基于卷积神经网络的安全防御模式研究

云计算、大数据、5G通信等技术的快速发展，有力的促进了计算机网络在多个领域的普及和使用，人们已经进入到了“互联网+”时代，实现了工作、生活和学习的数字化、智能化和共享化。计算机网络在为人们提供各种便利服务的同时也面临着许多的安全威胁，比如木马、病毒等，不法分子攻击网络数据中心，破坏用户服务终端及服务器，为人们带来严重的威胁。网络安全学者或企业机构也提出了许多安全防御技术，比如入侵检测技术、深度包过滤技术、防火墙技术、数据加密技术等，一定程度上提高了网络安全防御的能力，但是随着互联网数据流量的增多，需要引入更加先进的人工智能技术，以便能够快速的采集、分析网络数据流，确定数据流中是否存在病毒或木马，以便及时的对其进行查杀，进一步提高网络安全防御的主动性、积极性     

激光在线测厚振动分析与精度优化

激光测厚具有安全可靠、测量精度高、测量范围大等优点,广泛应用于纸张、电池极片等薄膜类材料厚度的在线测量。带材宽幅方向扫描测厚时由于扫描架往复运动会产生机械振动,影响在线测厚精度。针对该问题,以锂离子电池极片厚度测量为例,使用双激光差动式测厚平台对电池极片和铜箔分别进行厚度测量,然后对测厚数据进行频谱分析,探究其振动规律的相似性,并基于频谱分析结果采用滑动带阻滤波方式对测厚数据进行处理,滤波后极片和铜箔的厚度极差分别降低了33.4%和73.8%,有效过滤了机械振动导致的测量误差,可满足极片和铜箔厚度测量的精度要求。     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

基于光谱尺度空间与管道滤波的红外目标检测

为了准确地从复杂干扰背景下检测出真实弱小目标，本文引入视觉显著性，设计了基于快速光谱尺度空间与动态管道滤波的红外目标检测算法。基于真实目标与背景内容之间的整体差异，引入快速光谱尺度空间与阈值分割技术，设计视觉显著性机制，对红外图像完成处理，输出全局显著性映射，以高效过滤干扰背景内容。考虑目标与背景的局部特征差异，构建自适应局部对比度增强机制，对粗检测结果实施处理，获取对应的局部显著性映射，改善视觉显著性区域内目标的对比度。引入高斯差分理论，通过估算每一帧红外图像中的目标像素直径，形成动态管道滤波，充分消除虚警，准确识别出弱小目标。多组实验数据显示：较已有的红外目标检测技术而言，在各种不同的复杂背景干扰下，所提算法呈现出更好的检测能力，拥有更理想的接收机工作特性ROC曲线。     

Robust optical flow estimation via edge preserving filtering

It is known that optical flow estimation techniques suffer from the issues of ill-defined edges and boundaries of the moving objects. Traditional variational methods for optical flow estimation are not robust to handle these issues since the local filters in these methods do not hold the robustness near the edges. In this paper, we propose a non-local total variation NLTV-$L^1$ optical flow estimation method based on robust weighted guided filtering. Specifically, first, the robust weighted guided filtering objective function is proposed to preserve motion edges. The proposed objective function is based on the linear model which is computationally efficient and edge-preserving in complex natural scenarios. Second, the proposed weighted guided filtering objective function is incorporated into the non-local total variation NLTV-$L^1$ energy function. Finally, the novel NLTV-$L^1$ optical flow method is performed using the coarse-to-fine process. Additionally, we modify some state-of-the-art variational optical flow estimation methods by the robust weighted guided filtering objective function to verify the performance on Middlebury, MPI-Sintel, and Foggy Zurich sequences. Experimental results show that the proposed method can preserve edges and improve the accuracy of optical flow estimation compared with several state-of-the-art methods.     

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

This paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.     

A double laser filtering approach to vibration noise rejection for hot-rolled strip flatness measurement

This paper proposes a new filtering method based on the Kalman filtering algorithm for hot-rolled strip flatness measurement system. The system involves processing slowly changing signal, which can be considered as a bounded random process, and its model parameters are completely unknown. The noise rejection strategy in double lasers can generate a compensation signal. Since the initial and accumulative error would lead to negative filter effect or even cause divergence, Kalman filter is integrated to effectively deal with the initial error and enhance convergence. In this setting, the noise rejection strategy is used as a prediction model to constitute a similar Kalman filter. The correlated error caused by measurement error is coped with by a compensation model based on the feature of correlated error to enhance the filter effect. Both theoretical analysis and simulations show that the new algorithm has a better filter effect than the traditional Kalman filtering algorithm for the system.     

信号调理器对动态测试的影响

信号调理器是动态测试系统的重要组成部分，带宽、放大倍数和滤波是衡量其性能的主要指标。动态测试系统的输出信号幅值和相位是表征测试结果的重要参数。本文研究信号调理器性能对动态测试系统输出信号幅值和相位的影响，试验并分析带宽、放大倍数和滤波对输出信号幅值和相位造成的衰减。对提高动态测试结果准确度，优化测试系统动态性能具有重要意义。     

基于Vondark-Cepek滤波的氢铯时间尺度融合方法研究

氢原子钟和铯原子钟是当前国际原子时和各国标准时间产生的主要精密频率源,二者分别拥有优良的短期和长期稳定度特性。充分利用氢钟短稳和铯钟长稳进行时间保持成为时间产生过程中的一项关键技术。以提高时间尺度的长短期稳定性为目标,提出了Vondark-Cepek组合滤波的氢铯融合时间产生方法。首先利用AT1算法分别对氢钟组和铯钟组各产生一个钟组时间尺度,然后根据最小二乘原则对Vondark-Cepek组合滤波关键参数进行选取,进而通过氢钟组时间尺度时间序列的差分信息对铯钟组时间尺度进行性能增强,从而获得氢铯融合时间尺度。计算结果表明:该时间尺度1 h稳定度为3. 36×10-15,15 d稳定度为3×10-15,均优于氢铯单个时间尺度相同平均时间上的性能指标。     

相似系数阈值滤波的数据驱动控制束偏移

高斯束偏移不仅具有接近波动方程偏移的精度，而且具有Kirchhoff偏移灵活、高效的特点。然而当实际地震采集数据中含有较强噪声时，易产生偏移假象而影响成像质量。为此，在传统高斯束偏移的基础上，根据有效信号和干扰信号在τ-p域中的相干性差异，发展了一种基于相似系数阈值滤波的数据驱动控制束偏移方法。采用数据驱动策略，在高斯束偏移成像过程中，先计算τ-p道集的相似系数，再通过设定相似系数阈值控制干扰信号，从而降低偏移剖面中的随机噪声；控制束偏移可以直接提取角度域共成像点道集，无需复杂的角度映射变换且具有更高信噪比。模型测试及实际资料处理结果表明：对于低信噪比数据，控制束偏移剖面的信噪比明显高于常规高斯束偏移，但会损失相对振幅的可靠性；尽管控制束偏移在τ-p道集的滤波过程增加了一定的计算量，但总体与常规高斯束偏移方法的计算效率相当；相似系数阈值参数选取十分关键，阈值较小时偏移噪声较强，但过大阈值也可能压制部分有效信息或产生偏移假象，选取合适的阈值参数才能得到较理想的偏移剖面。