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Exploiting Language Models to Classify Events from Twitter
Vo, Duc-Thuan,Hai, Vo Thuan,Ock, Cheol-Young Hindawi Publishing Corporation 2015 Computational intelligence and neuroscience Vol.2015 No.-
<P>Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events.</P>
Thuan Nguyen Van,노혁천 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.4
In this study, the stochastic finite element solution is given to obtain the response variability in the natural frequency of Functionally Graded Material (FGM) beam due to uncertain structural parameters. Among others, the elastic modulus and mass density are chosen to have randomness along the beam axis direction, which are modeled as one-dimensional homogeneous stochastic processes. The stochastic analysis of the FGM beam is performed in conjunction with Monte Carlo simulation (MCS). Random samples of random parameters are generated based on the spectral representation method. The response variability in the natural frequency of FGM beam, due not only to the single random parameter of elastic modulus or mass density but also to both of the random parameters taken into account at the same time is investigated. The effect of respective random parameters on the response variability of natural frequency and the effect of correlation between the two random parameters as well are discussed in detail.
STS 304 스테인리스강의 대기중 1050~1200˚C, 1시간 동안의 산화
Thuan Dinh Nguyen,이동복 대한금속·재료학회 2009 대한금속·재료학회지 Vol.47 No.4
The STS304 stainless steel was oxidized isothermally and cyclically at temperatures between 1050 and 1200˚C for 1 hr in air. During isothermal oxidation, it displayed good oxidation resistance at 1050˚C. However, it suffered from breakaway oxidation above 1100˚C, being accompanied with internal oxidation. During cyclic oxidation, it also displayed good oxidation resistance at 1050˚C, but it suffered from massive weight loss above 1125˚C. The oxide scales formed consisted primarily of Fe2O3, Fe3O4 with and without Cr2O3. They were generally non-adherent.