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.     

基于集中式模型的我国各地区及三大产业的DEA效率评价

基于集中式模型, 首先在考虑地区内部三大产业的情况下, 对2010年我国31个地区展开DEA效率评价, 然后在保持各地区整体效率不变的前提下进行效率分解, 确定各产业的效率值和相对重要程度. 此外, 对各地区三大产业的规模收益问行了研究. 实证分析结果表明, 各地区的整体效率值偏低, 第一产业的效率重要程度偏低, 第二产业和第三产业的效率均值相差不大, 但大部分地区的产业比第二产业的相对重要程度高. 此外, 从规模收益角度来看, 大部分地区整体处于规模收益递减阶段, 大多数地区的第一和第三产业均处于规模收益递增阶段, 绝大多数地区的第二产业已处于规模收益递减阶段.     

关于酉不变范数的几个不等式

首先,得到并证明了酉不变范数的几个不等式;然后,将其与以前的不等式进行了比较.结果表明,新不等式比旧不等式更精细.     

基于烟花算法的基站群协作计算卸载模型研究

车联网、AR、AI等计算密集、时延敏感型应用迅速发展,而移动设备因自身计算能力相对不足,执行此类应用任务时会因高时延而严重影响用户体验甚至无法满足用户需求。针对此问题,提出综合考虑时延与成本的多用户、多MEC (mobile edge computing)服务器的基站群协作计算卸载模型。并提出基于凸优化的改进烟花算法（improved fireworks algorithm based on convex optimization, CVX-FWA）来对模型进行求解,对用户任务进行合理的卸载与资源分配。仿真结果表明,提出的计算卸载方案有效降低了任务总时延成本值,实现计算卸载资源的整体优化配置。     

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.     

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.     

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...     

MODELLING RANDOM CONVEX SETS

A model is developed for convex-set valued data,which are the Minkowski sum of aconvex parameter set and a convex noise set.This model is generalized to include location and scaleparameters.A further generalization results in a model for convex sets which is analogous to aone-way analysis of variance models.In each case,estimators of the parameters are given which,under certain conditions,are shown to be consistent.Data generated from the model and theestimators are studied.An outlier detection method is examined.