A Mathematical Model of Real-Time Simulation and the Convergence Analysis on Real-Time Runge-Kutta Algorithms

In this paper, a mathematical model of real-time simulation is given, and the problem of convergence on real-time Runge-Kutta algorithms is analysed. At last a theorem on the relation between the order of compensation and the convergent order of real-time algorithm is proved.

Conformal Wavelet Finite-difference Time-domain Algorithm for Analysis of Milimeter Wave Attenuation on Coplanar Waveguide

Addressed is the calculation of millimeter wave attenuation on coplanar waveguide(CPW). A novel conformal wavelet finite-difference time-domain(CWFDTD) algorithm is proposed with emphasis on its application in calculation of millimeter wave attenuation on CPW, which is the combination of conformal algorithm dealing with the deformed cell with Wavelet-FDTD using multi-resolution analysis(MRA). Derived is the difference formulation for multi-resolution time domain(MRTD) based on Daubechies wavelets, and also given is the stability conditions for wavelet-FDTD algorithm. To validate its accuracy and efficiency, this novel method is applied to calculate the millimeter wave attenuation on lithium niobate CPW. Numerical results demonstrate that this new CWFDTD algorithm has the same accuracy with the conformal finite-difference time-domain(CFDTD) and conformal finite-difference time-domain based on alternating-direction implicit method(ADI-CFDTD), but saves computational time and computer memory.

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

Real-time Design Constraints in Implementing Active Vibration Control Algorithms

Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.

基于小波变换Mallat算法的电网谐波检测方法

针对传统的傅里叶变换方法在分析非平稳运行电网的电量信号时误差较大的问题,提出了一种基于小波变换Mallat算法的电网谐波检测方法。该方法根据不同的分辨率将电量信号分解到不同的子频段,然后分别对子频段进行多次重构,得到原始信号的基波,最后将采样得到的原始信号与重构的基波信号相减,得到谐波信号。Matlab仿真结果表明,该方法能够有效地将电量信号中的基波与谐波成分分离,谐波检测精确度较高。

a尺度正交双向小波的Mallat算法

研究了a尺度正交双向小波的Mallat算法.引入了正交双向加细函数及a尺度正交双向小波的概念,在此基础上利用a尺度正交双向多分辨分析,得到了正交双向小波的分解与重构的Mallat算法,并给出其矩阵表示,推导了信号分解后完全重构的充要条件.该算法对于能量有限的离散信号的分解与重构有一定的实用价值.

小波Mallat谐波测量的带内泄漏误差分析与算法改进

电力谐波的小波变换分频带测量目前常采用Mallat算法,在信号同步采样条件下具有较高的准确度。当非同步采样时,通过理论分析指出该测量方法存在着很大的带内泄漏误差,并推导了带内泄漏误差的频域形式表达式。仿真和实验表明带内泄漏引起的相对误差可达5×10-2。针对带内泄漏误差,采用整系数交迭窗函数构造子带内抗混叠滤波器,提出一种抗带内泄漏的小波变换谐波测量算法。仿真表明抗带内泄漏算法的有效值测量相对误差小于1.3×10-4;实验表明抗带内泄漏算法的功率测量引用误差小于1.3×10-3,测量准确度比小波变换Mallat算法提高了1～2个数量级。

基于小波Mallat算法的次同步振荡监测

提出了1种基于小波Mallat算法的次同步振荡实时监测方法,该方法可以通过对发电机转速或定子电流2种不同类型的信号进行多分辨率分析,在不同尺度上实现对次同步振荡的分量提取和奇异性检测,从而使次同步振荡的监测在时频域均具有较高的分辨率。现场实际应用表明该方法具有较好的工程应用价值。

Mallat算法频率混叠原因及其改进模型

Mallat算法由于自身设计的原因,在信号分解过程中,存在频率混叠现象.在利用小波分析进行信号提取时,这种现象是一个不容忽视的问题.通过分析Mallat算法,找出了造成Mallat算法产生频率混叠的原因,给出了一个能有效消除频率混叠的改进算法模型.

Mallat算法与单子带重构算法的分析比较

主要论述了小波分析中的快速算法——Mallat算法和单子带重构算法。对Mallat分解起点的实用性、滤波器的滤波方法做了更加通俗的说明,并比较了Mallat算法与单子带重构算法的分析差别,论述了它们在各自实际工程中的应用场合。

a尺度正交小波的Mallat算法

本文研究了a尺度正交小波的Mallat算法,利用a重多分辨分析,得到了正交小波的分解与重构算法,给出了Haar小波的Mallat算法的矩阵表示,并简化了计算.

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.

Application Research of an Intelligent Detection Algorithm for Vehicle Trajectory Route Deviation

In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.

客户分级优先的即时配送路径规划方法

为了使即时配送企业能以较低的成本提高配送准时性,从而维护并发展高价值客户,首先,针对即时配送客户的特点改进RFM模型,基于已有数据使用DBSCAN算法进行客户聚类,根据聚类结果使用GBDT算法构建客户分级预测模型对即时配送客户进行分级预测。在此基础上,以即时配送的固定成本、变动成本及客户超时点种类、数量为优化目标,构建基于客户分级优先的即时配送路径优化模型,再设计遗传算法对该模型进行求解。最后,以沈阳市某一站式冷链即时配送企业为对象进行实例分析。结果显示,相比该企业原配送方案,应用客户分级优先的即时配送路径规划方法规划后的方案在配送总成本仅提高4.8%的情况下,高价值、潜在高价值客户超时点数量由6减少为2,且超时点均为边缘客户,同时配送总时间减少了7.3%,验证了该方法的有效性。采用该配送路径规划方法,企业的配送成本虽然会小幅增加,但因配送准时性提升,可以更好地维护高价值客户,同时发展潜在高价值客户向高价值客户转变,进而保持或提高长期收益。

基于特征组合优化的工业互联网恶意行为实时检测方法

工业互联网中节点数据具有高维、冗余和海量等特性,传统的恶意行为检测模型无法对工业互联网恶意攻击行为做出快速且准确的判断,提出基于特征组合优化的工业互联网恶意行为实时检测方法.采用改进的相关性快速过滤算法和基于奇异值分解的主成分分析算法对工业互联网恶意行为样本数据进行特征组合优化,基于对称不确定性信息度量指标和近似马尔科夫毯准则进行特征相关性计算、冗余特征识别与排除,通过参数特征维度的不同配置得到若干候选特征组合;利用决策树评估器筛选出准确率最高的候选特征组合;通过奇异值分解的主成分分析进一步进行特征降维,得到低维高信息量的最优特征组合;结合极端梯度提升算法和优化的特征组合对工业互联网恶意行为样本进行分类,基于密西西比州立大学多分类电力系统攻击样本数据对本文方法进行了验证;实验结果表明,特征组合优化检测模型训练时间可缩减57.53%,单个样本的平均检测时间为0.002 ms,可减少23.99%,基于特征组合优化的检测模型的准确率、召回率和F1值较特征优化前分别提升了1.11%、1.25%和1.01%.本文方法的突出优势表现为在提升模型检测效果的同时可明显降低模型检测时间,能更好适应工业互联网的实时性要求.

边坡安全监测GPS-RTK信号的降噪算法研究

全球定位系统实时动态差分技术(global positioning system-real time kinematic, GPS-RTK)是解决路基边坡安全监测问题的重要手段,但GPS-RTK信号易受到多路径误差和共模误差的影响。基于小波变换(wavelet transform, WT)和主成分分析(principal component analysis, PCA)分别可以有效去除多路径误差和共模误差,提出WT-PCA算法去除信号误差。首先设置仿真信号,通过参数调优进一步提高单一算法的降噪效果。其次提出组合算法WT-PCA改进单一算法的缺陷,并与其他组合算法进行对比分析。最后,对十天高速路基边坡的GPS-RTK监测数据进行实例分析。结果表明,WT-PCA算法的信噪比和均方根误差较于WT-VMD优于66%和50%左右,算法可以有效地消除GPS-RTK信号的多路径误差和共模误差影响。提高边坡位移监测信号处理精度,进一步评估边坡结构形变及安全状态。

多核处理器中混合关键级任务可调度及半分区划分算法

当前,多数多处理机中混合关键级任务可调度性分析以及半分区调度算法均针对单核利用率展开研究。但由于多核系统任务调度复杂性较高,现有研究结果存在各处理器负载不均衡以及任务可调度性不理想等问题。针对该问题,文中将动态需求边界函数(Dynamic Demand Boundary Function,DDBF)的应用范围扩展至多核处理器系统。根据半分区划分调度算法对DDBF改进,并加入了结转作业和前接作业分析提出了SDDBF(Super Dynamic Demand Boundary Function),可更精确地计算与利用资源。文中基于SDDBF提出了SDA(Stepper Dispatch Algorithm)可调度性分析法与半分区划分算法MCWF(Mixed-Criticality Worist First)。仿真结果表明,相较于AMC(Adaptive Mixed Criticality)、AMC-max以及XU算法,SDA可调度性分析判定提升了5%~10%,相较于WF_MY(Worst First_My)、WF_NEW(Worst First_New)算法,MCWF可使系统在任意关键等级下的CPU(Central Processing Unit)负载具有更良好的均衡性能。

源网荷储系统优化调度技术的应用研究

源网荷储系统优化调度技术已成为实现电力系统可持续发展和高效运行的关键,对于实现清洁能源转型、提高电力系统的效率具有重要意义。通过研究源网荷储系统的优化调度问题,提出源网荷储管理平台的技术架构和关键技术,即从线性规划模型、粒子群算法、遗传算法、模糊综合评判方法等方面剖析调度优化算法,平台通过数据采集与分析、实时调度方法、调度策略和调度优化算法等模块的协同配合,从而实现电力系统的高效和可靠运行。从清洁能源消纳、储能技术发展、智能化和自动化、协同调度和市场化等方面探讨源网荷储系统的发展方向与趋势,指出源网荷储系统未来将推动清洁能源的大规模消纳,提高电网的稳定性和经济性。

数据采集与分析技术在工业物联网中的应用

随着工业物联网技术的迅速发展,大量传感器和设备的接入使工业数据呈现爆炸式增长。文章对数据采集与分析技术在工业物联网的应用进行了研究,探讨了数据采集的方法、技术与数据分析的算法、工具,同时结合实际案例对其应用进行了分析和评估。