四旋翼无人机可视化轨迹跟踪仿真系统

为了便于对四旋翼无人机控制算法进行实验仿真和验证,联合Solidworks和Matlab/SimMechanics工具箱设计了一种四旋翼无人机可视化轨迹跟踪仿真系统;利用Solidworks搭建了四旋翼无人机三维实体模型,并通过Solidworks和Matlab转换接口将该实体模型导入到Matlab/SimMechanics中;Matlab/SimMechanics提供了了三维可视化窗口,可以显示无人机的实时仿真状态;仿真平台在Matlab/SimMechanics环境中实现,与Matlab/Simulink通信方便,可方便的将Simulink设计好的控制算法添加到仿真系统中,以进行验证和参数整定,还具有姿态分析和数据分析等功能;轨迹跟踪仿真结果表明,四旋翼无人机可视化轨迹跟踪仿真系统直观可视,准确可靠,能较好地对控制算法进行研究和测试,对四旋翼无人机以及控制算法的研究和开发具有重要价值。     

Weakly Supervised Building Semantic Segmentation Based on Spot-Seeds and Refinement Process

Automatic building semantic segmentation is the most critical and relevant task in several geospatial applications. Methods based on convolutional neural networks (CNNs) are mainly used in current building segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve the semantic segmentation of building by CNNs. In this paper, we propose a novel weakly supervised framework for building segmentation, which generates high-quality pixel-level annotations and optimizes the segmentation network. A superpixel segmentation algorithm can predict a boundary map for training images. Then, Superpixels-CRF built on the superpixel regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, we can train a more robust segmentation network and predict segmentation maps. To iteratively optimize the segmentation network, the predicted segmentation maps are refined, and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework achieves a marked improvement in the building’s segmentation quality while reducing human labeling efforts.     

Implementation of opto-digital stereo object tracking system

In this paper, a new opto-digital stereo object tracking system using the variable window mask and the optical binary phase extraction joint transform correlator (BPEJTC) is proposed. At the first step, with the distance information from the stereo camera to the tracking object easily acquired by the structural elements of a stereo vision system, the area of the tracking object can be digitally extracted by using the variable window mask. And, at the second step, by carrying out the optical BPEJTC between this reference image obtained from the variable window mask and the stereo input image, the coordinates of the tracking object's location can be acquired, and then with these values, the convergence angle and the pan/tilt of the stereo tracking camera can be finally controlled. From some experimental results, the proposed system is found to be able to effectively extract the area of the target object from the input image having the background noises by using the variable window mask. And, with the location values of the tracking object obtained by using the optical BPEJTC, the convergence angle and the pan/tilt of the stereo cameras can be controlled. Finally, a feasibility of real-time implementation of the adaptive stereo object tracking system using the proposed algorithm is also suggested.     

一种用于舰载无人机通信的天线伺服系统设计

为克服舰载设备使用稳定平台的可靠性问题,提出并设计了一种脱离稳定平台用于舰载无人机通信的天线伺服系统。该天线伺服系统用于舰艇对舰载无人机的实时跟踪,具有自动跟踪和手动跟踪两种工作模式,并且结构简单、工作稳定、响应速度快。从系统设计原理出发,阐述了系统的机电作动机构、角度跟踪算法、系统硬件电路设计以及伺服电机控制策略。实验表明,该伺服系统能够实时对舰载无人机进行精确跟踪,从而保障舰艇与无人机的有效通信。     

深度神经网络和高光谱显微图像的二维材料纳米片识别

近年来,二维材料由于其独特的性质而受到了广泛关注。在制备二维层状晶体的各种方法中,机械剥离法获得的薄层二维材料晶体质量高,适用于基础研究及性能演示。然而用机械剥离法从衬底上获得的材料具有一定的随机性,可能包含了少许相对较厚的部分。实现对这些二维薄层材料有效、快速且智能化的表征有利于促进二维材料性能的进一步研究。提出了一种基于深度学习的表征方法,通过搭建的编解码结构的卷积神经网络语义分割算法,可以根据光学显微镜图像进行分割和快速识别二维材料纳米片。卷积神经网络作为深度学习在图像处理领域中的典型算法,能够对光学显微镜图像中的复杂信息进行特征提取。首先采用机械剥离制备MoS₂纳米片样本,通过光学显微镜采集高光谱图像并对样本进行标记,根据样本的厚度范围标记出不同的区域,对标记后的图像进一步处理,包括图像的颜色校准和剪切操作,得到用于网络训练和测试的数据集。针对光学图像中二维纳米薄片存在的低对比度、碎裂等特点,编码时加入残差结构和金字塔池化模型,有助于特征信息的提取;解码时融合编码路径中提取的浅层特征信息,以提高网络分割精度。实验中采用带权重的交叉熵损失函数解决类别数量不平衡问题和采用数据增强扩大数据集。对训练后的网络测试结果表明,模型像素精度为97.38%,平均像素精度为90.38%,均交并比为75.86%。之后通过迁移学习成功地对剥离的单层和双层石墨烯纳米片样本进行了识别,均交并比达到了81.63%,表明该方法具有普适性。通过MoS₂和石墨烯纳米片的识别演示,实现了深度学习在二维材料的光学显微镜图像中的成功应用。该方法有望在更多的二维材料上得到扩展并突破自动动态处理光学显微镜图像的问题,同时为其他纳米材料的高光谱图像处理提供参考。     

基于自适应像素级注意力模型的场景深度估计

深度估计是传统的计算机视觉任务,在理解三维场景中起着至关重要的作用。基于单目图像的深度估计任务的困难在于如何提取图像特征中大范围依赖的上下文信息,提出了自适应的上下文聚合网络（adaptive context aggregation network,ACANet）用于解决该问题。该方法基于有监督的自注意力模型(supervised self-attention,SSA),能够自适应地学习任意像素之间的具有任务特性的相似性以模拟连续的上下文信息,并通过模型学习的注意力权重分布用来聚合提取的图像特征。将单目深度估计任务设计为像素级的多分类问题,经过设计的注意力损失函数减少RGB图像和深度图的语义不一致性,通过生成的像素级注意力权重对由位置索引的特征进行全局池化。最后提出一种软性有序推理算法（soft ordinal inference,SOI）,充分利用网络的预测置信度,将离散的深度标签转化为平滑连续的深度图,并且提高了准确率（rmse下降了3%）。在公开的单目深度估计基准数据集NYU Depth V2上的实验结果表明:rmse指标为0.490,阈值指标为82.8%,取得了较好的结果,证明了本文提出的算法的优越性。     

无人机对地目标自动检测与跟踪技术

针对无人机图像帧序列具有平台高速运动,视角旋转强烈,需要实时处理等特点,提出一种基于双级旋转不变特征空间检测（粗匹配-精细匹配）与并行特征提取跟踪的无人机对地目标图像帧序列自动快速目标检测与跟踪算法。采用图像子块的平均灰度值、灰度值方差、灰度值梯度构建特征空间。通过构造图像特征空间的方法来快速筛选待匹配图像的可疑区域,删除大量的背景区域,检测算法使用全局初步匹配加局部精细匹配的方法来规避算法复杂度的缺陷。理论及实验分析表明:该算法实时性强,对图像的旋转畸变具有抵消作用,对异常情况可以恰当处理,且全局初步匹配流程具有可移植性,可以在其他图像匹配跟踪算法中充当预处理器。实验结果表明:该算法在无人机对地的情况下可以保证对地面目标的稳定跟踪,配套检测算法具有较好的实时性,满足无人机图像目标检测跟踪实时处理的需要。     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.     

Optimal UAV Formation Tracking Control with Dynamic Leading Velocity and Network-Induced Delays

With the rapid development of UAV technology, the research of optimal UAV formation tracking has been extensively studied. However, the high maneuverability and dynamic network topology of UAVs make formation tracking control much more difficult. In this paper, considering the highly dynamic features of uncertain time-varying leader velocity and network-induced delays, the optimal formation control algorithms for both near-equilibrium and general dynamic control cases are developed. First, the discrete-time error dynamics of UAV leader–follower models are analyzed. Next, a linear quadratic optimization problem is formulated with the objective of minimizing the errors between the desired and actual states consisting of velocity and position information of the follower. The optimal formation tracking problem of near-equilibrium cases is addressed by using a backward recursion method, and then the results are further extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. Additionally, angle deviations are investigated, and it is proved that the similar state dynamics to the general case can be derived and the principle of control strategy design can be maintained. By using actual real-world data, numerical experiments verify the effectiveness of the proposed optimal UAV formation-tracking algorithm in both near-equilibrium and dynamic control cases in the presence of network-induced delays.     

Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.     

MS-KF融合算法用于锥套跟踪

针对无人机自主空中加油过程中锥套跟踪,提出一种均值漂移-卡尔曼滤波(mean shift-Kalman filter, MS-KF) 融合算法。分析了基于均值漂移算法的锥套目标模型、相似性度量、锥套目标定位的锥套定位原理;引入卡尔曼滤波器对锥套运动状态进行预测,将锥套运动信息融合到均值漂移算法中,以保证锥套跟踪算法的稳定性和鲁棒性;给出了MS-KF融合算法用于锥套识别跟踪的流程;搭建了锥套跟踪半物理实验验证系统,分别进行MS-KF融合算法用于锥套跟踪的半物理实验验证及数值仿真分析。实验结果表明:MS-KF融合算法可以对锥套精确定位跟踪,无人机3个轴向的跟踪误差保持在0.3 m的范围内,保证了无人机自主空中加油的顺利进行。     

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.     

高密度光盘循轨伺服中盘片径向倾斜的影响分析

基于标量衍射理论,根据实际光盘的参数,对数字多用途光盘(DVD光盘)的循轨行为进行了分析。在此基础上,针对盘片产生径向倾斜的情况引入了倾斜参数,对盘片模型进行了补充。对光盘的径向倾斜误差和系统循轨伺服的交叉影响进行了分析和讨论。     

An Improved Encoder-Decoder Network Based on Strip Pool Method Applied to Segmentation of Farmland Vacancy Field

In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.     

高密度光盘径向倾斜对DPD循轨误差信号影响分析

根据高密度光盘参数 ,计算了聚焦光斑在光盘表面扫描过程中每个位置的光瞳光强分布 ,得到DPD(Differ entialPhaseDetection)循轨误差信号。考虑盘片的径向倾斜 ,在光瞳光强分布方程中引入盘片的径向倾斜角度参数 ,计算了高密度盘片产生径向倾斜时DPD循轨误差信号的变化 ,进而分析了盘片的径向倾斜引入的循轨伺服误差及其对循轨伺服的负面影响。结果表明 ,高密度盘片产生 0 .5°的径向倾斜相当于引入了 0 .0 12 μm的循轨误差     

基于数字微镜阵列的协同目标跟踪系统设计

针对多镜头目标跟踪系统协同性差及单镜头系统目标跟踪观测过程中视场小、无法实现宽视场跟踪的现状,设计了能同时实现宽视场跟踪窄视场局部目标检测的协同跟踪系统。该系统基于数字微镜阵列高反射率、高性能视场分割的特点。验证实验中,宽窄视场分割系统实现了50 ms的协同成像周期;窄视场像中心调整系统不超过2%的绝对位置误差,为局部目标光束中心难以与窄视场成像系统视轴重合问题的解决提供了实验依据。     

Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration(PI) is introduced to solve the min-max optimization problem. The off-policy adaptive dynamic programming(ADP) algorithm is then proposed to find the solution of the tracking Hamilton–Jacobi–Isaacs(HJI) equation online only using measured data and without any knowledge about the system dynamics. Critic neural network(CNN), action neural network(ANN), and disturbance neural network(DNN) are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded(UUB) of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem.     

Analysis and demonstration of PID algorithm based on arranging the transient process for adaptive optics

The fastness and robustness of a control algorithm are highly important in the performance of adaptive optics systems. The proportional-integral-derivative control with arranging the transient process, which is designed using a tracking differentiator, is applied into an adaptive optics system. This control algorithm greatly improves the dynamic properties of the control system. To identify the underlying reasons for these improvements, the influence of the control algorithm is theoretically discussed. The control algorithm is verified by a simple adaptive optics system for tip/tilt correction. The experimental results demonstrate that the control algorithm is fast and robust.     

Hénon混沌系统广义预测控制无静差快速算法

提出了一种具有无静差跟踪性能的Hénon混沌系统广义预测控制快速算法.采用改进的时变遗忘因子递推最小二乘方法辨识混沌系统,通过在常规广义预测控制性能指标函数中引入前馈增益矩阵与柔化矩阵,并将MP神经元网络与BP算法相结合在线调整柔化因子,实现系统对参考信号的无静差快速跟踪.该算法避免了矩阵求逆计算,能够很好地跟踪参考信号.仿真结果验证了该方法的有效性. 关键词： 广义预测控制 Hénon混沌系统 前馈增益矩阵 柔化矩阵     

Modeling and analysis for the image mapping spectrometer

The snapshot image mapping spectrometer(IMS) has advantages such as high temporal resolution,high throughput,compact structure and simple reconstructed algorithm.In recent years,it has been utilized in biomedicine,remote sensing,etc.However,the system errors and various factors can cause cross talk,image degradation and spectral distortion in the system.In this research,a theoretical model is presented along with the point response function(PRF) for the IMS,and the influence of the mirror tilt angle error of the image mapper and the prism apex angle error are analyzed based on the model.The results indicate that the tilt angle error causes loss of light throughput and the prism apex angle error causes spectral mixing between adjacent sub-images.The light intensity on the image plane is reduced to 95%when the mirror tilt angle error is increased to ±100 "(≈ 0.028°).The prism apex error should be controlled within the range of 0-36"(0.01°)to ensure the designed number of spectral bands,and avoid spectral mixing between adjacent images.