一种基于波动方程的新高阶FDTD方法

 提出了一种基于二阶波动方程的（2M,4）高阶时域有限差分(FDTD)方法，通过使用辛积分传播子(SIP)在时域上获得4阶精度，使用离散奇异卷积(DSC)方法在空域上达到2M阶精度。与已有的(2M，4) 阶FDTD方法相比，虽然两者都采用SIP和DSC方法，但是此二者的不同点在于：第一，新方法基于二阶波动方程；第二，在离散计算空间时使用单一网格而不是传统的Yee网格；第三，单独计算某一场分量从而节约内存并减少计算量。数值计算结果表明，与传统高阶算法相比，基于波动方程的高阶FDTD方法耗费的机时只有它的50%,内存消耗下降10%, 而两者的计算结果之间相对误差小于5‰。     

三代微光像增强摄像系统选通成像系统研究

提出一种能够高速选通工作的三代微光像增强摄像系统(ICCD)。采用三代像增强器,设计了光阴极选通控制电路,使ICCD系统能够高速选通成像;通过分析、比较,设计了双高斯复杂化结构的中继镜头耦合方案,获得了高透过率、高调制传递函数(MTF)的图像耦合性能。采用数字CCD、数字图像采集卡及其集成的图像处理硬件,编写了相关处理软件,使ICCD系统的图像能够快速呈现。结果表明:设计的三代选通ICCD具有33 ns选通门宽,空间分辨力达到600 TVL。     

太阳模拟器中正方形光学积分器的设计与分析

为了解决太阳模拟器辐照均匀度问题,设计一种可以明显提高太阳模拟器辐照均匀度的正方形光学积分器。根据太阳模拟器以及光学积分器的组成与工作原理,对正方形光学积分器各光学参数进行理论设计,并且分析积分器像差对工作面均匀性的影响。利用Lighttools软件对太阳模拟器系统进行仿真,提出了投影镜离焦改善辐照面均匀性的优化方法。设计的积分器可使太阳模拟器在200 mm×200 mm正方形辐照面内的辐照不均匀度达到172 %。     

A study of extended optimization of U-type rod for LED projectors

The paper proposes a new U-type integral rod for a miniature DLP projector's optical engine with a high-power LED source and L-type optics in order to minimize volumetric size of optical engine. In addition, Taguchi method is applied in this research in order to extendedly optimize the performance of optical engine after initial optimization by damped least square (DLS). A miniature optical engine with LED light source has the advantages of good power consumption, useable projected image, reasonable brightness and uniformity, compactness in volumetric size and decent performance specifically for high spatial frequencies. However, there are still rooms for development with regard to light efficiency and uniformity due to non-uniformity of LED light distribution. Two critical design methods are proposed in this research. Firstly, a “U-type integrator” working with high-power LED which not only causes rays to make a “U-turn” in order to minimize volumetric size of optical engine but also, after optimization, controls output uniformity well after extended optimization by Taguchi method. With regard to gain of optical efficiency, we also consider the factors of a U-type integrator: shape and length with a Taguchi optimizer. It is concluded that the newly developed design achieves good results for the performance and volumetric size of the module. Extended optimized parameter design is able to improve the luminous flux by 4.84%, the illumination uniformity 5.62% and the packing size by 12%.     

用于投影显示的匀光杆照明系统设计

基于DMD和LCoS空间光调制器的投影显示对照明系统提出了更高的要求,有必要对原有的匀光杆照明系统进行分析和改进。着重于建立一个精确的匀光杆照明系统模型,对各个参数进行了细致的分析和总体的优化。同时提出了一种高效率的组合反射镜模型,通过LightTools软件模拟仿真,实现了ANSI照明均匀性为( 4．2％,-4．6％),光能利用率为82％的照明输出。能够满足投影显示对照明系统的指标要求。     

Boxcar积分器模型参数选择

利用计算机运算速度快、描述形象的特点设计一模型参数选择软件,可根据被恢复波形的要求灵活地选择、计算或修正参数值,给 Ｂｏｘｃａｒ 的使用带来了很大的方便     

COVID-19 Epidemic Prediction Based on Deep Learning

In this paper, a multi-layer gated recurrent unit neural network (multi-head GRU) model is proposed to predict the confirmed cases of the new crown epidemic (COVID-19). We extract the time series relationship in the data, and the rolling prediction method is adopted to ensure the simple structure of the model and achieve higher precision and interpretability. The prediction results of this model are compared with the LSTM model, the Transformer model and the infectious disease model (SIR). The results show that the proposed model has higher prediction accuracy. The mean absolute error (MAE) of epidemic prediction in most countries (the United States, Brazil, India, the United Kingdom and Russia) is respectively 197.52, 68.02, 200.67, 24.78 and 123.50, which is much smaller than the prediction error of the SIR model, LSTM model and Transformer model. For the spread of the COVID-19 epidemic, traditional infectious disease models and machine learning models cannot achieve more accurate predictions. In this paper, we use a GRU model to predict the real-time spread of COVID-19, which has fewer parameters and reduces the risk of overfitting to train faster. Meanwhile, it can make up for the shortcoming of the transformer model to capture local features.