Spatial quantum coherent modulation with perfect hybrid vector vortex beam based on atomic medium

The perfect hybrid vector vortex beam(PHVVB)with helical phase wavefront structure has aroused significant concern in recent years,as its beam waist does not expand with the topological charge(TC).In this work,we investigate the spatial quantum coherent modulation effect with PHVVB based on the atomic medium,and we observe the absorption characteristic of the PHVVB with different TCs under variant magnetic fields.We find that the transmission spectrum linewidth of PHVVB can be effectively maintained regardless of the TC.Still,the width of transmission peaks increases slightly as the beam size expands in hot atomic vapor.This distinctive quantum coherence phenomenon,demonstrated by the interaction of an atomic medium with a hybrid vector-structured beam,might be anticipated to open up new opportunities for quantum coherence modulation and accurate magnetic field measurement.

Hybrid vector beams with non-uniform orbital angular momentum density induced by designed azimuthal polarization gradient

Based on angular amplitude modulation of orthogonal base vectors in common-path interference method, we propose an interesting type of hybrid vector beams with unprecedented azimuthal polarization gradient and demonstrate in experiment. Geometrically, the configured azimuthal polarization gradient is indicated by intriguing mapping tracks of angular polarization states on Poincaré sphere, more than just conventional circles for previously reported vector beams. Moreover, via tailoring relevant parameters, more special polarization mapping tracks can be handily achieved. More noteworthily, the designed azimuthal polarization gradients are found to be able to induce azimuthally non-uniform orbital angular momentum density, while generally uniform for circle-track cases, immersing in homogenous intensity background whatever base states are. These peculiar features may open alternative routes for new optical effects and applications.

Multiple trapping using a focused hybrid vector beam

We propose a simple and efficient method that uses a single focused hybrid vector beam to confine metallic Rayleigh particles at multiple positions.We study the force mechanisms of multiple trapping by analyzing the gradient and scattering forces.It is observed that the wavelength and topological charges of the hybrid vector beam regulate the trapping positions and number of optical trap sites.The proposed method can be implemented easily in three-dimensional space, and it facilitates both trapping and organization of particles.Thus, it can provide an effective and controllable means for nanoparticle manipulation.

Hybrid gradient vector fields for path-following guidance

Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,

Hybrid Support Vector Machines-Based Multi-fault Classification

Support Vector Machines (SVM) is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification,a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies,1-v-1 (one versus one) and 1-v-r (one versus rest),are respectively adopted at different classifica-tion levels. At the parallel classification level,using 1-v-1 strategy,the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level,these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

An Improved Hybrid Space Vector PWM Technique for IM Drives

In this paper, an improved hybrid space vector pulse width modulation (HSVPWM) technique is proposed for IM (induction motor) drives. The basic principle involved in the proposed random pulse width modulation (RPWM) cuddled SVPWM is amalgamating the pre-calculated switching timings for various sections of hexagonal space vector boundary and the random selection of carrier between two triangular signals, in order to disband acoustic switching noise spectrum with improved fundamental component. The arbitrary selection between triangular carriers, which is decided by digital signal states (Low or High) of the linear feedback shift register (LFSR) based pseudo random binary sequence (PRBS) generator. The SVPWM offers a control degree of freedom in terms of positioning of vectors inside every sampling interval and hence it has six possible variants of the voltage vectors arrangements in each sector. The developed HSVPWM is thoroughly analyzed in using the MATLAB? based simulation for all SVPWM variants. From the simulation and experimental results viz. harmonic spectrum, harmonic spread factor (HSF), total harmonic distortion (THD) etc., and the superiority of the proposed scheme such as better utilization of DC bus and the randomization of the harmonic power are evidenced. For the practical implementation, Xilinx XC3S500E FPGA device has been used.

Predicting quantitative structure-activity relationship of substituted 17α-acetoxyprogesterones by molecular hybridization electronegativity-distance vector

A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (APs) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

基于容忍因子的近似最近邻混合查询算法

近似最近邻搜索(ANNS)是计算机领域中一种重要的高效相似度搜索技术,可用于在大规模数据集中进行快速信息检索。随着人们对高精度信息检索的需求不断增长,同时使用结构化信息和非结构化信息进行混合查询的方式也得到了广泛应用。然而,基于近邻图的过滤贪心算法在混合查询时可能会因结构化约束条件的影响导致连通性降低,进而损害搜索精度。为此,提出了一种基于容忍因子的过滤贪心算法,通过容忍因子控制不满足结构化约束条件的顶点参与路由,在不改变索引结构的前提下维持原有近邻图的连通性,克服了结构化约束条件对检索精度的负面影响。实验结果证明,新算法可以在不同结构化约束强度下实现ANNS的高精度搜索,同时保持检索效率。该研究解决了基于近邻图的ANNS在混合查询场景中的问题,为大规模数据集的快速混合查询信息检索提供了一种有效的解决方案。

基于混合模型的道岔综合监测系统研究

道岔作为关键的铁路信号设备,也是铁路线路三大薄弱环节之一,其工作质量直接影响列车的运行安全。传统的道岔检测方法过分依赖人工经验,检测效率低下,难以应对现有铁路运行中行车速度快、发车密度高等对道岔维护所带来的严峻挑战,并且现有道岔监测也存在监测项目不全面等问题。为满足工电融合需要,开发了一套基于混合模型的道岔综合监测系统,使用卷积神经网络自动进行特征提取,以获取道岔状态,充分发挥深度学习的自动特征提取优势;采用支持向量机和向量域的混合算法,对正常/故障数据进行分类和异常检测,从而提高故障检测的准确率。测试结果表明:与现有人工巡检方法相比,该系统能够为相关人员提供精准、实时的道岔故障预警,提高维护效率,有效减少人力成本且降低道岔病害的发生概率。

基于改进相关向量机的锂电池剩余使用寿命预测

精确预测锂离子电池剩余使用寿命对于保障设备安全运行十分重要。但电池寿命预测中存在诸如数据噪声和容量再生等不确定性来源,这将导致预测精度大幅下降。为解决这一问题,使用变分模态分解方法对从充电和容量数据中提取的健康因子进行滤波分解,并利用贝叶斯优化方法对相关参数进行优化,提出一种基于多核相关向量机的锂离子电池剩余使用寿命预测模型。利用美国国家航空航天局(NASA)和Oxford电池数据集对所提出的模型进行验证,研究结果表明:所提出的基于变分模态分解和贝叶斯优化的多核相关向量机(VMD-BAYES-HRVM)方法的预测性能不受预测起始点和截止电压的影响,预测结果准确性更高,95%置信区间的跨度更小,证明了所提出方法的有效性。

基于SCSO-SVM算法的光伏组件故障识别

光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine,SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine,SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine,PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine,GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine,SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine,GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine,WOA-SVM)算法。首先,六种SVM混合算法都克服了SVM诊断结果易受参数初始值影响的缺点,识别精度相较传统SVM算法都有所提升,但是识别时间都增加。其次,7种算法中SCSO-SVM识别效果最好,克服了SVM易受参数初始值的影响,相较SVM识别精度提高了约9.4594%;是因为更能有效找到SVM惩罚因子和核函数参数。然后,对于同一种算法而言,算法的识别精度是随输入特征减少而降低的,是因为输入特征越少,越不能有效表征光伏组件在不同故障类型下的输出属性。但算法的识别时间却不是随输入特征减少而减短。所以选取合适的输入特征才能兼顾算法的故障识别准确率和效率。最后,发现七种算法的识别效果依赖于数据集的影响。原因可能是各个算法参数选择过多导致泛化性有差异,且依赖参数初始值选择。

基于粒子图像分割的混合PIV-PTV算法

粒子图像测速法(particle image velocimetry,PIV)因其非接触场测量的特性,已成为空气动力学领域的主要测量工具。复杂流动的速度场往往具有非均匀性,示踪粒子难以在待测空间均匀分布。因此,在应用PIV互相关算法处理粒子稀疏区时,需要采用更大的查询窗口以降低测量的不确定度,但会带来空间分辨率低的实际问题。而粒子追踪测速法(particle tracking velocimetry,PTV)追踪单个示踪粒子的跨帧位移,具有比PIV更高的空间分辨率,但难以适用于粒子浓度高的稠密区。针对PIV、PTV各自的优点,本文发展了一种基于粒子图像分割的混合PIV-PTV测速技术。首先定义了基于维诺多边形的粒子局部浓度量度,用以计算示踪粒子在粒子图像上的局部浓度场;其次通过设定的浓度阈值对粒子进行二分类,使用基于高斯核函数的支持向量机寻找出最优的分类边界,从而实现对粒子图像的粒子稀疏区和稠密区的划分;最后对两个区域分别使用PIV和PTV进行速度场计算,并合并为完整的速度场输出。仿真结果表明,上述方法可实现对粒子图像中的示踪粒子稀疏区和稠密区的自动划分,有效提高速度场测量的空间分辨率。将该方法应用在马赫数Ma=6的湍流边界层近壁测量中,可有效解决高速条件下粒子因强剪切难以进入边界层近壁区的问题,显著提高对近壁流动的解析能力。

基于冗余矢量的T型三电平双向变换器中点电位平衡模型预测控制

T型三电平双向变换器因其具有损耗小、输出电能质量高等优势适合应用于低压交直流混合配电网互联等场景。针对T型三电平双向变换器存在直流侧中点电位不平衡等问题,模型预测控制因其易于实现多目标优化的特点具有良好的应用价值。为了解决传统有限集模型预测控制(FCS-MPC)权重因子整定困难,中点电位控制效果不佳的问题,提出一种基于冗余矢量的中点电位平衡模型预测控制,利用冗余小矢量的特性实现对中点电位平衡的控制,避免了权重因子的选择。在此基础上,利用代价函数计算矢量的作用时间并预测输出最优开关序列,提升系统的稳态性能。最后,通过实验测试验证了所提策略的有效性。

基于CNN-SVM的特高压三端混合直流线路故障区域识别方法

提出一种基于卷积神经网络–支持向量机(Convolutional neural network-support vector machine,CNN-SVM)的特高压三端混合直流线路故障区域识别方法。首先,对昆北侧、龙门侧的直流线路边界和柳北侧T区边界的频率特性进行分析,发现不同故障区域的故障特征存在一定差异。然后,使用经验小波变换提取故障特征,将其作为CNN-SVM的输入量,故障区域作为输出量,构建并训练CNN-SVM模型;将由测量点得到的故障特征量输入到训练完成的CNN-SVM模型中,进行故障区域识别。最后,搭建昆柳龙仿真模型,进行故障仿真实验验证。结果表明,该方法的故障区域识别率高,且可耐受300Ω的过渡电阻。

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

A novel data-driven, soft sensor based on support vector regression （SVR） integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization （HDS） process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm （GA） and sequential quadratic programming （SQP） methods （GA-SQP） was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search （PS）, and grid search （GS） indicated that the best average absolute relative error （AARE）, squared correlation coefficient （R2）, and computation time （CT） （AARE = 0.0745, R2 = 0.997 and CT = 56 s） was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization （VQ） technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

Simulation on effect of poloidal power spectrum on lower hybrid wave propagation

The coupling of lower hybrid wave to the plasma is a crucial issue for efficient current drive in tokamaks. This paper establishes a new coupling model which assumes the antenna to be a curved face and the plasma to be a cylinder. Power spectrum considering the coupling between wave-guides in both poloidal and toroidal direction is simply estimated and discussed. The effect of the poloidal wave vector on wave propagation, power deposition and driven current is also investigated with the help of lower hybrid current drive code. Results show that the poloidal wave vector affects the ray tracing, and also has effect on power deposition and driven current. The effect of the poloidal wave vector on power deposition and driven current profile depends on plasma parameters. Preliminary studies suggest that it seems possible to control the current profile by adjusting the poloidal phase difference between the waveguide in poloidal direction.