Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annea...Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.展开更多
当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node an...当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node and Edge Features,NE-GraphSAGE)用于浏览器指纹追踪。首先以浏览器指纹为节点、指纹之间特征相似度为边构建图数据。其次对图神经网络中的GraphSAGE算法进行改进使其不仅能关注节点特征,而且能捕获边缘信息并对边缘分类,从而识别指纹。最后将NE-GraphSAGE算法与Eckersley算法、FPStalker算法和LSTM算法进行对比,验证NE-GraphSAGE算法的识别效果。实验结果表明,NE-GraphSAGE算法在准确率和追踪时长上均有不同程度的提升,最大追踪时长可达80天,相比其他3种算法性能更优,验证了NE-GraphSAGE算法对浏览器指纹长期追踪的能力。展开更多
复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基...复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基于DETR_S(DEtection with TRansformer on Signal)的卫星信号智能检测方法.DETR_S以编码器-解码器架构为基础,利用Transformer网络全局建模能力捕获频谱信息,采用多头自注意力机制有效改善频谱信息长距离依赖的问题.基于匈牙利算法的预测框匹配模块摒弃了非极大值抑制的数据后处理步骤,将信号检测问题转变为集合预测问题,使模型并行输出检测结果.引入信号重构模块,将频谱重构损失函数加入损失函数中,辅助模型挖掘频谱深层表征,提升信号检测性能.实验结果表明,在仅使用信号频谱幅度信息条件下,DETR_S能够在信噪比等于0dB及以上对卫星信号进行精确检测(>95%),优于典型的目标检测方法.展开更多
基金the National Natural Science Foundation of China (60373089, 60674106, and 60533010)the National High Technology Research and Development "863" Program (2006AA01Z104)
文摘Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.
文摘当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node and Edge Features,NE-GraphSAGE)用于浏览器指纹追踪。首先以浏览器指纹为节点、指纹之间特征相似度为边构建图数据。其次对图神经网络中的GraphSAGE算法进行改进使其不仅能关注节点特征,而且能捕获边缘信息并对边缘分类,从而识别指纹。最后将NE-GraphSAGE算法与Eckersley算法、FPStalker算法和LSTM算法进行对比,验证NE-GraphSAGE算法的识别效果。实验结果表明,NE-GraphSAGE算法在准确率和追踪时长上均有不同程度的提升,最大追踪时长可达80天,相比其他3种算法性能更优,验证了NE-GraphSAGE算法对浏览器指纹长期追踪的能力。
文摘复杂电磁环境下卫星信号往往淹没在背景和噪声中,传统的信号检测算法在没有准确先验知识的情况下性能急剧降低,目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤,无法对信号进行端到端检测.针对上述缺陷,提出一种基于DETR_S(DEtection with TRansformer on Signal)的卫星信号智能检测方法.DETR_S以编码器-解码器架构为基础,利用Transformer网络全局建模能力捕获频谱信息,采用多头自注意力机制有效改善频谱信息长距离依赖的问题.基于匈牙利算法的预测框匹配模块摒弃了非极大值抑制的数据后处理步骤,将信号检测问题转变为集合预测问题,使模型并行输出检测结果.引入信号重构模块,将频谱重构损失函数加入损失函数中,辅助模型挖掘频谱深层表征,提升信号检测性能.实验结果表明,在仅使用信号频谱幅度信息条件下,DETR_S能够在信噪比等于0dB及以上对卫星信号进行精确检测(>95%),优于典型的目标检测方法.
