一种新型滑模控制算法在挠性多体卫星姿态控制中的应用

针对一类挠性多体卫星的复合控制问题，提出一种新型滑模变结构控制算法。新型算法利用闭环系统Lyapunov函数的一阶导数估计值设计控制器，且控制器采用了递归学习控制结构，能够有效解决传统滑模控制技术的颤振问题。随后根据Lyapunov稳定性理论证明闭环系统轨迹可以快速收敛到滑模面，并且系统状态误差可在滑模面上渐近收敛到零。此外，设计的控制器能够有效抑制外部干扰，而且控制器只需要控制输入矩阵信息而不需受控系统和未知参数的其他先验信息，使得算法具有较强鲁棒性。最后通过数值仿真与现有文献中控制算法进行对比，结果充分验证了本文设计控制算法的有效性和实用性。     

基于机器视觉的万用表数字识别技术研究

为提高手持式数字万用表校准系统的自动化水平，本文通过研究图像灰度阈值法、传统方法的目标分类和深度学习三种不同类型的字符识别技术，提出了两种基于机器视觉的数字识别方案。测试结果显示，两种方案的字符识别准确率均可达到99.8%，但其在硬件资源占比、编程难易程度上二者还存在显著差异。该机器视觉字符识别功能的成功开发与应用，可为更多无程控通信接口的计量测试设备，及一些不适于人工作业的危险计量工作环境进行类似的数字识别提供借鉴。     

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10⁻⁵) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.     

Dynamics and control for contactless interaction between spacecraft and tumbling debris

Tumbling debris has become a great threat to orbit activities. Contactless interaction is a novel concept for active debris removal, through which the tumbling debris no longer rotates freely but is under control. The contactless interaction method aims to de-tumble the debris and then maintain desired relative states between the spacecraft and debris. The spacecraft is simultaneously stabilized through three-axis attitude control, which makes the de-tumbling and capture operation much safer, more effective and accurate. The dynamics and control for the contactless interaction have been little studied in the past years. This paper considers a generic dynamics and control problem for contactless interaction between a spacecraft and debris. A translational and rotational dynamics model of contactless interaction is proposed and the 6-DOF equations are established. The contactless interaction control law is designed with the backstepping method, and the spacecraft three-axis control law is designed with the PD control. Simulation results show that the angular momentum is transferred from the debris to the spacecraft and the debris is thus de-tumbled. The desired relative states are achieved efficiently. Significantly, the spacecraft and debris no longer rotate in the inertial frame and, hence, the safety and accuracy for capture operation are guaranteed.     

高校思想政治教育工作队伍整体优化的内涵与进路

基于辩证系统思维引领,科学界定高校思想政治教育工作队伍整体优化的内涵,创新开启立足于微观、中观和宏观三个不同层面的多维研究视角,将思想政治教育工作队伍理论概括为三个不同层面上的"三位一体"化大军,并通过确定建构理想目标、把握发展趋势、整合资源力量、追求最高境界的进路,将高校思想政治教育工作队伍整体优化的理论研究引向深入,促使高校思想政治教育工作实效性进一步增强。     

强化词块训练 提高英语听说能力

以词块教学理论为依据,分析在听说教学中进行词块训练的可行性。研究词块训练在听说课堂中的具体实施过程,并通过问卷调查和访谈结果,论证了听说课上引入词块理念能提高英语学习者听说能力从而提出词块学习对语言习得的有效性。     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

Crustal deformation characteristics of Sichuan-Yunnan region in China on the constraint of multi-periods of GPS velocity fields

In order to obtain deformation parameters in each block of Sichuan-Yunnan Region (SYG) in China by stages and establish a dynamic model about the variation of the strain rate fields and the surface expansion in this area, we taken the Global Positioning System (GPS) sites velocity in the region as constrained condition and taken advantage of the block strain calculation model based on spherical surface. We also analyzed the deformation of the active blocks in the whole SYG before and after the Wenchuan earthquake, and analyzed the deformation of active blocks near the epicenter of the Wenchuan earthquake in detail. The results show that, (1) Under the effects of the carving from India plate and the crimping from the potential energy of Tibetan Plateau for a long time, there is a certain periodicity in crustal deformation in SYG. And the period change and the earthquake occurrence have a good agreement. (2) The differences in GPS velocity fields relative Eurasian reference frame shows that the Wenchuan earthquake and the Ya'an earthquake mainly affect the crustal movement in the central and southern part of SYG, and the average velocity difference is about 4–8?mm/a for the Wenchuan earthquake and 2–4?mm/a for the Ya'an earthquake. (3) For the Wenchuan earthquake, the average strain changed from 10 to 20 nanostrian/a before earthquake to 40–50 nanostrian/a after the earthquake, but before and after the Ya’an earthquake, the strain value increased from about 15 nanostrian/a to about 30 nanostrian/a. (4) The Wenchuan earthquake has changed the strain parameter of each active block more or less. Especially, the Longmen block and Chengdu block near the epicenter. The research provides fundamental material for the study of the dynamic mechanism of the push extrusion from the north-east of the India plate and the crimp from Qinghai Tibet Plateau, and it also provides support for the study of crustal stress variation and earthquake prediction in Sichuan Yunnan region.     

小天体主动附着制导与控制技术研究进展

结合目前已实施的小天体附着探测任务,对小天体探测器主动附着涉及的制导与控制技术进行了系统性的回顾。首先,针对小天体附着任务风险高、不确定性强的特点,明确了主动附着的内涵,并分析了现有附着探测技术在自主性和安全性上面临的挑战,阐述了提高探测器主动附着能力的重要性。在此基础上,归纳梳理了与主动附着相关的制导与控制技术最新研究进展。最后,结合未来小天体探测任务需求,总结了今后具有进一步研究价值的几个问题。     

大学生外语自主学习策略培养的文化研究

针对学院2011级196名学生自主学习情况进行了实证调查,发现学生外语学习和自主学习策略各要素的使用情况,在此基础上提出了一些符合中国文化及学生自身特点、适合不同任务的自主学习策略,以期达到提高学生外语学习效果的目的。