A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Exponential synchronization of chaotic Lur'e systems with time-varying delay via sampled-data control

In this paper, we study the exponential synchronization of chaotic Lur＇e systems with time-varying delays via sampled-data control by using sector nonlinearties. In order to make full use of information about sampling intervals and interval time-varying delays, new Lyapunov-Krasovskii functionals with triple integral terms are introduced. Based on the convex combination technique, two kinds of synchronization criteria are derived in terms of linear matrix inequal- ities, which can be efficiently solved via standard numerical software. Finally, three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

Designing of New Data Synchronization System

As the rapid development of Wireless Communications and the popularity of the Intelligent Terminal, data synchronization has been a social focus, meanwhile, user terminal devices are increasingly diversified, traditional synchronization technology based C/S mode has such deficiencies as insufficient amount of transmitting data and bad Real-time efficiency. It has become increasingly unable to meet the needs of future development. In this paper, we proposed and designed a new method and system by separating control with transmission to synchronize data to ensure Real-time data and improve efficiency.

Linear Network Coding Based Fast Data Synchronization for Wireless Ad Hoc Networks with Controlled Topology

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem.It is fundamental for efficient information fusion,control and decision in distributed systems.Previously,distributed data synchronization was mainly studied in the latency-tolerant distributed databases,or assuming the general model of wireless ad hoc networks.In this paper,we propose a pair of linear network coding(NC)and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator’s control.We consider both data block selection and transmitting node selection for exploiting the benefits of NC.Instead of using the store-and-forward protocol as in the conventional uncoded approach,a compute-and-forward protocol is used in our scheme,which improves the transmission efficiency.The performance of the proposed algorithms is studied under different values of network size,network connection degree,and per-hop packet error rate.Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.

Exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and mode-dependent probabilistic time-varying delays

In this paper, the problem of exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and Mode-dependent probabilistic time-varying coupling delays is investigated. The sam- pling period is assumed to be time-varying and bounded. The information of probability distribution of the time-varying delay is considered and transformed into parameter matrices of the transferred complex dynamical network model. Based on the condition, the design method of the desired sampled data controller is proposed. By constructing a new Lyapunov functional with triple integral terms, delay-distribution-dependent exponential synchronization criteria are derived in the form of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

Sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays

In this letter, a distributed protocol for sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays is proposed, and a brief procedure of convergence analysis for such algorithm over undirected connected graphs is provided. Furthermore, a simple yet generic criterion is also presented to guarantee synchronized oscillatory motions in coupled harmonic oscillators. Subsequently, the simulation results are worked out to demonstrate the efficiency and feasibility of the theoretical results.

Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control

We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks （MJFCNNs） with discrete, unbounded distributed delays, and the Wiener process based on sampled-data control using the linear matrix inequality （LMI） approach. The Lyapunov–Krasovskii functional combined with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure that the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions （e.g., time derivative is smaller than one） are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi Sugeno （T-S） fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov-Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.

Impulsive effect on exponential synchronization of neural networks with leakage delay under sampled-data feedback control

We consider the impulsive effect on the exponential synchronization of neural networks with leakage delay under the sampled-data feedback control. We use an appropriate Lyapunov-Krasovskii functional combined with the input delay approach and some inequality techniques to derive sufficient conditions that ensure the exponential synchronization of the delayed neural network. The conditions are formulated in terms of the leakage delay, the sampling period, and the exponential convergence rate. Numerical examples are given to demonstrate the usefulness and the effectiveness of the results.

Pinning sampled-data synchronization for complex networks with probabilistic coupling delay

We deal with the problem of pinning sampled-data synchronization for a complex network with probabilistic time-varying coupling delay. The sampling period considered here is assumed to be less than a given bound. Without using the Kronecker product, a new synchronization error system is constructed by using the property of the random variable and input delay approach. Based on the Lyapunov theory, a delay-dependent pinning sampled-data synchronization criterion is derived in terms of linear matrix inequalities （LMIs） that can be solved effectively by using MATLAB LMI toolbox. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

基于多层级时空图神经网络的风电机组在线异常检测

在风电场运营中,准确及时的故障检测是降低风电机组运行维护成本的关键。然而,现有检测方法未充分挖掘功能单元间的潜在时空关联,限制了检测准确性的提升。文中提出了一种基于多层级时空图神经网络的风电机组在线异常检测方法,以提高故障检测的准确性。该方法依据风电机组物理结构,将其功能单元划分为多个子图,从而构筑了一个多层级的时空图神经网络,通过图注意力机制和多头注意力机制全方位地分析风电机组各传感器节点与功能单元之间的关联强度。同时,针对数据采集与监控(SCADA)系统数据的时间关联,设计了动态图神经网络和时间注意力机制,使正常行为预测模型捕捉了SCADA系统数据的时间关联特性,实现了空间和时间特性的有效融合。最后,基于中国上海某风电场的实际数据验证了所提方法的显著有效性。

基于C++的三维闪电回击数据再处理系统设计

针对三维闪电数据处理算法改进之后需重新定位回击数据,以及省级三维闪电探测系统的回击数据通常更加完整,应用时要将多个省份闪电回击数据合并后重新处理出准确的云内闪电高度值等问题,本研究使用C/S架构和C++builder编程开发了三维闪电回击数据再处理系统。系统设计了回击数据查询处理界面,提供了监视控制功能。通过设定回击数据文件和配置文件,再运行软件系统对符合条件的回击数据进行排序,在完成排序的数据中提取同一闪电相关信息,经计算处理得到重新定位的三维闪电探测结果并存储。将系统部署于国家级平台查询处理全国组网的三维闪电历史回击数据,测试结果表明,再处理系统操作便捷、功能齐全,满足三维闪电回击数据再处理系统的任务要求。

基于面向TTE的新型数据综合系统的设计与实现

针对传统总线无法实时网络传输,新型总线时间触发以太网(TTE)兼容IP协议和IEEE802.3协议,但TT业务和BE业务需设备满足TTE协议,实际工程缺少适用性的问题,设计了一种基于FPGA的时间触发以太网数据综合系统。该数据综合系统以FPGA作为核心控制器,千兆网作为主要通信接口,在满足高速率、高宽带、高灵活的前提下,设计出数据综合模块,将多种传统总线与TTE总线良好兼容。试验结果表明:系统在同步精度高达40 ns、通信抖动仅为392 ns的前提下,数据转换收发无误码现象。同时将多种数据混合编帧,具备极高的稳定性与灵活性,为TTE网络数据服务于实际工程提供了可靠的解决方案。

应用于机床加工的总线数字量模块开发与试验研究

为了满足数控机床加工对信号量数据采集传输的实时性和稳定性的应用需求,如机床加工现场的轴运动位移硬件限位信号、气阀水阀信号、刀具测量等信号,同时能与可编程控制器建立通信进行数据交互,可实现信号量检测与控制功能。文章设计并开发基于以太网通讯技术的EtherCAT通信协议的数字量模块,对数字量模块软硬件设计内容与数据交互程序的执行流程进行研究,并通过配合标准EtherCAT通信主站TwinCAT进行试验,证明开发的数字量模块能与控制器主站正常建立通信并进行数据传输,且与EtherCAT总线通信数据周期保持同步。基于数字量信号检测设计出机床刀径测量方案,应用该数字量模块在机床加工中进行量刀试验,结果表明通过数字量模块对检测信号的实时响应计算得到的机床刀具磨损误差满足应用要求,具有工程实际价值,达到了研究的目的。

道路环境与驾驶行为数据同步采集系统研发

高精度、可移植、多维度的在途车辆及其周边驾驶环境数据在L3级及以上自动驾驶汽车场景库测试、不良驾驶行为谱构建以及在途实时交通管控等领域有至关重要的作用,由单一传感器构成的数据采集系统在精度、维度上都受到了较大限制,导致其可靠度较低;为实现多类数据传感器协同一体化,研发了一款包含激光雷达、车载运动测量与组合定位、车载踏板与方向盘位移传感器和雷视道路多维感知一体机的多模块道路环境数据与驾驶行为同步采集系统,支持多项交通数据采集任务的并行处理,融合了用户的真实驾驶行为,并基于该系统设计了依维柯某车型的数据实车采集平台;该系统由上位机与下位机两个子系统构成,集成了多模块数据采集、储存、传输与标定等功能,且对各个模块完成了时间同步处理,适用于两客一危场景,并设计了易操作的上位机用户交互界面,对多模块数据实现实时可视化,直观查看各时段数据的变化规律,具有较高的应用价值。

多导睡眠监测设备和肠鸣音设备之间数据同步采集方法研究

目的提出一种基于敲击脉冲信号的两个独立设备数据同步采集的方法,以实现数据的同步采集。方法利用多导睡眠监测设备的鼾声传感器与肠鸣音设备相互敲击产生瞬态脉冲信号作为同步标记信号,基于同步标记信号的三阶累积量实现同步标记信号的识别和对齐,并采用多组同步标记信号评价数据同步的精度。结果多导睡眠监测设备的鼾声传感器和肠鸣音设备相互敲击,产生了清晰明确的同步标记脉冲信号,结合时域特征和三阶累积量实现了同步标记信号的精确识别和对齐。经过多组同步标记信号识别验证表明,此种同步方法可保证同步精度范围在5 ms内。结论该数据同步采集方法简单易行,可保证多导睡眠监测设备和肠鸣音设备的高精度同步采集,同时也为其他完全独立的多个设备数据同步采集提供参考。

中国石油井下作业数字化监督发展问题与对策

阐述了中国石油井下作业数字化监督在监督人员数字化管理、施工队伍数字化监督、施工作业数字化监督、井场环境数字化监督4个方面的发展现状。分析了井下作业数字化监督发展面临数字化监督管理制度不健全、监督数据标准不统一、数字化智能监督技术不完善、缺少统一的数字化智能监督平台、监督数据价值挖掘不深入5个方面的主要问题。提出了制定井下作业数字化监督管理制度、建立井下作业监督数据标准、攻关数字化智能监督关键技术、研发井下作业数字化智能监督平台、提升监督大数据分析应用水平5项数字化监督发展对策。展望了井下作业数字化监督建设发展后在提升监督人员履职效率、降低监督人力资源成本、提升工程质量、保障施工安全与环保4个方面的预期成效。

基于FPGA的多通道高速串行数据采集系统设计

针对卫星导航信号抗干扰领域中进行高精度高速多通道数据采集的需要,系统采用ADI公司的16位高速串行AD转换芯片AD9653,完成模数转换,其转换精度可达16比特,转换速率最高为125MSPS。分析AD9653的高速串行数据接口特点,采用Xilinx公司的Kintex-7系列FPGA提供的片同步技术,进行位时钟自适应相位校准和数据帧调整研究,保证了高速串行数据传输的稳定性与正确性。采用内部FIFO数据缓存的方式实现多通道数据的片内同步以保证后续抗干扰算法的正确处理。研究结果表明,使用该方法进行高速串行数据采集时系统稳定可靠、正确无误。为多通道高速串行数据采集提供了一种切实可行的解决方案,对类似需求的数据采集系统具有一定的参考价值。