Surface passivation in n-type silicon and its application in silicon drift detector

Based on the surface passivation of n-type silicon in a silicon drift detector(SDD), we propose a new passivation structure of SiO2/Al2O3/SiO2 passivation stacks. Since the SiO2 formed by the nitric-acid-oxidation-of-silicon(NAOS)method has good compactness and simple process, the first layer film is formed by the NAOS method. The Al2O3 film is also introduced into the passivation stacks owing to exceptional advantages such as good interface characteristic and simple process. In addition, for requirements of thickness and deposition temperature, the third layer of the SiO2 film is deposited by plasma enhanced chemical vapor deposition(PECVD). The deposition of the SiO2 film by PECVD is a low-temperature process and has a high deposition rate, which causes little damage to the device and makes the SiO2 film very suitable for serving as the third passivation layer. The passivation approach of stacks can saturate dangling bonds at the interface between stacks and the silicon substrate, and provide positive charge to optimize the field passivation of the n-type substrate.The passivation method ultimately achieves a good combination of chemical and field passivations. Experimental results show that with the passivation structure of SiO2/Al2O3/SiO2, the final minority carrier lifetime reaches 5223 μs at injection of 5×10¹⁵ cm^-3. When it is applied to the passivation of SDD, the leakage current is reduced to the order of nA.     

Extinction time and the total mass of the continuous-state branching processes with competition

Consider a general continuous-state branching process with additional interaction, which destroys the branching property. We give precise conditions on the interaction term, in order to decide whether the extinction time of the process remains or not bounded as the initial value tends to infinity, and similarly for the total mass of the process.     

用灰色理论和神经网络理论建立陀螺漂移模型初探

本文就灰色理论和神经网络技术在建立陀螺漂移模型上的应用进行了初步探讨；介绍了这两种理论工程应用上的方法和特点以及相关的一些问题。     

一种快速有效的捷联系统初始对准算法

在捷联惯性系统中，初始对准是影响系统输出精度的最重要环节，陀螺漂移是引起对准误差的主要原因。本文在对捷联系统误差进行分析的基础上，结合卡尔曼滤波器的滤波特性，提出一种把陀螺随机常值漂移标定与初始对准进行多级组合的卡尔曼滤波方法。     

单向悬挂屋盖结构的风致气弹耦合效应数值模拟

综合运用计算流体力学和计算结构力学技术,建立了适用于索膜结构流固耦合风振分析的CFD数值模拟方法,并编制了相应的有限元计算程序。应用该程序对具有不同参数的大跨度单向屋盖结构进行了风振响应分析,探讨了来流风速、屋面质量和初始预张力等参数对结构流固耦合特性的影响。研究表明,由于屋盖前缘周期脱落的大尺度旋涡是诱导结构振动的主要原因,随着大尺度旋涡沿屋面向下游移动,其动能逐渐转化为结构变形能,使结构振动形态呈现明显的涡激振动特征。屋面质量、来流风速和初始预张力是影响结构流固耦合性能的主要参数。对于索膜结构,仅通过刚性模型来确定结构风压并进行风振响应分析,其结果是不可靠的。     

微机电陀螺零点漂移数据滤波方法的研究

微机电石英音叉陀螺虽然有突出的优点，但也存在噪声比较大的缺点。普通的数字滤波方法通常会造成比较大的滞后，减小了系统的相位裕度，而且对截止频率以内的噪声不起作用。作者在对石英音叉陀螺零点漂移数据建立时间序列模型（AR模型）的基础上，采用卡尔曼滤波算法对石英音叉陀螺的漂移数据进行了处理。仿真实验表明，所采用的滤波方法合理有效。     

在飞航导弹中用星敏感器修正捷联惯导陀螺漂移

星敏感器是一种高精度的姿态测量装置。研究了星敏感器和陀螺的特点,对星敏感器工作原理和修正陀螺漂移技术进行了原理分析。在不利用外界提供的姿态和位置信息的情况下,采用卡尔曼滤波的信息融合算法,建立组合导航系统的状态方程和量测方程,利用星敏感器输出的载体相对于惯性空间的姿态信息来修正捷联惯导的陀螺漂移。设计飞航导弹的典型飞行轨迹,通过数学仿真,对上述算法的有效性进行了验证,结果表明星敏感器能够有效地补偿捷联惯导由于陀螺漂移带来的误差,明显提高了导航定位精度。     

基于支持向量回归机的陀螺漂移预测模型

为了预测某导弹陀螺漂移趋势,以该陀螺漂移角速度时间序列为对象,建立了基于支持向量回归机的预测模型。针对该预测模型的特点,提出了支持向量预选取的模型优化方法。基于ε不敏感损失函数的支持向量回归机具有稀疏性,其结构由支持向量决定。因此从训练样本集中预选出有可能成为支持向量的样本,精简样本规模是提高该类支持向量回归机训练和预测效率的有效方法。针对该类支持向量回归机从分类和回归两个角度分析了支持向量的几何特征,提出了核函数空间免疫聚类的支持向量预选取方法并用于某导弹陀螺漂移预测模型的数据预处理。仿真结果表明优化后的预测模型运算量小、建模速度快,精度高。     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.