Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work pres...Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin.展开更多
Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the r...Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.展开更多
随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构...随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构——IP-AlexNet模型。首先,在卷积层之后,引入Inception模块,以捕获桑叶病害图像的多样化特征,并通过减少卷积核降低网络计算的复杂度;其次,利用金字塔卷积进行多尺度特征融合,以增强模型的准确性和鲁棒性;再次,加入SE(Squeeze and Excitation)注意力机制,使模型能够聚焦于图像中的关键区域或特征,从而提高识别的精确度和效率;最后,使用自适应平均池化替换传统的最大池化以生成更平滑的特征图,从而减少图像特征信息的损失。实验结果表明,IP-AlexNet模型在桑叶病害识别方面取得了较好的效果,识别准确率高达95.33%,较AlexNet模型提升了9.66个百分点。另外,精准率、召回率、F1值和混淆矩阵等多元评价指标的综合分析表明,IP-AlexNet模型具有很好的鲁棒性和稳定性。展开更多
针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete...针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。展开更多
基金sponsored by Key‐Area Research and Development Program of Guangdong Province,No.2020B0404020005.
文摘Spiking Neural Network is known as the third-generation artificial neural network whose development has great potential.With the help of Spike Layer Error Reassignment in Time for error back-propagation,this work presents a new network called SpikeGoogle,which is implemented with GoogLeNet-like inception module.In this inception module,different convolution kernels and max-pooling layer are included to capture deep features across diverse scales.Experiment results on small NMNIST dataset verify the results of the authors’proposed SpikeGoogle,which outperforms the previous Spiking Convolutional Neural Network method by a large margin.
基金supported by Balochistan University of Engineering and Technology,Khuzdar,Balochistan,Pakistan.
文摘Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.
文摘随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构——IP-AlexNet模型。首先,在卷积层之后,引入Inception模块,以捕获桑叶病害图像的多样化特征,并通过减少卷积核降低网络计算的复杂度;其次,利用金字塔卷积进行多尺度特征融合,以增强模型的准确性和鲁棒性;再次,加入SE(Squeeze and Excitation)注意力机制,使模型能够聚焦于图像中的关键区域或特征,从而提高识别的精确度和效率;最后,使用自适应平均池化替换传统的最大池化以生成更平滑的特征图,从而减少图像特征信息的损失。实验结果表明,IP-AlexNet模型在桑叶病害识别方面取得了较好的效果,识别准确率高达95.33%,较AlexNet模型提升了9.66个百分点。另外,精准率、召回率、F1值和混淆矩阵等多元评价指标的综合分析表明,IP-AlexNet模型具有很好的鲁棒性和稳定性。
文摘针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。
