This paper presents a potential approach to settle the problem of surviving major safety accidents in Submerged Floating Tunnel （SFT） that detachable emergency escape devices are set up outside SFT. The Computationa...This paper presents a potential approach to settle the problem of surviving major safety accidents in Submerged Floating Tunnel （SFT） that detachable emergency escape devices are set up outside SFT. The Computational Fluid Dynamics （CFD） technology is used to investigate the effect of emergency escape devices on the hydrodynamic load acting on SFT in uniform and oscillatory flows and water waves by numerical test. The governing equations, i.e., the Reynolds-Averaged Navier-Stokes （RANS） equations and k - ε standard turbulence equations, are solved by the Finite Volume Method （FVM）. Analytic solutions for the Airy wave are applied to set boundary conditions to generate water wave. The VOF method is used to trace the free surface. In uniform flow, hydrodynamic loads, applied to SFT with emergency escape device, reduce obviously. But, in oscillatory flow, it has little influence on hydrodynamic loads acting on SFT. Horizontal and vertical wave loads of SFT magnify to some extend due to emergency escape devices so that the influence of emergency escape devices on hydrodynamic loads of SFT should be taken into consideration when designed.展开更多
Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been c...Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus （SFT） based on latent Dirichlet allocation （LDA） and information gain （IG） models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine （SVM） and Naive Bayes Multinomial.展开更多
基金the China Postdoctoral Science Foundation (Grant Nos. 201003274, 20090460636)the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20090111120016)
文摘This paper presents a potential approach to settle the problem of surviving major safety accidents in Submerged Floating Tunnel （SFT） that detachable emergency escape devices are set up outside SFT. The Computational Fluid Dynamics （CFD） technology is used to investigate the effect of emergency escape devices on the hydrodynamic load acting on SFT in uniform and oscillatory flows and water waves by numerical test. The governing equations, i.e., the Reynolds-Averaged Navier-Stokes （RANS） equations and k - ε standard turbulence equations, are solved by the Finite Volume Method （FVM）. Analytic solutions for the Airy wave are applied to set boundary conditions to generate water wave. The VOF method is used to trace the free surface. In uniform flow, hydrodynamic loads, applied to SFT with emergency escape device, reduce obviously. But, in oscillatory flow, it has little influence on hydrodynamic loads acting on SFT. Horizontal and vertical wave loads of SFT magnify to some extend due to emergency escape devices so that the influence of emergency escape devices on hydrodynamic loads of SFT should be taken into consideration when designed.
基金Project (No. 20111081023) supported by the Tsinghua University Initiative Scientific Research Program, China
文摘Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus （SFT） based on latent Dirichlet allocation （LDA） and information gain （IG） models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine （SVM） and Naive Bayes Multinomial.
