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A Residual Learning-Aided convolutional autoencoder for SCMA

IEEE COMMUNICATIONS LETTERS 2023年第5期27卷 1337-1341页

作者： Jiang, Fang Chang, Da-Wei Ma, Song Hu, Yan-Jun Xu, Yao-Hua Anhui Univ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Internet Things Spectrum Sensing & Testing E Hefei 230601 Peoples R China Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China

Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) technology proposed to meet the access needs of large-scale intelligent terminal devices with high spectrum utilization. To improve the accuracy and computational complexity of SCMA to accommodate the internet of things (IoT) scenario, we design a new end-to-end autoencoder combining convolutional neural networks (CNNs) and residual networks. A residual network with multitask learning improves the decoding accuracy, and CNN units are used for SCMA codeword mapping, with sparse connectivity and weight-sharing to reduce the number of trainable parameters. Simulations show that this scheme outperforms existing autoencoder schemes in bit error rate (BER) and computational complexity.

关键词： Decoding Residual neural networks convolutional neural networks Deep learning Computational complexity Neural networks Network architecture SCMA deep learning residual learning convolutional autoencoder

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Damage mode classification in CFRP laminates using convolutional autoencoder and convolutional neural network on acoustic emission waveforms

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Krishna, Yelamarthi Sai Raju, Gangadharan Desarkar, Maunendra Sankar Indian Inst Technol Hyderabad Ctr Interdisciplinary Programs Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Mech & Aerosp Engn Hyderabad 502285 Telangana India Indian Inst Technol Hyderabad Dept Artificial Intelligence Hyderabad Telangana India

The acoustic emission (AE) technique is a widely used nondestructive method for in-situ health monitoring of composite structures. Unlike metals, failure mechanisms in composite structures are complex, involving multiple damage modes, and each damage mode has a distinct AE signature. This work uses deep learning algorithms called convolutional autoencoder (CAE) and convolutional neural network (CNN) to classify damage modes in carbon fiber-reinforced polymer laminates using AE waveforms. Tensile experiments are carried out on laminates of various stacking sequences, and the acquired raw AE waveforms are transformed into time-frequency planes called spectrograms using short-time Fourier transform. CAE is used for retrieving deep features associated with damage modes in the latent space from these spectrograms. Subsequently, k-means is used to cluster the deep features in the latent space. Each cluster is labeled with a damage mode by inspecting their damage signatures using the scalograms. This labeled data is then used to train the CNN. The CNN, once trained is used on the AE data of pristine and notched quasi-isotropic specimens, and its ability to classify and identify the damage modes is investigated. The trained CNN achieves satisfactory classification accuracy of 96.9% on pristine quasi-isotropic specimen data and 96.4% on notched quasi-isotropic specimen data. When compared to the prediction accuracy of pure damage modes, the prediction accuracy of mixed-mode damage is slightly lower, at 92.5% for pristine and 91.3% for notched quasi-isotropic specimens. This reduction in accuracy is due to the spectrograms of mixed-mode damage containing energy distributed across multiple frequency bands. By classifying the AE waveforms of both pristine and notched quasi-isotropic specimens, the progression of different damage modes is analyzed through the cumulative count of AE waveforms associated with each damage type. These findings enhance the understanding of damage mod

关键词： Acoustic emission CFRP laminate convolutional autoencoder convolutional neural network damage classification deep learning algorithms

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Robust Place Recognition Using Illumination-compensated Image-based Deep convolutional autoencoder Features

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第10期18卷 2699-2707页

作者： Park, Chansoo Chae, Hee-Won Song, Jae-Bok Korea Univ Sch Mechatron 145 Anam Ro Seoul South Korea Korea Univ Sch Mech Engn 145 Anam Ro Seoul South Korea

Place recognition is a method for determining whether a robot has previously visited the place it currently observes, thus helping the robot correct its accumulated position error. Ultimately, the robot will travel long distances more accurately. Conventional image-based place recognition uses features extracted from a bag-of-visual-words (BoVW) scheme or pre-trained deep neural network. However, the BoVW scheme does not cope well with environmental changes, and the pre-trained deep neural network is disadvantageous in that its computation time is high. Therefore, this paper proposes a novel place recognition scheme using an illumination-compensated image-based deep convolutional autoencoder (ICCAE) feature. Instead of reconstructing the raw image, the autoencoder designed to extract ICCAE features is trained to reconstruct the image, whose illumination component is compensated in the logarithm frequency domain. As a result, we can extract the ICCAE features based on a convolution layer that is robust to illumination and environmental changes. Additionally, ICCAE features can perform faster feature matching than the features extracted from existing deep networks. To evaluate the performance of ICCAE feature-based place recognition, experiments were conducted using a public dataset that includes various conditions.

关键词： convolutional autoencoder frequency image illumination compensation place recognition

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Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully convolutional autoencoder

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IEEE ACCESS 2019年 7卷 36433-36445页

作者： Li, Xingyu Radulovic, Marko Kanjer, Ksenija Plataniotis, Konstantinos N. Univ Toronto Edward S Rogers Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Inst Oncol & Radiol Serbia Dept Expt Oncol Natl Canc Res Ctr Belgrade 11000 Serbia

Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in a weak-supervised manner and generate a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and one-layer neural network. We apply the proposed method to a public breast cancer image set. Our results, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.

关键词： Breast cancer diagnosis abnormality detection convolutional autoencoder discriminative pattern learning histopathology image analysis

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A Real-Time Bearing Fault Diagnosis Model Based on Siamese convolutional autoencoder in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3820-3831页

作者： Hu, He-Xuan Cao, Chengcheng Hu, Qiang Zhang, Ye Lin, Zhen-Zhou Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 211100 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Univ Finance & Econ Off Teaching Affairs Nanjing 210023 Peoples R China

The extreme environment refers to the abnormal temperature, pressure, or vibration in the environment within a certain period of time, which will cause the fault of bearing equipment. Bearing fault diagnosis model can accurately identify the health status of bearing equipment, which can deal with the influence of extreme environments on the normal operation of bearings in a timely manner. However, current bearing fault diagnosis models have the following challenge: the sample size of faulty data is too small, which makes the parameters in the bearing fault diagnosis model unable to be effectively learned. Therefore, in order to solve the above issue in the field of bearing fault diagnosis, we draw on the siamese network and convolutional autoencoder, and propose a real-time bearing fault diagnosis model based on siamese convolutional autoencoder (RBFDSCA) in this work. First, we use an Industrial Internet of Things (IIoT) platform to collect, store and analyze bearing data. Second, to cope with the challenge of the small sample size of faulty data, RBFDSCA model constructs a siamese convolutional autoencoder. The siamese convolutional autoencoder contains a positive feature extraction network, a negative feature extraction network, and a prediction network. The four evaluation metrics of RBFDSCA model on the real bearing data set are 0.9638, 0.9640, 0.9641, and 0.9639, respectively, which verifies its excellent performance.

关键词： Bearing fault diagnosis model convolutional autoencoder extreme environment Industrial Internet of Things (IIoT) siamese network

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Novel Soft Smart Shoes for Motion Intent Learning of Lower Limbs Using LSTM With a convolutional autoencoder

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IEEE SENSORS JOURNAL 2021年第2期21卷 1906-1917页

作者： Yang, Jiantao Yin, Yuehong Shanghai Jiao Tong Univ Inst Robot State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Estimating the joint torques of lower limbs in human gait, known asmotion intent understanding, is of great significance in the control of lower limb exoskeletons. This study presents novel soft smart shoes designed for motion intent learning at unspecified walking speeds using long short-term memory with a convolutional autoencoder. The smart shoes serve as a wearable sensing system consisting of a soft instrumented sole and two 3D motion sensors that are nonintrusive to the human gait and comfortable for the wearers. A novel data structure is developed as a "sensor image" for the measured ground reaction force and foot motion. A convolutional autoencoder is established to fuse multisensor datasets and extract the hidden features of the sensor images, which represent the spatial and temporal correlations among the data. Then, long short-term memory is exploited to learn the multiscale, highly nonlinear inputoutput relationships between the acquired features and joint torques. Experiments were conducted on five subjects at threewalking speeds (0.8m/s, 1.2m/s, and 1.6m/s). Results showed that 98% of the r(2) valueswere acceptablein individual testing and 75% of the r(2) values were acceptable in interindividual testing. The proposedmethod is able to learn the join torques in human gait and has satisfactory generalization properties.

关键词： Footwear Intelligent sensors Convolution Feature extraction Image sensors Foot convolutional autoencoder long short-term memory motion intent learning soft smart shoes

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Low-Dose CT Image Reconstruction using Vector Quantized convolutional autoencoder with Perceptual Loss

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第2期48卷 1-5页

作者： Ramanathan, Shalini Ramasundaram, Mohan Natl Inst Technol Dept CSE Tiruchirappalli Tamil Nadu India

Computed Tomography (CT) has become a useful screening procedure to identify disease or injury within various regions of the human body. The human beings' health issues caused by CT radiation have attracted the interest of the researchers and academic community. Reducing the radiation dose is the solution, but the CT image generated with low-dose radiation results in excessive noise due to lower intensity and fewer angle measurements. Low-dose CT scan images reduce image quality and thus affect a doctor's diagnosis. Deep learning methods have become increasingly popular in recent years, many models have been proposed for Low-Dose CT image reconstruction. Low-Dose CT Image Reconstruction is an active area of modern medical imaging research. Deep learning-based medical image reconstruction methods will be helpful to reduce noise without compromising image quality. Therefore, this paper introduces a novel CT image reconstruction method based on the vector quantization technique utilized in the convolutional autoencoder network. The quality of the results is evaluated based on the perceptual loss function. Experimental evaluations are conducted on the LoDoPaB-CT benchmark dataset. Its result showed that the proposed network obtained better performance metric values and better noise elimination results, in terms of quantitative and visual evaluation, respectively.

关键词： Low-dose CT (LDCT) vector quantization deep learning convolutional autoencoder medical image reconstruction

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Development of de-noised image reconstruction technique using convolutional autoencoder for fast monitoring of fuel assemblies

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NUCLEAR ENGINEERING AND TECHNOLOGY 2021年第3期53卷 888-893页

作者： Choi, Se Hwan Choi, Hyun Joon Min, Chul Hee Chung, Young Hyun Ahn, Jae Joon Yonsei Univ Dept Informat & Stat Seoul South Korea Yonsei Univ Dept Radiat Convergence Engn Seoul South Korea

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based de noising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. convolutional autoencoder (CAE) was employed for de noising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images;the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Tomographic imaging Deep learning-based denoising process convolutional autoencoder Verification of fuel assemblies

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A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

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JOURNAL OF STRUCTURAL BIOLOGY 2018年第2期202卷 150-160页

作者： Zeng, Xiangrui Leung, Miguel Ricardo Zeev-Ben-Mordehai, Tzviya Xu, Min Carnegie Mellon Univ Computat Biol Dept Sch Comp Sci Pittsburgh PA 15213 USA Univ Oxford Wellcome Trust Ctr Human Genet Div Struct Biol Oxford OX3 7BN England Univ Utrecht Bijvoet Ctr Biomol Res Cryoelectron Microscopy Utrecht Netherlands

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.

关键词： Cellular electron cryo-tomography Macromolecular complex Subtomogram classification Visual proteomics Particle picking Structural pattern mining Deep learning convolutional neural network convolutional autoencoder Image semantic segmentation Machine learning Unsupervised learning Weakly supervised learning Pose normalization

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Laser stripe image denoising using convolutional autoencoder

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RESULTS IN PHYSICS 2018年 11卷 96-104页

作者： Fang, Zhuoqun Jia, Tong Chen, Qiusheng Xu, Ming Yuan, Xi Wu, Chengdong Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110004 Liaoning Peoples R China

convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system.

关键词： convolutional autoencoder Deep learning Depth perception Image denoising Laser stripe Structured light

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