Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
无人机遥感探测在军事侦察领域发挥着重要作用,偏振探测利用偏振光与物体相互作用产生的偏振变化来提高目标对比度。然而在复杂场景下,伪装小目标与背景特征差异较小且空间信息不足,存在检测困难的问题。为此提出一种偏振伪装小目标检...无人机遥感探测在军事侦察领域发挥着重要作用,偏振探测利用偏振光与物体相互作用产生的偏振变化来提高目标对比度。然而在复杂场景下,伪装小目标与背景特征差异较小且空间信息不足,存在检测困难的问题。为此提出一种偏振伪装小目标检测算法(Polarization Camouflaged Small Object Detection-YOLO,PCSOD-YOLO),设计了高效层注意力模块-坐标注意力特征提取模块和空间金字塔池化跨阶段局部通道-3D权重注意力感受野模块,捕获目标的偏振特征和语义信息,增强上下文信息理解能力;设计了动态小目标检测头,通过动态卷积增强对小目标特征提取能力的同时,利用不同尺度的特征信息,联合多通道特征信息输出小目标检测结果。构建伪装小目标偏振图像数据集(Polarization Image of Camouflaged Small Objects,PICSO)。在PICSO数据集上的实验表明,所提出的方法可以有效检测伪装小目标,mAP_(0.5)达到92.4%,mAP_(0.5:0.95)达到47.8%,检测速率达到60.6帧/s,满足实时性要求。展开更多
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘无人机遥感探测在军事侦察领域发挥着重要作用,偏振探测利用偏振光与物体相互作用产生的偏振变化来提高目标对比度。然而在复杂场景下,伪装小目标与背景特征差异较小且空间信息不足,存在检测困难的问题。为此提出一种偏振伪装小目标检测算法(Polarization Camouflaged Small Object Detection-YOLO,PCSOD-YOLO),设计了高效层注意力模块-坐标注意力特征提取模块和空间金字塔池化跨阶段局部通道-3D权重注意力感受野模块,捕获目标的偏振特征和语义信息,增强上下文信息理解能力;设计了动态小目标检测头,通过动态卷积增强对小目标特征提取能力的同时,利用不同尺度的特征信息,联合多通道特征信息输出小目标检测结果。构建伪装小目标偏振图像数据集(Polarization Image of Camouflaged Small Objects,PICSO)。在PICSO数据集上的实验表明,所提出的方法可以有效检测伪装小目标,mAP_(0.5)达到92.4%,mAP_(0.5:0.95)达到47.8%,检测速率达到60.6帧/s,满足实时性要求。
