Application of new void detection algorithm for analysis of feed pressure and rotation pressure of roof bolters

Roof and rib instability is an important issue in underground mining. To optimize ground support design,enhance ground stability, and reduce the possibility of roof or rib failure with minimal use of artificial ground support, it is essential to have an accurate understanding of ground conditions. This includes the location of voids, cracks, and discontinuities, as well as information about the different strata in the immediate roof. This paper briefly introduces ongoing research on void detection by using the roof bolter feed and rotation pressure. The goal of this project is to improve the sensitivity of detection programs to locate smaller joints and reduce the number of false alarms. This paper presents a brief review of the testing procedures, data analysis, logic, and algorithms used for void detection. In addition, this paper discusses the results of preliminary laboratory tests and statistical analysis of the data from these two drilling parameters used for void detection.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

基于改进松鼠搜索算法优化神经网络的数控机床进给系统热误差预测

为探究数控机床进给系统中各因素对热误差的影响规律,建立精准的热误差预测模型。在进给速度为10 m/min、环境温度20℃的条件下进行进给系统热误差测量实验,获得进给系统关键点的温升及热误差。为提高预测精度,采用Tent混沌改进松鼠搜索算法,并利用改进的算法对神经网络进行优化,建立热误差预测模型。利用热误差测量实验获得的数据进行验证,结果表明改进前的神经网络预测误差为12.23%,改进后的模型预测误差为8.92%,精度有较大提升。利用预测模型针对不同进给速度下相同位置处热误差进行分析,结果表明,进给系统中关键测温点的温度和丝杠各点的热误差随着进给速度的增加而增加。因此提出的预测模型可实现进给系统热误差的准确预测,为误差补偿提供理论依据。

数控机床直线进给轴温度测点优化研究

为分析数控机床直线进给系统的温度场,需要布置一定数量的温度测点来进行温度数据的采集。然而,测点的位置和数目都会对研究结果产生直接影响。为实现准确布置温度测点,文章提出一种基于统计学理论进行改进的Canopy-FCM-GRA温度测点优化模型。以某数控机床X向直线进给轴为例,首先根据测得的实验数据确定预聚类数,然后通过模糊矩阵和灰色关联度系数筛选出相应的温度敏感点,最后基于SVR理论分别建立温度测点优化前和优化后的温度-热误差预测模型,通过比较两个模型精度来验证温度测点优化的有效性。结果表明,温度测点优化效果良好,通过优化后的测温点可准确探究进给系统的热特性。

滑移转向无人车轨迹跟踪控制策略研究

针对滑移转向车辆构型的无人车,通过建立车辆动力学和运动学模型,对LQR(linear quadratic regulator)最优控制算法进行改进,在速度环采用一种自抗滑移的前馈控制,设计了一种轨迹跟踪控制策略。前馈控制有效提高了横摆角速度响应速度,可进一步优化LQR估计器的参数矩阵,保证车辆在提高轨迹跟踪控制精度的同时不影响跟随稳定性。采用Trucksim与Simulink联合仿真进行验证,结果表明:在双移线测试工况下,使用该控制策略的无人车最大横向误差比在双PID控制下小0.086 m,误差收敛速度2.1 s,稳态误差振荡幅值比滑模控制小约0.05 m;不同车速和不同附着系数路面下,无人车最大横向误差浮动小于0.02 m,误差收敛速度不变,设计算法控制效果稳定,验证了其鲁棒性。

某埋头弹机枪拨弹杠杆动力学分析及优化

某埋头弹机枪使用了与传统枪弹结构完全不同的埋头弹,为了适应这种弹药,该埋头弹机枪与传统机枪在结构上也有很大的差异。其中,相较于传统机枪,该埋头弹机枪的输弹行程更为靠后,为了减小全枪长度,不得不缩短拨弹杠杆中曲线槽部分的长度。这使得枪机框上的导柱进入拨弹杠杆曲线槽时,对拨弹杠杆的冲击力很大,从而对整个输弹机构的可靠工作造成极为不利的影响,同时也会严重降低枪机框的动能,使得输弹动作难以完成。现以导柱与拨弹杠杆之间的最大冲击力为目标函数,选用5次函数曲线作为拨弹杠杆曲线槽理论轮廓线的形状,运用遗传算法对该理论轮廓线进行优化,使得导柱与拨弹杠杆之间的最大冲击力降为优化前的1/3。试验验证表明,枪机框在进入拨弹杠杆曲线槽的过程中,速度损失由原来的0.94 m/s降低到0.28 m/s,达到了优化目的,该研究结果为该枪的改进提供了可靠的理论依据。

基于差分进化算法的数控机床进给系统加速试验优化模型

为了进一步提高数控机床进给系统工作性能及效率,该研究引入差分进化算法,基于差分进化算法的原理和应用流程,自动寻找最佳的参数配置,提高加速过程的效率。通过差分进化算法的迭代优化,寻找最佳的参数组合,以实现更快速、更平稳的加速过程。模型运行试验结果表明,优化后的数控机床进给系统在加速阶段具有更高的性能和更短的加速时间。研究结果能对提高数控机床的加工效率和性能提供有力支持,对于制造业具有重要的实际应用意义。

基于数据驱动的FAST主动反射面调节优化模型

针对500 m口径球面射电望远镜(five-hundred-meter aperture spherical radio telescope,FAST)的主动反射面调节问题,利用旋转抛物面在三维空间中的定义建立了一个对任意天体角度均适用的工作抛物面方程,该方法不同于以往的坐标旋转的方法。利用反射面板重心点作为反射点,将反射接收比定义为能把光线反射到馈源舱被其接收的面板数量与300 m口径工作抛物面下总面板数量的比值。结合主索节点径向伸缩距离、边界平稳过度和邻接距离变化幅度三个约束条件,以接收比最大化作为目标函数,建立了一个工作抛物面优化模型,并利用节点数据和模拟退火算法对其进行求解,得到了工作理想抛物面。选用两组天体角度进行实例分析,结果显示调节后的接收比增大12.82%,并给出了工作主索节点的部分径向调节方案。本研究为FAST对于任意天体角度的变形提供了一种新的建模方法,也对馈源舱反射接收比提出了一种新的计算方式。

Real-time Feed-forward Force Compensation for Active Magnetic Bearings System Based on H∞ Controller

There are two kinds of unbalance vibrations—force vibration and displacement vibration due to the existence of unbalance excitation in active magnetic bearings（AMB） system. And two unbalance compensation methods—closed-loop feedback and open loop feed-forward are presented to reduce the force vibration. The transfer function order of the control system directly influencing the system stability will be increased when the closed-loop method is adopted, which makes the real-time compensation not easily achieved. While the open loop method would not increase the primary transfer function order, it provides conditions for real-time compensation. But the real-time compensation signals are not easy to be obtained in the open loop method. To implement real-time force compensation, a new method is proposed to reduce the force vibration caused by the rotor unbalance on the basis of AMB active control. The method realizes real-time and on-line force auto-compensation based on H∞ controller and one novel feed-forward compensation controller, which makes the rotor rotate around its inertia axis. The time-variable feed-forward compensatory signal is provided by a modified adaptive variable step-size least mean square（VSLMS） algorithm. And the relevant least mean square（LMS） algorithm parameters are used to solve the H∞ controller weighting functions. The simulation of the new method to compensate some frequency-variable and sinusoidal signals is completed by MATLAB programming, and real-time compensation is implemented in the actual AMB experimental system. The simulation and experiment results show that the compensation scheme can improve the robust stability and the anti-interference ability of the whole AMB system by using H∞ controller to achieve close-loop control, and then real-time force unbalance compensation is implemented. The proposed research provides a new control strategy containing real-time algorithm and H∞ controller for the force compensation of AMB system. And the stability of the control system is finally improved.

BP神经网络在垃圾焚烧电厂酸性气体排放控制中的应用

对于采用半干法的大多数垃圾焚烧发电厂,控制酸性气体排放浓度的主要手段是调节脱酸塔入口石灰浆流量。传统的PID控制采用CEMS实测数据作为反馈信号调节石灰浆流量,但脱酸塔入口到烟囱烟气采样点距离远,反应时间长,控制效果不佳。为此,引入BP人工神经网络遗传算法,并利用人工控制模式下积累的大量过程数据训练人工神经网络,以替代人工控制。

Near-infrared Spectral Detection of the Content of Soybean Fat Acids Based on Genetic Multilayer Feed forward Neural Network

In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.

Emission-Reductive and Multi-Objective Coordinative Optimization of Binary Feed for Atmospheric and Vacuum Distillation Unit

A genetic algorithm based multi-objective coordinative optimization strategy is developed to optimize the operation of a binary feed atmospheric and vacuum distillation system, in which the objective functions cover the economic benefit, the furnace energy consumption and the CO_2 emissions, and meanwhile the simultaneous effect of binary feed composition is also investigated. A cross-call integration of software is developed to implement the optimization algorithm,and once the maximum economic benefit, the minimum furnace energy consumption and the minimum CO_2 emissions are obtained, the Pareto-optimal solution set is worked out, with the practical problems of the refinery being solved. The optimization result shows that under the same furnace energy consumption and the CO_2 emissions as the existing working condition, the economic benefit still allows for a considerable potential of increment by adjusting the heavy oil proportion of the binary feed crude oil.

ADAPTIVE FEED-FORWARD COMPENSATOR FOR HARMONIC CANCELLATION IN ELECTRO-HYDRAULIC SERVO SYSTEM

Since the dead zone phenomenon occurs in electro-hydraulic servo system, the output of the system corresponding to a sinusoidal input contains higher harmonic besides the fundamental input, which causes harmonic distortion of the output signal. The method for harmonic cancellation based on adaptive filter is proposed. The task is accomplished by generating reference signals with frequency that should be eliminated from the output. The reference inputs are weighted by the adaptive filter in such a way that it closely matches the harmonic. The output of the adaptive filter is a harmonic replica and is injected to the fundamental signal such that the output harmonic is cancelled leaving the desired signal alone, and the total harmonic distortion （THD） is greatly reduced. The weights of filter are adjusted on-line according to the control error by using least-mean-square （LMS） algorithm. Simulation results performed with a hydraulic system demonstrate the efficiency and validity of the proposed adaptive feed-forward compensator （AFC） control scheme

基于采食行为参数的奶牛个体采食量量化方法研究

受饲养环境和设备限制,现存奶牛个体采食量监测方法难以应用于商业生产。为找到简单高效的个体采食量量化方法,以采食行为参数(采食时间、躺卧时间、行走步数)、个体体重及日粮粗精比作为输入参数,构建4种采食量量化模型:反向传播神经网络(BP)模型、遗传算法优化BP神经网络(GA-BP)模型、粒子群算法优化BP神经网络(PSO-BP)模型、多项式学习率衰减算法优化BP神经网络(PDLR-BP)模型,并经模型评价验证找到适宜的算法函数。结果表明,结合采食行为数据与改进型BP算法可实现奶牛个体采食量量化研究,4个模型建模精度高,模型量化值与实测值呈显著相关,决定系数高于0.90;其中,PSO-BP模型精准性最优且收敛更快,模型MSPE,MAE和R2值分别为0.046 kg^(2)·d^(-1)、0.166 kg·d^(-1)和0.95,其均方相对误差RMSE=0.214 kg·d^(-1)分别低于BP模型、GA-BP模型和PDLR-BP模型10%、9.3%和6.9%。PSO-BP模型极值差降幅明显,说明PSO-BP模型稳定性更优。

信息流广告的种草效应与加热效应

文章探讨了信息流广告的间接效应,即在广告带动下,有更多用户通过算法推荐、商家发布的信息流内容产生订单。基于短视频平台数据,文章测算用户在信息流内容下产生的订单增量,检验广告投放量对订单增量是否存在显著作用。结果表明,信息流广告存在两类间接效应:其一,信息流广告对广告受众存在种草效应,算法后续会为广告受众持续推送商家发布的信息流内容;其二,信息流广告对商家发布的信息流内容具有加热效应,算法将热度更高的内容推送给更多非广告受众。此外,文章也揭示了间接效应的重要调节因素,即广告原生性与商家发布的信息流内容数量,这将有助于商家衡量信息流广告的真实效果,以及优化广告间接效应的表现。

基于轻量型S3D算法的鱼类摄食强度识别系统设计与试验

实际生产中,对鱼类的投喂控制仍是以人工经验判断和时序控制为主,易造成饲料浪费和环境污染。实时检测鱼群摄食强度,可用于指导投喂,进而提高饲料利用率,并降低残饲污染。基于此,本研究提出一种基于机器视觉和轻量型S3D算法的鱼群摄食强度实时识别算法,可精确定位视频流中“强、中、弱、无”4种鱼群摄食强度状态。首先将I3D网络作为基准,使用Inception模块和深度可分离卷积构建3D时空Sep-Inc模块;其次,利用3D时空Sep-Inc模块、池化层和3D卷积层交替搭建轻量型S3D网络;最后,开发了基于PyQt5的金鳟鱼摄食强度识别系统。结果显示:S3D算法对4类摄食强度的识别准确率可达92.68%,比C3D和R2+1D算法分别提高9.75%和14.15%,同时Parameters参数和GFLOPs参数也大幅下降,识别摄食强度标签的速率达到17 f/s。研究表明,本算法不仅适用于金鳟,也有望适用于其他游泳型鱼类,并可提供投喂决策建议。

基于蚁群优化算法的相控阵天线随机馈相方案的优化

为了降低相控阵天线的峰值副瓣电平,提高相控阵天线的抗干扰性能,以泰勒幅度加权的线阵为例,分析了随机馈相的数学模型,并基于蚁群优化算法提出了针对相控阵天线随机馈相的优化算法,并进行了仿真实验验证。仿真结果表明,采用优化后的随机馈相方案,相控阵天线的峰值副瓣电平相比优化前降低了8 dB以上。此外,所提出的优化算法经过200次左右的迭代即接近于最终的优化结果,具有较好的收敛性能。

基于GA优化的模糊PID控制螺旋给料计量系统

针对螺旋给料计量控制系统因存在的非线性因素导致系统计量精度低、稳定性差的问题,提出了一种基于遗传算法(GA)优化的模糊PID控制算法。根据系统特性,将GA与模糊逻辑控制结合,对PID参数进行实时调整,以适应系统存在的非线性变化,并基于Matlab/Simulink进行了仿真分析。仿真结果表明,相比于传统PID和模糊PID控制器,基于GA优化的模糊PID控制器超调量更低,抗干扰能力更强。经验证,算法具有很好的鲁棒性,能满足实际控制需求。

遗传算法辅助中药渣在饲料配方优化中的创新应用

饲料添加剂是生产饲料时为了平衡饲料营养、预防疾病而必须使用的一类商品。中药渣经益生菌发酵降解毒素后可作为安全的有机质饲料添加剂加入到饲料当中,作为抗生素替代物和营养补充剂。为了将发酵中药渣合理添加到饲料中,文章模拟实际饲料生产环境,建立饲料营养配方优化的多目标规划模型,通过遗传算法求解模型设计,希望获取保证精准营养、绿色安全的饲料配方。结果表明:这项技术能够实现饲料配方优化在2 s内实现;同时能够利用有机质添加剂替代无机质添加剂使用,降低67.48%无机质饲料添加剂的使用量。