深度图像的分块自适应压缩感知

针对基于空域上下采样的深度编码框架中,由边缘信息损失带来的视点绘制质量下降的问题,提出了一种面向视点绘制质量的深度图像分块自适应压缩采样方法。在基于分块压缩感知和光滑Landweber投影重构的BCS_SPL框架下,利用图像块的方差表征其边缘信息,并据此进行自适应采样,以提高深度图像重构和视点合成质量。结果表明,在相同的采样率下,相比上下采样和BCS_SPL方法,本文提出的分块自适应压缩感知方法在绘制视点的PSNR和主观质量上都有提高。     

小型无人直升机浸入–不变集自适应控制

本文基于浸入–不变集理论,针对小型无人直升机存在的参数不确定性问题,设计一种新型的自适应控制器.利用基于Lyapunov的分析方法和La Salle不变性原理,进行闭环系统的稳定性分析,确保无人直升机姿态角的跟踪误差全局渐进收敛,以及闭环系统的稳定性.在无人直升机姿态飞行控制实验平台上,进行了无人机姿态跟踪控制实验.实验结果表明,本文所提出的控制方法具有良好的跟踪控制效果.     

自适应混合滤波在脉搏实时监测系统中的应用

针对脉搏传感器在数据采集过程中干扰信号对系统测量精度带来的影响,提出了一种自适应混合滤波算法.该算法将限幅滤波、递推中位值平均滤波、递推平均滤波相结合,并针对采样长度对滤波效果的影响,自适应采样窗口大小的调整,可有效消除偶然脉冲与抑制周期性脉冲的干扰.通过对10名测试者进行测量实验,将心电图机测量数据、使用与未使用自适应混合滤波的脉搏实时监测系统测量数据进行对比.实验结果表明:使用滤波算法后,脉搏测量的算术平均差约为0.4 BPM,比未使用滤波算法整体提高约9倍.     

基于自适应混合非支配个体排序策略的改进型NSGA-Ⅱ算法

针对经典快速非支配排序遗传算法(NSGA-Ⅱ)中基于拥挤距离的种群多样性保持策略不能客观反映个体间真实拥挤程度的问题,提出了一种基于自适应混合非支配个体排序策略的改进型NSGA-Ⅱ算法(NSGA-Ⅱ^h)。首先,设计一种新的循环聚类个体排序策略;然后,根据Pareto分层信息来对基于经典拥挤距离和循环聚类的两种个体排序策略进行自适应的选择;最终,实现对进化后期的种群多样性保持机制的改进。通过5个标准测试函数进行算法验证,并与经典的NSGA-Ⅱ、多目标粒子群优化算法(MOPSO)和GDE3等算法进行对比分析,NSGA-Ⅱ^h算法获得了80%的最优反向世代距离(IGD)值,且显著性水平为5%的双尾t检验结果表明,新算法具有明显统计意义上的性能优势。改进算法不仅能提高进化种群的分布性,而且能增强算法的收敛性,有效提高了优化效果。     

基于超混沌Lorenz系统的自适应异步抗噪声保密通信方案

针对非实时通信对安全性要求高且信道中存在噪声的问题,提出了基于超混沌掩盖调制的自适应异步抗噪声保密通信方案。通信原理是由超混沌Lorenz系统生成四个状态变量的伪随机向量,并通过信号增益将它们调整到同一区间;再由分段线性混沌映射（PWLCM）生成两个向量,用于在四个状态变量之间随机切换选取和确定动态时滞步长,以生成载体信号;然后将预编码后的二进制信息成对掩盖到载体信号中,添加高斯噪声后发送出去。实验结果分析发现最小信号增益和噪声系数的比值稳定在一个小区间[0.08,0.11]内,如果把该比值设置成某个大于该区间上限的值,则比特误码率可达到零,因此接收端可在噪声信道中接收并完美恢复出被掩盖信号。该方案利用超混沌系统的非线性动力学特性,能够在噪声信道中自适应实现异步保密通信,数值仿真验证了其有效性。     

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multioutput (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO nonaffine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible "controller singularity" problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of "explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.      

Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms

A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS).     

基于分支分析的机翼摇晃预测与抑制

针对现代战斗机大迎角飞行时的机翼摇晃预测与抑制问题,根据其主要动态特性建立了新的横侧向多自由度模型,通过开环分支分析准确预测了机翼摇晃对应的临界迎角.在此基础上考虑模型不确定性和舵面位置限幅的影响,利用滤波器动态对跟踪误差进行补偿,设计了一种约束滤波自适应反步控制算法,通过采用补偿误差代替跟踪误差进行自适应律设计,确保了输入饱和条件下自适应过程的稳定,并结合闭环分支分析实现了对机翼摇晃运动的有效抑制.六自由度仿真验证了多自由度模型的有效性和控制算法的鲁棒性.     

Immersion and Invariance Based Robust Adaptive Control of High‐Speed Train with Guaranteed Prescribed Performance Bounds

This paper proposes a novel robust adaptive algorithm for train tracking control with guaranteed prescribed transient and steady‐state performance. As speed increases, the inherent time‐varying uncertainties and unmodeled dynamics in the longitudinal dynamics of a high‐speed train seriously impacts the tracking performance of automatic train operation. To improve train operation performance, an estimator based on immersion and invariance technology is developed to recover the unknown and time‐varying plant parameters, and it renders the estimation error converging to a bounded residual set exponentially while providing more freedom for the controller. After certain error transformation, the prescribed tracking performance is introduced into the controller design. Then, an input‐to‐stable stable controller is developed through the backstepping technique, and it is proven that stabilization of the transformed system is sufficient to guarantee the prescribed performance. Rigorous theoretical analysis for the presented algorithm is provided, and a series of simulation studies also are given to verify the effectiveness of it.     

基于自适应延迟切割的三角网格布尔运算优化

规则化的布尔运算被广泛应用在三维建模系统中.近年来,随着图形硬件的发展,基于三角网格的规则化布尔算法由于输出结果能直接被图形硬件处理,表现出了明显的优势.但是传统的算法由于采用CSG树局部评估策略,使得面片在相交测试中反复被切割,并且由于面片分类在切割后的模型之间直接进行,导致算法无法在保证鲁棒性的同时实现高性能.为了避免这些问题,本文呈现了一种CSG树全局评估算法来统一执行单次和连续布尔运算.算法由两部分组成：自适应的延迟切割和全局化面片分类.在自适应的延迟切割阶段,算法通过仔细处理多个三角面片相交导致的各种情况使得延迟切割被扩展到整个CSG树来避免由于面片的反复切割带来的数值误差累积并利用自适应的八叉树使得相交测试能在线性时间内完成.在全局化面片分类阶段,算法通过分治法使得分类始终在切割后的面片和原始输入模型之间进行来保证分类的精度;通过结合组分类策略和自适应的八叉树来进一步优化了分类性能。实验结果表明,本文提出的算法无论是在执行单次或连续布尔运算时都能在保证鲁棒性同时性能优于其他的算法,因此本文算法可广泛应用于交互式建模系统中,如数字雕刻、计算机辅助设计和制造（CAD/CAM）等.