高超声速滑翔飞行器全程总红外辐射最小的轨迹优化（特邀）

为降低高超声速飞行器再入过程中,产生的气动热辐射对红外探测窗口性能的影响,从轨迹优化的角度,以再入飞行全程驻点总红外辐射为目标函数,提出了一种基于改进鲸鱼优化算法(Whale Optimization Algorithm,WOA)的高超声速飞行器轨迹优化算法。首先,通过Tent混沌映射和控制因子余弦变化改进WOA,改进算法位置更新时的位置指向性,增强算法全局搜索能力;同时,将再入轨迹优化问题转化为控制量剖面参数优化问题,采用倾侧角一次翻转策略,利用普朗克公式计算驻点红外辐射,并设计目标函数,利用阻力加速度再入走廊处理路径约束,采用罚函数法将终端约束同目标函数相结合;最后,利用改进的WOA对设计的控制量剖面进行参数寻优,获得使目标函数最优的解。仿真实验表明: 文中改进的WOA能够有效完成全程总红外辐射最小的再入轨迹优化任务,全局搜索能力强,且具有较好的鲁棒性。     

基于少量样本学习的多目标检测跟踪方法

视频目标检测跟踪算法一直是计算机视觉领域的研究热点,目前大部分方法均需人工采集样本训练检测模型,搭建目标检测跟踪系统.当目标成像条件发生变化时,需重新采集样本,训练模型,调试整个检测跟踪系统,耗费大量人力、物力.本文提出一种基于少量样本学习的多目标检测跟踪算法,只需在监控视频第一帧指定待检测目标,即可自主生成混合分类模...     

Efficient facial landmark localization using spatial–contextual AdaBoost algorithm

Facial landmark detectors can be categorized into global and local detectors. Global facial landmark detectors rely on global statistical relations between landmarks, but do not sufficiently utilize local appearance information, whereas local detectors mainly focus on local appearance attributes of landmarks. Although the AdaBoost algorithm has been successfully employed in object localization, it cannot take advantage of geometric facial feature distribution very well. We propose an AdaBoost algorithm called SC-AdaBoost, which efficiently combines the global knowledge of landmark distribution, the regional shape model, and the local landmark attributes based on a coarse-to-fine strategy. The global prior distribution of landmarks is estimated using a face image set with landmark annotations. First, the face region is detected as a rectangular bounding box using a Haar-like feature-based boosting method, and the global distribution of landmarks is used to determine the facial component regions. Facial landmark localization is roughly performed by regional shape modeling. Posteriors of individual weak classifiers are determined by Gabor wavelet analysis at landmark candidate positions constrained by the regional shape model. SC-AdaBoost is established by empirical risk minimization, which decides the weights for the weak classifiers, and is used for the precise localization. The strength of the proposed approach is shown by extensive experiments using standard face datasets.     

基于蚁群算法的网络层析故障链路诊断

为了解决网络层析成像中链路故障诊断的NP难问题,提出一种基于蚁群算法的故障链路诊断方法。首先将问题建模成一个组合优化问题,利用蚁群算法在解决组合优化问题中独特的优势进行求解。不同于传统的蚁群算法,求解故障链路时蚁群在初始放置点和可行路径上都受约束。为了加快算法的收敛速度,对蚁群算法的初始信息素浓度进行优化。仿真结果表明,所提出的算法在故障链路检测中具有较好的精度和召回率。     

Application of computational geometry to multiuser detection in CDMA

The maximum-likelihood multiuser detection problem in code-division multiple-access is known to be an optimization problem with an objective function that is required to be optimized over a combinatorial decision region. Conventional suboptimal detectors relax the combinatorial decision region by a convex region, without altering the objective function to be optimized. We take an approach wherein the objective function is reduced to a form appropriate for the application of a polynomial complexity algorithm in computational geometry, while keeping the decision region combinatorial. The resulting detector allows a tradeoff between performance and computational complexity. The bit-error rate performance of the detector has been found to be better than the decorrelator and the linear minimum mean-square error detectors, for the same level of complexity.     

求解0-1背包问题的基于双禁忌对象的TS算法

0-1背包问题是一个典型的组合优化问题.针对这个问题,给出了一种基于双禁忌对象的禁忌搜索求解算法.该算法首先以解向量的分量为解对象进行禁忌搜索,当这个搜索过程完成后,然后以当前最优解为初始解对象再进行禁忌搜索.实验结果表明该算法可有效地解决0-1背包问题.     

一种解决组合优化问题的改进型量子遗传算法

在量子遗传算法(QGA)的基础上,提出了一种解决组合优化问题的改进型量子遗传算法(NIQGA).为充分利用量子态的干涉性和纠缠性,该算法引入了动态调整量子门旋转角步长机制、量子交叉操作和量子变异操作,因而具有更高的搜索效率.利用两种典型组合优化问题——0/1背包问题和路由选择问题进行验证.结果表明,相比于GA和QGA,NIQGA具有收敛速度快和全局搜索能力强的特点,在解决基因间弱关联性的组合优化问题时有更优的性能.     

模块化可重构服务机器人群的任务规划

对模块化可重构服务机器人群在医院中应用所产生的任务规划问题进行了分析和建模,提炼出一个多目标、多约束的多维组合优化问题.设计了改进二进制蜜蜂算法(IBBA)进行组合方案寻优.作为一种启发式群智能优化算法,其特点在于:(1)全局搜索和局部搜索的功能划分明确且并行实施;(2)在基本算法框架中融入了组合方案的表示与进化方法、多目标处理方法、约束处理方法等要素;(3)在算法原型的基础上改进了局部搜索策略.针对一个实际算例进行了优化计算,算法在可行性、稳定性、计算结果质量、计算效率、单目标优化等方面取得了较好表现,并从算法机制中得到了合理解释.扩展了模块化可重构机器人的研究范畴,为多目标、多约束的多维组合优化问题提出了通用的建模方法和优化算法.     

一种基于定时段区域补偿的视频对象分割算法

在传统时空联合算法的基础上,提出了一种基于定时段区域补偿的视频对象分割后处理算法。首先,通过对帧差图像进行噪声抑制和膨胀连接获得变化检测模板;然后,对原始图像进行开闭重构简化,求取形态学梯度,通过对形态学梯度图像进行非线性变换和梯度等级划分并最终由分水岭算法获得对象的精确边界,通过比例运算提取出视频对象的初始二值化模板;最后,通过定时段区域补偿获得最终的完整视频对象模板。实验结果证明了该算法的正确性和有效性。     

网格运动分析与形态学滤波相结合的视频对象分割

提出了一种基于二维网格运动分析与改进形态学滤波空域自动分割策略相结合的视频对象时空分割算法。该算法首先利用高阶统计方法对视频图像的二维网格表示进行运动分析，快速得到前景对象区域，通过后处理有效获得前景对象运动检测掩膜。然后，用一种结合交变序列重建滤波算法和自适应阈值判别算法的改进分水岭分割策略有效获得前景对象的精确边缘。最后，用区域基时空融合算法将时域分割结果和空域分割结果结合起来提取出边缘精细的视频对象。实验结果表明，本算法综合了多种算法的优点，主客观分割效果理想。     

基于自适应权重和模拟退火的鲸鱼优化算法

针对鲸鱼优化算法容易陷入局部极值和收敛速度慢的问题,提出了一种结合自适应权重和模拟退火的鲸鱼优化算法.通过改进的自适应权重策略来调整算法的收敛速度,通过模拟退火增强鲸鱼优化算法的全局寻优能力.仿真实验中计算了18个测试函数,对比了粒子群算法、海豚回声定位算法和标准鲸鱼算法并进行统计分析,同时比较了单独结合自适应权重和模拟退火对鲸鱼优化的影响,结果表明,改进的算法在测试函数的极值计算中,计算精度和收敛速度方面都有了明显提升,验证了改进算法的有效性.     

A CMA-ES-Based Adversarial Attack Against Black-Box Object Detectors

Object detection is one of the essential tasks of computer vision. Object detectors based on the deep neural network have been used more and more widely in safe-sensitive applications, like face recognition, video surveillance, autonomous driving, and other tasks. It has been proved that object detectors are vulnerable to adversarial attacks. We propose a novel black-box attack method, which can successfully attack regression-based and region-based object detectors. We introduce methods to reduce search dimensions, reduce the dimension of optimization problems and reduce the number of queries by using the Covariance matrix adaptation Evolution strategy (CMA-ES) as the primary method to generate adversarial examples. Our method only adds adversarial perturbations in the object box to achieve a precise attack. Our proposed attack can hide the specified object with an attack success rate of 86％ and an average number of queries of 5, 124, and hide all objects with a success rate of 74％and an average number of queries of 6, 154. Our work illustrates the effectiveness of the CMA-ES method to generate adversarial examples and proves the vulnerability of the object detectors against the adversarial attacks.     

基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

针对当前贝叶斯网络结构学习算法易陷入局部最优和寻优效率低的问题,该文提出一种基于改进鲸鱼优化策略的贝叶斯网络结构学习算法。该算法首先提出一种新的方法建立较优的初始种群,然后利用不产生非法结构的交叉变异算子构建适用于贝叶斯网络结构学习的改进捕食行为,同时采用动态调节参数增强算法个体寻优的能力,通过适应度排序更新种群,最终获得最优的贝叶斯网络结构。仿真结果表明,该算法具有全局收敛性,寻优效率高,精确率高于其它同类优化算法。     

High-level background prior based salient object detection

Salient object detection is a fundamental problem in computer vision. Existing methods using only low-level features failed to uniformly highlight the salient object regions. In order to combine high-level saliency priors and low-level appearance cues, we propose a novel Background Prior based Salient detection method (BPS) for high-quality salient object detection.Different from other background prior based methods, a background estimation is added before performing saliency detection. We utilize the distribution of bounding boxes generated by a generic object proposal method to obtain background information. Three background priors are mainly considered to model the saliency, namely background connectivity prior, background contrast prior and spatial distribution prior, allowing the proposed method to highlight the salient object as a whole and suppress background clutters.Experiments conducted on two benchmark datasets validate that our method outperforms 11 state-of-the-art methods, while being more efficient than most leading methods.     

Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

Adaptive Iterative Detectors for Phase-Uncertain Channels via Variational Bounding

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/ process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.     

基于增殖系数的混沌信号提取算法

本文提出一种基于混沌信号特性的信号盲提取算法,由于不同的混沌信号在相空间里面对应着不同的吸引子二阶增长率,利用这个特点定义了增殖系数（Proliferation Exponent,PE）并将其作为混沌信号提取的目标函数.首先分析基于增殖系数的梯度搜索方法在解决盲提取问题时存在不足,并将混沌信号的盲提取问题转化为带约束的优化问题,提出利用改进的粒子群优化算法解决信号盲提取的优化问题,通过惯性系数动态调整和最优位置的扰动,提高算法的寻优性能.实验结果表明基于增殖系数的信号提取算法能有效地提取混沌信号,提取的信号在时域和相空间与源信号接近,同时算法也表现出对噪声污染的鲁棒性.     

基于改进鲸鱼算法的含DG配电网故障区段定位

针对鲸鱼优化算法存在收敛速度较慢、定位精度不够高等问题,文中提出了一种基于改进鲸鱼算法的含分布式电源配电网故障区段定位方法。构建了一种适用于多电源配电网故障定位的数学模型,采用自适应惯性权重策略来优化鲸鱼算法,并利用改进后的鲸鱼算法对构建的定位模型进行求解。在33节点含分布式电源的配电网上进行算例仿真,仿真结果表明在配电网发生单重、多重故障的情况下,改进后的鲸鱼算法能快速准确地定位出故障区段,且具有良好的容错性能。相较于传统鲸鱼算法,改进鲸鱼算法收敛速度更快,定位准确性更高,定位的可靠性也更高。     

解旅行商问题的一个新进化算法

旅行商问题(Travelling Salesman Problem,TSP)是一个著名的组合优化问题。提出用进化算法求解该问题。算法采用自然编码方式表示个体,设计了一种利用种群的边产生后代个体的新交叉策略。采用随机DoubleBridge变异策略,最后设计了结合2-交换和特殊3-交换的局部搜索算子改善解(个体)的质量。数值模拟实验表明,该算法是有效的。     

基于ABC算法和模糊互信息的SAR图像分割

针对SAR图像分割的相干噪声、伪影、低图像对比度和图像亮度不均匀等问题,提出一种基于人工蜂群(Artificial Bee Colony,ABC)算法,并结合模糊互信息量的方法自动检测机场轮廓。对SAR图像进行Lee Sigma滤波和模糊增强等图像预处理,以最大模糊互信息作为图像分割的最优判决方法,用ABC算法寻找该判决的最优解,得到机场的轮廓。实验采用低分辨率SAR图像,使用ABC算法分别搜索源图像和二元分割图像(目标和背景)的最大传统互信息和最大模糊互信息,比较了2种情况下得到的分割图像,结果证明了算法的有效性。