Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label c...Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.展开更多
Ceramic Matrix Composite （CMC） turbine guide vanes possess multi-scale stress and strain with inhomogeneity at the microscopic scale. Given that the macroscopic distribution cannot reflect the microscopic stress flu...Ceramic Matrix Composite （CMC） turbine guide vanes possess multi-scale stress and strain with inhomogeneity at the microscopic scale. Given that the macroscopic distribution cannot reflect the microscopic stress fluctuation, the macroscopic method fails to meet the requirements of stress and strain analysis of CMC turbine guide vanes. Furthermore, the complete thermodynamic properties of 2D woven SiC/SiC-CMC cannot be obtained through experimentation, Accordingly, a method to calculate the thermodynamic properties of CMC and analyze multi-scale stress and strain of the turbine guide vanes should be established. In this study, the multi-scale thermodynamic analysis is investigated. The thermodynamic properties of Chemical Vapor Infiltration （CVI） pro- cessed SiC/SiC-CMC are predicted by a Representative Volume Element （RVE） model with porosity, leading to the result that the relative error between the calculated in-plane tensile modulus and the experimental value is 4.2%. The macroscopic response of a guide vane under given conditions is predicted. The relative error between the predicted strain on the trailing edge and the experimental value is 9.7%. The calculation of the stress distribution of micro-scale RVE shows that the maximum value of microscopic stress, which is located in the interlayer matrix, is more than 1.5 times that of macroscopic stress in the same direction and the microscopic stress distribution of the interlayer matrix is related to the pore distribution of the composite.展开更多
液态金属电池(liquid metal batteries,LMBs)作为一种极具潜力的大规模电化学储能技术,具备高电流密度、长循环寿命、低成本且易于规模化等核心优势,在可再生能源消纳、电网调峰填谷以及微电网稳定等领域呈现出广阔的应用前景。该综述...液态金属电池(liquid metal batteries,LMBs)作为一种极具潜力的大规模电化学储能技术,具备高电流密度、长循环寿命、低成本且易于规模化等核心优势,在可再生能源消纳、电网调峰填谷以及微电网稳定等领域呈现出广阔的应用前景。该综述系统地梳理了液态金属电池自诞生以来的发展历程,从电极材料体系优化、多物理场耦合机制解析、状态估计与成组管理技术三个关键维度,全面总结了近年研究进展。着重分析了锂基、钠基、钙基等主流体系的电极材料改性策略,磁流体不稳定性等多场耦合问题的调控方法,以及基于模型与数据驱动的状态估计技术突破。深入探讨了当前液态金属电池面临的材料成本、界面腐蚀、工程化应用等技术瓶颈,并结合材料科学、电化学工程与人工智能的交叉融合趋势,提出了多组元智能材料设计、多场协同调控、全生命周期智能化管理等未来发展方向。展开更多
基金Supported by the National Natural Science Foundation of China(No.61602191,61672521,61375037,61473291,61572501,61572536,61502491,61372107,61401167)the Natural Science Foundation of Fujian Province(No.2016J01308)+3 种基金the Scientific and Technology Funds of Quanzhou(No.2015Z114)the Scientific and Technology Funds of Xiamen(No.3502Z20173045)the Promotion Program for Young and Middle aged Teacher in Science and Technology Research of Huaqiao University(No.ZQN-PY418,ZQN-YX403)the Scientific Research Funds of Huaqiao University(No.16BS108)
文摘Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.
文摘Ceramic Matrix Composite （CMC） turbine guide vanes possess multi-scale stress and strain with inhomogeneity at the microscopic scale. Given that the macroscopic distribution cannot reflect the microscopic stress fluctuation, the macroscopic method fails to meet the requirements of stress and strain analysis of CMC turbine guide vanes. Furthermore, the complete thermodynamic properties of 2D woven SiC/SiC-CMC cannot be obtained through experimentation, Accordingly, a method to calculate the thermodynamic properties of CMC and analyze multi-scale stress and strain of the turbine guide vanes should be established. In this study, the multi-scale thermodynamic analysis is investigated. The thermodynamic properties of Chemical Vapor Infiltration （CVI） pro- cessed SiC/SiC-CMC are predicted by a Representative Volume Element （RVE） model with porosity, leading to the result that the relative error between the calculated in-plane tensile modulus and the experimental value is 4.2%. The macroscopic response of a guide vane under given conditions is predicted. The relative error between the predicted strain on the trailing edge and the experimental value is 9.7%. The calculation of the stress distribution of micro-scale RVE shows that the maximum value of microscopic stress, which is located in the interlayer matrix, is more than 1.5 times that of macroscopic stress in the same direction and the microscopic stress distribution of the interlayer matrix is related to the pore distribution of the composite.
文摘液态金属电池(liquid metal batteries,LMBs)作为一种极具潜力的大规模电化学储能技术,具备高电流密度、长循环寿命、低成本且易于规模化等核心优势,在可再生能源消纳、电网调峰填谷以及微电网稳定等领域呈现出广阔的应用前景。该综述系统地梳理了液态金属电池自诞生以来的发展历程,从电极材料体系优化、多物理场耦合机制解析、状态估计与成组管理技术三个关键维度,全面总结了近年研究进展。着重分析了锂基、钠基、钙基等主流体系的电极材料改性策略,磁流体不稳定性等多场耦合问题的调控方法,以及基于模型与数据驱动的状态估计技术突破。深入探讨了当前液态金属电池面临的材料成本、界面腐蚀、工程化应用等技术瓶颈,并结合材料科学、电化学工程与人工智能的交叉融合趋势,提出了多组元智能材料设计、多场协同调控、全生命周期智能化管理等未来发展方向。
