The complexity of multi-domain access control policy integration makes it difficult to understand and manage the policy conflict information. The policy information visualization technology can express the logical rel...The complexity of multi-domain access control policy integration makes it difficult to understand and manage the policy conflict information. The policy information visualization technology can express the logical relation of the complex information intuitively which can effectively improve the management ability of the multi-domain policy integration. Based on the role-based access control model, this paper proposed two policy analyzing methods on the separated domain statistical information of multi-domain policy integration conflicts and the policy element levels of inter-domain and element mapping of cross-domain respectively. In addition, the corresponding visualization tool is developed. We use the tree-maps algorithm to statistically analyze quantity and type of the policy integration conflicts. On that basis, the semantic substrates algorithm is applied to concretely analyze the policy element levels of inter-domain and role and permission mapping of cross-domain. Experimental result shows tree-maps and semantic substrates can effectively analyze the conflicts of multi-domain policy integration and have a good application value.展开更多
The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology play...The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.展开更多
近年来,神经辐射场在三维重建任务中展现出卓越性能。然而,应用在视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)中因缺乏全局优化机制容易导致系统定位精度不足以及重建失败。针对该问题,本文提出一种融合深度...近年来,神经辐射场在三维重建任务中展现出卓越性能。然而,应用在视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)中因缺乏全局优化机制容易导致系统定位精度不足以及重建失败。针对该问题,本文提出一种融合深度学习位姿估计与神经隐式表征的视觉SLAM系统。通过稠密束调整层以及高效的全局优化机制对相机位姿和深度进行像素级的循环迭代,并基于神经辐射场方法更新全局一致的隐式重建表面,使得系统在精准定位的同时能够重建高保真场景,并且在此基础上引入语言查询机制,增强系统的交互能力。在EuRoC和Replica数据集上进行大量实验,在不同的输入条件下,分别与3类基准方法进行对比,结果表明该系统在跟踪鲁棒性和重建精度方面相较于现有方法表现更优。本方法可为后续基于神经辐射场的视觉SLAM方法提供参考。展开更多
针对复杂城市环境中交通流量、人群密度等动态因素降低视觉地点识别(visual place recognition,VPR)算法精度的问题,提出一种融合语义引导的视觉地点识别(semantic-guided VPR,SG-VPR)算法。设计语义引导特征抑制模块,通过构建语义引导...针对复杂城市环境中交通流量、人群密度等动态因素降低视觉地点识别(visual place recognition,VPR)算法精度的问题,提出一种融合语义引导的视觉地点识别(semantic-guided VPR,SG-VPR)算法。设计语义引导特征抑制模块,通过构建语义引导模块与特征抑制层弱化动态物体干扰,精准提取关键静态特征;改进传统三元损失函数,提出自适应三元损失函数(adaptive triplet marginal loss,ATML),依据样本分布自适应调整边距,解决固定边距策略导致的次优解收敛问题,提升特征区分能力。实验结果表明:SG-VPR算法在Pittsburgh250k和Tokyo24/7城市复杂环境公开数据集上的表现优于现有方法,在动态干扰场景下的视觉地点识别性能得到提升。展开更多
即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是一种重要的定位与地图构建技术。传统的视觉SLAM假设环境完全静止,但在动态场景中,SLAM识别的特征点很可能位于运动物体上,这会导致定位误差和地图不准确。为解决该问...即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是一种重要的定位与地图构建技术。传统的视觉SLAM假设环境完全静止,但在动态场景中,SLAM识别的特征点很可能位于运动物体上,这会导致定位误差和地图不准确。为解决该问题,提出了一种基于深度学习的视觉SLAM系统,利用嵌入MobileNetV3轻量化改进的YOLOv8模型识别运动物体,并将其范围内的特征点剔除,从而提高系统在动态场景下的鲁棒性、稳定性和追踪精度。实验结果表明,改进后的YOLOv8在计算速度上加快了5%以上,满足视觉SLAM系统的需求。最终,基于KITTI数据集00序列的实验结果显示,改进后的SLAM系统在动态场景下相较于ORB-SLAM2,轨迹精度提高了12.53%以上。展开更多
深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力...深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力,近年来逐渐应用于有目标攻击的研究中。然而,现有的生成式攻击方法通常针对单一目标类别生成对抗样本,在多目标任务中表现出计算效率低下、灵活性不足和扩展性有限等问题。针对这些不足,提出了一种基于双重信息的多目标生成式攻击(multitarget generative attack based on dual-information,MTGA-DI)方法。该方法通过设计一个条件生成模型,充分融合目标类别的语义和视觉信息,不仅具备多目标攻击能力,还显著提升了对抗样本的迁移性和鲁棒性。实验结果表明,与现有多目标攻击方法相比,MTGA-DI在标准训练模型和鲁棒模型上的性能更优,在应对基于输入预处理的防御模型时也展现出更强的适应能力。展开更多
文摘The complexity of multi-domain access control policy integration makes it difficult to understand and manage the policy conflict information. The policy information visualization technology can express the logical relation of the complex information intuitively which can effectively improve the management ability of the multi-domain policy integration. Based on the role-based access control model, this paper proposed two policy analyzing methods on the separated domain statistical information of multi-domain policy integration conflicts and the policy element levels of inter-domain and element mapping of cross-domain respectively. In addition, the corresponding visualization tool is developed. We use the tree-maps algorithm to statistically analyze quantity and type of the policy integration conflicts. On that basis, the semantic substrates algorithm is applied to concretely analyze the policy element levels of inter-domain and role and permission mapping of cross-domain. Experimental result shows tree-maps and semantic substrates can effectively analyze the conflicts of multi-domain policy integration and have a good application value.
基金funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(grant number 22KJD440001)Changzhou Science&Technology Program(grant number CJ20220232).
文摘The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.
文摘近年来,神经辐射场在三维重建任务中展现出卓越性能。然而,应用在视觉同时定位与地图构建(simultaneous localization and mapping,SLAM)中因缺乏全局优化机制容易导致系统定位精度不足以及重建失败。针对该问题,本文提出一种融合深度学习位姿估计与神经隐式表征的视觉SLAM系统。通过稠密束调整层以及高效的全局优化机制对相机位姿和深度进行像素级的循环迭代,并基于神经辐射场方法更新全局一致的隐式重建表面,使得系统在精准定位的同时能够重建高保真场景,并且在此基础上引入语言查询机制,增强系统的交互能力。在EuRoC和Replica数据集上进行大量实验,在不同的输入条件下,分别与3类基准方法进行对比,结果表明该系统在跟踪鲁棒性和重建精度方面相较于现有方法表现更优。本方法可为后续基于神经辐射场的视觉SLAM方法提供参考。
文摘即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)是一种重要的定位与地图构建技术。传统的视觉SLAM假设环境完全静止,但在动态场景中,SLAM识别的特征点很可能位于运动物体上,这会导致定位误差和地图不准确。为解决该问题,提出了一种基于深度学习的视觉SLAM系统,利用嵌入MobileNetV3轻量化改进的YOLOv8模型识别运动物体,并将其范围内的特征点剔除,从而提高系统在动态场景下的鲁棒性、稳定性和追踪精度。实验结果表明,改进后的YOLOv8在计算速度上加快了5%以上,满足视觉SLAM系统的需求。最终,基于KITTI数据集00序列的实验结果显示,改进后的SLAM系统在动态场景下相较于ORB-SLAM2,轨迹精度提高了12.53%以上。
文摘深度神经网络在很多应用领域取得了显著成功。然而,近年来的研究表明,它们容易受到对抗攻击的威胁。尤其是有目标的对抗攻击,能够精确控制未知模型的输出,对数据隐私和系统安全构成严重挑战。生成式攻击方法因其高效生成对抗样本的能力,近年来逐渐应用于有目标攻击的研究中。然而,现有的生成式攻击方法通常针对单一目标类别生成对抗样本,在多目标任务中表现出计算效率低下、灵活性不足和扩展性有限等问题。针对这些不足,提出了一种基于双重信息的多目标生成式攻击(multitarget generative attack based on dual-information,MTGA-DI)方法。该方法通过设计一个条件生成模型,充分融合目标类别的语义和视觉信息,不仅具备多目标攻击能力,还显著提升了对抗样本的迁移性和鲁棒性。实验结果表明,与现有多目标攻击方法相比,MTGA-DI在标准训练模型和鲁棒模型上的性能更优,在应对基于输入预处理的防御模型时也展现出更强的适应能力。
