红外图像目标检测在交通领域中有很重要的应用价值,然而,由于红外图像存在分辨率低、缺乏颜色信息、对比度差、特征模糊的特点,导致现有模型在检测红外车辆与行人时精度不高。为此,文中对YOLOv8s进行了改进,首先对特征融合机制进行改进...红外图像目标检测在交通领域中有很重要的应用价值,然而,由于红外图像存在分辨率低、缺乏颜色信息、对比度差、特征模糊的特点,导致现有模型在检测红外车辆与行人时精度不高。为此,文中对YOLOv8s进行了改进,首先对特征融合机制进行改进,在网络中添加小目标检测层,充分利用目标的浅层特征信息,提高对小目标检测的准确性。其次引入了SPD(Space to Depth)细粒化模块来代替YOLOv8s中的3×3卷积进行下采样,避免了3×3卷积下采样导致红外图像细粒度信息丢失。并且还设计了一个新的混合注意力机制,使网络更好地聚焦感兴趣的区域,减少背景对行人和车辆检测的干扰,增强模型对目标特征的关注度。最后使用Focal EIOU损失函数代替CIOU损失函数,改善了CIOU在特殊情况失效和正负样本不平衡的问题。在交通场景红外图像数据集FLIR_ADAS_v2上进了行实验,验证了算法的有效性。与YOLOv8s相比,改进后的模型mAP@0.5从83.4%提升到了89.3%。展开更多
Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of ch...Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of chaos by the technique of reservoir computing(RC).In particular,by incorporating a parameter-control channel into the standard RC,it is demonstrated that the machine is able to not only replicate the dynamics of the training states,but also infer new dynamics not included in the training set.The new machine-learning scheme,termed parameter-aware RC,opens up new avenues for data-based analysis of chaotic systems,and holds promise for predicting and controlling many real-world complex systems.Here,using typical chaotic systems as examples,we give a comprehensive introduction to this powerful machine-learning technique,including the algorithm,the implementation,the performance,and the open questions calling for further studies.展开更多
对复合材料吸能结构盒段进行了耐撞性(crashworthiness)试验研究,得到吸能所关心的载荷-位移曲线,并获得相应的平均载荷、峰值载荷以及吸收总能量相关吸能参数.考虑复合材料的各向异性本构关系,对有限元软件进行了二次开发.考虑含刚度...对复合材料吸能结构盒段进行了耐撞性(crashworthiness)试验研究,得到吸能所关心的载荷-位移曲线,并获得相应的平均载荷、峰值载荷以及吸收总能量相关吸能参数.考虑复合材料的各向异性本构关系,对有限元软件进行了二次开发.考虑含刚度退化的Hashin失效准则对结构组件进行渐进失效数值分析讨论.基于扩展的失效准则,设置相应的渐进削弱式的薄弱环节,模拟得到了吸能评价参数平均载荷值,数值计算得到的平均载荷值为361.10 k N.并与试验结果进行了比较,其相对误差不超过7%,计算结果与试验结果取得较好一致性,表明这种方法模拟分析结构组件抗坠毁是有效可行的.展开更多
文摘红外图像目标检测在交通领域中有很重要的应用价值,然而,由于红外图像存在分辨率低、缺乏颜色信息、对比度差、特征模糊的特点,导致现有模型在检测红外车辆与行人时精度不高。为此,文中对YOLOv8s进行了改进,首先对特征融合机制进行改进,在网络中添加小目标检测层,充分利用目标的浅层特征信息,提高对小目标检测的准确性。其次引入了SPD(Space to Depth)细粒化模块来代替YOLOv8s中的3×3卷积进行下采样,避免了3×3卷积下采样导致红外图像细粒度信息丢失。并且还设计了一个新的混合注意力机制,使网络更好地聚焦感兴趣的区域,减少背景对行人和车辆检测的干扰,增强模型对目标特征的关注度。最后使用Focal EIOU损失函数代替CIOU损失函数,改善了CIOU在特殊情况失效和正负样本不平衡的问题。在交通场景红外图像数据集FLIR_ADAS_v2上进了行实验,验证了算法的有效性。与YOLOv8s相比,改进后的模型mAP@0.5从83.4%提升到了89.3%。
基金Project supported by the National Natural Science Foundation of China(Grant No.12275165)XGW was also supported by the Fundamental Research Funds for the Central Universities(Grant No.GK202202003).
文摘Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of chaos by the technique of reservoir computing(RC).In particular,by incorporating a parameter-control channel into the standard RC,it is demonstrated that the machine is able to not only replicate the dynamics of the training states,but also infer new dynamics not included in the training set.The new machine-learning scheme,termed parameter-aware RC,opens up new avenues for data-based analysis of chaotic systems,and holds promise for predicting and controlling many real-world complex systems.Here,using typical chaotic systems as examples,we give a comprehensive introduction to this powerful machine-learning technique,including the algorithm,the implementation,the performance,and the open questions calling for further studies.
文摘对复合材料吸能结构盒段进行了耐撞性(crashworthiness)试验研究,得到吸能所关心的载荷-位移曲线,并获得相应的平均载荷、峰值载荷以及吸收总能量相关吸能参数.考虑复合材料的各向异性本构关系,对有限元软件进行了二次开发.考虑含刚度退化的Hashin失效准则对结构组件进行渐进失效数值分析讨论.基于扩展的失效准则,设置相应的渐进削弱式的薄弱环节,模拟得到了吸能评价参数平均载荷值,数值计算得到的平均载荷值为361.10 k N.并与试验结果进行了比较,其相对误差不超过7%,计算结果与试验结果取得较好一致性,表明这种方法模拟分析结构组件抗坠毁是有效可行的.
