Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations

We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (48.4-773 nJ/image).Comment: 2017 IEEE Biomedical Circuits and Systems (BioCAS

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging

Numerical Simulation of Foam Flow in Annulus With Wellbore Heat Transfer

In the study of hydraulic parameters in foam drilling, most foam hydraulic models were built based on the assumption that wellbore temperature was equal to formation temperature. But energy exchange takes place among annular foam fluid, formation and drill string, there may be a discrepancy between wellbore temperature and formation temperature. The effects of heat transfer on foam flow in annulus were investigated by numerical simulation in this paper. Simulation results show that due to the influence of temperature on pressure, the changes of annular pressure are transient. And foam fluid density, viscosity and other physical parameters are change with pressure. During foam fluid transports the solid particles from bottom to surface, solid particles accelerate and foam decelerates. When high solid particles concentration zone is reached, foam velocity decreases to a minimum value, and then increases due to the decrease of solid particle concentration. When cuttings velocity increases to a certain value, cuttings transport with a constant velocity.Key words: Heat transfer; Foam drilling; Annulus pressure; Foam velocity; Numerical simulatio

Analysis on Influential Factors of Well Temperature for Deepwater Drilling

Wellbore circulating temperature must be predicted accurately to prevent gas hydrate and safe well construction operations during deepwater drilling. A model for predicting wellbore temperature distribution in deep water wells during circulation has been developed in terms of thermodynamics theory in this paper. And the influential factorsare analyzed. Model calculation results indicate that temperature profile is strongly dependent on mud specific heat and thermal conductivity, mud density and flow rate dependence of temperature effects is small. Wellbore temperature is dynamic, temperature increases with the increase of circulating time, and tend to be constant when circulating time reaches a certain value. And geothermal gradients of formation under mud line have a significant influence on wellbore temperature.Key words: Temperature distribution; Deepwater drilling; Thermodynamics theory; Influential factor

Mechanistic Modeling of Upward Gas-Liquid Flow in Deviated Wells

Underbalanced drilling (UBD) has increased in recent years because of the many advantages associated with it. The precise wellbore pressure prediction is the key for safe and efficient underbalanced drilling. With the quantity of deviated and horizontal wells using UBD increases, pressure prediction of these wells is important. In this paper, a new mechanistic model has been developed to predict flow pattern and calculate flow behavior for each pattern in deviated annular during UBD operation. And the proposed model has been validated with field data. In addition, a comparison of the model results against two empirical models indicating the presented models perform better in predicting two phase flow parameters in UBD operation.Key words: Underbalanced drilling (UBD); Deviated wells; Mechanistic modelin

Age-related sensitivity and pathological differences in infections by 2009 pandemic influenza A (H1N1) virus

<p>Abstract</p> <p>Background</p> <p>The highly pandemic 2009 influenza A H1N1 virus infection showed distinguished skewed age distribution with majority of infection and death in children and young adults. Although previous exposure to related antigen has been proposed as an explanation, the mechanism of age protection is still unknown.</p> <p>Methods</p> <p>In this study, murine model of different ages were inoculated intranasally with H1N1 (A/Beijing/501/09) virus and the susceptibility and pathological response to 2009 H1N1 infection were investigated.</p> <p>Results</p> <p>Our results showed that the younger mice had higher mortality rate when infected with the same dose of virus and the lethal dose increased with age. Immunohistochemical staining of H1N1 antigens in mice lung indicated infection was in the lower respiratory tract. Most bronchial and bronchiolar epithelial cells in 4-week mice were infected while only a minor percentage of those cells in 6-month and 1-year old mice did. The young mice developed much more severe lung lesions and had higher virus load in lung than the two older groups of mice while older mice formed more inducible bronchus-associated lymphoid tissue in their lungs and more severe damage in spleen.</p> <p>Conclusions</p> <p>These results suggest that young individuals are more sensitive to H1N1 infection and have less protective immune responses than older adults. The age factor should be considered when studying the pathogenesis and transmission of influenza virus and formulating strategies on vaccination and treatment.</p