闪电光电信号的同步观测与闪电类型模式识别

 利用宽带电场仪和光学纹波探头,对2006年夏季广东省从化地区发生的自然闪电的光电信号进行了同步观测,并对结果进行了统计分析。统计结果表明：闪电光信号脉冲的发生时间和峰值到达时间总是滞后于电信号,并且脉冲上升沿和宽度也比电信号宽;云闪的光信号在上述特征参数的滞后量、上升沿时间和脉宽时间差要比地闪回击放电大。从闪电光电信号时域信号特征的角度,利用BP人工神经网络,对闪电类型进行了模式识别研究。总体识别结果基本上能够满足要求,但是由于云闪放电过程比较复杂,获得的数据较少,所以对云闪识别的效果并不理想。     

重氮化法合成磺胺间甲嘧啶免疫抗原的研究

采用重氮化法将磺胺间甲嘧啶(SM1)与牛血清蛋白(BSA)偶联制备合成抗原BSA-SM1作免疫抗原;用紫外扫描法和聚丙烯酰胺电泳法对其进行鉴定,并测得偶联的结合比。通过紫外扫描和聚丙烯酰胺电泳法的测定证明偶联成功,偶联的结合比为9∶1。用BSA-SDM免疫BALB/C小鼠,间接ELISA测定多抗上清(pAb)效价,结果表明,获得了高效价的pAb,为SM1残留免疫学检测方法的建立奠定了基础。     

青海世居土族、藏族和回族人发中微量元素的人工神经网络研究

用原子吸收光谱法对青海土族、藏族和回族青年人体头发中的Ca、Fe、Zn、Cu、Mg、Mn和Cd等7种元素的含量进行了测定,采用概率神经网络方法进行了比较研究。研究表明,在3个民族的人发之间,7种元素含量的综合水平存在着较显著的差别,概率神经网络方法可以用于对这3个民族样品进行判别和预测,其预测结果优于F isher法或Bayes法。     

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.     

基于非线性光谱混合模型的遥感影像提取城市不透水层

针对传统方法在提取城市不透水层中的许多局限性,采用两种非线性光谱混合分解模型,包括混合调谐匹配滤波和多层感知器神经网络,通过混合像元分解获取城市不透水层.混合调谐匹配滤波利用用户选择的端元,通过最大化端元响应并减少未知背景信息的影响,进行局部分解端元.多层感知器由多个感知器组成,能够很好的进行非线性学习.对Landsa...     

两阶段串联可修系统的统计过程控制与机会维修整合研究

针对两阶段串联可修系统,研究统计过程控制(SPC)与机会维修策略整合的问题。首先,考虑系统异常因素和完全故障情形下,根据过程演变分析可能的场景,给出每个场景发生的概率;其次,基于上述场景,分析各种维修行为发生的概率;然后,考虑人力成本、生产率以及停时损失,从成本的角度,根据更新报酬理论构建整合统计过程控制与机会维修策略的成本模型;再次,采用具体实例来比较分析所提出的成本模型与单独的维修策略模型,其结果表明构建的模型在节约成本方面有着明显的优势;最后,运用分式析因设计对模型输入参数进行了敏感性分析。     

基于填充函数与DCD的全局优化算法及其反演

岩土工程中以监测位移为已知信息的反演问题可通过带未知变量约束空间的优化模型去求解.该模型中的优化函数常具有非线性、非凸性等特点,使得反演结果容易陷入局部最优的困境.为了应对在运用优化算法反演此类问题时存在的困境,并提高其算法效率,依据填充函数优化思想与DCD(Dynamic Canonical Descent)思想在反...     

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.     

锂金属负极界面修饰及其在硫化物全固态电池中的应用

随着我国新能源产业的快速发展,全固态电池由于其理论上的高能量密度和高安全性受到广泛关注,而硫化物全固态电池具有离子电导率高的优势成为目前的研发热点,但是金属锂负极的锂枝晶生长和与硫化物电解质之间的不稳定性严重阻碍了硫化物全固态电池的研发.本工作在高温150℃下制备了均匀的LiF界面层来抑制金属锂负极/硫化物电解质之间的界面反应和锂枝晶.LiF/Li之间具有较高的界面能,所以可以有效抑制锂枝晶的生长.LiNbO₂@LiCoO₂//Li₆PS₅Cl//LiF@Li (LNO@LCO//LPSCl//LiF@Li)全电池0.05 C, 0.1 C, 0.2 C和0.5 C倍率的正极放电克容量分别为138.4 mAh/g, 105.0 mAh/g, 80.3 mAh/g和60.4 mAh/g, 0.05 C循环50周后,正极容量保持率为80.2%.该方法为后续金属锂负极在全固态电池中的应用提供了新的方案.     

辐照处理对油菜籽光谱特性影响的研究

针对油菜籽经过核辐照处理后其光谱反射特性会发生改变的特点,提出了应用可见／近红外光谱技术进行油菜籽的快速无损鉴别。利用偏最小二乘法和BP神经网络建立鉴别模型,并比较了不同光谱预处理方法、主成分数据变换方式及隐含层节点数对模型预测结果的影响。实验采用五种剂量辐照(50, 100, 150, 200Gy和不经核辐照处理)的油菜籽共135个样本进行建模,49个进行预测。结果显示,最优模型是原始光谱数据先经过中值滤波平滑法、附加散射校正及一阶求导法预处理。经PLS方法提取6个主成分经自然对数变换后,选取神经网络隐含层结点数为4个或9个。最优模型对是否经过核辐照处理的样本识别率达100%,对核辐照剂量预测精度为85.71%, 说明提出的方法可以用于评估核辐照处理对油菜籽光谱特性产生的明显影响。