Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

A novel dynamic step size LMS optimization scheme for interference reducing in FBMC-QAM

Filter bank multicarrier quadrature amplitude modulation(FBMC-QAM)will encounter inter-ference and noise during the process of channel transmission.In order to suppress the interference in the communication system,channel equalization is carried out at the receiver.Given that the con-ventional least mean square(LMS)equilibrium algorithm usually suffer from drawbacks such as the inability to converge quickly in large step sizes and poor stability in small step sizes when searching for optimal weights,in this paper,a design scheme for adaptive equalization with dynamic step size LMS optimization is proposed,which can further improve the convergence and error stability of the algorithm by calling the Sigmoid function and introducing three new parameters to control the range of step size values,adjust the steepness of step size,and reduce steady-state errors in small step sta-ges.Theoretical analysis and simulation results demonstrate that compared with the conventional LMS algorithm and the neural network-based residual deep neural network(Res-DNN)algorithm,the adopted dynamic step size LMS optimization scheme can not only obtain faster convergence speed,but also get smaller error values in the signal recovery process,thereby achieving better bit error rate(BER)performance.

Adaptive Method’s Application for the Identification Systems in Dynamic Weighing

In this work,the possibility of adaptive algorithm in WIM(weight-in-motion)systems,in which fibre optic sensors are used,is shown.Appointment of dynamic weighing device consists in determining the weight and type of vehicle.In this work an algorithm for processing the input data and fiber optic sensor to create the database used in the algorithm is presented.The results of the algorithm for the identification of vehicles are given.The conclusions are made and options of increasing the accuracy of the identification algorithm are considered.

Obtaining vehicle parameters from bridge dynamic response:a combined semi-analytical and particle filtering approach

Dynamic load imposed on the bridge by mov- ing vehicle depends on several bridge-vehicle parameters with various uncertainties. In the present paper, particle filter technique based on conditional probability has been used to identify vehicle mass, suspension stiffness, and damping including tyre parameters from simulated bridge accelerations at different locations. A closed-form expres- sion is derived to generate independent response samples for the idealized bridge-vehicle coupled system consider- ing spatially non-homogeneous pavement unevenness. Thereafter, it is interfaced with the iterative process of particle filtering algorithm. The generated response sam- ples are contaminated by adding artificial noise in order to reflect field condition. The mean acceleration time history is utilized in particle filtering technique. The vehicle- imposed dynamic load is reconstructed with the identified parameters and compared with the simulated results. The present identification technique is examined in the presence of different levels of artificial noise with bridge response simulated at different locations. The effect of vehicle velocity, bridge surface roughness, and choice of prior probability density parameters on the efficiency of the method is discussed.

Optimal Filtering Correction forMarine Dynamical Positioning Control System

This paper focuses on the problem of control law optimization for marine vessels working in a dynamical positioning （DP） regime. The approach proposed here is based on the use of a special unified multipurpose control law structure constructed on the basis of nonlinear asymptotic observers, that allows the decoupling of a synthesis into simpler particular optimization problems. The primary reason for the observers is to restore deficient information concerning the unmeasured velocities of the vessel. Using a number of separate items in addition to the observers, it is possible to achieve desirable dynamical features of the closed loop connection. The most important feature is the so-called dynamical corrector, and this paper is therefore devoted to solving its optimal synthesis in marine vessels controlled by DP systems under the action of sea wave disturbances. The problem involves the need for minimal intensity of the control action determined by high frequency sea wave components. A specialized approach for designing the dynamical corrector is proposed and the applicability and effectiveness of the approach are illustrated using a practical example of underwater DP system synthesis.

Research on Dynamic Mathematical Resource Screening Methods Based on Machine Learning

The current digital educational resources are of many kinds and large quantities, to solve the problems existing in the existing dynamic resource selection methods, a dynamic resource selection method based on machine learning is proposed. Firstly, according to the knowledge structure and concepts of mathematical resources, combined with the basic components of dynamic mathematical resources, the knowledge structure graph of mathematical resources is constructed;according to the characteristics of mathematical resources, the interaction between users and resources is simulated, and the graph of the main body of the resources is identified, and the candidate collection of mathematical knowledge is selected;finally, according to the degree of matching between mathematical literature and the candidate collection, machine learning is utilized, and the mathematical resources are screened.

基于筛选策略的动态环境下激光SLAM算法

现有的同步定位与建图(SLAM)方法在理想条件下运行稳定,但在动态环境中会因移动物体特征点云的误匹配导致定位误差增大。为解决此问题,提出了一种动态点云检测算法。首先利用惯性测量装置信息对点云数据预处理,包含去畸变等操作;然后剔除地面点云,采用弯曲体素结构对非地面点云进行聚类;接着,通过匈牙利算法关联和匹配两帧之间的聚类,同时利用惯性信息统一坐标系;最后设计一种筛选策略,先用边界框交并比和质心速度粗略筛选动态聚类,再用z轴(高度)分布相似性进行精细筛选。实验结果表明,所提算法能够识别并滤除实验环境中的大部分动态点云聚类;与LIO-SAM算法相比,四种场景下的定位均方根误差平均降低了17.75%;平均精确率和召回率相比Removert分别提升14.81%和5.90%。

基于FBG传感器阵列反演结构振动位移的监测方法

针对现有振动位移监测方法易受环境影响且精度不高的问题,提出了一种基于光纤布喇格光栅(FBG)传感器阵列反演结构振动位移的监测方法。该方法使用FBG传感器阵列获取结构振动过程中的多点应变和单点加速度数据,先利用基于正交曲率的曲线重构算法和获得的应变数据重构结构各点的动态位移,然后运用卡尔曼滤波算法将曲线重构算法导出的位移数据与同位置处的单点加速度数据相融合,获得更为准确的位移数据。实验结果表明:该方法可成功重构出监测结构任意时刻的振动形态,悬臂铝板和简支管道的振动位移重构相对均方根误差分别小于5.5%和7%。

基于模型预测控制的电-气互联系统动态优化调度研究

在当前电气、天然气与人们生活密不可分的背景下,电—气互联综合能源系统(IPGES)的调度运行研究具有极其重要的价值和意义。目前电—气互联系统的运行策略大多基于日前概况设计,而负荷预测误差往往导致次优日前调度运行。针对这种情况,模型预测控制因为其实时性和鲁棒性的优点,在处理不确定性问题方面表现出卓越效果。本研究在长时间尺度上对日前调度进行了优化,并基于模型预测控制方法进行了实时在线优化调度。在日内调度中,采用卡尔曼滤波对负荷进行短期预测,并根据最新预测信息建立滚动优化和实时反馈校正模型以进行多时间尺度的调度。最后通过案例分析验证了该方法在动态优化调度电力系统和天然气系统组成的电—气互联系统中具有更好经济性和供需匹配性,并能有效应对负荷波动。

基于AGPF的目标定位精度改善方法

针对传统遗传算法粒子滤波容易因遗传操作参数恒定不变而陷入局部最优的问题,在遗传算法粒子滤波中引入自适应方法,提出自适应遗传算法粒子滤波。根据粒子适应度的大小,动态调节遗传操作的交叉、突变概率,从而在尽可能多地保留优势粒子的同时更有效地产生新的优势粒子,跳出局部最优。将自适应遗传算法粒子滤波应用于动态目标定位模型,并将其与遗传算法粒子滤波的性能进行比较。结果表明,自适应方法的引入可以增加算法有效粒子数,有效解决算法早熟问题,改善滤波精度,对于提高动态目标定位精度是有效的。

基于负荷预测和无迹粒子滤波的配电网动态状态估计

随着汽车充电成为新型重要负荷,为确保此时配电网运行与控制安全,对其进行实时准确的态势感知,提出一种基于卷积神经网络和门控循环单元的短期负荷预测与无迹粒子滤波算法自适应混合的配电网动态状态估计方法。结合使用卷积神经网络和门控循环单元进行短期负荷预测,将预测得到的有功与无功功率进行潮流计算,再与无迹粒子滤波量测估计值自适应加权得到电压幅值和相角状态估计结果。以IEEE33节点配电网为例,验证了所提状态估计方法的准确性与面对不良数据时的鲁棒性。

多普勒效应下的跳频信号定时同步算法研究

由于收发信双方的相对高速运动会影响接收信号的同步和解调,特别是对符号速率每跳变化的跳频信号影响更大。针对跳频信号在高动态场景下的时钟同步问题,通过分析多普勒钟偏对时钟同步的影响,提出了基于同步信息的时钟频率估计加时钟相位估计定时同步方法,将二者的估计结果进行卡尔曼滤波,估计出钟偏及钟偏变化率,实时跟踪、调整接收机的时钟,达到收发信号时钟同步的目的。与三阶锁相环进行了仿真与对比,仿真结果证明,该方法比传统的锁相环方法跟踪范围大,估计精度高,解调性能更优良;解调性能损失与理论值相比小于0.2 dB。

奇异值分解五阶容积卡尔曼滤波汽车状态估计

针对三阶滤波对高维汽车非线性模型估计精度有限的问题,以电动汽车为研究对象,提出了一种基于奇异值分解的五阶容积卡尔曼滤波(SVD-FCKF)车辆状态估计器。首先基于Dugoff轮胎模型,构建高维非线性7自由度车辆动力学模型。然后根据三阶球面-径向容积规则将CKF拓展到五阶,使其具有五阶泰勒级数展开精度,同时利用奇异值分解代替传统Cholesky分解,提高估计器的鲁棒性。最后利用Carsim和Matlab/Simulink联合仿真平台对SVD-FCKF进行验证,结果表明:改进的SVD-FCKF估计器能够有效提高电动汽车纵向速度、侧向速度、质心侧偏角和四轮转速的估计精度和稳定性,多工况适应能力强,整体估计效果优于CKF估计器。研究结果为电动汽车主动安全研究提供了理论支撑,具有实际应用价值。

基于指令滤波器的无人艇抗饱和动态面跟踪控制器设计

本文对带有外界干扰、输入受限情形下的无人水面艇轨迹控制问题开展了研究。首先,给出了考虑前进、横漂和艏摇的无人艇三自由度跟踪控制模型。其次,基于反步法、跟踪微分器和抗饱和辅助系统,提出了有限时间饱和动态面跟踪控制器,并通过引入补偿信号消除了滤波器带来的微分误差,提高了控制系统的性能。最后,借助李雅普诺夫稳定性理论证明了所设计系统状态为有限时间稳定的,并通过数字仿真验证了所设计控制策略的有效性。

推荐系统中混合难负样本的生成模型

负样本对协同过滤推荐任务影响巨大,高质量的负样本能帮助模型精准描述用户画像。针对现存的假负样本及计算量大的问题,基于难负样本的思想提出一种混合动态负采样模型。首先,通过动态负采样方法和服务推荐模型确定每个用户的负样本范围与序列;其次为每个用户快速采样到大量的难负样本候选项;再次,使用混合思想将采样到的负样本集合装配成一个难负样本,扩大感知域和融入的信息量;最后,引入一种注意力机制指导负样本的融合,以此提升系统稳定性。在Alibaba、Yelp2018和Amazon公开数据集上与基线模型进行的比较实验表明,所提模型在多个评价指标下均优于现有基线模型,证明了模型的有效性。

覆膜方法对纳米纤维膜除尘滤料动态过滤性能的影响

为解决聚四氟乙烯(PTFE)覆膜滤料使用中阻力增长快且膜易破损问题,基于静电纺丝技术制备了涤纶(PET)纳米纤维膜,通过直接覆膜法、热处理覆膜法及一步共纺覆膜法将PET纳米纤维膜作为表面膜与聚苯硫醚(PPS)针刺毡滤料进行复合,并对比研究3种覆膜方法制备的PET纳米纤维膜滤料、PPS滤料以及PTFE覆膜滤料的动态过滤性能,结果表明,与PTFE覆膜滤料相比,3种覆膜方法制备的PET纳米纤维膜滤料均表现出优异的过滤性能.稳定过滤阶段,一步共纺覆膜法制备的PET/TPU纳米纤维膜滤料完成单个周期所需时间是PTFE覆膜滤料的2倍,粉尘剥离率为99.79%,其阻力增长速率低、完成单个周期所需时间平稳,降低能耗并减少了气流对膜造成的磨损,在工业除尘领域具有较大应用潜力.

面向稀疏卷积神经网络的CGRA加速器研究

本文针对规模日益增长和演变迅速的稀疏卷积神经网络(CNN)应用,提出一款高能效且灵活的加速结构DyCNN来提升其性能和能效。DyCNN基于兼具灵活性和高能效的粗粒度可重构架构(CGRA)设计,可以利用其指令的高并行性来高效支持CNN的操作。DyCNN使用基于数据感知的指令动态过滤机制来滤除各计算单元中由于稀疏CNN中权值静态稀疏性和激活值动态稀疏性产生的大量无效计算和访存指令,使它们能像执行稠密网络一样高效复用一组指令。此外DyCNN利用基于负载感知的动静结合负载调度策略解决了稀疏导致的负载不均衡问题。实验结果表明,DyCNN运行稀疏CNN与运行密集CNN相比实现了平均1.69倍性能提升和3.04倍能效提升,比先进的GPU(cuSPARSE)和Cambricon-X上的解决方案分别实现了2.78倍、1.48倍性能提升和35.62倍、1.17倍能效提升。

结合区块链和关系数据库管理的会计信息系统安全方案

会计信息系统(AIS)是企业资源规划(ERP)的核心模块,过去一直被设计为集中式系统,存在单点故障、可扩展性、安全性、灵活性等方面的缺陷。为此,提出了结合联盟区块链和关系数据库管理系统(RDMBS),支持事务防篡改和签名验证的分布式AIS系统。通过将区块链嵌入关系表中,在数据库中以防篡改的方式存储可信信息,并支持数据的溯源和验证。整个系统由多个经授权的参与方共同维护,支持AIS数据的安全整合,并通过签名方案明确数据所有权。为支持大规模查询,应用了基于动态布隆过滤树(DBFT)的优化算法,以最大限度降低查询和验证作业的总时间成本。实验评估表明,所提方法在AIS系统中成功实现了基于区块链的身份验证和篡改溯源,并显著提升了大规模分析查询和验证的效率。

基于CKF-SLAM改进的无人水下航行器动态目标跟踪算法研究

针对容积卡尔曼滤波(cubature Kalman filter,CKF)同步定位与建图(simultaneous localization and mapping,SLAM)算法在动态目标跟踪(object tracking,OT)的应用中,存在算法实时性不高、计算复杂以及对动态目标物跟踪精度较低的问题,提出基于平方根容积卡尔曼滤波SLAM的无人水下航行器(unmanned underwater Vehicle,UUV)目标跟踪算法(SRCKF-SLAM-OT).该算法将CKF-SLAM-OT中复杂的计算部分,利用3阶容积准则选取一组相同权值的容积点来近似计算,再利用数值积分法计算非线性方程模型的后验状态估计平均值和方差,并对协方差矩阵的平方根因子进行更新.仿真结果表明:SRCKF-SLAM-OT算法简化了计算量和改善了数值精度,提高了UUV在未知水下环境中自身定位的精度和动态目标物跟踪的能力.

融合词性语义扩展信息的事件检测模型

事件检测是事件抽取中的关键步骤,依赖于触发词进行事件类型分类。现有主流事件检测方法在稀疏标记数据上性能较差,模型过度拟合密集标注的触发词,在稀疏标记的触发词或者未见过的触发词上容易失效。改进方法通常通过扩充更多训练实例来缓解这一问题,但扩充后的数据分布不平衡,存在内置偏差,仍然表现不佳。为此,建立一种融合词性语义扩展信息的事件检测模型。对词粒度扩展信息进行分析,在不增加训练实例的条件下缩小候选触发词的范围,并对候选触发词进行语义扩展,挖掘候选触发词的上下文中蕴含的丰富语义,缓解了标记数据稀疏造成模型训练不充分的情况。通过词性筛选模块寻找候选触发词并对其进行语义扩展挖掘词粒度语义信息,融合句子粒度语义信息提升语义表征的鲁棒性,最终利用Softmax分类器进行分类完成事件检测任务。实验结果表明,该模型在ACE2005和KBP2015数据集上的事件检测任务中的F1值分别达到79.5%和67.5%,有效提升了事件检测性能,并且在稀疏标记数据实验中的F1值达到78.5%,明显改善了标记数据稀疏带来的不良影响。