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
本文提出了一种改进U-Net散斑抑制方法,该方法结合了Inception、残差结构和注意力模块,应用于具有不同噪声级别的包裹相位图像。将所提出的方法与传统的降噪方法以及现有的深度学习降噪方法进行了对比,仿真与实验结果表明,所提出的方法...本文提出了一种改进U-Net散斑抑制方法,该方法结合了Inception、残差结构和注意力模块,应用于具有不同噪声级别的包裹相位图像。将所提出的方法与传统的降噪方法以及现有的深度学习降噪方法进行了对比,仿真与实验结果表明,所提出的方法在不同噪声级别下具有更好的散斑抑制效果。此外,我们对降噪后的包裹相位进行了相位重建,对比了不同方法降噪后的相位精度,结果表明,该方法在实际应用中能够有效抑制散斑噪声,取得了较好的效果。This paper proposes an improved U-Net speckle suppression method that integrates Inception and residual structures with attention modules, applied to wrapped phase images with different noise levels. The proposed method is compared with traditional denoising methods as well as existing deep learning-based denoising techniques. Experimental results show that our method achieves better speckle suppression across various noise levels. Furthermore, we performed phase reconstruction on the denoised wrapped phase images and compared the phase accuracy of different denoising methods. The results show that the proposed method can effectively suppress speckle noise in practical applications and achieve satisfactory performance.展开更多
针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间...针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间构建特征序列HF,并采用Pearson相关性系数分析HF和容量之间的相关性;另外针对特征变量的特征提取不够全面问题,采用Inception模型进行特征提取,采用LSTM进行时序建模,随后利用注意力机制进一步提取对电池健康度影响较大的特征来估计电池健康状态,利用该深度学习模型来挖掘电池在复杂使用条件下的动态变化特征。实验结果表明文章模型SOH估计最大均方根误差在3.86%以内,RUL预测最大误差在1个循环。实验结果表明该方法在SOH估计和RUL预测方面优于传统模型。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。
文摘本文提出了一种改进U-Net散斑抑制方法,该方法结合了Inception、残差结构和注意力模块,应用于具有不同噪声级别的包裹相位图像。将所提出的方法与传统的降噪方法以及现有的深度学习降噪方法进行了对比,仿真与实验结果表明,所提出的方法在不同噪声级别下具有更好的散斑抑制效果。此外,我们对降噪后的包裹相位进行了相位重建,对比了不同方法降噪后的相位精度,结果表明,该方法在实际应用中能够有效抑制散斑噪声,取得了较好的效果。This paper proposes an improved U-Net speckle suppression method that integrates Inception and residual structures with attention modules, applied to wrapped phase images with different noise levels. The proposed method is compared with traditional denoising methods as well as existing deep learning-based denoising techniques. Experimental results show that our method achieves better speckle suppression across various noise levels. Furthermore, we performed phase reconstruction on the denoised wrapped phase images and compared the phase accuracy of different denoising methods. The results show that the proposed method can effectively suppress speckle noise in practical applications and achieve satisfactory performance.
文摘针对锂电池健康状态(State of Health,SOH)估计和剩余使用寿命(Remaining Useful Life,RUL)预测过程中健康特征提取单一、估计精度低等问题,提出了一种Inception-LSTM模型用于锂电池SOH估计与RUL预测。首先选取合适的恒压恒流充电时间构建特征序列HF,并采用Pearson相关性系数分析HF和容量之间的相关性;另外针对特征变量的特征提取不够全面问题,采用Inception模型进行特征提取,采用LSTM进行时序建模,随后利用注意力机制进一步提取对电池健康度影响较大的特征来估计电池健康状态,利用该深度学习模型来挖掘电池在复杂使用条件下的动态变化特征。实验结果表明文章模型SOH估计最大均方根误差在3.86%以内,RUL预测最大误差在1个循环。实验结果表明该方法在SOH估计和RUL预测方面优于传统模型。
