基于并行计算的盾构机刀盘三维切削仿真

针对盾构机工作时刀盘切削土体的数值仿真，建立了细致分析模型，并利用任意拉格朗日欧（ALE）和流固耦合（FSI）理论及方法未解决传统有限元方法进行切削仿真时易引发的网格大变形问题；在国产曙光4000A超级计算机上利用LS-DYNAMPP970完成了并行计算，采用了传统递归坐标对分、以夏根据仿真模型特点提出的基于耦合均衡坐标对分等两种区域分解策略。结果表明，ALE-FSI方法能有效处理刀盘切割土体仿真时的网格大变形，仿真数据与试验数据对比也比较一致：通过促进负载均衡，在计算时CBCB法比RCB法体现出了更好的加速比和并行效率。     

Parametric adaptive time-frequency representation based on time-sheared Gabor atoms

A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

极端嗜热微生物分子伴侣蛋白研究与嗜热功能蛋白质的规模化制备技术研究

极端微生物是丰富的资源宝库,有着巨大的生物技术开发前景。本文从特殊功能蛋白质的发现与表征、结构与功能及潜在性应用．以及特殊功能蛋白质的规模化制备技术两个方面,对极端微生物的资源开发与利用进行了探讨。重点介绍了极端嗜热古菌Pyrococcus furiosus分子伴侣蛋白系统的一些研究,并以P．furiosus胞外α-淀粉酶PFA为例,对工业生物技术领域重组蛋白质的规模化制备技术进行了讨论。     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

5G移动通信发展趋势与若干关键技术

第5代移动通信系统(5G)是面向2020年之后的新一代移动通信系统,其技术发展尚处于探索阶段.结合国内外移动通信发展的最新趋势,本文对5G移动通信发展的基本需求、技术特点与可能发展途径进行了展望,并分无线传输和无线网络两个部分,重点论述了富有发展前景的7项5G移动通信关键技术,包括大规模天线阵列、基于滤波器组的多载波技术、全双工复用、超密集网络、自组织网络、软件定义网络及内容分发网络.本文还概括性地介绍了国内5G移动通信的相关研发活动及其近期发展目标.     

单样本条件下基于图像增强和Fourier频谱的人脸识别

目前有许多处理正面视觉人脸的识别方法,当有充分数量的有代表性的样本时,能取得较好的识别效果。然而当处理单样本识别问题时,现有的许多方法的识别率将明显下降或甚至不适用。为了加强单训练样本的分类信息,训练样本与其基于受扰动的奇异值的重构图组合成新样本,Fourier频谱作为人脸识别特征,在ORL人脸库上的实验结果表明了该方法的有效性。     

基于EMD能量占比的海面漂浮小目标特征检测

针对传统的时频分析方法对海杂波分析有限的问题,提出一种基于经验模态分解(empirical mode decomposition,EMD)能量占比的海面漂浮小目标特征检测方法.首先,采用EMD将接收回波分为独立不同尺度的若干个固有模态(intrinsic mode function,IMF)分量,实现对接收回波的频率从...     

基于熵评价模态分解的雷达信号双谱特征识别

针对在低信噪比下雷达信号调制识别准确率低、抗噪性差的问题,提出一种基于熵评价模态分解和双谱特征提取的识别方法.利用双谱可以抑制高斯噪声的特点,分析了在低信噪比下进行信号调制识别的可行性并引入了噪声项.由于噪声项的干扰,双谱在0 dB以下时,噪声抑制效果变差,提出了基于信息熵评价的经验模态化分解对信号进行预处理,提高信噪...