基于数字式红外传感器的姿态测量盲区补偿法

单轴红外姿态测量系统在测量小型无人机姿态角时存在盲区,通过增加一组与原轴线相垂直的红外传感器,形成双轴姿态测量系统,对盲区进行补偿。两组红外传感器一定有一组位于倾角可测区域,通过测试目标轴的输出温差的大小,判断位于可测区域的轴线,可实现对目标轴姿态角的解算。首次采用数字式输出红外热电堆传感器设计实现了180°无盲区的姿态角测量模块,经过测试,其静态误差小于2°。     

基于Agent的移动组播算法的优化

随着科学技术的飞速发展,作为网络信息领导者的移动通信在日常生活工作中的应用也越来越广泛,但是移动通信在应用中也会遇到很多问题,比如移动网络切换延迟现象较为普遍,组播包的丢包率比较高等等.本文针对于以上问题,分析了多种组播协议,提出了新型的基于Agent的移动组播算法,此算法可以有效地改进切换延迟和丢包率等问题.     

State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements

This paper is concerned with the state estimation problem for the complex networked systems with randomly occurring nonlinearities and randomly missing measurements. The nonlinearities are included to describe the phenomena of nonlinear disturbances which exist in the network and may occur in a probabilistic way. Considering the fact that probabilistic data missing may occur in the process of information transmission, we introduce the randomly data missing into the sensor measurements. The aim of this paper is to design a state estimator to estimate the true states of the considered complex network through the available output measurements. By using a Lyapunov functional and some stochastic analysis techniques, sufficient criteria are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the state estimator gain is also obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed state estimation conditions.     

Fisher discrimination based low rank matrix recovery for face recognition

In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results.     

Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts

The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.     

Older adults’ use of smart phones: an investigation of the factors influencing the acceptance of new functions

After adopting mobile phones, most older adults use them only for calling and SMS. The purpose of this study is to extend their usage of mobile phones to new functions. To understand older adults’ requirements of mobile phones, a questionnaire was constructed and 351 Chinese older adults were recruited to complete the questionnaires. Data collected through the questionnaires were analysed using explorative factor analysis. The results revealed that older adults’ requirements were composed of 10 factors: Find a Specific Function, Awareness and Attractiveness, Readability, Personal Concern, Soft Keys and Multi-tap, Hardware Capacity, Touch Screen, Concern of Learning, Connectivity, and Social Influence. Then, from the above 10 factors, the 6 most important factors were revealed using multiple linear regression analysis. The results imply that accepting new functions is different from accepting a product. Readability and Find a Specific Function, which are critical for older adults’ acceptance of feature phones, are not determinants of their acceptance of new functions in smart phones.     

纳米SiO2粉体的制备与研究

纳米SiO2粉体的制备是以硅酸钠和盐酸为原料，添加适宜的稳定剂(非离子表面活性剂)和分散剂，在适宜的pH值和温度下，采用化学沉淀法合成。研究表明，要得到性能优良纳米的SiO2粉体，最佳工艺条件为：温度20～40℃，pH=6，反应液质量浓度P1=20g／L，P2=1．20g／L，反应时间15min。结果表明：制备的纳米SiO2粒径30～50nm，比表面积大，分散性好，质量优良，可达到产业化的生产。     

烟丝整丝率变化率对卷烟卷接质量控制的影响研究

长期以来,烟丝的物理特征都被认为是影响卷烟质量的主要因素。随着生产工艺和企业烟丝标准的不断提高,如今烟丝的物理特征对卷烟质量的影响不再显著。研究人员转而将目光转向了烟丝的整丝率变化率。     

个险再保险信息管理系统的设计与研究

再保险是保险人将其承担的保险业务,以承保形式,部分转移给其他保险人。再保险业务的网络化和信息化管理,很大程度上满足了现代再保险业务管理的需求,推动了再保险公司管理的发展。介绍个险再保险信息管理系统的需求背景,描述系统的总体设计。在系统总体实现方面详细阐述了软件开发平台、相关设备性能容量需求分析和再保险业务风险评估模型。最后对相关设备性能容量进行了评估。