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
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
The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. Ho...The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).展开更多
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。
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%。
基金support of the National Natural Science Foundation of China(No.52322603)the Science Center for Gas Turbine Project of China(Nos.P2022-B-II-004-001 and P2023-B-II-001-001)+1 种基金the Fundamental Research Funds for the Central Universities,Chinathe Beijing Nova Program of China(Nos.20220484074 and 20230484479).
文摘The utilization of Inlet Guide Vane (IGV) plays a key factor in affecting the instability evolution. Existing literature mainly focuses on the effect of IGV on instability inception that occurs in the rotor region. However, with the emergence of compressor instability starting from the stator region, the mechanism of various instability inceptions that occurs in different blade rows due to the change of IGV angles should be further examined. In this study, experiments were focused on three types of instability inceptions observed previously in a 1.5-stage axial flow compressor. To analyze the conversion of stall evolutions, the compressor rotating speed was set to 17 160 r/min, at which both the blade loading in the stator hub region and rotor tip region were close to the critical value before final compressor stall. Meanwhile, the dynamic test points with high-response were placed to monitor the pressures both at the stator trailing edges and rotor tips. The results indicate that the variation of reaction determines the region where initial instability occurs. Indeed, negative pre-rotation of the inlet guide vane leads to high-reaction, initiating stall disturbance from the rotor region. Positive pre-rotation results in low-reaction, initiating stall disturbance from the stator region. Furthermore, the type of instability evolution is affected by the radial loading distribution under different IGV angles. Specifically, a spike-type inception occurs at the rotor blade tip with a large angle of attack at the rotor inlet (−2°, −4° and −6°). Meanwhile, the critical total pressure ratio at the rotor tip is 1.40 near stall. As the angle of attack decreases, the stator blade loading reaches its critical boundary, with a value of approximately 1.35. At this moment, if the rotor tip maintains high blade loading similar to the stator hub, the partial surge occurs (0° and +2°);otherwise, the hub instability occurs (+4° and +6°).
