Numerical simulation and experimental analysis of the sintered micro-parts using the powder injection molding process

Microsystem Technologies - This paper discusses in detail the development of numerical simulations capable of simulating structural evolution and macroscopic deformation during a powder injection...     

一种稳健的基于Visemic LDA的口形动态特征及听视觉语音识别

视觉特征提取是听视觉语音识别研究的热点问题。文章引入了一种稳健的基于Visemic LDA的口形动态特征,这种特征充分考虑了发音时口形轮廓的变化及视觉Viseme划分。文章同时提出了一利利用语音识别结果进行LDA训练数据自动标注的方法。这种方法免去了繁重的人工标注工作,避免了标注错误。实验表明,将'VisemicLDA视觉特征引入到听视觉语音识别中,可以大大地提高噪声条件下语音识别系统的识别率;将这种视觉特征与多数据流HMM结合之后,在信噪比为10dB的强噪声情况下,识别率仍可以达到80％以上。     

Double diffusive Buoyancy-driven flow in a fluid-saturated elliptical annulus with a neural network-based prediction of heat and mass transfer

This paper presents a numerical study of buoyancy-driven double-diffusive convection within an elliptical annulus enclosure filled with a saturated porous medium. An in-house built FORTRAN code has been developed, and computations are carried out in a range of values of Darcy–Rayleigh number Ra_m (10 ≤ Ra_m ≤ 500), Lewis number Le (0.1 ≤ Le ≤ 10), and the ratio of buoyancy forces N (−5 ≤ N ≤ 5). In addition, three methods are used, namely the multi-variable polynomial regression, the group method of data handling (GMDH), and the artificial neural network (ANN) for the predictions of heat and mass transfer rates. First, results are successfully validated with existing numerical and experimental data. Then, the results indicated that temperature and concentration distributions are sensitive to the Lewis number and thermal and mass plumes are developing in proportion to the Lewis number. Two particular values of Lewis number Le = 2.735 and Le = 2.75 captured the flow's transition toward an asymmetric structure with a bifurcation of convective cells. The average Nusselt number tends to have an almost asymptotic value for Le » 5. For the case of aiding buoyancies N > 1, the average Nusselt Number $\overline{\mathit{Nu}}$ decreased by 33% when the Lewis number increased to its maximum value. Then, it increased by 10% when the Lewis number increased to Le = 1 for the case of opposing buoyancies N < 1 and then decreased by 33% when the Lewis number increased to its maximum value., contrary to the behavior of the average Sherwood number $\overline{\mathit{Sh}}$ that increased by 700% for both cases N > 1 and N < 1. New correlations of $\overline{\mathit{Nu}}$, and $\overline{\mathit{Sh}}$ as a function of Ra_m, Le, and N are derived and compared with GMDH and ANN methods, and the ANN method showed higher performance for the prediction of $\overline{\mathit{Nu}}$ and $\overline{\mathit{Sh}}$ with R² exceeding 0.99.