工序问题的数学模型

本文讨论了加工的次序问题，对于不同的、具有实际意义的问题，我们得到了相应的优化准则，这些准则在实际应用中便于操作，利用这些优化准则得到了加工次序问题的最优的结果。     

基于SIFT和仿射变换的航空器起飞图像跟踪方法

针对机场监视的需要,结合航空器起飞特征,研究了尺度不变特征变换( SIFT)算法和仿射变换模型,在对图像进行灰度图像序列处理的基础上,提出了将SIFT特征点跟踪与仿射变换相结合对航空器起飞信息识别、跟踪的方法。以滨海国际机场的一个起飞过程为例,对图像序列进行了运动目标识别和帧间特征点的匹配,验证了方法的可行性,为机场航空器的起飞跟踪方法提供参考。     

系统可靠性优化方法

介绍了性优化的各种模型：组合优化模型、两类失效模式优化模型、可靠必优化分配模型、多目标优化模型以及可靠性模糊优化模型等。分析了可靠性优化分配模型各种解法的优缺点。研究认为，蒙特卡罗法具有简单和广泛适用性特点，因而推荐使用该法。     

便携式红外寻的防空导弹——单通道控制的自动驾驶仪

导弹采用单通道控制，虽然系统简单，但控制技术复杂。从这类自动驾驶仪特点出发，在介绍其功能、组成、控制方式基础上，对设计、分析、试验中遇到的技术问题提出了解决途径和方法，并对此类驾驶仪的数字化问题提出了设想和看法。     

从美的流变浅析美的本质

自从十八世纪中页鲍姆嘉通为美学奠定基础以来,众多哲学家、美学家写了无数论著来探讨美的含义.那么,美的本质究竟是什么呢?为什么美的问题会如此难以明晰?直到马克思主义美学产生后,这一美学命题才得到突破性地进展.马克思主义美学以实践为基础,认为实践是人类存在和发展的本质特征,从而提出"美是人的本质力量的对象化."将审美主体和审美客体有机地统一起来.     

Sailing at the brink – The no-limits of near-/now-term-technology solar sails and SEP spacecraft in (multiple) NEO rendezvous

Near-Earth object (NEO) in-situ exploration can provide invaluable information for science, possible future deflection actions and resource utilisation. This is only possible with space missions which approach the asteroid from its vicinity, i.e. rendezvous. This paper explores the use of solar sailing as means of propulsion for NEO rendezvous missions. Given the current state of sail technology, we search for multiple rendezvous missions of up to ten years and characteristic acceleration of up to 0.10 mm/s². Using a tree-search technique and subsequent trajectory optimisation, we find numerous options of up to three NEO encounters in the launch window 2019–2027. In addition, we explore steerable and throttleable low-thrust (e.g. solar-electric) rendezvous to a particular group of NEOs, the Taurid swarm. We show that an acceleration of 0.23 mm/s² would suffice for a rendezvous in approximately 2000 days, while shorter transfers are available as the acceleration increases. Finally, we show low-thrust options (0.3 mm/s²) to the fictitious asteroid 2019 PDC, as part of an asteroid deflection exercise.     

低空风切变系统建模及其对直升机飞行安全威胁定性分析

系统地建立了含湍流的三维低空风切变模型，并综合分析了其对直升机飞行安全的威胁。建立了微下击暴流风场及大气湍流场组合的风切变模型，在不增加计算量的前提下，选取特征点发展直升机飞行动力学模型，有效捕捉了风切变的切变特性，提高了在风场中的动态响应计算精度。模型配备姿态保持功能的控制增稳系统已符合一般直升机的飞行状态，并改善机体响应。根据风速分布的特点，选取不同飞行速度、不同风场位置进行飞行仿真，定性地对比分析状态量变化与风场对应关系，并且以垂向通道为例，从动力学角度分析验证了响应的理论计算表达式。结果表明：湍流主要导致高频姿态角响应，风切变对飞行状态量变化占主导作用，且垂向风是引起威胁的主要因素，据此提出危险风场规避建议。     

Boundary condition modelling and identification for cantilever-like structures using natural frequencies

The actual boundary conditions of cantilever-like structures might be non-ideally clamped in engineering practice, and they can also vary with time due to damage or aging. Precise modelling of boundary conditions, in which both the boundary stiffness and the boundary mass should be modelled correctly, might be one of the most significant aspects in dynamic analysis and testing for such structures. However, only the boundary stiffness was considered in the most existing methods. In this paper, a boundary condition modelling and identification method for cantilever-like structures is proposed to precisely model both the boundary stiffness and the boundary mass using sensitivity analysis of natural frequencies. The boundary conditions of a cantilever-like structure can be parameterized by constant mass, constant rotational inertia,constant translational stiffness, and constant rotational stiffness. The relationship between natural frequencies and boundary parameters is deduced according to the vibration equation for the lateral vibration of a non-uniform beam. Then, an iterative identification formulation is established using the sensitivity analysis of natural frequencies with respect to the boundary parameters. The regularization technique is also used to solve the potential ill-posed problem in the identification procedure.Numerical simulations and experiments are performed to validate the feasibility and accuracy of the proposed method. Results show that the proposed method can be utilized to precisely model the boundary parameters of a cantilever-like structure.     

Improving orbit prediction accuracy through supervised machine learning

Due to the lack of information such as the space environment condition and resident space objects’ (RSOs’) body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs’ trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO’s orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.     

Computation and Elicitation of Valleyness

Abstract

Existing methods for land surface form characterisation often focus on relatively simple landform element classifications and do not evaluate results with large scale participant experiments. This piece of research takes a semantics-grounded approach to characterising the landform valley. Based on definitions three algorithms to characterise valleys in a fuzzy way are introduced. Comparison of the results to assessments regarding the degree of being in a valley gained from over 800 participants in a questionnaire survey yields significant amounts of explained variance (R ² = 0.35–0.37). Furthermore, accounting for very ambiguously perceived stimuli showing vast low places leads to markedly improved regressions (R ² = 0.45–0.49), weighting of the data with a measure of uncertainty in judgment even more so (R ² = 0.50–0.55).