Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details o...Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.展开更多
为解决传统目标检测算法在地铁、商场以及交通堵塞等地区高密度人群中因目标重叠和尺寸偏小而难以检测的问题,文中提出一种基于YOLOv5(You Only Look Once version 5)网络的目标检测算法。在算法模型的锚框部分引入新特征图来设计添加...为解决传统目标检测算法在地铁、商场以及交通堵塞等地区高密度人群中因目标重叠和尺寸偏小而难以检测的问题,文中提出一种基于YOLOv5(You Only Look Once version 5)网络的目标检测算法。在算法模型的锚框部分引入新特征图来设计添加小目标检测层,以此提升检测小目标的准确性。通过重新定义一个卷积层,在YOLOv5中添加SOCA(Second-Order Channel Attention)注意力机制,提高了模型对复杂场景和遮挡的鲁棒性。引入Focal_EIoU(Focal and Efficient Intersection over Union)替换原始模型的损失函数CIoU(Complete Intersection over Union),从而提高了模型在高密度目标上的检测精度。实验结果表明,改进YOLOv5算法在CrowdHuman数据集上的平均检测精度比原模型提高了6.7百分点,召回率提高了3.8百分点,优于FPN(Feature Pyramid Network)和RetinaNet算法,实现了对高密度人群的目标检测优化。展开更多
A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lac...A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lacks interpretability in its results,obscuring the causal relationships between data distribution characteristics and the outcomes.Furthermore,the current approach to battery type selection is not included in traditional BESS planning,hindering comprehensive optimization.To tackle these BESS planning problems,this paper presents a universal method for BESS planning,which is designed to enhance the interpretability of DRO.First,mathematical definitions of interpretable DRO(IDRO)are introduced.Next,the uncertainties in wind power,photovoltaic power,and loads are modeled by using second-order cone ambiguity sets(SOCASs).In addition,the proposed method integrates selection,sizing,and siting.Moreover,a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems.Finally,the effectiveness of the proposed method is demonstrated through case studies,offering planners richer decision-making insights.展开更多
基金Supported by the National Natural Science Foundation of China(No.61901183)Fundamental Research Funds for the Central Universities(No.ZQN921)+4 种基金Natural Science Foundation of Fujian Province Science and Technology Department(No.2021H6037)Key Project of Quanzhou Science and Technology Plan(No.2021C008R)Natural Science Foundation of Fujian Province(No.2019J01010561)Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province 2019(No.JAT191080)Science and Technology Bureau of Quanzhou(No.2017G046)。
文摘Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.
文摘为解决传统目标检测算法在地铁、商场以及交通堵塞等地区高密度人群中因目标重叠和尺寸偏小而难以检测的问题,文中提出一种基于YOLOv5(You Only Look Once version 5)网络的目标检测算法。在算法模型的锚框部分引入新特征图来设计添加小目标检测层,以此提升检测小目标的准确性。通过重新定义一个卷积层,在YOLOv5中添加SOCA(Second-Order Channel Attention)注意力机制,提高了模型对复杂场景和遮挡的鲁棒性。引入Focal_EIoU(Focal and Efficient Intersection over Union)替换原始模型的损失函数CIoU(Complete Intersection over Union),从而提高了模型在高密度目标上的检测精度。实验结果表明,改进YOLOv5算法在CrowdHuman数据集上的平均检测精度比原模型提高了6.7百分点,召回率提高了3.8百分点,优于FPN(Feature Pyramid Network)和RetinaNet算法,实现了对高密度人群的目标检测优化。
基金supported by the National Natural Science Foundation of China(No.51977046)。
文摘A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lacks interpretability in its results,obscuring the causal relationships between data distribution characteristics and the outcomes.Furthermore,the current approach to battery type selection is not included in traditional BESS planning,hindering comprehensive optimization.To tackle these BESS planning problems,this paper presents a universal method for BESS planning,which is designed to enhance the interpretability of DRO.First,mathematical definitions of interpretable DRO(IDRO)are introduced.Next,the uncertainties in wind power,photovoltaic power,and loads are modeled by using second-order cone ambiguity sets(SOCASs).In addition,the proposed method integrates selection,sizing,and siting.Moreover,a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems.Finally,the effectiveness of the proposed method is demonstrated through case studies,offering planners richer decision-making insights.
