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
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.展开更多
从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解...从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解决上述问题,本文首先提出一种包含两个部分的特征交互适应模块,第一部分特征交互在保留左右手分离特征的同时生成两种新的特征表示,并通过交互注意力模块捕获双手的交互特征;第二部分特征适应则是将此交互特征利用交互注意力模块适应到每只手,为左右手特征注入全局上下文信息。其次,引入三层图卷积细化网络结构用于精确回归双手网格顶点,并通过基于注意力机制的特征对齐模块增强顶点特征和图像特征的对齐,从而增强重建的手部网格和输入图像的对齐。同时提出一种新的多层感知机结构,通过下采样和上采样操作学习多尺度特征信息。最后,设计相对偏移损失函数约束双手的空间关系。在InterHand2.6M数据集上的定量和定性实验表明,与现有的优秀方法相比,所提出的方法显著提升了模型性能,其中平均每关节位置误差(Mean Per Joint Position Error,MPJPE)和平均每顶点位置误差(Mean Per Vertex Position Error,MPVPE)分别降低至7.19 mm和7.33 mm。此外,在RGB2Hands和EgoHands数据集上进行泛化性实验,定性实验结果表明所提出的方法具有良好的泛化能力,能够适应不同环境背景下的手部网格重建。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Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
文摘从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解决上述问题,本文首先提出一种包含两个部分的特征交互适应模块,第一部分特征交互在保留左右手分离特征的同时生成两种新的特征表示,并通过交互注意力模块捕获双手的交互特征;第二部分特征适应则是将此交互特征利用交互注意力模块适应到每只手,为左右手特征注入全局上下文信息。其次,引入三层图卷积细化网络结构用于精确回归双手网格顶点,并通过基于注意力机制的特征对齐模块增强顶点特征和图像特征的对齐,从而增强重建的手部网格和输入图像的对齐。同时提出一种新的多层感知机结构,通过下采样和上采样操作学习多尺度特征信息。最后,设计相对偏移损失函数约束双手的空间关系。在InterHand2.6M数据集上的定量和定性实验表明,与现有的优秀方法相比,所提出的方法显著提升了模型性能,其中平均每关节位置误差(Mean Per Joint Position Error,MPJPE)和平均每顶点位置误差(Mean Per Vertex Position Error,MPVPE)分别降低至7.19 mm和7.33 mm。此外,在RGB2Hands和EgoHands数据集上进行泛化性实验,定性实验结果表明所提出的方法具有良好的泛化能力,能够适应不同环境背景下的手部网格重建。
