虚拟现实技术在反恐训练中的研究进展

介绍了虚拟现实技术的国内外研究现状,以及仿真技术与虚拟现实技术结合使用的发展趋势.为了更好地履行反恐使命,适应形势的发展,有必要提高反恐教学训练质量,创新反恐行动教学训练手段.将仿真技术和虚拟现实技术应用于路边炸弹的虚拟及仿真,能使反恐受训者不受时间、空间、经费和器材的限制,利用各种3D虚拟模型任意组装不同起爆方式下的路边炸弹,拓展其想象空间和创新思维,使之对路边炸弹的结构组成、工作原理及爆炸效应等有深刻理解.     

利用Visual C＋＋和Direct3D设计雷达显示器

利用Visual C＋＋和Direct3D对某雷达显示器的实现过程作了详细论述,包括数据采集、PPI型显示器、A型显示器、B型显示器、余晖显示、航迹显示等。通过试验证明,采用Direct3D绘图方式比采用GDI绘图方式效果好,更适用于数据处理量大、天线扫描速度快的雷达显示器。     

基于地面先验的3D目标检测算法

为了提高自动驾驶汽车环境感知的性能,增强单目相机对障碍物三维和边界信息的感知能力,提出了一种基于地面先验的3D目标检测算法。基于优化的中心网络(CenterNet)模型,以DLA(deep layer aggregation)为主干网络,增加目标3D边沿框中心点冗余信息预测。根据自动驾驶场景的地面先验信息,结合针孔相机模型,获取目标3D中心深度信息,以优化深度网络学习效果。使用KITTI 3D数据集评测算法性能,结果表明:在保证2D目标检测准确性的基础上,该算法运行帧率约20 fps,满足自动驾驶感知实时性要求;同时相比于CenterNet模型,在平均方位角得分(average orientation score)和鸟视图平均准确率(bird eye view AP)上分别有4.4和4.4%的性能提升。因而,该算法可以提高自动驾驶汽车对障碍物三维和边界信息的感知能力。     

卷积融合文本和异质信息网络的学术论文推荐算法

针对交互数据稀疏和推荐多样性问题,基于卷积协同过滤推荐框架提出卷积融合文本和异质信息网络的学术论文推荐算法（WN-APR）。首先学习不同语义下用户和论文的多样化特征,缓解数据稀疏问题;然后基于外积设计不同语义特征相互增强的方式融合它们,并使用三维卷积神经网络代替二维卷积神经网络充分挖掘不同特征对性能的影响;最后改进贝叶斯个性化排序损失函数增强推荐多样性。在CiteuLike-a、CiteuLike-t数据集上的实验结果表明,相比于基线模型,WN-APR在准确率和多样性的四个指标上都有所提升。     

Improvement of rainwater infiltration and storage capacity by an enhanced seepage well: From laboratory investigation to HYDRUS-2D numerical analysis

Seepage well is an emerging Low Impact Development (LID) technology that can effectively control the storm runoff. However, its rainwater infiltration rate and storage capacity still require further enhancement. By setting a horizontal infiltration structure at the bottom of conventional rainwater seepage well (CSW), an enhanced seepage well (ESW) was proposed in this study, and its infiltration performances compared with the permeable pavement (PP) and the CSW were systemically investigated using static infiltration experiment and HYDRUS-2D simulation. The results showed that the infiltration efficiency of ESW was significantly higher than that of PP and CSW, and the process of water infiltrated through soil mainly controlled the macroscopic infiltration rate. The Nash-Sutcliff Efficient (NSE) index was used to evaluate the accuracy and reliability of the HYDRUS-2D model, and the results of NSE values greater than 0.75 (varied between 0.75 and 0.91) confirmed the applicability of HYDRUS-2D to describe correctly the hydraulic behavior of the ESW system. Simulation infiltration tests showed that the ESW performed a higher average infiltration rate and fewer total runoff volume than the CSW, indicating the effectively enhancement of the infiltration and water retention capacity of ESW, especially under heavy rainfall intensities. Additionally, the ESW system exhibited an excellent runoff-control and rainwater retention capacity in an actual rainfall scenario.     

3D human pose estimation in motion based on multi-stage regression

3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.     

Bayesian network based general correspondence retrieval method for depth sensing with single-shot structured light

This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

Film and television industry cloud exhibition design based on 3D imaging and virtual reality

The three-dimensional virtual scene can provide users with a visual three-dimensional virtual environment, with various multimedia channels such as sound, video, force feedback equipment, etc., to bring users a completely immersive interactive experience. This paper introduces 3D imaging and virtual reality technology in the film and television industry cloud exhibition, and develops a virtual display platform. First of all, this paper divides the registration into two processes: camera calibration and joint calibration of the camera and laser scanner based on the calibration results. Camera calibration can determine the plane model of the calibration board in the camera coordinate system, and the joint calibration uses the RANSAC algorithm to extract the point cloud of the plane model of the calibration board, and then optimizes the distance between the points in the plane model point cloud and the corresponding plane in the camera coordinate system Find the optimal transformation between the two sets of data, and then calculate the registration relationship between the point cloud and the image. Secondly, this article conducts a demand analysis of the film and television industry cloud exhibition platform based on virtual reality technology, including the business goals set by the platform, platform system analysis, overall design, and system operating environment and configuration requirements. This model provides a feasible solution for the visual interaction of the cloud exhibition design of the film and television industry.