An alternative strategy process for government space activities

This article is a revised and updated version of a paper presented at the 49th International Astronautical Congress, held in Melbourne, Australia from September 28 to October 2, 1998. It presents a methodical approach to the future planning of government space activities. Rather than generating detailed programme plans that are hard to implement in a dynamic environment, the method described herein is rather modelling the priorities of different project alternatives. This is less detailed as the plans that usually result from the classic space planning approach, yet is highly usable as a roadmap for implementation. This approach enables a dynamic planning with inherent learning cycles that can easily be adapted to the dynamic changes which are plaguing today’s space policies.     

基于过程神经网络的热平衡温度预测研究

为缩短航天器热平衡试验周期，以降低航天器研制成本，提出了一种基于过程神经网络的热平衡温度预测模型。为简化该模型的学习过程，提出了一种基于正交基函数展开的基本学习算法，利用基函数的正交性不仅可以简化模型中的时间累积运算过程，而且能提高模型对解决实际问题的适应性。同时，为增强模型的外推预测能力，在基本学习算法的基础上给出了一种基于新增样本的学习算法，使模型既能对新增样本进行快速学习又不损失对原有样本的记忆。实际应用表明，该预测模型能够利用某型号卫星热平衡试验中某监测点进入稳定工况后40小时内的试验数据提前42．5—68小时获得该监测点的极限热平衡温度。     

Learning in an immersive digital theater

The Houston Museum of Natural Science, in collaboration with Rice University has an outreach program taking portable digital theaters to schools and community sites for over five years and has conducted research on student learning in this immersive environment. By using an external independent evaluator, the effectiveness of NASA-funded Education and Public Outreach (EPO) projects can be assessed. This paper documents interactive techniques and learning strategies in full-dome digital theaters. The presentation is divided into Evaluation Strategies and Results and Interactivity Strategies and Results. All learners from grades 3–12 showed statistically significant short-term increase in knowledge of basic Earth science concepts after a single 22-min show. Improvements were more significant on items that were taught using more than one modality of instruction: hearing, seeing, discussion, and immersion. Thus immersive theater can be an effective as well as engaging teaching method for Earth and Space science concepts, particularly those that are intrinsically three-dimensional and thus most effectively taught in an immersive environment. The portable system allows taking the educational experience to rural and tribal sites where the underserved students could not afford the time or expense to travel to museums.     

卫星姿态控制系统故障重构观测器设计

对于卫星姿态控制系统,提出一种基于PD型学习观测器(Learning observer, LO)的系统故障重构方法。在P型LO的学习算法基础上引入测量输出估计误差的微分项,设计了一种PD型LO,估计卫星姿态角速度和姿态角的同时,快速精确重构卫星执行机构故障。给出了所提观测器的稳定性条件,并基于线性矩阵不等式技术提出一种系统化PD型LO设计方法。进一步,将所提PD型LO设计扩展用于卫星姿态敏感器故障的快速重构。最后,将所提方法应用于微小卫星推力器故障重构和陀螺故障重构,仿真结果校验了所提方法的有效性。     

对英语专业四级成绩的多元线性回归分析

本研究以英语专业大学生为调查对象,旨在分析英语专业大学生学习动机和努力程度对专业四级成绩的预测能力,以及不同类型动机和努力程度间的关系。统计结果表明,学习动机和努力程度对专四成绩均有一定的预测能力;积极动机与努力程度间存在显著的正相关关系。英语专业教师应当有针对性地增强学生的积极动机,弱化消极动机,以提高学生的努力程度和学业水平。     

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.     

Safety lifetime analysis method for multi-mode time-dependent structural system

It is important to determine the safety lifetime of Multi-mode Time-Dependent Structural System (MTDSS). However, there is still a lack of corresponding analysis methods. Therefore, this paper establishes MTDSS safety lifetime model firstly, and then proposes a Kriging surrogate model based method to estimate safety lifetime. The first step of proposed method is to construct the Kriging model of MTDSS performance function by using extremum learning function. By identifying possible extremum mode of MTDSS, the performance function of MTDSS can be equivalently transformed into the one of Single-mode Time-Dependent Structure (STDS). The second step is to use the Advanced First Failure Instant Learning Function (AFFILF) to train the Kriging model constructed in the first step, so that the convergent Kriging model can identify the possible First Failure Instant (FFI) of STDS. Then safety lifetime can be searched quickly by dichotomy search. By using AFFILF, the minimum instant that the state is not accurately identified by the current Kriging model is selected as the training point, which avoids the unnecessary calculation which may be introduced into the existing First Failure Instant Learning Function (FFILF). In addition, the Candidate Sample Pool (CSP) reduction strategy is also adopted. By adaptively deleting the random candidate sample points whose FFI have been accurately identified by the current Kriging model, the training efficiency is further improved. Three cases show that the proposed method is accurate and efficient.     

Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data

In this paper, we implement the AdaBoost algorithm to optimize the classifications results of precipitations intensities carried out by One versus All strategy using Support Vector Machine (OvA-SVM). The model developed which combines the AdaBoost algorithm with a multiclass SVM is applied to images from the MSG (Meteosat Second Generation) satellite. Other variants to build multiclass SVMs, such as the OvO-SVM (One versus One SVM), SBT-SVM (Slant Binary Tree SVM) and DDAG-SVM (Decision Directed Acyclic Graph) are also implemented on which we tested the AdaBoost algorithm. The study showed that the AdaBoost algorithm performed better in the case of the OvA-SVM variant compared to the other variants.In order to evaluate the elaborated model, some classification techniques, such as the ECST Enhanced Convective Stratiform Technique (ECST), the SART where the Support vector machine, Artificial neural network and Random forest classifiers are combined, the Convective/Stratiform Rain Area Delineation Technique (CS-RADT) and the Random Forest technique (RFT) are applied. The classification results obtained show that AdaBoost with OvA-SVM (AdaOvA-SVM) presents very interesting performances where the evaluation parameters POD, POFD, FAR, BIAS, CSI and PC indicate the values 95.2%, 12.4%, 14.7%, 0.9, 88.1% and 96.5% respectively. Indeed, the AdaOvA-SVM technique has surpassed the CS-RADT, ECST and RFT techniques. As for the comparison with the SART, we noted that OvA-SVM presents very close results. The same trend was also observed when estimating precipitation. At the end of this study, it is shown that the AdaBoost algorithm performs better on a weak classifier or on a strong classifier operating in an unfavorable environment.     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.     

近距平行跑道配对进近方式的安全区域

为了提高我国近距平行跑道的运行效率,对配对进近方式的安全区域进行研究.根据配对过程中快机和慢机的相对运动关系和速度特征,将配对进近分为快机不可超越慢机和快机超越慢机两种方式.将慢机作为参考量,分析快机的相对运动状态,定性地给出两种配对方式的安全区域范围.在此基础上,考虑慢机错误地闯入快机航向道,建立防撞安全边界计算模型;考虑配对前机在最大不利侧风影响下的尾流对后机的影响,建立尾流安全边界模型.利用虹桥机场的相关数据,采用C类航空器B737-800,D类航空器B747-400作为配对进近的两架飞机,对模型进行验证.结果表明:该方法可以实现实时定量地计算两种配对方式下的安全区域范围.