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adversarial autoencoder concurrent projection to latent structure and its application

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

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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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An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 247卷

作者： Bao, Yuequan Sun, Huabin Guan, Xiaoshu Tian, Yuxuan Harbin Inst Technol Key Lab Smart Prevent Mitigat Civil Engn Disaster Minist Ind & Informat Technol Harbin 150090 Peoples R China Harbin Inst Technol Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China

Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this challenge, surrogate model methods are frequently employed. One way to improve the efficiency of surrogate model methods involves selecting informative samples that significantly enhance the accuracy of the surrogate model. This paper introduces a novel approach to facilitate the construction of surrogate models and selection of informative samples in high-dimensional reliability analysis, through an active learning method based on a deep adversarial autoencoder-based sufficient dimension reduction (AAE-SDR) neural network. The AAE-SDR neural network serves as a surrogate model, transforming complex high-dimensional variables into tractable, low-dimensional embeddings relevant to the target. These embeddings are Gaussian-distributed with a distinct latent limit state boundary. A new sampling strategy is proposed to select informative misclassified samples by iteratively identifying candidate samples near the latent limit state boundary and uniformly sampling from the candidate sample dataset based on the latent Gaussian distribution. The effectiveness of the proposed approach is demonstrated through two highdimensional numerical examples and a cable-stayed bridge case study. Results show that the proposed method simplifies complex high-dimensional reliability problems and provides a relatively accurate estimated failure probability with a limited number of samples.

关键词： Structural reliability Deep neural network Sufficient dimension reduction adversarial autoencoder Surrogate model

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Anomalous Radio Signal Detection Based on an adversarial autoencoder

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ELECTRONICS 2025年第9期14卷 1785-1785页

作者： Wang, Huan Luo, Yushu Long, Fan Fu, Yaxin Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621000 Peoples R China

In complex and ever-changing electromagnetic environments, a large number of anomalous radio signals illegally occupy spectrum resources and interfere with legitimate communications. To address this challenge, this paper proposes an anomalous radio signal detection method based on an adversarial autoencoder (AAE). The method comprises an autoencoder and a Generative adversarial Network. The autoencoder is used to extract time-frequency features of radio signals, generate latent feature vectors, and reconstruct the signals. By comparing the original signals to their reconstructions, anomalies can be rapidly and accurately detected via the reconstruction error. Meanwhile, the adversarial network regularizes the latent vectors produced by the encoder, forcing them to approximate a predefined prior distribution, thereby improving the model's generative capability and enhancing the structural consistency of the latent representations. We used a Pluto SDR device to capture real-world radio data in the field, performed frequency-domain analysis, and constructed a high-resolution power spectrum time-frequency dataset for model training and testing. Experimental results show that the proposed method achieves a detection accuracy of 95.5% for anomalous radio signals, exhibiting excellent performance on multiple metrics across diverse scenarios.

关键词： anomalous radio signal detection generative adversarial network adversarial autoencoder time-frequency features unsupervised learning

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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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AUTH: An adversarial autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 10954-10965页

作者： Zhu, Xingjian Dong, Jiankuo Qi, Jin Zhou, Zhenguo Dong, Zhenjiang Sun, Yanfei Wang, Moyu Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Dept Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr HPC & Intelligent Proc Nanjing 210003 Peoples R China Hefei Yun Micro Elect Co Ltd Hefei 230088 Peoples R China

Deep learning has shown broad research prospects in addressing insider threats, a serious problem currently facing industrial information systems. Although deep learning is able to capture effective feature representations from complex multidimensional data, there are still issues such as strong stealth of insider threat behavior and the imbalance data that need to be solved. Therefore, we propose an adversarial autoencoder based Unsupervised insider Threat detection scHeme (AUTH). Compared to other methods, AUTH fully considers the role of time feature and event feature in threat detection. In addition, in order to improve the performance of autoencoder models to detect covert threat behaviors, AUTH drives a temporal convolutional network and long short-term memory network-based adversarial autoencoder (TL-AAE). Generative adversarial Theory is introduced to solve the problem of uncertainty in the latent feature of the encoder. Finally, with the sufficient experiments on public datasets, we demonstrate that the usefulness of adding time features and the proposed TL-AAE model to improve threat detection performance. Compared with the baseline, AUTH obtains the area under curve value of 0.932, which is 4.95% higher than the highest result obtained by the baseline. In addition, AUTH obtains the EER value of 0.146, which is 12.57% lower than the lowest result of the baseline.

关键词： Feature extraction Threat assessment Machine learning Long short term memory Hidden Markov models Training Deep learning adversarial autoencoder insider threat detection time series data unsupervised learning

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Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 155卷

作者： Yang, Xu Xiao, Jieshi Huang, Jian Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Background: The significant nonlinearity between the monitoring variables introduces challenges in the task of features extraction when implementing fault detection for an industrial process. Recently, neural network with complex hierarchical structure and layer-by-layer nonlinear transformation, especially autoencoder (AE), have attracted considerable attention from the process monitoring community. However, the latent features of AE cannot fully reflect process information, and there is redundancy between features. Methods: This study introduces an online convolutional adversarial autoencoder (AAE) model to learn nonlinear features with representative information of industrial processes. The structure of generative adversarial networks (GAN) in AAE aims to extract features that can reflect the manifold information and subject to the Gaussian distribution. Given the advantages of convolutional kernels in weight sharing and local perception, convolutional kernels are embedded in AAE to capture the spatial structure information of process data. On the basis of this model, the fault-relevant features selection strategy is designed to remove redundant information online and improve the accuracy of fault detection. Significant findings: The results show that the average fault detection rate of the penicillin fermentation process can be improved to 94% using the proposed algorithm comparing with the current fault detection methods.

关键词： Nonlinear process monitoring adversarial autoencoder Convolutional kernel Penicillin fermentation process

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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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Estimation of 6D Pose of Objects Based on a Variant adversarial autoencoder

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NEURAL PROCESSING LETTERS 2023年第7期55卷 9581-9596页

作者： Huang, Dan Ahn, Hyemin Li, Shile Hu, Yueming Lee, Dongheui South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany South China Univ Technol Sch Automot Sci & Engn Guangzhou 510640 Peoples R China

The goal of this paper is to estimate object's 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant adversarial autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018).

关键词： adversarial autoencoder 6D pose RGB image Self-supervised learning

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