have an prolonged survival compared to those harvested at 24 hour. Coculture of BAL and PB EOS resulted in significant viability enhancement than expecteed. Direct neutralization of GM-CSF activity, not IL-3 and EL-5, markedly decreased tile survival of BAL EOS in culture, and abrogated tile viability enhancing activity of their culture supernatants in a dose dependent manner. We conclude that BAL EOS activated in vivo possess enhanced viability compared to PB EOS. Mixing and neutralization experiments suggest a role for autocrine production of GM-CSF.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.24-31
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• 1994

In this paper, we report on the development of a speaker independent continuous speech recognition system using continuous hidden Markov models. The continuous hidden Markov model consists of mean and covariance matrices and directly models speech signal parameters, therefore does not have quantization error. Filter bank coefficients with their 1st and 2nd-order derivatives are used as feature vectors to represent the dynamic features of speech signal. We use the segmental K-means algorithm as a training algorithm and triphone as a recognition unit to alleviate performance degradation due to coarticulation problems critical in continuous speech recognition. Also, we use the one-pass search algorithm that Is advantageous in speeding-up the recognition time. Experimental results show that the system attains the recognition accuracy of $83\%$ without grammar and $94\%$ with finite state networks in speaker-indepdent speech recognition.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.326-336
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• 2022

This study aims to examine Korean learners' production of American English vowel focused on feature analysis. Specifically, the present study adopts feature analysis so that vowel production is analyzed in terms of vowel features as well as overall segmental accuracy. To this end, 22 Korean college students participated in a production test which contained 11 English vowels /i, ɪ, eɪ, ɛ, æ, ɑ, oʊ, ɔ, ʊ, u, ʌ/. The results revealed that the degree of difficulty varied depending on features; the Korean participants showed higher accuracy for front/back features than for tongue height features and tense/lax features. In particular, the participants had more difficulty producing back vowels and non-high vowels than front vowels and high vowels with respect to tongue height features and lip rounding features. Among the individual vowels, /eɪ/ showed the highest accuracy in feature analysis. On the other hand, /ɑ, ɔ, ʌ/ showed low accuracy with respect to height features and lip rounding features, and high vowels /i, ʊ, u/ showed low accuracy with respect to tense/lax features. As for the correlation between the vowel features, tongue height features and lip rounding features are significantly correlated. Also, tongue height features and tense/lax features showed a strong correlation. Finally, pedagogical implications for teaching English vowels were further discussed based on the findings of the current study.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.374-379
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• 2002

In this paper, a nobel method to reduce noise using voice/unvoice classification is proposed. Voice and unvoice are an important feature of speech and the proposed method processes noisy speech differently for each voice/unvoice part. Speech is classified into voice/unvoice using zero-crossing rate and energy, and a modified speech/noise dominant-decision is proposed based on voice/unvoice classification. The proposed method was tested on conditions of white noise and airplane noise, and on the basis of comparing segmental SNR with the existing method and listening to the enhanced speech, a performance of the proposed method was superior to that of the existing method.