In-Situ Measuring the Particle Mean Size and Dust Concentration by Near-Forward Small Angle Light Scattering

This paper presents a method of measuring the particle mean size and dust concentration by small angle near forward light scattering optics and the extinction theory. Its theory is based on Fraunhofer diffraction theory which is the approximation of Mie scattering within the forward Fraunhofer diffraction lobe, and Rosin Rammler function is introduced to describe the particle size distribution in two phase flow in advance. Compared with the values by the sample weight method, the measurement results have a reasonable agreement. The present work has demonstrated that this method will be probably used to monitor the parameters of two phase flow.

Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction

Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.

A Generalization of the Mean Size Formula of Wavelet Packets in L_p

The purpose of this paper is to investigate the mean size formula of wavelet packets in Lp for 0 〈 p ≤ ∞. We generalize a mean size formula of wavelet packets given in terms of the p-norm joint spectral radius and we also give some asymptotic formulas for the Lp-norm or quasi-norm on the subdivision trees. All results will be given in the general setting,

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

Influence of Mean Particle Size on Magnetic Properties of Sm （ Co0.72Fe0. 15 Cu0. 1Zr0. 03 ）7.5 Sintered Magnets

The influence of mean particle size on magnetic properties ofSm （ Co0.72Fe0. 15 Cu0. 1Zr0. 03 ） 7.5 sintered magnets, prepared by the conventional powder metallurgy method, was studied. With increasing ballmilling time, mean particle size decreases, specific surface increases, and sintering temperature decreases. The optimum sintering temperature of powders fabricated by baH-milling for 5, 7, 9 and 11 h are 1225, 1225, 1215 and 1215℃ respectively. The optimum value of Br, （BH）max, Hob and Hci of Sm （ Co0.72Fe0. 15 Cu0. 1Zr0. 03 ） 7.5 sintered magnets with powders ball-milling for 9 h and sintering at 1215 ℃ can reach 0.94 T, 708.4 kA·m^-1, 171.9 kJ·m^-3 and 2276.6 kA·m^-1 respectively, and the irreversible flux loss is less than 5 % after the sample ageing at 550 ℃ for 2 h, so the temperature stability improves and the magnets may be expected to be applied in the circumstances of 550 ℃.

Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

基于sigmoid-sinh分段函数的变步长FxLMS算法

为改善滤波-x最小均方(filtered-x least mean square,FxLMS)算法在噪声主动控制时无法兼顾收敛速度和稳态误差的问题,提出了基于sigmoid-sinh分段函数的FxLMS(SSFxLMS)算法,并引入蚁狮算法对SFxLMS(sigmoid filtered-x least mean square)、ShFxLMS(sinh filtered-x least mean square)、SSFxLMS算法的参数进行优化。分别采用高斯白噪声和实测簇绒地毯织机噪声为输入信号,采用FxLMS、SFxLMS、ShFxLMS、SSFxLMS算法进行噪声主动控制仿真,对比分析这4种算法的性能。结果表明:与其他3种算法相比,采用SSFxLMS算法对高斯白噪声和簇绒地毯织机噪声进行控制时,误差信号的平均绝对值更小,平均降噪量与收敛速度也有大幅度提升。由此可知,SSFxLMS算法有效改善了FxLMS算法无法兼顾收敛速度和稳态误差的问题,研究结果为噪声主动控制算法设计提供了一定的参考。

基于Mean Shift点法向量分类的目标三维姿态估计

目标三维姿态信息在目标运动分析、目标识别和目标跟踪等领域的应用越来越广泛。现有的OPDVA算法采用基于距离的K-means算法对点法向量进行分类后确定目标坐标系MCS的正方向,求取目标三维姿态角。针对点法向量分类效果不理想的情况,提出了基于Mean Shift点法向量分类的目标三维姿态估计算法(PEMSPNC)。该算法利用不依赖初始参数设定、基于密度聚类的Mean Shift算法,对密度分布不同的不同平面点法向量分类,寻找密度最大处点法向量做为每类代表法向量确定MCS的正方向,然后计算目标姿态角,并根据目标姿态估计结果计算目标尺寸。采用矩形拟合法、OPDVA和PEMSPNC算法分别对仿真和实测目标距离像进行实验。实验结果表明:采用PEMSPNC算法得到的姿态估计结果误差最小,相比于OPDVA算法,平均误差降低了0.4434°,且对实测数据有较好的处理结果。

基于浅地层剖面数据和改进地声模型的底质反演方法

浅地层剖面仪发射的声脉冲能够穿透海底面进入沉积层内部,其回波中携带了丰富的底质信息。地声模型是底质声学与物理性质关系的数学描述,广泛用于海底声学与地声反演研究。本文通过对浅地层剖面数据的处理、解译得到海底反射系数,与考虑底质松密影响的改进Biot-Stoll模型相结合,提出底质反演新方法并开展实例验证。研究结果表明:通过对浅地层剖面原始记录的读取、解译,提取反射波振幅,并结合设备声源级,可有效求取海底反射系数。通过引入相对密度改进孔隙度计算公式,进而在基于Biot-Stoll模型构建海底反射系数和底质平均粒径关系过程中进一步考虑了底质松密的影响。基于山东威海某海域及文献的算例均显示,本文提出的改进地声模型可缩小底质反演与实测结果之间的相对误差、提升基于浅地层剖面数据的海底底质地声反演精度。

飞机货舱可燃物在干扰源状态下火灾特征参数研究

目前民用飞机货舱主要采用传统光电式烟雾探测器来识别火灾烟雾,但由于飞机货舱中悬浮有其他干扰颗粒(灰尘、水蒸气等)会触发火灾探测器报警,传统光电式烟雾探测器存在误报率高的问题。采用基于双波长及索特平均粒径的光电式烟雾探测器采集飞机货舱典型燃烧物、干扰源的红外光散射功率、蓝光散射功率,以此作为火灾参数,通过对颗粒物红外光散射功率与蓝光散射功率进行分析,结合索特平均粒径分析,为研发复合型烟雾探测器优化火灾参数。结果表明:红外光和蓝光对火灾烟雾颗粒有较好地响应,结合索特平均粒径,可以有效区分火灾烟雾颗粒与非火灾烟雾颗粒,降低误报率,干扰源误报率不超过6.7%。

不同催化剂对聚乙二醇改性油酸产物性能的影响

为提高液压液的稳定性,保障液压支架跟机移架的可靠性,采用聚乙二醇对液压液常用润滑原料油酸进行改性,研究了不同催化剂对改性产物性能的影响。以500 mg/L的人工硬水为配液水,将改性产物按相同质量比各配稀释液,考察稀释液的外观、电导率、Zeta电位、表面张力和平均粒径。实验结果表明,无机和有机强酸分别催化后的产物,在人工硬水中分散性较好,外观呈透亮状态,电导率较人工硬水自身略有提高,表面张力均在40 mN/m以上,表现出良好的亲水能力,平均粒径在30 nm以下,在人工硬水中表现出良好的稳定性。

基于产状Fisher分布的岩石节理最小样本容量确定方法

节理产状是影响岩体结构及其工程性质的关键参数之一,因此准确计算节理的平均产状是岩体工程性质研究的基础。由于节理产状的离散性,合理确定节理的最小样本容量对于计算节理组的平均产状具有重要意义。基于Fisher分布,阐释了节理产状随机数的生成方法,提出一种考虑“超越上半球投影产状”(OEBEUHP)的平均产状计算方法。随机生成不同数量、不同κ值的Fisher分布产状来研究节理样本容量对平均产状准确性的影响,在保证准确率不低于0.95的情况下,建立了最小样本容量与Fisher分布常数κ值的经验关系。研究结果表明:节理样本容量对平均产状的统计准确性影响显著,样本容量越大,平均产状的统计准确率越高;最小样本容量与κ值呈明显的反比例关系,反比例系数为1 007.23。通过对三峡地下厂房上游边墙和乌东德水电站平洞PD5-2节理的统计分析,验证了该经验关系的适用性,能够有效指导节理产状的采样工作。

基于改进K-means图像分割算法的细叶作物覆盖度提取

植被覆盖度是重要的农学指标,图像法作为一种方便、快捷、准确度较高的地面测量方法,在该领域得到了广泛应用。图像背景分割是获取植被覆盖度最关键的步骤,已有分割算法的分割对象局限于大叶植物或者长势较为稀疏的作物,针对细叶作物的研究较少,或者未根据分割结果得出更有价值的规律。本文以小麦为例,提出了基于HSV空间的自适应果蝇均值聚类算法(IFOA-K-means),用来分割图像背景,以此作为获取覆盖度变化的理论基础。采用小波分析按比例去噪算法单独对亮度分量去噪,主体分割算法采用自适应步长果蝇算法(IFOA)改进的Kmeans算法对小麦图像进行背景分割,综合了自适应果蝇算法的全局最优和K-means算法的局部最优特点,使分割效果达到最优。其分割效果优于基于遗传算法的最大类间方差分割法,较好地去除了滴灌带等较明显干扰因素,与传统的K-means算法相比,运行时间和峰值信噪比指标都较优,小麦覆盖度准确率在90%以上,与作物系数之间的决定系数为0. 953 1。

基于信令数据的中型城市通勤公交站点优化方法

大中型城市之间的手机通信基站密度和通勤出行结构不同,公交站点布设呈现出显著差异。在此背景下,研究了基于改进Mean Shift聚类算法的中型城市通勤公交站点优化方法。该方法采用荆州市中心城区信令数据中的通勤记录,以系统总成本(包括运营成本和乘客步行时间成本)作为主要评价指标。根据中心城区早高峰的通勤出行需求,制定通勤公交站点优化方案。通过对比优化结果和现有公交站点布局,验证了优化方法的有效性;比较不同聚类算法,证明改进的Mean Shift聚类算法的性能优越性;考虑基站和等时圈的影响,对比不同场景,证明了考虑二者影响的必要性。结果表明:①针对荆州市研究区域的早高峰出行需求,优化方法共设置28个公交站,乘客步行时间成本下降51.98%,系统总成本下降17.82%,表明本方法能够得到系统总成本更优的站点布设方案,有效减少研究区域内乘客步行时间成本;②与不同聚类算法的比较中,改进Mean Shift算法得到的方案有明显提升,系统总成本比K-means聚类算法下降8.73%,比近邻传播聚类算法(affinity propagation,AP)下降2.48%;③与未考虑基站和等时圈影响的情况相比,本算法步行时间成本有所下降。上述指标表明改进Mean Shift聚类方法在聚类质量上优于其他方法,可以获得更优的公交站点布设方案,为中型城市的公交线路规划提供基础。

CCD散射图样法测量粉尘质量浓度影响因素研究

为减小连续在线式电荷耦合器件(charge-coupled device,CCD)传感器相机散射图样法测量粉尘质量浓度相对误差,提高该方法准确性。针对不同入射色光、颗粒粒径、相机传感器感光元件位置以及交互影响因素进行理论研究和实验验证;由于光子具有随机粒子统计特性,所以对测量数据进行独立样本T检验以及基于单因素验方分析的均值差异检验。研究结果表明:大粒径颗粒测量质量浓度偏低、曲线平滑,小粒径颗粒测量由于具有“双峰”现象,所以质量浓度曲线会有波动;为减小测量相对误差,应在不改变相机传感器位置前提下尽量选择红色线激光入射。研究结果可为粉尘质量浓度光学测量装置的改进和校准提供参考。

基于K-means的金属矿物光片嵌布粒度测量

目前金属矿物嵌布粒度的测定主要依靠技术人员在显微镜下观测,存在工作量大、观测粒度准确度和精度不高等问题。因此提出了基于K-means聚类算法的金属矿物嵌布粒度测量方法。初始聚类中心数K值的准确预设是K-means算法的核心之一,利用金属矿物光片显微镜下图像中的颜色特征,对原矿图像中的矿物种类进行准确判别,再将识别到的矿物颜色特征作为设置K值的依据。通过K-means算法将目的矿物的颜色聚类为单一的颜色,对聚类的图像进行颜色分割,最后利用Feret Diameter精确测量出该金属矿物嵌布粒度。结果表明:利用建立的方法明显提高了金属矿物嵌布粒度测量的准确度、精度和效率,具有极大的应用价值。

基于DAE和改进RFKM的负荷数据精准特征提取与标签定义

针对目前配电网用户负荷数据高维度时序数据特征提取难、交叉数据聚类处理难、负荷数据精准标签化难等问题,文章提出面向用户负荷数据的基于降噪自编码器和改进粗糙模糊K均值的特征提取与标签定义模型(feature extraction and label definition model based on DAE and improve RFKM,FLMbD-iR)。FLMbD-iR通过降噪自编码器对原始用户负荷数据进行深度特征提取后,利用基于类簇规模不均衡度量的粗糙模糊K均值进行聚类,处理聚类中簇间交叉数据存在误差的缺陷,最后构建描述指标对典型日负荷曲线进行标签定义。实验采用美国电力负荷数据进行仿真模拟,实验结果显示本方法在用户负荷数据聚类处理上效果显著。

变步长CMA和DD-LMS双模式切换盲均衡算法

针对经典盲均衡算法收敛速度较慢和稳态误差较大的问题,提出了一种基于变步长恒模算法(Constant Modulus Algorithm, CMA)和判决引导的最小均方(Decision Directed Least Mean Square, DD-LMS)算法的双模式切换盲均衡算法。在算法收敛初期采用CMA算法,以确保算法可以较快收敛。在收敛之后切换至DD-LMS算法,以进一步降低稳态误差。通过设定阈值来切换算法,取相邻多次迭代误差的平均值作为算法的切换值,以确保算法切换时机的合理性。另外,引入Softsign变步长函数并加入3个参数对该函数进行改进,使得Softsign变步长函数可以依据不同信道环境设定最佳参数,同时提高算法的收敛速度。仿真结果表明,在卫星通用信道条件下,所提算法的收敛迭代次数约为1 000次,稳态误差为-12 dB,在信噪比为15 dB时,误码率为1×10~(-6)。与相关算法对比,所提算法的收敛速度较高,误码率和稳态误差较低。

Observations and analyses of floc size and floc settling velocity in coastal salt marsh of Luoyuan Bay, Fujian Province, China

In coastal environments, fine-grain sediments often aggregate into large and porous flocs. ElectroMagnetic Current Meters （EMCM） and Laser In Situ Scattering and Transmissometry （LISST-ST） have been deployed within a Spartina alterniflora marsh of the Luoyuan Bay in Fujian Province, China, to measure the current velocity, the floc size and the settling velocity between 15 and 22 January 2008. During the observations, the near-bed water was collected in order to obtain the suspended sediment concentration （SSC） and constituent grain size. Data show that： （1） the nearbed current velocities vary from 0.1 to 5.6 cm/s in the central Spartina alterniflora marsh and 0.1–12.5 cm/s at the edge; （2） the SSCs vary from 47 to 188 mg/dm 3 . The mean grain size of constituent grains varies from 7.0 to 9.6 μm, and the mean floc sizes （MFS） vary from 30.4 to 69.4 μm. The relationship between the mean floc size and settling velocity can be described as： w s =ad b , in which w s is the floc settling velocity （mm/s）, a and b are coefficients. The floc settling velocity varies from 0.17 to 0.32 mm/s, with a mean value of 0.26 mm/s, and the floc settling velocity during the flood tide is higher than that during the ebb tide. The current velocity and the SSC are the main factors controlling the flocculation processes and the floc settling velocity.

基于最大均值差异的卷积神经网络故障诊断模型

针对工程场景中轴承故障数据采集困难,小样本下轴承故障诊断准确率较低且稳定性不高的问题,提出了一种小样本下滚动轴承故障的诊断方法,即基于最大均值差异(MMD)的卷积神经网络(CNN)诊断模型(方法)。首先,根据轴承故障机理,获取了滚动轴承故障的仿真信号,基于生成式对抗网络构建了仿真信号与少量真实样本间的对抗训练模型,得到了伪域样本,并将其扩充为训练数据集;其次,以交叉熵损失和最大均值差异(MMD)为卷积神经网络(CNN)的优化准则,引入了缩放因子,对网络进行了动态优化,根据测试结果选取缩放因子为0.05作为最优网络结构参数,构建了故障诊断的训练模型;最后,将结构均为1024个数据点的伪域样本和真实样本共同构成模型的训练集,对其进行了归一化处理,然后将其输入到构建的网络模型中,并以MMD作为约束,进行了卷积、池化操作,以实现特征提取的目的,经反向传播对模型进行了优化,实现了诊断模型参数的迭代更新目标。研究结果表明:基于MMD的CNN诊断模型(方法)对小样本下轴承的故障诊断识别精度有明显的改善,当样本数仅为16时,识别率可达95%以上,证明该方法在小样本下的轴承故障诊断中依然能获得较高的故障识别率。