基于FPGA的三电平变换器SVPWM调制方法的实现

本文在研究二极管箝位型三电平NPC变换器拓扑结构的基础上,搭建三电平变换器控制系统的样机试验平台,针对60°坐标系下的SVPWM调制算法的数学模型,提出了一种基于FPGA硬件实现的SVPWM调制方案,最后通过实验验证了此调制方案的可行性和有效性。     

联合模板先验概率和稀疏表示的目标跟踪

目的 虽然基于稀疏表示的目标跟踪方法表现出了良好的跟踪效果,但仍然无法彻底解决噪声、旋转、遮挡、运动模糊、光照和姿态变化等复杂背景下的目标跟踪问题。针对遮挡、旋转、姿态变化和运动模糊问题,提出一种在粒子滤波框架内,基于稀疏表示和先验概率相结合的目标跟踪方法。方法 通过先验概率衡量目标模板的重要性,并将其引入到正则化模型中,作为模板更新的主要依据,从而获得一种新的候选目标稀疏表示模型。结果 在多个测试视频序列上,与多种流行算法相比,该算法可以达到更好的跟踪性能。在5个经典测试视频下的平均中心误差为6.77像素,平均跟踪成功率为97%,均优于其他算法。结论 实验结果表明,在各种含有遮挡、旋转、姿态变化和运动模糊的视频中,该算法可以稳定可靠地跟踪目标,适用于视频监控复杂场景下的目标跟踪。     

特征学习的单幅图像去雾算法

目的 雾霾天气使图像降质,严重影响军事、交通和安全监控等领域信息系统的正常运作,因此图像去雾具有重要研究意义。目前主流的单幅图像去雾算法主要利用各种与雾相关的颜色特征实现,但不同的颜色先验知识往往存在各自的场景局限性。为提高图像去雾的普适性,提出一种特征学习的单幅图像去雾方法。方法 首先通过稀疏自动编码机对有雾图像进行多尺度的纹理结构特征提取,同时抽取各种与雾相关的颜色特征。然后采用多层神经网络进行样本训练,得到雾天条件下纹理结构特征及颜色特征与场景深度间的映射关系,并估算出有雾图像的场景深度图。最后结合大气散射模型,根据场景深度图复原无雾图像。结果 与主流去雾算法的结果定性对比,复原后的图像细节更清晰,颜色更自然。采用均方误差和结构相似度定量评价各算法去雾结果与真实无雾图像的相似度,本文算法结果与真实无雾场景间的相似度最高,达到99.9%。结论 对实验结果的定性及定量分析表明,本文算法能有效获取有雾图像的场景深度,复原出视觉效果理想的无雾图像,且具有很好的场景普适性。     

不同池化模型的卷积神经网络学习性能研究

目的 基于卷积神经网络的深度学习算法在图像处理领域正引起广泛关注。为了进一步提高卷积神经网络特征提取的准确度,加快参数收敛速度,优化网络学习性能,通过对比不同的池化模型对学习性能的影响提出一种动态自适应的改进池化算法。方法 构建卷积神经网络模型,使用不同的池化模型对网络进行训练,并检验在不同迭代次数下的学习结果。在现有算法准确率不高和收敛速度较慢的情况下,通过使用不同的池化模型对网络进行训练,从而构建一种新的动态自适应池化模型,并研究在不同迭代次数下其对识别准确率和收敛速度的影响。结果 通过对比实验发现,使用动态自适应池化算法的卷积神经网络学习性能最优,在手写数字集上的收敛速度最高可以提升18.55%,而模型对图像的误识率最多可以降低20%。结论 动态自适应池化算法不但使卷积神经网络对特征的提取更加精确,而且很大程度地提高了收敛速度和模型准确率,从而达到优化网络学习性能的目的。这种模型可以进一步拓展到其他与卷积神经网络相关的深度学习算法。     

认知无线电网络中安全的协作频谱检测算法

在认知无线电网络中,当认知用户存在虚假感知信息SSDF时,协作频谱感知的性能会受到巨大影响。为了确保频谱感知的鲁棒性,提出了一种基于信誉度加权序贯频谱检测算法来识别恶意用户并减轻他们造成的影响。该算法通过使用认知用户的近期感知信息计算本地信誉度函数,再综合考虑其信誉值与感知过程中表现的稳定程度以消除恶意用户对主用户的传输效率带来的影响。仿真结果表明,在恶意攻击环境下,新算法优于现有典型协作频谱感知算法。     

WSNs入侵检测中实值否定选择算法研究

针对无线传感器网络免疫入侵检测中否定选择算法采用r-连续位二进制串匹配度作为亲和力,检测率低且无法反映WSNs在一段时间内的动态特性这一现象,提出采用RNS-WSNs算法,该算法用一段时间内属性值的变化率构成向量作为抗原和抗体,通过计算向量间的曼哈顿距离作为亲和力。在NS3上模拟WSNs进行实验,结果显示在能量消耗相当且误报率相同的情况下,RNS-WSNs算法具有更高的检测率。     

Active disturbance rejection control approach to stabilization of lower triangular systems with uncertainty

In this paper, we apply the active disturbance rejection control (ADRC) to stabilization for lower triangular nonlinear systems with large uncertainties. We first design an extended state observer (ESO) to estimate the state and the uncertainty, in real time, simultaneously. The constant gain and the time‐varying gain are used in ESO design separately. The uncertainty is then compensated in the feedback loop. The practical stability for the closed‐loop system with constant gain ESO and the asymptotic stability with time‐varying gain ESO are proven. The constant gain ESO can deal with larger class of nonlinear systems but causes the peaking value near the initial stage that can be reduced significantly by time‐varying gain ESO. The nature of estimation/cancelation makes the ADRC very different from high‐gain control where the high gain is used in both observer and feedback. Copyright © 2015 John Wiley & Sons, Ltd.     

Rotation,scaling and translation invariant texture recognition by Bessel-Fourier moments

The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) invariant texture recognition using Bessel-Fourier moments. Compared with the others moments based methods, the radial polynomials of Bessel-Fourier moments have more zeros and these zeros are more evenly distributed. It makes Bessel-Fourier moments more suitable for invariant texture recognition as a generalization of orthogonal complex moments. In the experiment part, we got three testing sets of 16, 24 and 54 texture images by way of translating, rotating and scaling them separately. The correct classification percentages (CCPs) are compared with that of orthogonal Fourier-Mellin moments and Zernike moments based methods in both noise-free and noisy condition. Experimental results validate the conclusion of theoretical derivation: BFM performs better in recognition capability and noise robustness in terms of RST texture recognition under both noise-free and noisy condition when compared with orthogonal Fourier-Mellin moments and Zernike moments based methods.     

Topology Optimization in Aircraft and Aerospace Structures Design

Topology optimization has become an effective tool for least-weight and performance design, especially in aeronautics and aerospace engineering. The purpose of this paper is to survey recent advances of topology optimization techniques applied in aircraft and aerospace structures design. This paper firstly reviews several existing applications: (1) standard material layout design for airframe structures, (2) layout design of stiffener ribs for aircraft panels, (3) multi-component layout design for aerospace structural systems, (4) multi-fasteners design for assembled aircraft structures. Secondly, potential applications of topology optimization in dynamic responses design, shape preserving design, smart structures design, structural features design and additive manufacturing are introduced to provide a forward-looking perspective.     

Pure spatial rich model features for digital image steganalysis

The SRM (Spatial Rich Model) is a very effective steganalysis method. It uses statistics of neighboring noise residual samples as features to capture the dependency changes caused by embedding. Because the noise residuals are the high-frequency components of image and closely tied to image content, the residuals of different types of image regions have different statistical properties and effectiveness for steganalysis. In this paper, the effectiveness of the residuals is investigated. Then the effectiveness of the statistics collected from different types of neighboring residual samples is investigated from the FLD (Fisher Linear Discriminant) viewpoint, and ineffective, effective and high-effective neighboring residual samples are defined. The ineffective neighboring residual samples are not likely to change during embedding, and if they are counted in statistics, they may mix the features with noise and make the features impure. Pure SRM features are extracted based on neighboring noise residual sample selection strategy. Furthermore, multi-order statistical features are proposed to increase the statistical diversity. Steganalysis performances of the statistical features collected from different types of neighboring residual samples are investigated on three content adaptive steganographic algorithms. Experimental results demonstrate that the proposed method can achieve a more accurate detection than SRM.