激光窗口热效应和应力双折射的分析

 分析了平板窗口在激光加热和内外压强差作用下对光束带来的附加相移，详细讨论了应力双折射部分的相移，计算了CaF₂窗口的温升、应力、附加相移、远场Strehl比和退偏度等的分布。结果表明：窗口变形和折射率随温度变化是产生附加相移的主要原因，窗口的非均匀温升将导致光束质量下降，应力双折射部分的相移与窗口表面Miller指数有关，对入射光产生一定的退偏效应，从而影响光束质量。     

主客观相结合眼波面像差分析系统

利用Hartmann-Shack波面传感器对眼出瞳位置的波面像差进行测量,根据所测像差控制可变形镜对像差进行实时补偿.眼像差以Zernike多项式表示,通过对可变形镜的精确控制,可在光路中生成特定模式和幅值的像差.因此,被测者根据视觉的主观感觉,调节视标像差,直至获得最满意的视觉效果.该系统采用移动三棱镜和柱镜的方法对离焦和散光进行预补偿,弥补了波面传感器测量范围和可变形镜矫正范围受限制的缺点.该系统把快速、客观的测量及补偿和主观调整相结合,更加符合人眼工作的特性,可用于在人眼像差与视觉分辩的基础和临床研究.     

反馈调节的局部正则化鲁棒光流估计

提出了一种引入反馈调节机制的新局部鲁棒光流场计算思想.该思想结合局部法和全局法的特点,首先划分整场为有公共边界的子块,定义距离来判断每个点的子块归属,然后在每个子块内进行光流约束和平滑约束正则化的优化计算.将光流约束中的光强残差作为控制参量,衡量光流准确度的峰值信噪比作为输出,经反馈处理,调节控制参量使光流估计准确度趋向最优.实验结果表明,这种新的结合反馈调节机制局部光流场计算法能使光流准确度获得提升.     

气溶胶前向散射大气能见度测量系统传递系数的标定及校准方法

分析了气溶胶的前向角散射特性,利用标准的大气透射仪标定前向散射大气能见度测量系统的传递系数。当系统结构发生变化时,传递系数也随之改变,为保证系统测量的准确性和简化系统传递系数的标定过程,提出了一种利用标准漫透射板校准系统传递系数的方法。该方法使用了两块具有相同透射系数的漫透射板,使气溶胶粒子团在前向半球上产生漫散射,此时探测器的测量值即为包含了仪器常数的定值。当系统结构改变时,这个测量值相对于原测量值的变化率作为比例系数代入系统传递系数的计算,实现校准。     

空间摄像机的热设计

根据空间摄像机的所处空间环境和结构特点,对其进行了热设计.采用被动热控措施进行热隔离和热疏导,充分利用了摄像机所搭载的卫星平台的热容;采用主动热控措施进行温差补偿,将温度拉平至热控指标范围之内.根据所采用的热控措施,针对空间摄像机进行了仿真分析,得到了满意的结果.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

Small world effects in the harmonious unifying hybrid preferential model (HUHPM) networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE model, so-called HUHPM-BA, HUHPM-BBV and HUHPM-TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-weighted models but also for the weighted models.     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

冲击加载条件下材料之间摩擦系数的确定

尝试利用自制分离式霍普金森压剪装置对聚氨酯泡沫塑料、硅橡胶和MDF水泥材料与铝合金在冲击加载条件下的摩擦系数进行测试.结果表明:在冲击加载条件下,聚氨酯泡沫塑料、硅橡胶和MDF水泥与铝杆之间的摩擦系数与材料的性质关系不大,其摩擦系数测试结果存在一定的分散性,摩擦曲线出现抖动,且与加载条件有关,摩擦系数比通常意义下所得到的摩擦系数小;不同加载条件对所测试材料与铝压杆之间的摩擦系数数值影响不大,只是曲线的走势稍有不同.在冲击加载条件下硬质聚氨酯泡沫塑料与铝杆之间的静摩擦系数为0.29,动摩擦系数为0.25;硅橡胶与铝杆之间的静摩擦系数为0.285,动摩擦系数为0.24;MDF水泥与铝合金杆之间的静摩擦系数为0.28～0.29,动摩擦系数约0.23.     

具有数据丢失的网络化控制系统的基于观测器的镇定

研究具有数据丢失的网络化控制系统的基于观测器的镇定问题。受控对象是由T-S模糊模型描述的非线性时滞系统。把数据丢失过程建模成独立同分布过程。利用模糊Lyapunov函数方法得到了闭环系统随机稳定的充分条件,其基于观测器的反馈控制器可通过求解一组线性矩阵不等式来设计。一个仿真例子说明方法的有效性。