基于人工势场法的月球表层采样装置避障规划

表层采样技术是获取地外天体土壤样品的重要手段,为在月表复杂地形、不确定着陆姿态、不规则器表包络约束下,快速获取安全的表层采样动态轨迹,文章根据四自由度表层采样装置设计,分析了工作过程中的正逆运动学关系;根据表层采样装置动、静态目标点运动要求,将约束空间分解为有效障碍检测区与不可达集,进行表层采样避障规划;为加快规划速度,针对表层采样装置瞬态构型构建了动态障碍检测区、动态不可达集;结合人工势场法提出了避障规划算法。对典型姿态下表层采样过程的避障规划仿真表明,该算法可实现约束条件下的表层采样轨迹规划,并对表层采样装置适应性设计提供支持。     

未来智能空战发展综述

随着装备战斗力生成模式逐渐向机械化、信息化、智能化"三化融合"发展演变,未来航空主战装备的定位、形态及运用将可能发生根本性变革。为应对新时期空战任务所面临的环境高复杂性、博弈强对抗性、响应高实时性、信息不完整性、边界不确定性等一系列挑战,交叉融合人工智能理论与空战对抗技术,研发智能空战系统,将有望在下一代无人制空装备谱系中构建不对称"智能代差",成为制胜未来空天战场的核心关键。本文完整梳理了智能空战研究的发展脉络,总结了以专家机动逻辑、自动规则生成、规则演进、机器学习等方法为代表的智能空战基础理论。从体系、应用及技术视角全面剖析了智能空战的发展趋势,以智能空战的不确定性、安全性、解释性、迁移性、协同性为切入点阐述了智能空战应用落地的若干问题,以期为未来智能空战技术研究勾勒出一条新的探索路径,为人工智能理论与航空科学技术的跨领域交叉融合提供新的发展思路。     

Determination of visible coordinates of the low-orbit space objects and their photometry by the CCD camera with the analogue output. Initial image processing

The number of artificial space objects in the low Earth orbit has been continuously increasing. That raises the requirements for the accuracy of measurement of their coordinates and for the precision of the prediction of their motion. The accuracy of the prediction can be improved if the actual current orientation of the non-spherical satellite is taken into account. In so doing, it becomes possible to directly determine the atmospheric density along the orbit. The problem solution is to regularly conduct the photometric surveillances of a large number of satellites and monitor the parameters of their rotation around the centre of mass. To do that, it is necessary to get and promptly process large video arrays, containing pictures of a satellite against the background stars.     

基于长短期记忆网络时序数据趋势预测及应用

为了研究状态监测大数据对设备运行状态的估计和预测,提出了一种人工经验与主成分分析相结合的长短期记忆网络方法（AEPCA-LSTM）,利用运行过程中的监测时序数据对设备运行趋势进行预测。首先,通过基于人工经验的主要成分分析方法（AEPCA）从状态监测系统中提取与目标变量最相关的状态变量作为输入;其次,利用长短期记忆网络（LSTM）对目标变量趋势变化进行预测,并考虑运行过程中新数据样本的持续产生,对模型进行定期更新,以提高模型的动态适应性。最后,将所提出的方法应用于船舶副机系统的涡轮增压器转速预测中,结果表明该方法相对于传统的PCA-LSTM和LSTM,具有更小的预测平均误差0.18037,即展现了其在时序数据趋势预测的优势。     

Development of a regional rain retrieval algorithm for exclusive mesoscale convective systems over peninsular India

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T_bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM – Precipitation Radar. It shows that T_b–R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T_b–R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.     

卫星密封舱湿度控制技术的应用研究

通过实验验证和热力学分析相结合的方法,对卫星密封舱湿度控制技术进行了应用研究。由卫星的热试验和在轨飞行试验可知,通过利用相机胶片的水汽含量、设置湿控子系统及进行临射前舱内湿度调控等方法,能够使卫星密封舱内局部区域的相对湿度控制在30%~60%的范围内,满足相机要求。通过研究可得,热控系统所采取的卫星湿度控制技术是成功的,保证了卫星任务的圆满完成。     

The knowledge of knots: an interdisciplinary literature review

ABSTRACT

Knots can be found and used in a variety of situations in the 3D world, such as in vines, in the DNA, polymer chains, electrical wires, in mountaineering, seamanship and when ropes or other flexible objects are involved for exerting forces and holding objects in place. Research on knots as topological entities has contributed with a number of findings, not only of interest to pure mathematics, but also to statistical mechanics, quantum physics, genetics, and chemistry. Yet, the cognitive (or algorithmic) aspects involved in the act of tying a knot are a largely uncharted territory. This paper presents a review of the literature related to the investigation of knots from the topological, physical, cognitive and computational (including robotics) standpoints, aiming at bridging the gap between pure mathematical work on knot theory and macroscopic physical knots, with an eye to applications and modeling.     

An experimental method to obtain the hard alpha anomaly distribution for titanium alloy aeroengine disks

A probability-based damage tolerance methodology has been proposed to improve the recognition of material anomalies, especially hard alpha (TiN) anomalies for aeroengine rotor disks. A key input to this method is hard alpha anomaly distribution, which reflects the occurrence rate and size of anomalies present in the finished part material of titanium rotors. Since anomalies rarely occur naturally, an experimental method is proposed to obtain the anomaly distribution for titanium alloy aeroengine disks to reflect and equivalently replace the manufacturing development in titanium industry. In general, the anomaly distribution information can be divided into two parts: the Probability of Detection (POD) curve and the detected anomaly distribution, which contains the size and frequency data of the detected anomalies. The distribution can be established based on several appropriate assumptions and derivation steps with different confidence levels of POD curves and detected anomaly distributions. In this case, the distribution can be obtained in a relatively short time as a key input to the probability-based damage tolerance methodology. Then the Probability of Failure (POF) can be calculated, and the value is found to vary with different confidence levels. On this basis, the conservative estimated POF can be obtained in conjunction with confidence levels.     

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

As an advanced combat weapon, Unmanned Aerial Vehicles (UAVs) have been widely used in military wars. In this paper, we formulated the Autonomous Navigation Control (ANC) problem of UAVs as a Markov Decision Process (MDP) and proposed a novel Deep Reinforcement Learning (DRL) method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments. To solve the problem of limited training experience, the proposed Imaginary Filtered Hindsight Experience Replay (IFHER) generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences. The well-designed goal, episode, and quality filtering strategies ensure that only high-quality augmented experiences can be stored, while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities. By training in a complex environment constructed based on the parameters of a real UAV, the proposed IFHER algorithm improves the convergence speed by 28.99% and the convergence result by 11.57% compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent. Moreover, the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.     

Review on ultra-lightweight flapping-wing nano air vehicles:Artificial muscles,flight control mechanism,and biomimetic wings

Flying insects are capable of flapping their wings to provide the required power and control forces for flight. A coordinated organizational system including muscles, wings, and control architecture plays a significant role, which provides the sources of inspiration for designing flapping-wing vehicles. In recent years, due to the development of micro-and meso-scale manufacturing technologies, advances in components technologies have directly led to a progress of smaller Flapping-Wing Nano Air V...