Continuous speech recognition for the TIMIT database using neural networks

Four types of neural networks which have previously been established for speech recognition and tested on a small, seven-speaker, 100-sentence database are applied to the TIMIT database. The networks are a recurrent network phoneme recognizer, a modified Kanerva model morph recognizer, a compositional representation phoneme-to-word recognizer, and a modified Kanerva model morph-to-word recognizer. The major result is for the recurrent net, giving a phoneme recognition accuracy of 57% from the si and sx sentences. The Kanerva morph recognizer achieves 66.2% accuracy for a small subset of the sa and sx sentences. The results for the word recognizers are incomplete.

Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Dynamic learning with the EM algorithm for neural networks

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We find that the method is intrinsically very powerful, simple and stable.

Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.

Physical Blocking Neural Tube Closure Affects Radial Intercalation and Neural Crest Midline-directed Migration in Xenopus Dorsal Explants

神经管闭合缺陷(NTDs)是一种严重的先天畸形疾病,在新生儿中有千分之一的发病率.神经管融合前后,多种组织参与形态发生运动.神经管一经融合,神经嵴细胞就会向背侧中线方向产生单极突出并向此方向迁移形成神经管的顶部.与此同时,神经管从腹侧开始发生辐射状切入以实现单层化.在此,我们在非洲爪蟾的移植体中机械阻断神经管的闭合以检测其细胞运动及随后的图式形成.结果显示神经管闭合缺陷的移植体不能形成单层化的神经管,并且神经嵴细胞滞留在侧面区域不能向背侧中线迁移,而对神经前体标记基因的检测显示神经管的背腹图式形成并未受到影响.以上结果表明神经管的融合对于辐射状切入和神经嵴细胞向背侧中线方向的迁移过程是必需的,而对于神经管的沿背腹轴方向的图式形成是非必需的.

Xenopus RCOR2 (REST corepressor 2) interacts with ZMYND8, which is involved in neural differentiation

Regulation of neuronal gene expression is critical to nervous system development. REST (RE1-silencing transcription factor) regulates neuronal gene expression through interacting with a group of corepressor proteins including REST corepressors (RCOR). Here we show that Xenopus RCOR2 is predominantly expressed in the developing nervous system. Through a yeast two-hybrid screen, we isolated Xenopus ZMYND8 (Zinc finger and MYND domain containing 8) as an XRCOR2 interacting factor. XRCOR2 and XZMYND8 bind each other in co-immunoprecipitation assays and both of them can function as transcriptional repressors. XZMYND8 is co-expressed with XRCOR2 in the nervous system and overexpression of XZMYND8 inhibits neural differentiation in Xenopus embryos. These data reveal a RCOR2/ZMYND8 complex which might be involved in the regulation of neural differentiation. (C) 2010 Elsevier Inc. All rights reserved.