基于Selenium的Ajax异步加载页面的数据采集技术

互联网中的许多网站均为Ajax站点,而Ajax页面的原始源代码中并无通过异步加载方式呈现到页面中的数据,因此其数据的采集颇为困难。针对此问题,介绍一种基于Selenium的Ajax页面数据采集方案,并通过具体的实例说明其关键技术。

A Joint Activity and Data Detection Scheme for Asynchronous Grant-Free Rateless Multiple Access

This paper considers the frameasynchronous grant-free rateless multiple access(FAGF-RMA)scenario,where users can initiate access at any symbol time,using shared channel resources to transmit data to the base station.Rateless coding is introduced to enhance the reliability of the system.Previous literature has shown that FA-GFRMA can achieve lower access delay than framesynchronous grant-free rateless multiple access(FSGF-RMA),with extreme reliability enabled by rateless coding.To support FA-GF-RMA in more practical scenarios,a joint activity and data detection(JADD)scheme is proposed.Exploiting the feature of sporadic traffic,approximate message passing(AMP)is exploited for transmission signal matrix estimation.Then,to determine the packet start points,a maximum posterior probability(MAP)estimation problem is solved based on the recovered transmitted signals,leveraging the intrinsic power pattern in the codeword.An iterative power-pattern-aided AMP algorithm is devised to enhance the estimation performance of AMP.Simulation results verify that the proposed solution achieves a delay performance that is comparable to the performance limit of FA-GF-RMA.

Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems

This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.

基于数据接口的Ajax页面数据采集技术

互联网中的许多网站均已采用了Ajax技术,相应的Ajax页面中的数据是通过异步加载的方式予以呈现的,并未包含在页面源代码中,因此难以对其进行采集。针对此问题,介绍一种基于数据接口的Ajax页面数据采集技术,并通过具体的实例说明其编程模式。

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Review on the Usage of Synchronous and Asynchronous FIFOs in Digital Systems Design

First-Input-First-Output (FIFO) buffers are extensively used in contemporary digital processors and System-on-Chips (SoC). There are synchronous FIFOs and asycnrhonous FIFOs. And different sized FIFOs should be implemented in different ways. FIFOs are used not only for the pipeline design within a processor, for the inter-processor communication networks, for example Network-on-Chips (NoCs), but also for the peripherals and the clock domain crossing at the whole SoC level. In this paper, we review the interface, the circuit implementation, and the various usages of FIFOs in various levels of the digital design. We can find that the usage of FIFOs could greatly facilitate the signal storage, signal decoupling, signal transfer, power domain separation and power domain crossing in digital systems. We hope that more attentions are paid to the usages of synchronous and asynchronous FIFOs and more sophististicated usages are discovered by the digital design communities.

AJAX在前端开发的设计及应用

文章通过解析AJAX运行机理,在前端开发中,利用PHP语言工具操作数据库,调用、读取用户名,完成表单的验证,实现了前端静态页面的异步读取和验证,加快了网页验证效率,为数据库的快速前端调用提供了参考。

基于PHP+Ajax的工单系统设计与实现

工单系统是企业用于客户服务的重要工具,也是企业内部评价客服工作的重要依据。在此提出了基于浏览器/服务器(B/S)结构,发现应用PHP+Ajax技术对工单系统前端开发具有无刷新交互的优势。以客户注册与登录、选择工单类型、提交工单、信息反馈等功能模块的开发为例,进行系统需求分析和概要设计,讲解基于B/S结构的工单系统开发详细过程。

Harmonics in the Squirrel Cage Induction Motor Analytic Calculation-Part Ⅰ: Differential Leakage, Attenuation, Asynchronous Parasitic Torques

The magnetic field generated in the air gap of the cage asynchronous machine and the harmonics of the magnetomotive forces creating that magnetic field, as well as the related differential leakage, attenuation, asynchronous parasitic torques have been discussed in great detail in the literature, but always separately, for a long time. However, systematization of the phenomenon still awaits. Therefore, it is worth summarizing the completeness of the phenomena in a single study – with a new approach at the same time-in order to reveal the relationships between them. The role of rotor slot number is emphasized much more than before. An existing, commonly used, but still impractical basic figure has been modified to more clearly demonstrate the response of the rotor for the harmonics of the stator. The need to treat differential leakage, asynchronous parasitic torques and attenuation together will be demonstrated: new formula for asynchronous parasitic torque is derived;the long-used characteristic curves for differential leakage and attenuation used separately so far was merged into one, correct curve in order to provide a correct design guide for the engineers.

Inference of interactions between players based on asynchronously updated evolutionary game data

The interactions between players of the prisoner's dilemma game are inferred using observed game data.All participants play the game with their counterparts and gain corresponding rewards during each round of the game.The strategies of each player are updated asynchronously during the game.Two inference methods of the interactions between players are derived with naive mean-field(n MF)approximation and maximum log-likelihood estimation(MLE),respectively.Two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick models,varying the data length,asymmetric degree,payoff,and system noise(coupling strength).We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half(benefit from the extra information of update times)of that by n MF.Both methods are robust to the asymmetric degree but work better for large payoffs.Compared with MLE,n MF is more sensitive to the strength of couplings and prefers weak couplings.

Fully asynchronous distributed optimization with linear convergence over directed networks

We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λ^(k)) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem.

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Asynchronous federated learning(AsynFL)can effectivelymitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security.However,the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning(FL)algorithm uses synchronous or asynchronous aggregation.Therefore,there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction.This paper proposes a novel Fixed-point Asynchronous Federated Learning(FixedAsynFL)algorithm,which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity.FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL.In order to balance energy consumption and learning accuracy,this paper proposed a quantization scale selection mechanism.This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL.Based on considering the upper bound of quantization noise,this paper optimizes the quantization scale by minimizing communication and computation consumption.This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency.The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3%and save the computation energy in the training phase by 74.9%without significant loss of accuracy.According to the experimental results,we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.

Integrated system of traditional THz time-domain spectroscopy and asynchronous optical sampling

Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the traditional THz-TDS system and the complex control of the asynchronous optical scanning(ASOPS)system,which requires frequent calibration,we combine traditional THz-TDS and ASOPS systems to form a composite system and propose an all-fiber trigger signal generation method based on the time overlapping interference signal generated by the collinear motion of two laser pulses.Finally,the time-domain and frequency-domain spectra are obtained by using two independent systems in the integrated systems.It is found that the full width at half maximum(FWHM)of the time-domain spectra and the spectral width of the frequency-domain spectra are almost the same,but the sampling speed of the ASOPS system is significantly faster than that of the traditional THz-TDS system,which conduces to the study of the transient characteristics of substances.

基于柔直控制的南方电网异步联网频率同步运行控制器设计与应用

电网分区异步运行将成为未来南方电网发展趋势,针对分区异步电网间频率失去了同步性和一致性问题,开展分区异步电网频率同步运行控制策略研究,提出了分区异步电网频率同步运行控制器结构,优化确定运行关键控制参数并通过仿真验证其有效性。鲁西柔直工程作为试点实施分区异步电网频率同步运行,现场实施效果表明该控制能够有效保证分区异步运行电网间实际运行中频率的一致性和同步性,且与电网AGC策略具有良好的适配性,实施中未出现两侧电网功率振荡和频率振荡发散情况,有效改善分区电网频率稳定性和提升分区电网间功率的灵活性支援性能,同时也实现全网调频资源的实时、自动共享,推动构建全网统一的调频辅助服务市场。

基于Ajax的强对流多灾种协同发布机制研究与应用

强对流天气是影响范围小、持续时间短的局地灾害性天气,由于其发生突然、强度剧烈,带来的经济损失和人员损伤往往非常严重。预警早一秒,风险少一分,为解决强对流天气预警服务灾种多、时间紧的难题,针对强对流天气预警发布流程和服务内容特征开展研究,从信息采集前、采集中、采集后分别采取措施,设计了多灾种预警信息协同发布工作机制,并基于Ajax异步通信技术实现具体应用,提升了预警采集的便捷度,提高了预警发布效率,具有较好的应用效果。

基于AJAX技术和RSS技术的网络考试系统设计

为提高考试系统在运行中的流畅度,引进RSS技术与AJAX技术,以某高校为例,对网络考试系统展开设计。参照B/S三层架构,开发网络考试系统架构。选用英国DGFSG公司开发的数据库存储器、IHGFS44FA无线监控设备,作为网络考试系统的主要设备;引进RSS技术,设计网络考试内容的整合;引入AJAX异步传送技术,建立系统web终端的AJAX异步传输模型,设计网络考试试卷生成与异步传输模式;利用NET技术提供的RSA和DPAPI加密方式,设计考试中的交互行为。对比实验结果证明:设计的系统可以在降低卡顿次数的条件下,缩短累计卡顿时间。

智能化视角下造纸机传动系统变频调速控制研究

在当今造纸行业向智能化转型的背景下,造纸机作为纸张生产的核心设备,其传动系统的高效运行对整体生产效率和产品质量起着决定性作用。电气自动化与变频调速技术的深入应用,极大地推动了造纸机控制系统的技术进步,为实现造纸机械高效、稳定生产提供了坚实的技术基础。基于此,在探讨造纸机传动控制系统的构成与设计关键点,并针对常见的变频调速技术进行分析,特别是直接转矩控制(DTC)算法在优化传动系统中减少异步电机电磁转矩波动、降低能耗、提升效率方面的应用,旨在为造纸机传动系统的优化提供切实可行的解决方案。

基于空间目标异步观测的惯导误差快速确定

针对惯性/天文组合导航系统中大的初始状态误差影响惯导误差收敛速度的问题,在星相机观测具有先验位置信息的有限空间目标的辅助下,提出一种基于目标-恒星角距异步测量的惯导误差在线快速确定方法。首先,在星相机光轴旋转角度和视场角大小受限的情况下,设计了通过异步照相观测方式获取有效空间目标参考信息的方案;其次,在利用惯导误差状态传播模型实现异步测量信息同步处理的基础上,构建基于空间目标与恒星之间角距的非线性最小二乘优化模型,避免了星相机的光轴扰动和安装误差对测量精度的影响;最后,基于高斯牛顿法设计了两轮迭代优化估计惯导位置误差和速度误差的方法。蒙特卡洛仿真结果表明,所提方法利用对空间目标和恒星的有限观测信息,可以有效估计惯导位置误差和速度误差,在初始位置误差约十千米量级的情况下,可以估计补偿约97.73%的位置误差以及66.25%的速度误差,优化求解误差参数的计算耗时为0.0160 s。

边缘计算网络中区块链赋能的异步联邦学习算法

由于数据量激增而引起的信息爆炸使得传统集中式云计算不堪重负,边缘计算网络(ECN)被提出以减轻云服务器的负担。此外,在ECN中启用联邦学习(FL),可以实现数据本地化处理,从而有效解决协同学习中边缘节点(ENs)的数据安全问题。然而在传统FL架构中,中央服务器容易受到单点攻击,导致系统性能下降,甚至任务失败。本文在ECN场景下,提出基于区块链技术的异步FL算法(AFLChain),该算法基于ENs算力动态分配训练任务,以提高学习效率。此外,基于ENs算力、模型训练进度以及历史信誉值,引入熵权信誉机制评估ENs积极性并对其分级,淘汰低质EN以进一步提高AFLChain的性能。最后,提出基于次梯度的最优资源分配(SORA)算法,通过联合优化传输功率和计算资源分配以最小化整体网络延迟。仿真结果展示了AFLChain的模型训练效率以及SORA算法的收敛情况,证明了所提算法的有效性。