Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

Fast M-fold matching pursuit algorithm for image approximation

A simple and effective greedy algorithm for image approximation is proposed. Based on the matching pursuit approach, it is characterized by a reduced computational complexity benefiting from two major modifications. First, it iteratively finds an approximation by selecting M atoms instead of one at a time. Second, the inner product computations are confined within only a fraction of dictionary atoms at each iteration. The modifications are implemented very efficiently due to the spatial incoherence of the dictionary. Experimental results show that compared with full search matching pursuit, the proposed algorithm achieves a speed-up gain of 14.4-36.7 times while maintaining the approximation quality.

A non-linear spatial hearing model based on bases pursuit algorithm

In the research on spatial hearing and realization of virtual auditory space,it is important to effectively model the head-related transfer functions(HRTFs)or head-related impulse responses(HRIRs).In our study,we managed to carry out adaptive non-linear approximation in the field of wavelet transformation.The results show that the HRIRs’adaptive non-linear approximation model is a more effective data reduction model,is faster,and is 5 dB on average better than the traditional principal component analysis(PCA)(Karhunen-Loève transform)model based on relative mean square error(MSE)criterion.Furthermore,we also discussed the best bases’choice for the time-frequency representation of HRIRs,and the results show that local cosine bases are more propitious to HRIRs’adaptive approximation than wavelet and wavelet packet base.However,the improved effect of local cosine bases is not distinct.Here,for the sake of modeling the HRIRs more truthfully,we consider choosing optimal time-frequency atoms from redundant dictionary to decompose this kind of signals of HRIRs and achieve better results than all the previous models.

Algorithm for source recovery in underdetermined blind source separation based on plane pursuit

In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.

Matching Pursuits方法综述

Matching Pursuits(匹配逼近)算法是在一个确定的函数集合中自适应地选择一些函数来表示一个信号的计算过程,函数集合中的每个函数都称为原子.多样化的信号特征决定了可以精确刻画信号特征的原子的类型,而重复迭代逼近的贪婪算法又确定了运算效率是MP算法的存在和发展的问题核心.本文围绕MP算法中原子库的生成,原子参数的搜索索引方式和迭代逼近过程中的快速算法等方面,阐述了MP算法发展变化过程.

Pure Pursuit算法在移动机器人路径跟踪的位姿修正研究

文章介绍了Pure Pursuit算法在移动机器人路径跟踪方面的原理。着重探讨了采用Pure Pursuit算法对移动机器人路径跟踪的位置和姿态调整问题,提出了保证移动机器人在路径的关键点处的位置和姿态都能同时满足要求的算法。通过仿真程序对算法的正确性进行了验证。文章可以为采用Pure Pursuit算法进行路径跟踪的移动机器人提供参考。

基于Pure Pursuit算法的智能缩微车路径跟踪

为实现缩微智能车沿预设路径稳定行驶,本文以多传感器缩微智能车为载体,搭建了1套包含无线通信系统、视频监控系统、终端控制系统的缩微道路交通实验台,实现了图像数据处理、道路识别跟踪、路径规划判断以及运动控制执行等主要功能。重点研究了缩微智能车路径跟踪的Pure Pursuit控制算法,该算法能实时调整缩微智能车当前位置与预设路径之间的偏差,从而使得缩微智能车的航向在一定程度上能与预设路径保持一致。实验结果表明:Pure Pursuit算法响应速度快、超调量小,缩微智能车能更快速稳定地实现路径跟踪。

Projection Pursuit Dynamic Cluster Model and its Application to Water Resources Carrying Capacity Evaluation

The research shows that projection pursuit cluster (PPC) model is able to form a suitable index for overcom-ing the difficulties in comprehensive evaluation, which can be used to analyze complex multivariate prob-lems. The PPC model is widely used in multifactor cluster and evaluation analysis, but there are a few prob-lems needed to be solved in practice, such as cutoff radius parameter calibration. In this study, a new model-projection pursuit dynamic cluster (PPDC) model-based on projection pursuit principle is developed and used in water resources carrying capacity evaluation in China for the first time. In the PPDC model, there are two improvements compared with the PPC model, 1) a new projection index is constructed based on dynamic cluster principle, which avoids the problem of parameter calibration in the PPC model success-fully;2) the cluster results can be outputted directly according to the PPDC model, but the cluster results can be got based on the scatter points of projected characteristic values or the re-analysis for projected character-istic values in the PPC model. The results show that the PPDC model is a very effective and powerful tool in multifactor data exploratory analysis. It is a new method for water resources carrying capacity evaluation. The PPDC model and its application to water resources carrying capacity evaluation are introduced in detail in this paper.

基于改进的Pure Pursuit智能客车轨迹跟踪算法研究

以传统的Pure Pursuit为轨迹跟踪算法基础,采用Stanely控制算法来补偿其计算得到的方向盘转角。仿真和试验结果均表明该改进算法对轨迹跟踪效果有明显提升。

Approach to Weighted Geometric Evaluation Based on Projection Pursuit

Weighted geometric evaluation approach based on Projection pursuit (PP) model is presented in this paper to optimize the choice of schemes. By using PP model, the multi-dimension evaluation index values of schemes can be synthesized into projection value with one dimension. The scheme with a bigger projection value is much better, so the schemes sample can be an optimized choice according to the projection value of each scheme. The modeling of PP based on accelerating genetic algorithm can predigest the realized process of projection pursuit technique, can overcome the shortcomings of large computation amount and the difficulty of computer programming in traditional projection pursuit methods, and can give a new method for application of projection pursuit technique to optimize choice of schemes by using weighted geometric evaluation. The analysis of an applied sample shows that applying PP model driven directly by samples data to optimize choice of schemes is both simple and feasible, that its projection values are relatively decentralized and profit decision-making, that its applicability and maneuverability are high. It can avoid the shortcoming of subjective weighing method, and its results are scientific and objective.

Benefit Evaluation Model of Small Watershed Control Based on Projection Pursuit

A projection pursuit model is presented in this paper for comprehensive evaluation of benefits of small watershed control.By using the model,small watershed control samples with many benefit evaluation indexes can be synthesized projective values with one dimension.The samples can be naturally evaluated according to the projective values.The parameters of the model is optimized by using real coding beased accelerating genetic aglrothm,which overcomes the shortcomings of large computation amount and difficulty of computer programming in traditional projection prusuit methods,and provides a new way for wide applications of projection pursuit technique to different evaluation problems in agricultural systems engineering.

基于蜣螂算法优化的投影寻踪生态环境评价方法构建

矿山开采会破坏和占用大量土地资源,对生态环境造成持久的负面影响,因此评价矿区生态环境质量变化迫在眉睫。为了准确监测矿区生态环境质量,以陕煤集团张家峁矿区2000—2023年24期逐年的Landsat影像为基础数据,通过计算绿度(Normalized Difference Vegetation Index,NDVI)、湿度(Humidity Index,WET)、热度(Land Surface Temperature,LST)、干度(Normalized Differential Build-up and bare Soil Index,NDBSI)4项生态指标,并采用基于果蝇优化算法的投影寻踪(Fruit Fly Optimization Algorithm-Projection Pursuit Clustering,FOA–PPC)、基于粒子群优化算法的投影寻踪(Particle Swarm Optimization-Projection Pursuit Clustering,PSO–PPC)、基于灰狼优化算法的投影寻踪(Grey Wolf Optimizer-Projection Pursuit Clustering,GWO–PPC)和基于蜣螂优化算法的投影寻踪(Dung Beetle Optimizer-Projection Pursuit Clustering,DBO–PPC)4种群智能优化算法构建矿区生态环境质量评价方法,并利用平均相关度进行精度验证。结果表明:①DBO–PPC模型的平均相关度和类内聚集度均高于PSO–PPC模型、FOA–PPC模型和GWO–PPC模型,且与EI指数更接近,表明DBO–PPC能更好的评估研究区生态环境;②基于DBO–PPC模型的张家峁矿区2000—2023年生态环境质量均值为0.4,生态环境质量以差和较差等级为主,面积占比约55.94%,空间上呈西部差东部好,常家沟水库的生态环境在研究期内表现为优等级,矿区东北部和中部区域的生态环境较好,植被覆盖较多;③矿区发生沉陷面积占比为81.28%,沉陷最大值达−0.15 m;采区发生沉陷现象面积占比明显高于矿区,占采区面积的89.56%,生态环境质量以−0.0004的速率下降,表明采矿活动使得研究区的地表发生了沉陷,进而影响到生态环境。综上所述DBO–PPC模型在监测评价矿区生态环境质量方面具有较强的合理性,从而为矿区生态环境可持续发展提供技术手段。

Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

The image signal is represented by using the atomic of image signal to train an over complete dictionary and is described as sparse linear combinations of these atoms. Recently, the dictionary algorithm for image signal tracking and decomposition is mainly adopted as the focus of research. An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition. A new segmentation algorithm of brain MRI image, which uses the noise reduction method with adaptive dictionary based on genetic algorithm, is presented in this paper, and the experimental results show that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation. In a very complicated situation, the results show that the segmentation of brain MRI images can be accomplished successfully by using this algorithm, and it achieves the ideal effect and has good accuracy.

一种新型的电能质量扰动信号分析的CDMSPSO-MP算法

针对匹配追踪(matching pursuit,MP)算法在检测电能质量扰动信号时存在的计算量大、重构信号质量不佳的问题,利用混沌动态多种群粒子群优化(chaos dynamic multi-swarm particle swarm optimization,CDMSPSO)算法对MP算法进行优化,提出了CDMSPSO-MP算法。首先,CDMSPSO算法使用Logistic映射替代伪随机数更新种群,提高信号重构时搜索时频原子的随机性;然后,将种群划分为多个小规模种群并设置相应的重组期,增加信号重构时频原子的多样性;最后,以扰动信号与原子内积的绝对值作为CDMSPSO算法的适应度函数,替代MP算法的遍历计算,提升信号的重构速度。实验结果表明,CDMSPSO-MP算法有效提高了计算速度,减少了无关时频原子作为扰动信号分量的计算,提高了重构信号的质量。

CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

五相感应电机变采样周期模型预测控制策略

针对无调制模块的模型预测控制器MPC(model predictive controller)存在的谐波含量高问题,提出1种新型基于模型的变采样周期MPC策略,并将其应用于五相感应电机驱动系统。分析了MPC对时间固定离散化所带来的问题,而引入调制或调制替代来解决此问题将使控制系统复杂度增加,故遵循更简单、自然的思路,基于追击算法来改变采样间隔,结合MPC确定最优控制动作和实施时间,实现变采样周期MPC方案。利用五相感应电机驱动系统开展实验,实验结果验证了新型变采样周期MPC优良的参考跟踪和电流谐波性能。

时空异质性视角下长江经济带水资源系统韧性评估及调控因子分析

水资源系统韧性的评估与调控是缓解水资源危机和规避水资源风险的重要途径之一。从水资源的自然资源、社会经济、生态环境等属性出发,综合考虑水资源系统韧性的抵抗性、恢复性、适应性特征,构建了包含水资源、水经济、水福利、水管理、水生态和水环境6个维度的水资源系统韧性评价指标体系,建立了基于麻雀搜索算法的投影寻踪模型,运用自然断点法识别区域空间分异特征,采用因子探测识别影响水资源系统韧性空间分布格局的调控因子。通过对2010—2019年长江经济带11个省(市)水资源系统韧性的分析发现,在空间上,长江经济带水资源系统韧性表现为西部最优、东部次之、中部最末,且南部优于北部;在时间上,长江经济带水资源系统韧性虽有波动但整体呈现上升的趋势。从6个维度来看,水生态韧性维度的韧性值区间跨度较大,在0.08~0.52之间波动,水福利韧性最高,水环境韧性次之。水资源和水福利的相关因子对水资源系统韧性的影响较为明显,水经济中的工业废水治理投资占GDP的比重和万元工业增加值用水量2个指标以及水福利中的供水管道密度和人均供水量2个指标的影响力逐渐上升。通过对长江经济带水资源系统韧性和调控因子的分析,有针对性地提出了提高长江经济带水资源系统韧性的对策建议。

基于改进WOA-MP算法的多普勒海流计回波信号频率估计研究

受海洋环境复杂干扰影响,多普勒式海流计的超声波回波信号易受干扰影响,包含多种频率成分,这给海流流速信息的解算带来困难。为解决多普勒海流计回波信号频率估计精度较差的问题,提出一种基于信号匹配追踪(MP)的频率估计方法,并融合改进型鲸鱼优化算法(MWOA)提高估计过程的效率。该方法利用混沌映射提升初始种群多样性,并引入自适应权重和非线性收敛因子提高算法局部寻优能力、搜索速度和求解精度。而后,对改进算法与其他优化算法进行对比测试,结果显示所设计改进算法在收敛速度和寻优能力上具有有效性和优越性。此外,通过对多普勒回波信号进行特征重构,开展MWOA-MP算法在噪声环境下的频率估计精度性能测试。在回波信号信噪比为–10 dB条件下,MWOA-MP算法相对传统方法抗噪声能力提高30%。与其他智能方法融合MP后的频率估计算法相比,MWOA-MP算法的估计误差在10 Hz以内,可满足多普勒海流计频率测量精度要求。

权重化QR分解的正交匹配追踪算法硬件实现

为在小型化、低成本的硬件平台实现正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法,针对OMP算法中最小二乘法的问题,该文构造一个确定性的传感矩阵,提出一种低复杂度、低资源的权重化QR分解的OMP(Weighted QR decomposition OMP,WQR-OMP)算法硬件结构,在ZYNQ 7020型号芯片上搭建WQR-OMP SOC系统.WQR-OMP算法在传感矩阵进行QR分解后,根据三角矩阵R中元素的分布特性,通过权重化运算只保留主对角线上的元素而其他余元素归零,得到对角矩阵D,然后近似计算稀疏向量的解.实验结果表明:与基于QR分解的OMP(QR decomposition OMP,QR-OMP)和Batch-OMP算法的硬件结构相比,WQR-OMP算法硬件结构的重构速度更快、存储资源更少.在压缩率为0.25的条件下,WQR-OMP SOC系统对256×256分辨率图像的重构时间为400 ms左右,其速率比仅使用ARM处理器的重构速率提高了约6.3倍.与其他现有研究对比,该系统在Block RAM存储资源消耗较少的情况下,进一步提升了重构速度,适用于存储资源受限的硬件平台.

数字乡村建设对防返贫能力的影响及空间效应研究

防止规模性返贫是扎实推进乡村振兴的关键所在,数字乡村建设对提升农村防返贫能力具有重要意义。本文基于2016~2022年中国30个省(区、市)的面板数据,运用基于加速遗传算法的投影寻踪模型测度数字乡村建设水平与防返贫能力,通过核密度估计刻画两者的时空分异特征,利用空间杜宾模型深入分析数字乡村建设对防返贫能力的空间溢出效应。研究发现:2016~2022年,数字乡村建设水平与防返贫能力整体上在不断提高,数字乡村建设存在多极化趋势,防返贫能力两极化现象逐渐减弱。农村防返贫能力具有显著的空间聚集特征,主要表现为“高—高”和“低—低”聚集模式。空间杜宾溢出效应分解表明:数字乡村建设对防返贫有着显著的正向影响以及正的空间溢出效应。据此,对未来建设数字乡村提出相关建议,并探索出提升防返贫能力的新路径,未来可尝试从溢出边界进一步探索空间溢出效应。