Grouping bi-chi-squared method for pulsar navigation experiment using observations of Rossi X-ray timing explorer

X-ray pulsar-based navigation is a revolutionary technology which is capable of providing the required navigation information in the solar system. Performing as an important pulsar-based navigation technique, the Significance Enhancement of Pulse-profile with Orbit-dynamics (SEPO) can estimate orbital elements by using the distortion of pulse profile. Based on the SEPO technique, we propose a grouping bi-chi-squared technique and adopt the observations of Rossi X-ray Timing Explorer (RXTE) to carry out experimental verification. The pulsar timing is refined to determine spin parameters of the Crab pulsar (PSR B0531+21) during the observation periods, and a short-term dynamic model for RXTE satellite is established by considering the effects of diverse perturbations. Experimental results suggest that the position and velocity errors are 16.3 km (3σ) and 13.3 m/s (3σ) with two adjacent observations split by one day (exposure time of 1.5 ks), outperforming those of the POLAR experiment whose results are 19.2 km (3σ) and 432 m/s (3σ). The approach provided is particularly applicable to the estimation of orbital elements via using high-flux observations.     

基于AI+神经网络的数字化故障电弧识别方案研究

线路故障电弧的特征随着电力电子技术的提高更趋复杂，传统的故障电弧识别方式已无法应对新型用电系统。对比传统故障电弧识别方案，研究故障电弧产生类型和新型检测技术，提出基于谐波因素、总谐波畸变率、电流零休时间、电流变化速率、电流周期性等典型时域、频域特征的AI+神经网络的数字化故障电弧识别方案。该方案运用回归算法向量机对特征图进行分类处理，通过正向传输运算、反向传输运算和迭代回归运算三个步骤执行卷积神经网络，频繁迭代回归以获得最佳特征识别图。本文以调光器的故障电弧识别为例，对故障识别方案进行验证，结果表明采用此方案可实现更高效、更精准、更稳定地识别故障电弧。     

Modelling of grinding mechanics: A review

Grinding is one of the most widely used material removal methods at the end of many process chains. Grinding force is related to almost all grinding parameters, which has a great influence on material removal rate, dimensional and shape accuracy, surface and subsurface integrity, thermodynamics, dynamics, wheel durability, and machining system deformation. Considering that grinding force is related to almost all grinding parameters, grinding force can be used to detect grinding wheel wear, energy calculation, chatter suppression, force control and grinding process simulation. Accurate prediction of grinding forces is important for optimizing grinding parameters and the structure of grinding machines and fixtures. Although there are substantial research papers on grinding mechanics, a comprehensive review on the modeling of grinding mechanics is still absent from the literature. To fill this gap, this work reviews and introduces theoretical methods and applications of mechanics in grinding from the aspects of modeling principles, limitations and possible future trendencies.     

Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising are...     

安全约束下的空间翻滚目标涡流消旋稳定控制

针对空间翻滚目标涡流消旋任务执行效率低和抵近安全无保证的问题，首先基于椭球包络法给出了服务星机动轨迹的直接线性凸化安全约束，以确保机动过程的安全性和最优轨迹跟踪问题的有限时间可解性；设计了垂直构型下空间消旋任务的抵近期望轨迹以增强服务星消旋力矩的作用强度，缩短任务周期。在此基础上，提出了一种反馈线性化的收缩模型预测控制(FLC MPC)算法，有效跟踪所提出的期望轨迹，并严格保证安全约束及控制输入约束下受控系统的稳定性。最后，利用所提出的控制方法对阿丽亚娜 4火箭上面级进行消旋仿真，结果表明该方法能有效提高消旋效率，并保证服务星的安全稳定。     

多视角多尺度陨石坑特征检测识别方法

针对序列图像间视角尺度变化大带来的特征无法检测和识别效率低等问题，提出了多视角多尺度陨石坑特征检测识别方法。基于明暗区域信息和形态学处理实现陨石坑图像特征边缘粗提取，利用快速傅里叶变换计算模板与图像特征的匹配度，从而实现图像特征所在区域粗定位。在此基础上，通过引入惯性测量信息计算图像间重叠面积，更新模板形状，并预测搜索区域，解决了序列图像间视角尺度变换及信息冗余带来的特征无法检测和识别效率低的困难，最终在单一陨石坑区域内实现其精细检测。     

A clustering based method to complete frame of discernment

When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness.     

在轨高效目标检测加速技术

针对深度卷积网络目标检测算法参数量大、计算量大以及受星上计算资源、存储资源及功耗的限制，难以实现在轨部署的问题，提出了一种在轨高效目标检测算法加速框架与实现方法。首先，设计了一种可以同时兼容三种卷积算子的计算引擎，有效提高了资源利用率;其次，从通道和卷积核两个维度将目标检测算法模型展开，实现了加速器的高度并行化和可扩展性;最后，在多种FPGA平台上实现了该加速器并对其性能进行了评估。实验结果表明：所提出的加速器计算性能可以达到1843.2 GFLOPs(每秒千兆次浮点运算)，推理时间为0.22 ms。与同类加速器方案相比，所提出的加速器框架在性能、功耗、能效比及推理时间方面具有很大优势，适合部署在资源受限环境中，具有良好的星上应用前景和价值。     

槽道式侧推器水动力性能研究进展和展望

侧推器在从事港口作业、科考、救援和海洋资源开发等具有较高操纵性要求的船舶和海洋平台上具有广泛的应用。本文分别对槽道式侧推器的水动力性能预报方法、螺旋桨设计、槽道口减阻降噪设计、做功能力影响因素、空化噪声和脉动压力、减摇作用以及操纵效能等方面的研究进展进行了回顾；重点对影响侧推器做功能力的因素进行了梳理，将其分为槽道口形状、槽道内流场情况、船舶航速与康达效应、槽道及船体形状、侧推器同其他推进器的相互干扰、自由液面和限制水域等几个方面，并对现有相关研究工作进行了阐述。最后对侧推器水动力性能研究方面需要进一步深化以及可以拓展的方向进行了展望。     

Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter ...