基于STEP和改进神经网络的STEP-NC制造特征识别方法

特征识别是实施STEP-NC重要的一步，也是实现开放式、智能化和网络化STEP-NC数控系统的关键。本文提出了一种基于STEP和改进神经网络的STEP-NC制造特征识别方法。该方法首先在对STEP AP203中性文件进行几何拓扑信息提取后，基于边的凹凸性判断构建了零件最小子图。然后，将混沌算法、遗传算法与BP神经网络算法有机相结合提出了改进的BP神经网络。最后，通过将获得的零件模型最小子图信息数据输入到改进的BP神经网络，实现了对STEP-NC制造特征高效精准地识别。通过实例验证了该方法的有效性和可行性。     

构建数学模型 优化贴装过程

贴装优化问题直接关系到表面贴装工艺的效率。本文以JUKI-2010/2020贴片机为试验平台,根据该机型结构及贴装过程,把贴装优化问题的描述为最小权重匹配问题(MWMP)以及非对称旅行商(TSP)问题,构建了一种新的优化模型:把时间优化转化为距离优化,并应用启发式算法进行软件编程。通过生产企业的6块PCB板的对比实验,证明了该数学模型和算法是合理的。     

OFDM系统的自适应低秩信道估计

为了降低正交频分复用OFDM(Orthogonal Frequency division Multiplexing)系统中最小均方误差MMSE(Minimum Mean Square Error)信道估计算法的复杂度,并且改善由于信道的统计特性与先验知识不匹配而导致的MMSE估计性能恶化,提出了一种自适应的低秩信道估计算法.该算法利用信道的时间平均相关取代统计相关,结合了基于特征值分解的低秩建模,从而近似地实现MMSE估计.借助于子空间跟踪,该算法可以自适应地估计信道相关矩阵的主特征空间及噪声方差,以迭代的方式逼近最优的MMSE估计,而且复杂度较低.进一步分析指出基于信道延时子空间跟踪的估计算法是该算法的一种特例,理论分析和仿真结果均表明这种新算法在低信噪比时可以显著改善信道估计的准确性.      

使用复小波包的MIMO-OFDM无线系统

为了在频率选择性信道中提供高速数据业务, 提出了一种新的多入多出-正交频分复用系统MIMO-OFDM(Multi-Input Multi-Output -Orthogonal Frequency Division Multiplexing).该系统使用复小波包变换CWPT(Complex Wavelet Packet Transform)来实现OFDM,而不是使用传统的快速傅立叶变换FFT(Fast Fourier Transform).由于复小波包函数具有很好的特性,通过对有2个用户的MIMO-OFDM系统进行仿真的结果表明,基于CWPT的MIMO-OFDM系统性能要比使用传统的FFT的MIMO-OFDM 系统好,但是复杂度略高.      

一种确定燃气涡轮轴发动机起动最小所需扭矩的工程方法

依据某型涡轴发动机冷运转试验数据,给出了发动机阻力矩和净力矩的计算方法。按照发动机在此温度条件下的最大允许起动时间,提出了确定发动机在该大气温度条件下起动时最小所需扭矩的方法,并通过试验表明用这种方法得到的计算结果和试验结果较吻合。      

基于熵权的灰色关联概率数据关联算法

在多目标信息融合中,通过数据关联判断来自不同传感器的数据是否代表同一个目标,传统数据关联一般采用联合概率数据关联算法,单纯依靠状态量测且计算复杂。而基于灰色关联的概率数据关联算法,能够利用目标的多个特征信息进行数据关联,且计算简单准确。特征信息一般由专家主观判断给出。提出了基于熵权的灰色关联概率数据关联算法,由量测数据自适应的给出各特征的权重系数,不依赖于专家。仿真表明,该关联算法具有很好的工程实用性。     

基于样本熵的语音信号检测算法

船舶自身发出的巨大噪声,严重影响了通讯设备通讯端船舶工作者的正常语音交流和通讯,须要进行语音信号增强,将语音信号从背景噪声中检测分离出来。为此,文章在分析语音信号与船舶背景噪声信号时域序列复杂性基础上,提出基于信号序列样本熵的语音信号检测算法。该算法将含噪信号进行分帧后,首先计算每帧信号的样本熵,并根据自适应预设阈值对信号进行初始判断；然后,利用信号通常持续一定时间的连续性,对信号初始判断进行平滑,得到信号的最终判别类别,实现对语音信号的检测。经过大量实测实验数据的验证,环境噪声信号的辨别准确度为 90.33%,语音信号为 95.05%,该算法取得了较好的判别精度。     

Analysis of the solar sail deformation based on the point cloud method

The deformation of the solar-sail membrane is an important factor for causing inaccuracies in the solar-sail missions. This paper describes the solar sail under deformation by using a new modelling technique based on point cloud and triangular mesh generation. Two types of deformation, stemming from wrinkling and billowing, are modelled. The changes in the solar radiation pressure force and the moment caused by deformation are calculated and compared to the ideal non-deformed case. The heliocentric spiral trajectory and the orbital angular momentum reversal trajectory are taken as examples to quantify the influence of the deformation from an orbit point of view. Additionally, point cloud simplification, based on the normal vector and bounding box, is utilized to simplify the original deformed-sail model. It involves a reasonable reduction and renewal of the points in the model considering the variation of surface curvature. The simplification and its modelling accuracy are numerically investigated as well as computational efficiency.     

Impact of a novel hybrid accelerometer on satellite gravimetry performance

The state-of-the-art electrostatic accelerometers (EA) used for the retrieval of non-gravitational forces acting on a satellite constitute a core component of every dedicated gravity field mission. However, due to their difficult-to-control thermal drift in the low observation frequencies, they are also one of the most limiting factors of the achievable performance of gravity recovery. Recently, a hybrid accelerometer consisting of a regular EA and a novel cold atom interferometer (CAI) that features a time-invariant observation stability and constantly recalibrates the EA has been developed in order to remedy this major drawback. In this paper we aim to assess the value of the hybrid accelerometer for gravity field retrieval in the context of GRACE-type and Bender-type missions by means of numerical closed-loop simulations where possible noise specifications of the novel instrument are considered and different components of the Earth’s gravity field signal are added subsequently. It is shown that the quality of the gravity field solutions is mainly dependent on the CAI’s measurement accuracy. While a low CAI performance (10^?8 to 10^?9?m/s²/Hz^1/2) does not lead to any gains compared to a stand-alone EA, a sufficiently high one (10^?11?m/s²/Hz^1/2) may improve the retrieval performance by over one order of magnitude. We also show that improvements which are limited to low-frequency observations may even propagate into high spherical harmonic degrees. Further, the accelerometer performance seems to play a less prominent role if the overall observation geometry is improved as it is the case for a Bender-type mission. The impact of the accelerometer measurements diminishes further when temporal variations of the gravity field are introduced, pointing out the need for proper de-aliasing techniques. An additional study reveals that the hybrid accelerometer is – contrary to a stand-alone EA – widely unaffected by scale factor instabilities.     

Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast. Thus safety of aircraft taking off and landing and air flight can be guaranteed. Thresholding is a kind of simple and effective method of cloud classification. It can realize automated ground-based cloud detection and cloudage observation. The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect. Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation. In view of the above-mentioned problems, multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization （UPSO） are proposed. Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy. In addition, exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster. Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy. In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection, the UPSO algorithm is adopted. It can find the optimal thresholds quickly and accurately. As a result, the real-time processing of segmentation of groundbased cloud image can be realized. The experimental results show that, in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy, the proposed methods can extract the boundary shape, textures and details feature of cloud more clearly. Therefore, the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.