针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型G...针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型GVBNet(global variable block net),使用攻击密度自适应计算稀疏注意力。利用信息熵以及信息增益分析提取攻击流量的连续字节作为特征向量,通过构建基于GVBNet的网络模型在两种数据集上进行训练。实验结果表明,该方法具有良好的识别效果、检测速度以及抗干扰能力,在不同的环境下具有应用价值。展开更多
针对锂离子电池健康状态(state of health,SOH)预测,提出了一种基于概率化稀疏自注意力机制(probsparseself-attentionmechanism,PSM)和长短期记忆(longshort-term memory,LSTM)神经网络的预测模型。首先,提取锂离子电池容量数据并进行...针对锂离子电池健康状态(state of health,SOH)预测,提出了一种基于概率化稀疏自注意力机制(probsparseself-attentionmechanism,PSM)和长短期记忆(longshort-term memory,LSTM)神经网络的预测模型。首先,提取锂离子电池容量数据并进行窗口化处理,利用位置嵌入获取高维数据之间的特征信息并对数据进行位置编码。然后,引入PSM对输入数据的权重进行稀疏性判断,增加对SOH预测具有关键影响的因素的权重。最后,利用LSTM神经网络捕获数据之间的时序特征进行锂离子电池SOH预测。实验结果表明,与其他常用的锂离子电池SOH预测模型相比,所提模型可以减少预测误差,具有更好的预测性能。展开更多
为了解决传统火焰烟雾检测算法在森林树木遮挡与雨雾天气因素影响下存在漏检误检、准确性下降、小目标检测效果不佳的缺陷,提出了基于交叉可变特征融合和动态稀疏注意力YOLOv8的遥感森林野火检测模型。首先,针对火焰烟雾目标特征复杂的...为了解决传统火焰烟雾检测算法在森林树木遮挡与雨雾天气因素影响下存在漏检误检、准确性下降、小目标检测效果不佳的缺陷,提出了基于交叉可变特征融合和动态稀疏注意力YOLOv8的遥感森林野火检测模型。首先,针对火焰烟雾目标特征复杂的问题,在C2f模块中融合可变形卷积网络(DCNv3)实现特征融合,提升对网络图像中不同尺度火焰烟雾空间位置变化的细节感知能力,增强了网络在不同尺度下的特征表示能力。然后,在主干检测网络加入BiFormer注意力模块,达到抑制干扰信息,提升模型表征能力的效果。最后,引入小目标检测层,进一步提高了检测精度。改进后的算法相比于传统算法,mAP50值提高了1.3%,P值提高了1.5%,R值提高了0.4%。In order to solve the shortcomings of the traditional flame and smoke detection algorithm under the influence of forest tree occlusion and rain and fog weather factors, such as missing detection, false detection, reduced accuracy and poor detection effect of small targets, a remote sensing wildfire detection model based on cross-variable feature fusion and dynamic sparse attention YOLOv8 is proposed. Firstly, in order to solve the problem of complex features of flame smoke targets, the C2f module is fused with a Deformable Convolution Network v3 (DCNv3) to achieve feature fusion, which improves the detail perception ability of the spatial position changes of flame smoke at different scales in the network image, and enhances the feature representation ability of the network at different scales. Then, the attention module of BiFormer was added to the backbone detection network to suppress the interference information and improve the model representation ability. Finally, small object detection layer is introduced to further improve the detection accuracy. Compared with the traditional algorithm, the mAP50 value is increased by 1.3%, the P value is increased by 1.5%, and the R value is increased by 0.4%.展开更多
文摘针对锂离子电池健康状态(state of health,SOH)预测,提出了一种基于概率化稀疏自注意力机制(probsparseself-attentionmechanism,PSM)和长短期记忆(longshort-term memory,LSTM)神经网络的预测模型。首先,提取锂离子电池容量数据并进行窗口化处理,利用位置嵌入获取高维数据之间的特征信息并对数据进行位置编码。然后,引入PSM对输入数据的权重进行稀疏性判断,增加对SOH预测具有关键影响的因素的权重。最后,利用LSTM神经网络捕获数据之间的时序特征进行锂离子电池SOH预测。实验结果表明,与其他常用的锂离子电池SOH预测模型相比,所提模型可以减少预测误差,具有更好的预测性能。
文摘为了解决传统火焰烟雾检测算法在森林树木遮挡与雨雾天气因素影响下存在漏检误检、准确性下降、小目标检测效果不佳的缺陷,提出了基于交叉可变特征融合和动态稀疏注意力YOLOv8的遥感森林野火检测模型。首先,针对火焰烟雾目标特征复杂的问题,在C2f模块中融合可变形卷积网络(DCNv3)实现特征融合,提升对网络图像中不同尺度火焰烟雾空间位置变化的细节感知能力,增强了网络在不同尺度下的特征表示能力。然后,在主干检测网络加入BiFormer注意力模块,达到抑制干扰信息,提升模型表征能力的效果。最后,引入小目标检测层,进一步提高了检测精度。改进后的算法相比于传统算法,mAP50值提高了1.3%,P值提高了1.5%,R值提高了0.4%。In order to solve the shortcomings of the traditional flame and smoke detection algorithm under the influence of forest tree occlusion and rain and fog weather factors, such as missing detection, false detection, reduced accuracy and poor detection effect of small targets, a remote sensing wildfire detection model based on cross-variable feature fusion and dynamic sparse attention YOLOv8 is proposed. Firstly, in order to solve the problem of complex features of flame smoke targets, the C2f module is fused with a Deformable Convolution Network v3 (DCNv3) to achieve feature fusion, which improves the detail perception ability of the spatial position changes of flame smoke at different scales in the network image, and enhances the feature representation ability of the network at different scales. Then, the attention module of BiFormer was added to the backbone detection network to suppress the interference information and improve the model representation ability. Finally, small object detection layer is introduced to further improve the detection accuracy. Compared with the traditional algorithm, the mAP50 value is increased by 1.3%, the P value is increased by 1.5%, and the R value is increased by 0.4%.
