基于自适应侧抑制网络的红外弱小目标检测

 为了提高远距离红外弱小目标的检测效率,提出了一种基于自适应侧抑制网络的复杂背景下的红外弱小目标检测方法。该方法建立了改进的侧抑制网络数学模型,利用各向异性滤波来自适应地确定侧抑制网络的抑制系数,不需要人为干预,实现了侧抑制网络与各向异性高斯滤波的有机结合。同时,在各向异性高斯滤波器两轴确定方面进行了改进,两轴分别采用对比度尺度模型和强度尺度传播模型来独立确定。通过与传统弱小目标检测方法的对比实验,验证了方法的有效性。     

An Adaptive Nonmonotone Projected Barzilai-Borwein Gradient Method with Active Set Prediction for Nonnegative Matrix Factorization

In this paper, we first present an adaptive nonmonotone term to improve the efficiency of nonmonotone line search, and then an active set identification technique is suggested to get more efficient descent direction such that it improves the local convergence behavior of algorithm and decreases the computation cost. By means of the adaptive nonmonotone line search and the active set identification technique, we put forward a global convergent gradient-based method to solve the nonnegative matrix factorization (NMF) based on the alternating nonnegative least squares framework, in which we introduce a modified Barzilai-Borwein (BB) step size. The new modified BB step size and the larger step size strategy are exploited to accelerate convergence. Finally, the results of extensive numerical experiments using both synthetic and image datasets show that our proposed method is efficient in terms of computational speed.     

基于自适应主动及滑模控制的分数阶超混沌系统异结构反同步

以超混沌Chen系统和超混沌Lorenz系统为例,研究了慢时变参数超混沌系统的反同步问题.首先利用主动控制的思想,消去超混沌系统中的非线性部分,然后基于Lyapunov稳定性理论,合理地选取参数自适应控制律,很好的解决了时变参数的参数摄动问题,从而实现了两个超混沌系统的反同步.在此基础之上,又进一步研究了分数阶超混沌系统,使用滑模控制方法对其进行控制,理论上分析了该方法的可行性.数值模拟实验进一步验证了所提出方法的有效性. 关键词： 超混沌 分数阶 自适应 滑模     

Synchronization between two different noise-perturbed chaotic systems with unknown parameters

In this paper, a general method of synchronizing noise-perturbed chaotic systems with unknown parameters is proposed. Based on the LaSalle-type invariance principle for stochastic differential equations and by employing a combination of feedback control and adaptive control, some sufficient conditions of chaos synchronization between these noise-perturbed systems with unknown parameters are established. The model used in the research is the chaotic system, but the method is also applicable to the hyperchaotic systems. Unified system and noise-perturbed RSssler system, hyperchaotic Chen system and nolse-perturbed hyperchaotic RSssler system are taken for illustrative examples to demonstrate this technique.[第一段]     

基于粒子群优化的自适应模糊制冷控制算法

为解决空间斯特林制冷机和探测器热负载不确定及存在变化的问题,提出了自适应模糊PID制冷控制。在空间环境中使用的斯特林制冷机参数会随着时间的变化而发生改变,探测器负载也会随着工作模式和工作时间的变化而变化,整个制冷系统涉及的变量多,参数非线性。采用传统的控制方法,在固定的单一条件、环境下得到的控制参数,环境和负载发生变化后容易性能变差甚至不稳定,控制精度和稳定性不能满足使用要求。设计了一种自适应斯特林制冷机控制器,通过综合自适应模糊PID控制的方法,采用粒子群优化算法调整控制参数以减小代价函数。通过仿真和试验验证算法的有效性和鲁棒性。     

竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害

基于竞争性自适应重加权算法(CARS)和相关系数法(CA)特征波长选择方法,提出了利用可见-近红外高光谱成像技术检测番茄叶片灰霉病的方法。首先获取380～1 023 nm波段范围内80个染病和80个健康番茄叶片的高光谱图像,然后提取染病和健康叶片感兴趣区域(ROI)的光谱反射率值,作为番茄叶片灰霉病鉴别模型的输入来建立支持向量机(SVM)鉴别模型,训练集和验证集的鉴别率都是100%。研究进一步通过CARS和CA提取特征波长,分别得到5个(554, 694, 696, 738和880 nm)和4个(527, 555, 571和633 nm)特征波长,然后分别建立CARS-SVM和CA-SVM鉴别模型。结果显示,CARS-SVM模型中训练集和验证集的鉴别率都是100%,CA-SVM模型中训练集和验证集的鉴别率分别是91.59%和92.45%。以上结果说明了从可见-近红外高光谱图像中提取的光谱反射率值用于检测番茄叶片的灰霉病是可行的。     

军用运输机机舱有源消声实验系统的设计与实现

为验证有源消声技术在军用运输机机舱低频噪声消除方面的有效性,设计和实现了一套机舱有源消声实验系统。采用“激振器+舱壁板”方式实现了飞机螺旋桨对机舱诱导噪声的声源模拟,设计了基于前馈控制结构的自适应有源噪声控制系统,构建了基于FX-LMS算法的自适应消声控制器,采用监测麦克风组对舱内空间的消声效果进行监测。实验结果验证了自适应有源噪声控制技术在军用飞机舱室消声降噪领域的有效性,并表明初、次级声源间距对自适应有源消声系统的消声效果具有重要的影响。     

基于时间窗延迟的资源约束项目调度双层优化研究

本研究从业主—承包商交互的视角构建了一种RCPSP(resource-constrained project scheduling problem)双层优化模型,即在可更新资源约束条件下,项目双方如何进行交互决策达到双方NPV(Net present value)最大化的目标。首先对研究问题进行界定,构建资源约束下的max-NPV项目调度双层优化模型;然后利用延迟优先规则设计了一种基于时间窗延迟的嵌套式自适应遗传算法来求解该模型,以达到双方NPV最大化;最后用一个算例验证算法的有效性,同时通过PSPLIB数值实验说明算法的稳定性,并分析关键参数对项目双方收益的影响。研究结果为项目进程的安排以及奖励机制的设计提供依据,以提高双方利益。     

Stabilization of novel multi-layer networks with noise-based nonlinear superior couplings via aperiodically adaptive intermittent pinning control

In this paper, novel multi-layer networks with superior couplings are proposed firstly which are established on a non-strongly connected digraph. Within the multi-layer networks, a nonlinear coupling based on white noises is introduced, which is the feature of superior couplings. We adopt aperiodically adaptive intermittent pinning control to stabilize the multi-layer networks. An concrete analysis framework about selecting the target vertex of the control is revealed. Aperiodically adaptive intermittent control is employed on the vertex systems of the first layer networks, to achieve the stabilization of the first layer networks, where the couplings of drift terms are treated as negative effects on stabilization. With the help of noise stabilization, the stabilization of the other layers networks is realized based on the stability of the first layer networks and the characteristics of the superior coupling that is based on white noises. By employing graph theory and the Lyapunov method, an almost sure exponential stabilization criterion of the multi-layer networks is acquired. As a subsequent result, the proposed theory is applied to a class of stochastic coupled oscillators with sufficient conditions being given to ensure their stability. Finally, a numerical example is provided to illustrate the feasibility of the stated theoretical results.     

基于液晶光调制器的夏克-哈特曼波前探测技术

微透镜阵列是夏克-哈特曼波前探测器的关键器件。利用液晶器件的空间相位调制特性,可实现微透镜阵列功能,且具有孔径和焦距可变的优点。论述了基于液晶光调制的夏克-哈特曼波前探测校正技术的特征、技术进展、及应用前景。