基于FPGA硬件实现的谐波检测方法

针对现有系统对谐波检测实时性差和精度低的问题,介绍一种基于傅立叶变换和FPGA硬件实现的谐波检测方法.分析了谐波检测中影响测量精度的关键因素,采用数字锁相环来同步被测信号,以减小由非同步采样所产生的误差.基-4FFT 处理器的硬件设计采用全并行的乘法运算单元结构和并行的存储分配方法,最大限度地提高谐波检测的速度.数字锁相环和基-4 FFT 算法用VHDL语言设计实现,并用MAX plus Ⅱ软件进行仿真,仿真结果表明,所设计的数字锁相环可以很好地跟踪被测信号,在180ms时,误差仅为0.01Hz,很好地消除了非同步采样所引起的测量误差;采用所设计的基-4FFT运算器对给定的谐波数据进行运算,得到的谐波幅值和相位误差小于0.05%,运算时间仅为8μs.     

一类欠驱动系统的分层FTSMC控制

研究了一类具有扰动的欠驱动非线性系统的控制问题及其渐近稳定的判定.提出一种基于快速终端滑模(FTSMC)的分层控制方法:分层快速终端滑模控制(HFTSMC).方法将系统按状态变量的耦合关系分成多个子系统,应用快速终端滑模控制分别构造滑动面.在此基础上采用Lyapunov方法设计控制量,给出了一种新的滑模而渐近稳定性判据,并提出了两种构造第二层滑模面的方法.对旋转式倒立摆的稳定控制进行的MATLAB仿真与实物实验,均得到较好的控制效果.实验结果证明了控制方法及判据的有效性.     

国产平台下可靠文件传输软件的设计及实现

在国产自主可控平台加速推广的当下,为实现文件在测控数据传输网不同软硬件平台之间的可靠、快速传输,本文根据相关技术规范,基于文件交换协议(File Exchange Protocol,简称FXP协议),设计并实现功能完备、运行稳定的可靠文件传输软件。软件具备模块化、可移植、基于TCP协议满足用户可靠性需求、基于UDP协议满足用户传输效率需求、兼容不同软硬件平台等的功能特点,并具备满足安全编程需求的软件特性,立足核心代码设计、验证,对软件功能和稳定运行两个方面做了进一步的改良和提升,以满足跨不同软硬件平台稳定运行、恶劣网络环境中可靠传输、畅通网络环境中尽力快速传输的新要求。     

Intelligent analysis method of dam material gradation for asphalt-core rock-fill dam based on enhanced Cascade Mask R-CNN and GCNet

Reasonable dam materials’ gradation design for asphalt-core rock-fill dams is one of the main ways to control permeability. It is a challenge to test whether it can meet the requirements of dam construction. The computer vision method provides a new idea for asphalt-core rock-fill dam material gradation testing. However, due to the characteristics of densely overlapping and multi-scale sizes of dam material particles, the traditional image segmentation methods and algorithms cannot achieve accurate segmentation of dam materials’ images, and it is hard to apply the segmentation result to quantify the gradation curve. In this research, the enhanced Cascade Mask R-CNN with ResNet and PAFPN (Path Aggregation Feature Pyramid Networks) is proposed. Multi-scale features extracted by ResNet and feature ensemble can be realized using PAFPN. Data augmentation (DA) and online hard example mining (OHEM) are also applied in segmentation model training. Moreover, the GCNet is proposed to calibrate the gradation curve. The nonlinear relationship between the real gradation and the one based on the segmentation results can be revealed and the model of dam materials’ gradation analysis can be established. In the research, the enhanced Cascade Mask R-CNN can achieve 84.2 mAP, which is higher than that of Cascade Mask R-CNN with 74.9 mAP. The effectiveness of the proposed module and training strategies is proved using ablation experiments. The average error of each level for the gradation calibration using GCNet is 0.55%, 1.87%, 2.22%, 1.18%, and 2.42% respectively. The accuracy can meet the requirements of hydraulic engineering construction, which verifies the effectiveness of the GCNet network for gradation calibration, and the research provides a new method and technology for intelligent gradation testing of the asphalt-core rock-fill dam.     

Fast k most similar neighbor classifier for mixed data (tree k-MSN)

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. For this reason, many fast k-NN classifiers have been developed, some of them are based on a tree structure, which is created during a preprocessing phase using the prototypes in T. Then, in a search phase, the tree is traversed to find the nearest neighbor. The speed up is obtained, while the exploration of some parts of the tree is avoided using pruning rules which are usually based on the triangle inequality. However, in soft sciences as Medicine, Geology, Sociology, etc., the prototypes are usually described by numerical and categorical attributes (mixed data), and sometimes the comparison function for computing the similarity between prototypes does not satisfy metric properties. Therefore, in this work an approximate fast k most similar neighbor classifier, for mixed data and similarity functions that do not satisfy metric properties, based on a tree structure (Tree k-MSN) is proposed. Some experiments with synthetic and real data are presented.     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

A systematic method for efficient computation of full and subsets Zernike moments

A new method is proposed for fast and accurate computation of Zernike moments. This method presents a novel formula for computing exact Zernike moments by using exact complex moments where the exact values of complex moments are computed by mathematical integration of the monomials over digital image pixels. The proposed method is applicable to compute the full set of Zernike moments as well as the subsets of individual order, repetition and an individual moment. A comparison with other conventional methods is performed. The results show the superiority of the proposed method.     

一类非张量积区域快速傅立叶变换算法在匡产并行机上的可扩展性测试

本文给出了一类基于六边形非张量积区域上的广义离散快速傅立叶变换算法（HFFT）以及它在国产百万亿次超级计算机（曙光5000A）上的测试运行情况．文章介绍了该算法在曙光5000A上的大规模集群测试加速比和可扩展性特性,并通过分析,说明HFFT在国产超级计算机的大规模并行环境下拥有良好的可扩展性．在使用8192个处理器核的情况下,HFFT加速比达到了277倍．我们同样对FFTw软件包进行了测试．本文的分析为解决其他科学计算程序在国产百万亿次规模集群上的可扩展性问题,提供了一些先行的参考和建议．     

GPS软件接收机信号的快速捕获与跟踪

根据GPS软件接收机对数据块进行信号处理的工作方式,研究基于快速傅里叶变换的循环相关法伪码快速捕获与跟踪技术。基于实测GPS中频数据,采用循环相关法在频域计算输入信号与本地信号的相关值,由相位关系得到精细载频。仿真结果表明,该技术能减少运算量,缩短捕获时间。得到的载频能满足跟踪环对频率分辨率的要求。对跟踪结果进行子帧匹配和奇偶校验后,可以获得导航电文信     

eXist数据库结构连接算法的改进和实现

为了进一步提高eXist数据库的查询效率,针对数据库现有的数据存储特点和查询策略,提出一种快速定位的结构连接算法FL-Stack。算法采用栈缓存已遍历过的但仍可能与尚未遍历的后代结点匹配的祖先结点,并对能预先判断不可能满足结构连接匹配的祖先或后代结点,提出相应方法实现快速定位,以批量跳过这类结点。与现有算法必须逐个扫描祖先后代结点序列相比,这种快速定位的结构连接算法避免了逐个扫描带来的多次重复无意义的比较,可大大提高结构连接效率。