Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the i...Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.展开更多
Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvat...Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.展开更多
The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear ...The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.展开更多
The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dy...The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.展开更多
A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized fle...A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.展开更多
服装已经成为网络购物的重要商品之一,实现精准的符合用户个性化审美的服装推荐系统,已经成为热门研究内容。针对提取用户的细粒度兴趣特征不全面,导致推荐系统的准确性低问题,提出融合长短期偏好的服装推荐算法;针对数据稀疏以及数据...服装已经成为网络购物的重要商品之一,实现精准的符合用户个性化审美的服装推荐系统,已经成为热门研究内容。针对提取用户的细粒度兴趣特征不全面,导致推荐系统的准确性低问题,提出融合长短期偏好的服装推荐算法;针对数据稀疏以及数据单一性,导致推荐结果个性化、多样性低的问题,利用跨模态数据和注意力机制使模型学习出更为精准的差异性用户特征。在真实数据集Clothing Shoes and Jewelry上,将所设计的模型(PCR)与经典的循环神经网络RNN、基于矩阵分解MF-BPR模型以及改进的矩阵分解TARMF模型进行性能比对,PCR模型在关键性能评价指标NDCG、Precision@K和Recall@K均有提升。实验结果表明该模型在服装推荐系统中是可行与有效的。展开更多
市场营销领域对数据的研究正在逐渐地从传统的单模态数据向信息更加丰富的多模态数据过渡,回顾和展望市场营销研究经历的多模态数据形塑过程具有学术价值和实践意义。笔者基于Web of Science与中国知网数据库资源(2005—2025),运用CiteS...市场营销领域对数据的研究正在逐渐地从传统的单模态数据向信息更加丰富的多模态数据过渡,回顾和展望市场营销研究经历的多模态数据形塑过程具有学术价值和实践意义。笔者基于Web of Science与中国知网数据库资源(2005—2025),运用CiteSpace和内容分析两种研究工具,从研究主题、理论基础及研究方法等维度,对筛选自核心期刊的407篇相关研究样本文献进行了系统梳理和深度解构,呈现出多模态数据对市场营销研究的形塑:研究主题的演进主轴为“静态内容呈现”—“动态互动参与”—“长期价值转化”;理论基础展现由市场营销学扩展到信息科学、传播学、心理学等多学科的相互交叉融合;研究方法趋向以人工智能计算为主导,辅之定性阐释的多元化格局;基于样本文献研究构建的多模态数据形塑市场营销研究的整合性理论框架,系统揭示多模态信息影响力的完整作用机制;涵盖理论深化、方法创新、应用拓展等一系列拓展预景均可宏观勾勒。研究结论为该领域的理论深化与实践创新提供了整合性的认知框架与前瞻性指引。展开更多
Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sens...Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.展开更多
该研究通过文献计量分析,探讨人工智能在阿尔茨海默病研究中的发展趋势和应用热点。基于Web of Science核心数据库,检索了2013—2023年间的3 680篇相关文献,利用CiteSpace软件进行共现分析与关键词聚类,分析了发文趋势、国家和机构的合...该研究通过文献计量分析,探讨人工智能在阿尔茨海默病研究中的发展趋势和应用热点。基于Web of Science核心数据库,检索了2013—2023年间的3 680篇相关文献,利用CiteSpace软件进行共现分析与关键词聚类,分析了发文趋势、国家和机构的合作情况、核心作者及共被引文献等。研究结果表明,人工智能在阿尔茨海默病领域的应用主要集中在影像数据分析与早期诊断、多模态数据融合以及脑网络功能连接三个方向。同时,任务分析和迁移学习作为新兴热点,显示了人工智能在个体化诊断和长期病情管理中的潜力。从结果分析可知,人工智能在阿尔茨海默病诊断与治疗中的应用正处于快速发展阶段,未来研究将聚焦于算法的泛化能力提升和多模态数据处理能力,以提供更加精准的诊断和个体化治疗方案。展开更多
基金supported by the National Natural Science Foundation of China(no.62072153)the Anhui Provincial Key Technologies R&D Program(no.2022h11020015)the 111 Center(no.B14025).
文摘Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.
基金Project supported by the National Natural Science Foundation of China (No. 50378041) Specialized Research Fund for Doctoral Programs of Higher Education (No. 20030487016).
文摘Many multi-story or highrise buildings consisting of a number of identical stories are usually considered as periodic spring-mass systems. The general expressions of natural frequencies, mode shapes, slopes and curvatures of mode shapes of the periodic spring-mass system by utilizing the periodic structure theory are derived in this paper. The sensitivities of these mode parameters with respect to structural damages, which do not depend on the physical parameters of the original structures, are obtained. Based on the sensitivity analysis of these mode parameters, a two-stage method is proposed to localize and quantify damages of multi-story or highrise buildings. The slopes and curvatures of mode shapes, which are highly sensitive to local damages, are used to localize the damages. Subsequently, the limited measured natural frequencies, which have a better accuracy than the other mode parameters, are used to quantify the extent of damages within the potential damaged locations. The experimental results of a 3-story experimental building demonstrate that the single or multiple damages of buildings, either slight or severe, can be correctly localized by using only the slope or curvature of mode shape in one of the lower modes, in which the change of natural frequency is the largest, and can be accurately quantified by the limited measured natural frequencies with noise pollution.
文摘The accurate mathematical models for complicated structures are verydifficult to construct.The work presented here provides an identification method for estimating the mass, damping , and stiffness matrices of linear dynamical systems from incompleteexperimental data. The mass, stiffness, and damping matrices are assumed to be real,symmetric, and positive definite. The partial set of experimental complex eigenvalues and corresponding eigenvectors are given. In the proposed method the least squaresalgorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters. several illustrative examples, are presented to demonstrate the reliability of the proposed method .It is emphasized thatthe mass, damping and stiffness martices can be identified simultaneously.
文摘The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.
文摘A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.
文摘服装已经成为网络购物的重要商品之一,实现精准的符合用户个性化审美的服装推荐系统,已经成为热门研究内容。针对提取用户的细粒度兴趣特征不全面,导致推荐系统的准确性低问题,提出融合长短期偏好的服装推荐算法;针对数据稀疏以及数据单一性,导致推荐结果个性化、多样性低的问题,利用跨模态数据和注意力机制使模型学习出更为精准的差异性用户特征。在真实数据集Clothing Shoes and Jewelry上,将所设计的模型(PCR)与经典的循环神经网络RNN、基于矩阵分解MF-BPR模型以及改进的矩阵分解TARMF模型进行性能比对,PCR模型在关键性能评价指标NDCG、Precision@K和Recall@K均有提升。实验结果表明该模型在服装推荐系统中是可行与有效的。
文摘市场营销领域对数据的研究正在逐渐地从传统的单模态数据向信息更加丰富的多模态数据过渡,回顾和展望市场营销研究经历的多模态数据形塑过程具有学术价值和实践意义。笔者基于Web of Science与中国知网数据库资源(2005—2025),运用CiteSpace和内容分析两种研究工具,从研究主题、理论基础及研究方法等维度,对筛选自核心期刊的407篇相关研究样本文献进行了系统梳理和深度解构,呈现出多模态数据对市场营销研究的形塑:研究主题的演进主轴为“静态内容呈现”—“动态互动参与”—“长期价值转化”;理论基础展现由市场营销学扩展到信息科学、传播学、心理学等多学科的相互交叉融合;研究方法趋向以人工智能计算为主导,辅之定性阐释的多元化格局;基于样本文献研究构建的多模态数据形塑市场营销研究的整合性理论框架,系统揭示多模态信息影响力的完整作用机制;涵盖理论深化、方法创新、应用拓展等一系列拓展预景均可宏观勾勒。研究结论为该领域的理论深化与实践创新提供了整合性的认知框架与前瞻性指引。
文摘Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.
文摘该研究通过文献计量分析,探讨人工智能在阿尔茨海默病研究中的发展趋势和应用热点。基于Web of Science核心数据库,检索了2013—2023年间的3 680篇相关文献,利用CiteSpace软件进行共现分析与关键词聚类,分析了发文趋势、国家和机构的合作情况、核心作者及共被引文献等。研究结果表明,人工智能在阿尔茨海默病领域的应用主要集中在影像数据分析与早期诊断、多模态数据融合以及脑网络功能连接三个方向。同时,任务分析和迁移学习作为新兴热点,显示了人工智能在个体化诊断和长期病情管理中的潜力。从结果分析可知,人工智能在阿尔茨海默病诊断与治疗中的应用正处于快速发展阶段,未来研究将聚焦于算法的泛化能力提升和多模态数据处理能力,以提供更加精准的诊断和个体化治疗方案。
