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A pencil lead break-triggered, adversarial autoencoder-based approach for rapid and robust rail damage detection

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 150卷

作者： Dang, Da-Zhi Su, Bo-Yang Wang, You-Wu Ao, Wai Kei Ni, Yi-Qing Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Natl Rail Transit Electrificat & Automat Engn Tech Hong Kong Branch Hung HomKowloon Hong Kong Peoples R China

Detecting early-stage damage is essential for railway maintenance, ruling out potential risks that could undermine railway ride comfort and safety. Ultrasonic testing methods, featuring high precision and non-destructive characteristics, have gained widespread use for on-site inspections in modern railway systems. However, current ultrasonic testing remains a highly complex technique that requires expensive ultrasonic devices and trained professionals for operation. This study presents a novel approach for rail damage detection utilizing a disposable mechanical pencil. By intentionally breaking the pencil lead on rail surface, the accumulated potential energy is released in the form of ultrasonic bursts which are acquired by sensors mounted on the rail. The rail damage diagnosis is empowered by an adversarial autoencoder (AAE) which learns representations of ultrasonic signals induced by pencil lead break (PLB). A damage-sensitive indicator is developed based on the Jensen-Shannon Divergence (JSD) between the AAE model output distributions of the baseline and an unknown signal, facilitating rapid and accurate damage diagnosis. Both laboratory experiments and on-site verifications were conducted to validate the proposed approach. The results demonstrate the effectiveness of the damage detection framework in identifying rail damage, exhibiting excellent robustness and reliability. Comparative studies are also conducted to demonstrate the adaptability and effectiveness of the proposed method against field testing environments. The research outcomes of this study will significantly contribute to the development of more efficient on-site inspection techniques for railway maintenance and sustainability.

关键词： adversarial autoencoder Pencil lead break Railway track Damage detection Non-destructive testing

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DiffusionAAE: Enhancing hyperspectral image classification with conditional diffusion model and adversarial autoencoder

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ECOLOGICAL INFORMATICS 2025年 87卷

作者： Cao, Zeyu Li, Jinhui Xu, Xiangrui Hangzhou City Univ Sch Spatial Planning & Design Hangzhou 310015 Peoples R China Hangzhou City Univ Res Inst Spatial Planning & Design Hangzhou 310015 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

Hyperspectral image (HSI) classification is essential for ecological monitoring, but faces significant challenges due to high dimensionality, complex spectral-spatial relationships, and limited labeled data. This study introduces DiffusionAAE, a novel framework that uniquely combines adversarial autoencoders (AAE) with conditional diffusion models to address these challenges. Unlike existing approaches, DiffusionAAE incorporates spectral similarity constraints and class label guidance into the diffusion process, ensuring the generation of physically realistic synthetic samples. Our framework's key innovation lies in its two-stage architecture: first, an AAE extracts robust latent features capturing intricate spectral-spatial relationships;second, a conditional diffusion model refines these features through progressive denoising, enabling class-specific feature generation while maintaining physical constraints inherent to hyperspectral data. Comprehensive experiments on three benchmark datasets demonstrate DiffusionAAE's superior performance: compared to state-of-theart methods, our approach achieves significant improvements with an overall accuracy (OA) of 96.77% on Indian Pines (3.21% higher than CNN-based methods), 99.56% on University of Pavia (1.24% improvement over Transformer-based approaches), and 99.62% on Salinas (0.98% better than the best competing method). Notably, DiffusionAAE shows remarkable performance on minority classes, with an average 7.35% accuracy improvement across underrepresented classes in the Indian Pines dataset. The framework demonstrates particular strength in scenarios with limited training data, maintaining 95.3% accuracy even when using only 5% of available labeled samples. These results establish DiffusionAAE as a significant advancement for ecological informatics applications, especially for biodiversity monitoring and land cover classification where labeled data scarcity and class imbalance are prevalent challenges.

关键词： Land cover classification Generative model Feature extraction Hyperspectral image classification Conditional diffusion model adversarial autoencoder

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Latent Space Representation of adversarial autoencoder for Human Activity Recognition: Application to a low-cost commercial force plate and inertial measurement units

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Smart Health 2025年 35卷

作者： Kamikokuryo, Kenta Venture, Gentiane Hernandez, Vincent Department of Mechanical Engineering - The university of Tokyo Japan Department of Mechanical Systems Engineering - Tokyo University of Agriculture and Technology Japan

Human Activity Recognition (HAR) is a key component of a home rehabilitation system that provides real-time monitoring and personalized feedback. This research explores the application of adversarial autoencoder (AAE) models for data dimensionality reduction in the context of HAR. Visualizing data in a lower-dimensional space is important to understand changes in motor control due to medical conditions or aging, to aid personalized interventions, and to ensure continuous benefits in remote rehabilitation settings. This makes patient assessment effective, easier, and faster. In this study, the classification performance of the latent space created by the AAE is evaluated using the Wii Balance Board (WiiBB) and/or three Inertial Measurement Units (IMUs) placed on the forearms and hip. Various sensor configurations are considered, including only WiiBB, only IMUs, combinations of WiiBB with the IMU at the hip, and combinations of WiiBB with the 3 IMUs. The accuracy of the latent space representation is compared with two common supervised classification models, which are the Convolutional Neural Network (CNN) and the neural network called CNNLSTM, which is composed of convolution layers followed by recurrent layers. The approach was demonstrated for two different sets of exercises consisting of upper and lower body exercises collected with 19 participants. The results show that the latent space representation of the AAE achieves a strong classification accuracy performance while also serving as a visualization tool. This study is an initial demonstration of the potential of integrating WiiBB and IMU sensors for comprehensive activity recognition for upper and lower body movement analysis. © 2025 Elsevier Inc.

关键词： adversarial autoencoder Ground reaction force Human activity recognition Inertial Measurement Unit Machine learning

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adversarial autoencoder Empowered Joint Anomaly Detection and Signal Reconstruction From Sub-Nyquist Samples

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 618-628页

作者： Zhang, Han Song, Zihang Yang, Jian Gao, Yue Univ Surrey Inst Commun Syst 5GIC & 6GIC Guildford GU2 7XH England China Acad Informat & Commun Technol Mobile Commun Innovat Ctr Beijing 100191 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200483 Peoples R China

Anomaly detection is an essential part of spectrum monitoring applications. Malicious users and malfunctioning nodes could be identified via anomaly detection methods. Meanwhile, the spectrum bands that would be utilized in future 6G or satellite communication system settings are going to be wider than ever. Acquiring Nyquist sampled data from such a spectrum would require components with a very high sampling rate. To monitor a wide spectrum, a compressive sensing recovery algorithm combined with a sub-sampling approach could accomplish the task with a lower hardware cost. To solve the anomaly detection problem using a sub-sampled data stream, a joint signal recovery and anomaly detection solution utilizing an adversarial autoencoder (AAE) structure are proposed in this article. An AAE is constructed via an autoencoder and a discriminator weaved together. The discriminator would guide the autoencoder to drive its extracted feature onto a designed feature space, while the autoencoder would provide a reconstruction of the original Nyquist sampled signal. The proposed AAE structure could learn the distribution of the signal from either labelled or unlabelled training data, enabling it to work on both supervised and unsupervised data sets. The proposed AAE has shown superior reconstruction and detection performance on very sparse sampling scenarios.

关键词： Anomaly detection Monitoring Feature extraction 6G mobile communication Wideband Generators Compressed sensing signal reconstruction adversarial autoencoder multicoset sampling compressive sensing

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adversarial autoencoder concurrent projection to latent structure and its application

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第1期102卷 274-290页

作者： Liu, Dongyu Zhou, Jinglin Jiang, Huixia Beijing Univ Chem Technol Sch Informat Sci & Technol Beijing Peoples R China Army Engn Univ Dept Missile Engn Shijiazhuang Peoples R China

Multivariate monitoring plays an important role in process monitoring. Among the multivariate monitoring methods, the projection to latent structure method (PLS) has been most widely used in the fields of quality control and fault diagnosis. To improve the monitoring capability of traditional PLS methods in nonlinear and non-Gaussian multivariate systems, this paper proposes an innovative multivariate monitoring strategy, which combines adversarial autoencoder (AAE) and the concurrent projection to latent structure method (CPLS). In the proposed strategy, the original data are mapped to the high-dimensional space using the AAE method with the Gaussian prior distribution to realize data transformation. The mapped data are linearly divisible and approach the Gaussian distribution. Then, the projection with orthogonality is realized using the CPLS method. In addition, the reconstruction error and distribution properties are used as evaluation indexes of the high-dimensional mapping performance. The corresponding monitoring strategy is established using the traditional statistical method based on the Gaussian distribution. Finally, the simulations are performed on the Tennessee-Eastman process platform, and the results show that the proposed method could efficiently extract the principal components, especially in quality-relevant fault monitoring.

关键词： adversarial autoencoder concurrent projection to latent structure non-Gaussian process monitoring Tennessee-Eastman process

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adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards

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JOURNAL OF ELECTRONIC IMAGING 2020年第4期29卷 p41013-41013页

作者： Goto, Keisuke Kato, Kunihito Saito, Takaho Aizawa, Hiroaki Gifu Univ Fac Engn Gifu Japan Aisin AW Co Ltd Anjo Aichi Japan

The inspection of solder joints on printed circuit boards is a difficult task because defects inside the joints cannot be observed directly. In addition, because anomalous samples are rarely obtained in a general anomaly detection situation, many methods use only normal samples in the learning phase. However, sometimes a small number of anomalous samples are available for learning. We propose a method to improve performance using a small number of anomalous samples for training in such situations. Specifically, our proposal is an anomaly detection method using an adversarial autoencoder (AAE) and Hotelling's T-squared distribution. First, the AAE learns features of the solder joint following the standard Gaussian distribution from a large number of normal samples and a small number of anomalous samples. Then, the anomaly score of a solder joint is calculated by Hotelling's T-squared method from the features learned by the AAE. Finally, anomaly detection is performed by thresholding using this anomaly score. In experiments, we show that our method performs anomaly detection with few false positives in such situations. Moreover, we confirmed that our method outperforms the conventional method using handcrafted features and a one-class support vector machine. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

关键词： anomaly detection adversarial autoencoder Hotelling's T-squared x-ray computed tomography

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adversarial autoencoder for trajectory generation and maneuver classification 25

Adversarial Autoencoder for trajectory generation and maneuv...

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25th IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Rakos, Oliver Becsi, Tamas Aradi, Szilard Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Budapest Hungary

ISBN: (纸本)9781665444996

For the development of self-driving cars, it is essential to perceive the environment as accurately as possible and to interpret the movement of the surrounding vehicles. It makes sense to draw conclusions based on the past trajectories of these vehicles, whether it is maneuver detection or, in more complex cases, maneuver or trajectory prediction. Trajectories are time series data, so it is obvious to deploy recurrent neural networks for their analysis, or 1-dimensional convolutional networks that can capture temporal patterns. The concept is presented that trajectories starting from the origin can be compressed efficiently so that the reconstructed trajectory is quite similar to the original, and the latent space code obtained by compression can be used for maneuver detection. Using a variational autoencoder, assuming a normal distribution, the latent spatial distribution can be approximated. However, in this article, the goal was to test this concept with adversarial training, so the so-called adversarial autoencoder is trained. It has been shown that this method is suitable for twelve-fold compression of trajectories, and the latent code is suitable for maneuver detection. This proves that the encoder has learned useful features about the distribution that generates trajectories.

关键词： adversarial autoencoder NGSIM maneuver deteciton trajectory compression

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Image scrambling adversarial autoencoder based on the asymmetric encryption

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第18期80卷 28265-28301页

作者： Bao, Zhenjie Xue, Ru Jin, Yadong Xizang Minzu Univ Sch Informat Engn Xianyang Shaanxi Peoples R China

For the purpose of information transmission security, image scrambling is to encrypt the image by changing the image pixel values and pixel positions. Based on the asymmetric encryption, we propose a model of Image Scrambling adversarial autoencoder. Firstly, we describe an encoder-decoder framework to imitate the procedure of image scramble, descramble and the key generation. Secondly, we employ the generator network of CycleGAN as the encoder and decoder structure of our method to transfer the secret image to totally meaningless image and reconstruct it. Thirdly, the parameters of the encoder and decoder can be regarded as the public key and private key. Then, the patchGANs discriminator is used to distinguish encoded images and evenly distributed noise by image blocks. Moreover, we combine the encode-then-decode loss function with the adversarial loss function by an adjustable parameter in order to make the model training results more stable. Experiments show that our method can accomplish automatic image scrambling in ten different scenes which include Africa people and villages, beach, buildings, buses, dinosaurs, elephants, flowers, horses, mountains and glaciers, food. Compared with 3D Arnold transformation and CycleGAN, scrambled pixels by our method are more evenly distributed intuitively. What is more, extensive experiments show that the proposed method can address security requirements partly and achieve a good encryption efficiency. In addition, contrast experimental results show that the combination of the encode-then-decode loss function and the adversarial loss function is essential to achieve the ideal results of image scrambling and restoration.

关键词： Transmission security Image scrambling adversarial autoencoder Asymmetric encryption

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Multi-task adversarial autoencoder network for face alignment in the wild

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NEUROCOMPUTING 2021年 437卷 261-273页

作者： Yue, Xiaoqian Li, Jing Wu, Jia Chang, Jun Wan, Jun Ma, Jinyan Wuhan Univ Sch Comp Sci Wuhuan 430072 Peoples R China Macquarie Univ Fac Sci & Engn Dept Comp Sydney NSW 2109 Australia Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China

Face alignment has been applied widely in the field of computer vision, which is still a very challenging task for the existence of large pose, partial occlusion, and illumination, etc. The method based on deep regression neural network has achieved the most advanced performance in the field of face alignment in recent years, and how to learn more representative facial appearance is the key to face alignment. Based on the idea of Multi-task Learning, we propose a Multi-task adversarial autoencoder (MTAAE) net -work, which can learn more representative facial appearance for heatmap regression and improve the performance of face alignment in the wild. MTAAE is composed of three tasks. The main task uses the heatmap regression method to locate the position of landmarks and introduces a discriminator on the landmark heatmaps to generate more realistic heatmaps. Facial attribute estimation tasks and face recon-struction task based on adversarial autoencoder respectively extract discriminative and generative rep-resentations to improve the effect of heatmap regression. At the same time, the dynamic weight network is designed to assign a weight coefficient dynamically and reasonably for each auxiliary task. Extensive experiments on 300 W, MTFL, and WFLW datasets demonstrate that our method is more robust in com-plex environments and outperforms state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved. Face alignment has been applied widely in the field of computer vision, which is still a very challenging task for the existence of large pose, partial occlusion, and illumination, etc. The method based on deep regression neural network has achieved the most advanced performance in the field of face alignment in recent years, and how to learn more representative facial appearance is the key to face alignment. Based on the idea of Multi-task Learning, we propose a Multi-task adversarial autoencoder (MTAAE) network, which can learn more representative facial appeara

关键词： Multi-task learning adversarial autoencoder Heatmap regression Face alignment

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LLP-AAE: Learning from label proportions with adversarial autoencoder

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NEUROCOMPUTING 2023年第1期537卷 282-295页

作者： Wang, Bo Sun, Yingte Tong, Qiang Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

This paper presents an effective weakly supervised learning algorithm LLP-AAE to leverage the adversar-ial autoencoder (AAE) for learning from label proportions (LLP), in which only the bag-level proportional information is available. Our LLP-AAE utilizes an autoencoder backbone and performs adversarial training in latent space to match the aggregated posterior distribution of hidden coding with the prior distribu-tions. In this way, apart from the reconstruction task, the encoder is also dedicated to producing fake samples, in order to deceive discriminators as far as possible. Ultimately, the encoder is employed as a competent label predictor for unseen data. In addition to the LLP classifier, our model can also achieve controllable samples generation by feeding the decoder with gradually changing latent code, which is proven to be useful for a better LLP performance. We also provide a panoramic explanation for LLP-AAE by regarding the LLP problem as an alternative learning procedure between proportion-based pseudo label generation and discriminative reconstruction. Experiments on six benchmark image data -sets demonstrate the advantage of our method both in style manipulation with the latent feature repre-sentation and comparable multi-class LLP performance with the state-of-the-art models.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Learning from label proportions adversarial autoencoder Multi-class classification Weakly supervised learning

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