基于IAGA-LSSVM的切削加工表面粗糙度的智能预测

提出了一种基于改进自适应遗传算法与最小二乘支持向量机(IAGA-LSSVM)的切削加工表面粗糙度的智能预测方法。通过设定LS-SVM模型主要参数的取值范围,采用IAGA进行寻优,提高了LS-SVM预测模型的精度。最后采用平均相对预测误差作为检验指标,比较了多元线性回归模型、BP神经网络模型、AGA-LSSVM模型及IAGA-LSSVM模型对表面粗糙度的预测能力。结果表明:IAGA-LSSVM预测模型的建模时间更短,平均相对预测误差更小,对切削加工表面粗糙度的预测具有一定的参考意义。     

基于LS-SVM的机床加工误差预测模型探讨

提出一种基于最小二乘支持向量机(LS-SVM)的机床加工误差回归模型和预测方法,给出了相应的步骤和算法.通过与BP神经网络和RBF神经网络预测方法比较,仿真结果表明,在较少的误差数据条件下,该模型能够有效的描述和预测加工误差的变化,且模型预测误差比神经网络模型小60%左右;应用该预测模型预测机床加工误差有更高的预测精度,对其实施补偿和控制,将有效提高机床的加工精度.     

基于海上风电功率特性的预测误差对比分析

海上风电功率预测是大型风电场并网稳定运行的先决条件,风电功率预测的准确性对提高电网的质量和一致性具有重要作用。为提高风电功率预测误差,采用自回归移动平均(ARIMA)和改进的k-最近邻(kNN)两种预测方法对海上风电功率进行预测,结果表明,风电功率根据不同的特性可以用不同的预测方法进行预测,预测误差小于20%,通过改进预测方法,能够提高预测精度。最后,通过具体算例对改进的两种预测方法进行了验证,并将结果进行了对比,验证了两种算法在不同预测时间范围的有效性。     

侧铣加工刀具回转轮廓误差预测技术研究

在侧铣加工中，刀具磨损和变形引起的刀具回转轮廓误差在实际加工前难以准确预测。提出一种工件形状刀具轮廓映射的辨识试验方法来获取加工过程刀具回转轮廓误差，并通过多因素正交试验获取了不同工况下刀具回转轮廓误差数据库。基于误差数据，采用最小二乘支持向量机（LS-SVM）技术建立了刀具回转轮廓误差预测模型。运用遗传算法优化对所提模型有重要影响的核函数参数和错误惩罚因子, 建立了基于遗传算法优化的最小二乘支持向量机(GA-LS-SVM)模型，并与未经遗传算法优化的LS-SVM模型进行了对比，试验结果表明，GA-LS-SVM预测模型能更好地适用于刀具回转轮廓误差预测。     

基于PSO优化的LS-SVM的混沌时间序列预测

在非线性混沌动力学的发展中,混沌时间序列预测已成为一个非常重要的研究方向。最小二乘支持向量机（LS-SVM）是标准支持向量机的再形成形式,包含了分类、回归、预测等多种算法。微粒群优化（PSO）是当今智能寻优算法中较为简单快速的算法。运用PSO算法可以很好地解决LS—SVM的参数优化问题。针对以上问题。提出了基于PSO优化的LS—SVM预测方法。并对混沌时间序列预测进行了仿真。仿真结果证明了该算法的有效性。     

风电场混合储能的小波包-SOC分区功率控制

大规模可再生能源并网引起的一系列问题使得储能技术成为未来能源转型的重要依托与突破方向。为减小系统调节压力,采用蓄电池和超级电容组成混合储能以补偿风功率预测误差。利用小波包分解可获取信号更多细节信息的优势,根据混合储能的性能特点和响应速度,确定分解层次并实现混合储能充放电功率的初始分配。考虑实际应用中容量约束,提出荷电状态(SOC)分区功率控制策略对储能的功率指令进行修正,实现充放电功率的优化分配,提高补偿效果。应用结果表明,所提策略能够充分利用混合储能互补的性能优势有效补偿风功率预测误差,同时保证储能的长期稳定运行。     

滚动轴承振动的非线性超混沌特性研究

为研究滚动轴承不同振动状态的非线性混沌特性,采用不同方法从多角度进行探讨和判定。使用递归图分析振动的周期性,然后采用CLY方法对信号的混沌特性进行判定,再结合功率谱探讨振动的频率分布特征,最后采用最小二乘支持向量机(LS-SVM)方法,通过对各状态振动的Lyapunov指数谱进行计算,研究轴承振动的超混沌特性。研究结果表明:滚动轴承不同状态振动信号的非线性混沌特征存在明显的差异,但均表现为混沌特征并且都是超混沌的,且故障状态越大,振动的超混沌性越强。     

基于LS-SVM的机械式温度仪表误差预测研究

机械式温度仪表在测量过程中易受到环境温度、毛细管长度以及内部机构影响而出现测量精度不高、非线性的情况,针对这些问题以液体压力式温度仪表作为研究对象,提出了基于最小二乘支持向量机(LS-SVM)的温度误差建模预测的方法。通过分析液体压力式温度仪表的测温结构和误差影响因素,将环境温度及毛细管长度等特征参数作为模型输入,将误差值及误差随毛细管长度的变化率作为输出。根据回归预测的原理,利用网格搜索和交叉验证的方法寻找最优参数组合,建立液体压力式温度仪表的误差预测模型。实验结果表明,该模型可以有效地描述温度误差,并将此建模方法与常用的支持向量机回归建模方法进行比较,基于LS-SVM得到的误差预测模型精度较高、推广能力强,可以对机械式温度仪表进行补偿,为探索机械式温度仪表自适应补偿机构提供理论依据。     

基于 IWOA-SVM 的风电功率预测

针对支持向量机预测精度低、收敛速度慢等问题,提出一种改进鲸鱼算法优化支持向量机的风电功率预测模型。将 Tent 混沌映射引入鲸鱼算法中,使初始种群的分布更加均匀;由于随机抽取猎物具有盲目性,不能充分结合迭代经验对种群进行更新,采用轮盘赌法寻找目标猎物来加快鲸鱼算法的收敛速度,得到改进鲸鱼算法优化支持向量机的风电功率预测模型。将该模型应用到我国东北某处风电场进行风电功率预测,并与其他常用的功率预测模型进行对比分析,仿真结果表明,该模型具有更高的预测精度。     

基于OE-CM算法的机床主轴热误差建模与补偿分析

针对机床热误差建模过程中,误差信息不透明、数据特性不全面等不利因素,根据机床主轴热误差实验数据,分别采用GM(1,n) 模型和最小二乘支持向量机(LS-SVM)模型建立主轴热误差预测模型并进行线性叠加,然后采用预测有效度算法调整模型加权系数,建立了最优有效度复合预测模型（OE-CM）以获取最佳预测效果。在VXC-560型三轴数控机床上进行在线实验建模,实验结果表明：OE-CM具有预测精度高、鲁棒性好等特点,整体预测效果优于灰色GM(1,n)模型和LS-SVM模型,适合在复杂工况条件下对机床主轴热误差进行预测和补偿,为提高机床热误差补偿精度建立了理论模型。为了验证该预测模型的有效性,对所研究的机床主轴进行热误差在线补偿,机床主轴Z向最大误差从23.8μm减小到8μm,减幅达到66.4%,较好地提高了机床精度,具有一定的工程化推广前景。     

INTEGRAL EQUATION METHOD＇S APPLICATION IN HOLE-EDGE STRESS OF COMPOSITE MATERIAL PLATE WITH DIFFERENT SHAPED HOLES

The strength of composite plate with different hole-shapes is always one of the most important but complicated issues in the application of the composite material. The holes will lead to mutations and discontinuity to the structure. So the hole-edge stress concentration is always a serious phenomenon. And the phenomenon makes the structure strength decrease very quickly to form dangerous weak points. Most partial damage begins from these weak points. According to the complex variable functions theory, the accurate boundary condition of composite plate with different hole-shapes is founded by conformal mapping method to settle the boundary condition problem of complex hole-shapes. Composite plate with commonly hole-shapes in engineering is studied by several complex variable stress fimction. The boundary integral equations are founded based on exact boundary conditions. Then the exact hole-edge stress analytic solution of composite plate with rectangle holes and wing manholes is resolved. Both of offset axis loadings and its influences on the stress concentration coefficient of the hole-edge are discussed. And comparisons of different loads along various offset axis on the hole-edge stress distribution of orthotropic plate with rectangle hole or wing manhole are made. It can be concluded that hole-edge with continuous variable curvatures might help to decrease the stress concentration coefficient; and smaller angle of outer load and fiber can decrease the stress peak value.     

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity ar...     

End-to-End Multiview Gesture Recognition for Autonomous Car Parking System

The use of hand gestures can be the most intuitive human-machine interaction medium.The early approaches for hand gesture recognition used device-based methods....     

ANGULAR DISTORTION ALLOWABLE VALUE AND ASSESS METHOD OF WELD ON COKE TOWER

A graph as the new engineering method for estimate the safety of bulging deformation of coke tower is proposed. Through stresses analysis of circumferential weld of coke tower and comparing the stresses produced by pressure with heat stress of steady state, residual stress, bending stress produced by both itself weight and wind loads, it showed that the stresses produced by pressure on the angle distortion are the main factor of equivalent stress of the combined stress. After comparing four kinds of stress controlling conditions, the relation to stress with depth of angular distortion, grade of curvature of angular distortion and half of region of angular distortion has been inferred. Graph of deformation allowable value of coke tower for different condition by angular distortion and half of region of angular distortion has been plotted. The five steps for its engineering use have been explained. The lighter the grade of curvature is, the larger of bulge allowance, may be, and the bigger of depth of angular distortion may pose too. For the coke tower with a popular structure of Dg 5 400 mm×28 mm, the result by graph is nearly more than the result of two formulas formed by other research, the error is less than 7.0%. But, the graph can be easily applied to different size of angular distortion.     

Multiple-tapped-delay-line hardware-linearisation technique based on wire load regulation

This article describes designing, implementation and tuning processes of multiple-tapped-delay-line (MTDL). Obtained MTDL can be implemented in various field-programmable-logic-devices (FPGA) devices and applied for time-to-digital-converters (TDC) construction. The task of tuning process is the tapped-delay-line (TDL) linearisation, and consists of two stages. The first stage depends on selecting an appropriate configurable-logic-block (CLB) for particular delay-segment realization and selecting proper connection between these blocks. The second tuning stage, that is essential from this article viewpoint, depends on inter CLBs connecting wires delay regulation realized directly by load regulation. The Load regulation depends on connecting an appropriate number of unused three-state-buffers or CLB inputs to the wire which delay is adjusted. Depending on the number of inputs connected to the wire its capacitance changes that influences its time-constant and finally changes its time-delay.The MTDL mathematical model, obtained characteristics and results of time-interval (TI) measurements are also presented. The derived TDL model provides information about how the particular wire delay should be changed and in which order the changes should be executed. This makes the designing process predictable and easy to carry out. Presented characteristics confirm the proper operation of presented linearisation technique. The proper operation of the whole measuring module is confirmed by obtained TIs histograms presentation.     

Quantum Entanglement and Cryptography for Automation and Control of Dynamic Systems

This paper addresses the application of quantum entanglement and cryptography for automation and control of dynamic systems.A dynamic system is a system where t...     

原子力显微镜在生物分子间相互作用力的研究

近年来原子力显微镜在测量生物分子间的相互作用力方面取得显著的进步。本文综述原子力显微镜原理以及在生物分子间相互作用方面的研究,为人们理解分子的识别进程,提供一个新的研究方法。     

DYNAMICS ANALYSIS OF SPECIAL STRUCTURE OF MILLING-HEAD MACHINE TOOL

The milling-head machine tool is a sophisticated and high-quality machine tool of which the spindle system is made up of special multi-element structure. Two special mechanical configurations make the cutting performance of the machine tool decline. One is the milling head spindle supported on two sets of complex bearings. The mechanical dynamic rigidity of milling head structure is researched on designed digital prototype with finite element analysis（FEA） and modal synthesis analysis （ MSA ） for identifying the weak structures. The other is the ram structure hanging on milling head. The structure is researched to get dynamic performance on cutting at different ram extending positions. The analysis results on spindle and ram are used to improve the mechanical configurations and structure in design. The machine tool is built up with modified structure and gets better dynamic rigidity than it was before.     

Detection method of spot welding based on multi-information fusion and fractal

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.