In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time con...In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).展开更多
针对当前非侵入式负荷技术在低功率、多状态设备的时序负荷上存在分解精度不足、模型泛化性能低的问题,提出一种融合多尺度通道增强注意力机制与改进双向时序卷积网络的负荷分解模型。该模型结合多种卷积与残差网络,克服传统卷积神经网...针对当前非侵入式负荷技术在低功率、多状态设备的时序负荷上存在分解精度不足、模型泛化性能低的问题,提出一种融合多尺度通道增强注意力机制与改进双向时序卷积网络的负荷分解模型。该模型结合多种卷积与残差网络,克服传统卷积神经网络无法捕捉全局信息、难以处理时间序列以及随着网络深度增加带来梯度爆炸的局限性,通过双向结构使模型能从历史数据推断出当前状态,并利用未来短暂波动修正当前状态,从而减少状态转换延迟或瞬时噪声导致的误判。同时,多尺度通道增强注意力机制通过并行多尺度池化,自适应提取不同粒度的时序特征,并结合动态通道交互模块增强关键特征的权重分配。实验结果表明,所提模型在Reference Energy Disaggregation Data(REDD)数据集上对低功率、多状态设备负荷分解误差低,模型泛化能力强。展开更多
文摘In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).
文摘针对当前非侵入式负荷技术在低功率、多状态设备的时序负荷上存在分解精度不足、模型泛化性能低的问题,提出一种融合多尺度通道增强注意力机制与改进双向时序卷积网络的负荷分解模型。该模型结合多种卷积与残差网络,克服传统卷积神经网络无法捕捉全局信息、难以处理时间序列以及随着网络深度增加带来梯度爆炸的局限性,通过双向结构使模型能从历史数据推断出当前状态,并利用未来短暂波动修正当前状态,从而减少状态转换延迟或瞬时噪声导致的误判。同时,多尺度通道增强注意力机制通过并行多尺度池化,自适应提取不同粒度的时序特征,并结合动态通道交互模块增强关键特征的权重分配。实验结果表明,所提模型在Reference Energy Disaggregation Data(REDD)数据集上对低功率、多状态设备负荷分解误差低,模型泛化能力强。
