基于增强高阶非凸全变分模型的图像去噪算法

为了在缓解阶梯效应的同时更好地保留去噪后图像的细节信息,提出一种基于增强高阶非凸全变分(higher order non-convex total variation, HONTV)模型的图像去噪算法。该算法将每一次去噪后的图像和原始图像取平均作为增强HONTV模型下一次循环的输入并更新参数,然后采用增广拉格朗日乘子法和交替方向乘子法进行循环求解,经过多次迭代,最终得到的去噪图像包含较多的细节信息。在基于全变分的图像去噪方法中,对添加不同标准差大小的高斯白噪声的测试图像和视频进行实验。实验结果表明,所提算法在视觉性能和客观评价指标方面均优于对比算法。     

净浆裹石工艺在配制高强混凝土中的应用研究

采用普通原材料和净浆裹石工艺,通过正交设计试验研究,配制了强度为80 M Pa的高强混凝土,给出了优选配比。试验结果表明:净浆裹石搅拌工艺可以改善界面过渡区结构,提高混凝土的强度和耐久性。     

外加轴向磁场对短路过渡的影响

为提高短路过渡频率、减少焊接飞溅,分别通过施加直流与交变轴向磁场,研究了轴向磁场对短路过渡的影响规律,分析了高速摄像、电流电压信号以及熔滴在磁场作用下的短路过渡受力情况.结果表明:当直流磁场励磁电流为4 A时,短路过渡频率达到最大值;当交流磁场励磁电流为9 A、磁场频率为100 Hz时,短路过渡频率达到最大值,这与电流...     

A decision rule to minimize daily capital charges in forecasting value‐at‐risk

Under the Basel II Accord, banks and other authorized deposit‐taking institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of value‐at‐risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realized losses are above the estimated risk. In this paper we analyze the profit‐maximizing problem of an ADI subject to capital requirements under the Basel II Accord as ADIs have to choose an optimal VaR reporting strategy that minimizes daily capital charges. Accordingly, we suggest a dynamic communication and forecasting strategy that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor's 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits. Copyright © 2009 John Wiley & Sons, Ltd.     

水肥高效利用灌排系统调控面临的问题研究

通过调查我国水肥利用的现状,指出我国农业水利中干旱缺水、非点源污染、水资源利用率低下和灌排系统不配套等问题,提出灌区水肥高效利用需深入研究的热点问题,对探讨水肥高效利用和灌排系统管理理论,指导我国灌排系统精量调控,以最少的水肥投入量获取最佳的灌区产量和经济及生态环境效益,具有重要意义.     

一种适用于高阶QAM载波恢复的改进型稳定性算法

本文提出了一种结合频率相位载波恢复的数字QAM接收系统,将非零均值引入到Stop—and—Go控制器中,有效地解决了稳定状态的误差偏移较大的问题。仿真结果表明系统在15dB信噪比时,采用载波恢复电路只需7500个符号周期即可捕获达到6％符号率的频偏和30。的相偏,同时其稳态相位抖动亦显著减少到0．07。     

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.     

磁流体-泡沫金属减振器磁路优化设计

利用ANSYS软件对磁流体-泡沫金属减振器的磁路进行数值模拟研究,对影响其性能的磁路级数及相邻两级线圈中电流流向的异同进行了分析.研究结果表明,相同条件下采用多级磁路较单级磁路具有较多的优点,并且多级磁路中相邻激励线圈绕向相反能够更好的改善磁路性能.本研究为磁流体-泡沫金属减振器的设计提供依据.     

便携式发电机整流稳压电源设计

针对便携式发电机的特殊要求,给出了一种能够满足宽压和宽频输入的整流电源的设计方案。为了使系统获得良好的稳态和动态性能,在系统中加入了反馈控制和前馈控制相结合的复合控制。实验证明,该系统不仅能够适应宽压和宽频输入,而且在输入电压和频率大幅度变化时仍能稳定可靠地工作。     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.