• 한국콘텐츠학회논문지
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• 제19권11호
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• pp.632-643
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• 2019

최근 지구 기후의 변화, 해외 여행객의 증가 및 국가 간 물류 이동의 증가 등과 같은 요인으로 인해 모기와 같은 절지동물이 매개하는 아보바이러스(arthropod-borne virus, arbovirus) 감염으로 인한 대규모의 피해가 전 세계적으로 끊임없이 발생하고 있다. 그 중에서도 플라비바이러스 속에 해당하는 지카 바이러스와 뎅기바이러스에 의한 피해가 대표적이다. 본 연구에서는 다양한 생물정보학 데이터베이스를 바탕으로 지카 바이러스 및 뎅기 바이러스가 숙주 감염에 필수적인 기능을 수행하는 외피 단백질에 대한 심층적인 분석을 수행했다. 외피 단백질을 구성하는 도메인들에 대한 분석을 통해 도메인의 종류, 위치 및 기능을 파악했으며 각 도메인별 상동성을 분석했다. 이로부터 낮은 상동성을 보이는 도메인인 EDIII를 도출하였으며, EDIII를 구성하는 펩타이드에 대한 상동성 및 면역원성 분석과 3차원 구조 모델링을 수행했다. 더 나아가 이들이 갖는 생물학적 의미와 활용 방안에 대해 논의했다.

• Nutrition Research and Practice
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• 제17권5호
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• pp.945-958
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• 2023

BACKGROUND/OBJECTIVES: Food literacy (FL) is a crucial skill for selecting sustainable and healthy food options, necessitating the identification of vulnerable groups in the community using valid measurement tools. Identifying weak domains in FL is essential for enhancing the overall FL. This study examined the FL levels of Seoul citizens based on their sociodemographic characteristics and assessed the relationship between FL, food intake, and weight status. SUBJECTS/METHODS: This study utilized the data from the Seoul Food Survey, a cross-sectional study employing representative samples of Seoul citizens. Data collection occurred from September to October 2021, with 4,039 citizens aged 18 yrs and above participating in face-to-face surveys. Thirty-three FL items were assessed, comprising 14 items in the nutrition and safety (NS) domain, eight items in the cultural and relational (CR) domain, and 11 items in the socio-ecological (SE) domain. In addition, data on food intake sufficiency and obesity status were collected. The descriptive statistics, t-tests, analysis of variance, and logistic regression analysis were used for analysis. RESULTS: Men, students, young adults, older citizens, and people experiencing food insecurity had the lowest scores for all the FL domains. The highest quartile group of NS scores had a higher probability of consuming adequate servings of vegetables and fruits, with significant linear trends observed (P for trend < 0.05). In all three FL domains, the odds ratio for obesity was significantly lower in the groups with high FL scores (P < 0.05). CONCLUSIONS: A close relationship was observed between low FL, obesity, and food intake, even after controlling for other covariates. Vulnerable groups with low FL were also identified. Therefore, it is essential to develop programs to improve FL and the health and well-being of these groups.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 추계학술대회 논문집
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• pp.183-185
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• 2008

Forming Limit Diagram(FLD) is a representative tool for evaluating formability of sheet metals. This paper presents a methodology to determine the FLD using Finite Element Method. For predicting the forming limits numerically. Previous methods such as using the thickness strain or the ductile fracture criterion are limited at plane strain domain. These results suggest that behavior of the void growth in sheet metals is different from real one. In contrast to previous methods, a more exact model which takes void growth into account is used. This result agrees with the experimental result qualitatively.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2005년도 추계학술대회 논문집
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• pp.302-305
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and denpends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• 음성과학
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• 제11권1호
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2005년도 춘계 학술기술논문발표대회 논문집
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• pp.119-124
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• 2005

It is necessary for prediction of recycled aggregate concrete(RAC) strength at the early stage that facilitate concrete form removal and scheduling for construction. However, to predict RAC strength is difficult because of being influenced by complicated many factors. Therefore, this research suggest optimized estimation method that can reflect many factors. One way is Case-Based Reasoning(CBR) that solved new problems by adapting solutions to similar problems solved in the past, which are solved in the case library. Other way is Artificial Neural Networks(ANN) that solved new problems by training using a set of data, which is representative of problem domain. This study is to propose comparing accuracy of the estimating the compressive strength of recycled aggregate concrete using Case-Based Reasoning(CBR) and Artificial Neural Networks(ANN).

• 해양환경안전학회지
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• 제5권2호
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• pp.35-44
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• 1999

High negative pressure coefficient is formed in the corner of the bluff body structures. For many curtain wall designers this phenomena is of interest because this high negative pressure coefficient is adopted in structural calculation. The present study is aimed to investigate shedding vortex characteristics of two-dimensional rectangular prism flow. Unsteady calculation by finite difference method based upon SOLA is carried out for three aspect ratios(1:1, 1:2, 1:3) of Re=10$^4$ in viscous incompressible flow within infinite domain. Fluctuation of velocity components at various pick-up points and time variation of drag and lift coefficients are analysed by FFT method to reveal shedding vortex frequency patterns. At aspect ratio 1:1, one primary Strouhal number appears for about all pick-up points. At aspect ratio 1:2, two representative Strouhal numbers are classified by pick-up positions and their flows show two different reattachment patterns. For aspect ratio 1:3, frequency spectrum maintains multiple peaks.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.288-293
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• 2011

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.91-94
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• 2005

The performance of speech recognition is degraded by the mismatch between training and test environments. Many methods have been presented to compensate for additive noise and channel effect in the cepstral domain, and Cepstral Mean Subtraction (CMS) is the representative method among them. Recently, high order cepstral moment normalization method has introduced to improve recognition accuracy. In this paper, we apply high order moment normalization method and smoothing filter for real-time processing. In experiments using Aurora2 DB, we obtained error rate reduction of 49.7% with the proposed algorithm in comparison with baseline system.