剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长...剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长短时记忆网络-变分自编码器(Bidirectional Long Short Term-Memory-Variational Auto Encoder, BLSTM-VAE)的RUL预测方法。该方法首先进行数据预处理,包括数据降噪、滑动窗口分段和标签修正等步骤。然后设计基于BLSTM的VAE型特征提取器,以有效提取时间序列数据中的非线性关系和长距离依赖关系。最后提出一种基于流形学习的退化趋势平滑约束模块,通过局部不变性假设来增强模型的稳健性和泛化能力。通过航空发动机数据集数据集进行验证,结果表明所提出的RUL预测方法在数据集上的表现优于现有的多种RUL预测方法,具有更低的预测误差和更高的稳定性。展开更多
Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone miner...Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model.展开更多
Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.T...Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.The low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the images.The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal images.The dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature fusion.The Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal images.Asparse autoencoder was trained on the grayscale and colorized thermal images.The SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of 0.066.The autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of 0.0705.The multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,respectively.Hence,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.展开更多
As a form of discrete representation learning,Vector Quantized Variational Autoencoders(VQ-VAE)have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capaci...As a form of discrete representation learning,Vector Quantized Variational Autoencoders(VQ-VAE)have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity.However,existing VQ-VAEs often perform quantization in the spatial domain,ignoring global structural information and potentially suffering from codebook collapse and information coupling issues.This paper proposes a frequency quantized variational autoencoder(FQ-VAE)to address these issues.The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform(2D-FFT)and performs adaptive quantization on these frequency components to preserve image’s global relationships.The codebook is dynamically optimized to avoid collapse and information coupling issue by considering the usage frequency and dependency of code vectors.Furthermore,we introduce a post-processing module based on graph convolutional networks to further improve reconstruction quality.Experimental results on four public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of Structural Similarity Index(SSIM),Learned Perceptual Image Patch Similarity(LPIPS),and Reconstruction Fréchet Inception Distance(rFID).In the experiments on the CIFAR-10 dataset,compared to the baselinemethod VQ-VAE,the proposedmethod improves the abovemetrics by 4.9%,36.4%,and 52.8%,respectively.展开更多
文摘剩余寿命(Remaining Useful Life, RUL)预测对于维护工业设备的可靠性和安全性至关重要,但现有的RUL预测方法在处理高维传感器数据以及捕捉时间退化模式方面仍然面临诸多挑战。为了解决上述问题,提出一种退化趋势平滑约束下基于双向长短时记忆网络-变分自编码器(Bidirectional Long Short Term-Memory-Variational Auto Encoder, BLSTM-VAE)的RUL预测方法。该方法首先进行数据预处理,包括数据降噪、滑动窗口分段和标签修正等步骤。然后设计基于BLSTM的VAE型特征提取器,以有效提取时间序列数据中的非线性关系和长距离依赖关系。最后提出一种基于流形学习的退化趋势平滑约束模块,通过局部不变性假设来增强模型的稳健性和泛化能力。通过航空发动机数据集数据集进行验证,结果表明所提出的RUL预测方法在数据集上的表现优于现有的多种RUL预测方法,具有更低的预测误差和更高的稳定性。
基金Beijing Natural Science Foundation-Haidian original Innovation Joint Foundation,Grant/Award Number:L192016Joint Funds of the National Natural Science Foundation of China,Grant/Award Number:U21A20489+3 种基金National Natural Science Foundation of China,Grant/Award Number:62003330Shenzhen Fundamental Research Funds,Grant/Award Numbers:JCYJ20220818101608019,JCYJ20190807170407391,JCYJ20180507182415428Natural Science Foundation of Guangdong Province,Grant/Award Number:2019A1515011699Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen Institute of Advanced Technology。
文摘Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model.
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydneysupported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Project RSP2025 R14.
文摘Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time realtime.Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems.The low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the images.The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal images.The dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature fusion.The Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal images.Asparse autoencoder was trained on the grayscale and colorized thermal images.The SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of 0.066.The autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of 0.0705.The multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,respectively.Hence,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.
基金supported by the Interdisciplinary project of Dalian University DLUXK-2023-ZD-001.
文摘As a form of discrete representation learning,Vector Quantized Variational Autoencoders(VQ-VAE)have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity.However,existing VQ-VAEs often perform quantization in the spatial domain,ignoring global structural information and potentially suffering from codebook collapse and information coupling issues.This paper proposes a frequency quantized variational autoencoder(FQ-VAE)to address these issues.The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform(2D-FFT)and performs adaptive quantization on these frequency components to preserve image’s global relationships.The codebook is dynamically optimized to avoid collapse and information coupling issue by considering the usage frequency and dependency of code vectors.Furthermore,we introduce a post-processing module based on graph convolutional networks to further improve reconstruction quality.Experimental results on four public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of Structural Similarity Index(SSIM),Learned Perceptual Image Patch Similarity(LPIPS),and Reconstruction Fréchet Inception Distance(rFID).In the experiments on the CIFAR-10 dataset,compared to the baselinemethod VQ-VAE,the proposedmethod improves the abovemetrics by 4.9%,36.4%,and 52.8%,respectively.
