• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.266-269
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• 2002

This paper explores a fundamental study of acquiring the users' KANSEI information regarding the recognition of shape models. Since there are many differences such as background differences and knowledge differences among users, they will produce different evaluations based on their KANSEI even when an identical shape model is presented. Cluster analysis is proved to be available for catching a group tendency and for constructing a mapping relation between a description of the shape model and the HANSEl database. In order to investigate an analogical relation and a mutual influence in our consciousness, first, we made a questionnaire that asked subjects to represent images having different colors and shape cones by using 4 pairs of adjectives (KANSEI words). Next, based on the cluster analysis of the questionnaire using a fuzzy set theory, we proposed a hypothesis showing how the analogical relation and the mutual influence work in our mind while viewing the shape models. Furthermore, how the properties of KANSEI depend on their descriptions was also investigated by virtue of the cluster analysis. This work will be valuable to construct a personal KANSEI database regarding the Shape Model Processing System.

• Annals of Pediatric Endocrinology and Metabolism
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• 제23권4호
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• pp.182-195
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• 2018

Increasing prevalence of childhood obesity poses threats to the global health burden. Because this rising prevalence cannot be fully explained by traditional risk factors such as unhealthy diet and physical inactivity, early-life exposure to endocrine disrupting chemicals (EDCs) is recognized as emerging novel risk factors for childhood obesity. EDCs can disrupt the hormone-mediated metabolic pathways, affect children's growth and mediate the development of childhood obesity. Many organic pollutants are recently classified to be EDCs. In this review, we summarized the epidemiological and laboratory evidence related to EDCs and childhood obesity, and discussed the possible mechanisms underpinning childhood obesity and early-life exposure to non-persistent organic pollutants (phthalates, bisphenol A, triclosan) and persistent organic pollutants (dichlorodip henyltrichloroethane, polychlorinated biphenyls, polybrominated diphenyl ethers, per- and polyfluoroalkyl substances). Understanding the relationship between EDCs and childhood obesity helps to raise public awareness and formulate public health policy to protect the youth from exposure to the harmful effects of EDCs.

• 대한임상검사과학회지
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• 제50권4호
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• pp.375-381
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• 2018

세포 유전학적 분석은 인간에서의 다양한 종류의 질환을 연구하고 진단하는데 매우 유용하게 사용되고 있다. 지난 수년 동안 세포 유전학적 분석을 통해 매우 의미 있는 결과를 얻을 수 있었으며, 현재 임상검사실에서 일반적인 검사로 확대되어 질병을 진단하고 결과를 평가하는데 매우 유용하게 사용 되고 있다. Microarray는 분자 세포 유전학적인 방법과 기존의 세포유전학적 방법이 융합된 검사방법으로 기존 검사 방법의 단점을 보완하여 유전 관련 질환을 진단하는데 매우 유용하게 사용되고 있다. 따라서 본 논문은 유전질환 진단에 있어 기존의 일반적인 세포유전학적 방법에서 마이크로어레이를 통한 분자세포유전학적 방법으로 어떻게 전환되어 왔는지, 유전 진단을 하는데 앞으로 이 검사방법들이 얼마나 의미 있게 사용될 것인지에 관하여 고찰하였다.

• Advances in nano research
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• 제7권4호
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• pp.277-292
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• 2019

In this present paper, a new two dimensional (2D) and quasi three dimensional (quasi-3D) nonlocal shear deformation theories are formulated for free vibration analysis of size-dependent functionally graded (FG) nanoplates. The developed theories is based on new description of displacement field which includes undetermined integral terms, the issues in using this new proposition are to reduce the number of unknowns and governing equations and exploring the effects of both thickness stretching and size-dependency on free vibration analysis of functionally graded (FG) nanoplates. The nonlocal elasticity theory of Eringen is adopted to study the size effects of FG nanoplates. Governing equations are derived from Hamilton's principle. By using Navier's method, analytical solutions for free vibration analysis are obtained through the results of eigenvalue problem. Several numerical examples are presented and compared with those predicted by other theories, to demonstrate the accuracy and efficiency of developed theories and to investigate the size effects on predicting fundamental frequencies of size-dependent functionally graded (FG) nanoplates.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Steel and Composite Structures
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• 제52권4호
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• pp.461-473
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• 2024

This study focuses on the reliability of a transmission line under wind excitation and evaluates the failure probability using explicit data resources. The data-driven framework for calculating the failure probability of a transmission line subjected to wind loading is presented, and a probabilistic method for estimating the yearly extreme wind speeds in each wind direction is provided to compensate for the incompleteness of meteorological data. Meteorological data from the Xuwen National Weather Station are used to analyze the distribution characteristics of wind speed and wind direction, fitted with the generalized extreme value distribution. Then, the most vulnerable tower is identified to obtain the fragility curves in all wind directions based on uncertainty analysis. Finally, the failure probabilities are calculated based on the presented method. The simulation results reveal that the failure probability of the employed tower increases over time and that the joint probability distribution of the wind speed and wind direction must be considered to avoid overestimating the failure probability. Additionally, the mixed wind climates (synoptic wind and typhoon) have great influence on the estimation of structural failure probability and should be considered.

• Bioinformatics and Biosystems
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• 제1권1호
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• pp.28-37
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• 2006

Recent studies in molecular biology and proteomics have identified a significant number of novel diagnostic, prognostic, and therapeutic disease markers. However, validation of these markers in clinical specimens with traditional histopathological techniques involves low throughput and is time consuming and labor intensive. Tissue microarrays (TMAs) offer a means of combining tens to hundreds of specimens of tissue onto a single slide for simultaneous analysis. This capability is particularly pertinent in the field of cancer for target verification of data obtained from cDNA micro arrays and protein expression profiling of tissues, as well as in epidemiology-based investigations using histochemical/immunohistochemical staining or in situ hybridization. In combination with automated image analysis, TMA technology can be used in the global cellular network analysis of tissues. In particular, this potential has generated much excitement in cardiovascular disease research. The following review discusses recent advances in the construction and application of TMAs and the opportunity for developing novel, highly sensitive diagnostic tools for the early detection of cardiovascular disease.

• Parasites, Hosts and Diseases
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• 제55권1호
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• pp.15-20
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• 2017

The aim of this study was to identify antigens for a vaccine or drug target to control rabbit coccidiosis. A combination of 2-dimensional electrophoresis, immunoblotting, and mass spectrometric analysis were used to identify novel antigens from the sporozoites of Eimeria stiedae. Protein spots were recognized by the sera of New Zealand rabbits infected artificially with E. stiedae. The proteins were characterized by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF/TOF-MS) analysis in combination with bioinformatics. Approximately 868 protein spots were detected by silver-staining, and a total of 41 immunoreactive protein spots were recognized by anti-E. stiedae sera. Finally, 23 protein spots were successfully identified. The proteins such as heat shock protein 70 and aspartyl protease may have potential as immunodiagnostic or vaccine antigens. The immunoreactive proteins were found to possess a wide range of biological functions. This study is the first to report the proteins recognized by sera of infected rabbits with E. stiedae, which might be helpful in identifying potential targets for vaccine development to control rabbit coccidiosis.

• 한국가스학회지
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• 제16권5호
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• pp.21-27
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• 2012

대학 실험실이나 연구시설에서 실험실 사고에 의해 인적 및 물적 손실이 꾸준히 발생되고 있어서 실험실 사고예방의 필요성이 제기되고 있다. 실험실 사고예방을 위해 무엇보다 중요한 것은 사고의 체계적인 분석이다. 국내에서 산업재해에 관한 분석 연구는 수행되어 왔으나, 실험실 사고 분석에 관한 연구는 수행되지 않았다. 본 연구에서는 실험실 사고예방 대책 수립을 위해 설문지를 개발하였고, 설문지는 대학과 연구기관에 전자우편이나 방문조사로 사고사례를 수집하였다. 사고사례 데이터는 계절별, 사고 분류형태, 발생형태, 기인물 및 상해종류 별로 분석 고찰하였다. 이 연구결과는 실험실 종사자의 안전 확보와 사고예방을 위한 기초자료로 활용하고자 하였다.

• 한국수소및신에너지학회논문집
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• 제27권6호
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• pp.693-701
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• 2016

Steam gasification of low rank coals is possible at relatively low temperature and low pressure, and thus shows higher efficiency compared to high rank coals. In this study, the gasification reactivity of four different Indonesian low rank coals (Samhwa, Eco, Roto, Kideco-L) was evaluated in $T=700-800^{\circ}C$. The low rank coals containing $53.8{\pm}3.4$ wt% volatile matter in proximate analysis and $71.6{\pm}1.2$ wt% carbon in ultimate analysis showed comparable gasification reactivity. In addition, $K_2CO_3$ catalyst rapidly accelerated the reaction rate at $700^{\circ}C$, and all of the coals were converted over 90% within 1 hour. The XRD analysis showed no significant difference in carbonization between the coals, and the FT-IR spectrum showed similar functional groups except for differences due to moisture and minerals. TGA results in pyrolysis ($N_2$) and $CO_2$ gasification atmosphere showed very similar behavior up to $800^{\circ}C$ regardless of the coal species, which is consistent with the steam gasification results. This confirms that the indirect evaluation of the reactivity can be made by the above instrumental analyses.