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Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartA期86卷

作者： Zou, Yongxiang Cheng, Long Han, Lijun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China

Surface electromyography (sEMG) signals are often incomplete due to interferences during data measurement, which can degrade sEMG-based applications. To address this issue, this paper proposes a novel adversarial autoencoder model, called the SGMD-AAE, which includes a self-mask generator and a multi-view discrimi-nator. The generator's binary mask is replaced with a self-mask mechanism, and an adversarial loss is added to promote the reconstruction performance. The multi-view discriminator extracts and fuses deep features of sEMG from time and frequency domains to enhance the generator's reconstruction ability. The SGMD-AAE model is evaluated on the benchmark NinaPro DB2 database, and the experimental results show that it significantly outperforms incomplete sEMG signals, reducing NRMSE by 88.04% and increasing PSNR by 116.21%. The proposed model also achieves high recognition accuracy for hand gesture recognition even in extreme cases where 90% of the sEMG signals are missing, with an average accuracy exceeding 84%. The effectiveness of the SGMD-AAE model is further verified on a self-collected dataset, demonstrating similar recognition results.

关键词： adversarial autoencoder Missing sEMG Signal reconstruction Self-mask Multi-view discriminator

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A3N: Attention-based adversarial autoencoder network for detecting anomalies in video sequence

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2022年 87卷

作者： Aslam, Nazia Rai, Prateek Kumar Kolekar, Maheshkumar H. Indian Inst Technol Patna Dept Elect Engn Video Surveillance Lab Bihta 801106 India

This paper presents a novel attention-based adversarial autoencoder network (A3N) that consists of a twostream decoder to detect abnormal events in video sequences. The first stream of the decoder is a reconstructive model responsible for recreating the input frame sequence. However, the second stream is a future predictive model used to predict the future frame sequence through adversarial learning. A global attention mechanism is employed at the decoder side that helps to decode the encoded sequences effectively. The training of A3N is carried out on normal video data. The attention-based reconstructive model is used during the inference stage to compute the anomaly score. A3N delivers a considerable average speed of 0.0227 s (similar to 44 fps) for detecting anomalies in the testing phase on used datasets. Several experiments and ablation analyses have been performed on UCSD Pedestrian, CUHK Avenue and ShanghaiTech datasets to validate the efficiency of the proposed model.

关键词： Anomaly detection Attention mechanism adversarial autoencoder Generative adversarial network

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Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning

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NMR IN BIOMEDICINE 2021年第2期34卷 e4433-e4433页

作者： Ghodrati, Vahid Bydder, Mark Ali, Fadil Gao, Chang Prosper, Ashley Nguyen, Kim-Lien Hu, Peng Univ Calif Los Angeles David Geffen Sch Med Dept Radiol Sci Los Angeles CA 90095 USA Univ Calif Los Angeles Biomed Phys Interdept Grad Program Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Div Cardiol Dept Med Los Angeles CA 90095 USA

The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05);however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstra

关键词： adversarial autoencoder cardiovascular magnetic resonance deep learning magnetic resonance imaging respiratory motion correction

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Generating spectral samples with analyte concentration values using the adversarial autoencoder

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 252卷

作者： Huang, Guangzao Zhao, Xinyu Chen, Xiao Ali, Shujat Shi, Wen Xie, Zhonghao Chen, Xiaojing Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Inst Ind & Sci Wenzhou 325035 Peoples R China

The prediction of analyte concentration by spectral responses using a calibration model is a commonly used method in chemical analysis. However, insufficient modeling samples will limit the performance of the calibration model. Artificial generation of spectral samples with analyte concentration values is an effective way to address the shortage of modeling samples. However, traditional methods for generating spectral samples with concentration values still have problems in terms of diversity and accuracy. We proposed a method for generating spectral samples with analyte concentration values based on an adversarial autoencoder (AAE). The proposed method combined spectral responses and analyte concentration as the inputs and fitted the extracted latent variables into a prior distribution. By decoding the random sampling points of the prior distribution, the spectral samples with analyte concentration values were generated. Four spectral datasets were used to validate the effectiveness of the proposed method. Two traditional spectral generation methods were used to evaluate the performance of the proposed methods. It was found that the proposed method performed significantly better than traditional ones. The spectral responses generated by the proposed method had good diversity and similarity to the real ones. In addition, the generated spectral samples could also accurately simulate the actual relationship between spectral responses and analyte properties. The proposed method is an effective solution to the problem of insufficient modeling samples in the quantitative analysis of spectral technology.

关键词： Analyte concentration Spectral responses Calibration model Artificial generation adversarial autoencoder

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Hyperspectral Pansharpening Based on Spectral Constrained adversarial autoencoder

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REMOTE SENSING 2019年第22期11卷 2691-2691页

作者： He, Gang Zhong, Jiaping Lei, Jie Li, Yunsong Xie, Weiying Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the pro

关键词： hyperspectral pansharpening spectral consistency adversarial autoencoder adaptive fusion

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Unsupervised anomalous sound detection method based on Gammatone spectrogram and adversarial autoencoder with attention mechanism

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Yan, Hao Zhan, Xianbiao Wu, Zhenghao Cheng, Junkai Wen, Liang Jia, Xisheng Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang 050003 Peoples R China Northwestern Polytech Univ Sch Automat Xian Peoples R China

Anomalous sound detection (ASD) is an important technology in the fourth industrial revolution, which can monitor the abnormal state of machine by identifying whether the sound of the machine is normal or not. However, in practical applications where there are few anomalous sound samples from machines, achieving effective ASD is still a challenge. In this paper, an unsupervised ASD algorithm based on adversarial autoencoder with attention mechanism is proposed. Different from the traditional reconstruction-based ASD model, in order to make the features learned by the model more representative, complex sound timing signals are converted into Gammatone spectrogram with richer features through filtering. Then the spectrogram is used as the input of the convolutional autoencoder. At the same time, the attention mechanism is introduced in the encoder to enhance adaptive learning of the normal patterns. Then the discriminator is used in the generative adversarial network to perform adversarial learning with the improved convolutional autoencoder to improve the reconstruction ability of the model for normal samples. Experimental results demonstrate that the proposed algorithm significantly outperforms commonly used industry methods for anomaly detection and exhibits advantages over other deep learning approaches in terms of system complexity.

关键词： Anomalous sound detection unsupervised learning adversarial autoencoder attention mechanism Gammatone spectrogram

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An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Zideh, Mehdi Jabbari Khalghani, Mohammad Reza Solanki, Sarika Khushalani West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA Florida Polytech Univ Dept Elect & Comp Engn Lakeland FL 33805 USA

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time -series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data -driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13 -bus and 123 -bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real -world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data -driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.

关键词： False data injection attacks adversarial autoencoder Power distribution grids Unsupervised data-driven method Generative adversarial networks Cyber attack detection

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Precursor Chemical Mixtures Analysis Using Joint VMD adversarial autoencoder and Multitask CNN Algorithm via Gas Sensor

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1-1页

作者： Wang, Hao Zhao, Yong Yuan, Zhenyu Ji, Hanyang Meng, Fanli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Natl Frontiers Sci Center Ind Intelligence & Syst Shenyang 110819 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China Northeastern Univ Key Lab Data Ana lyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China

Precursor chemical mixture gases (PMGs) analysis plays a significant role in combatting drug-related crimes. However, existing intelligent methods for analyzing PMG components are constrained by weak signals, as well as signal coupling issues related to semiconductor gas sensors. To address this issue, a variational mode decomposition (VMD) adversarial autoencoder joint multitask convolutional neural network (VAE-MTCNN) algorithm is proposed. The proposed algorithm uses a semiconductor gas sensor MQ4, to collect signals in square wave temperature modulation, which are then processed by VMD to minimize signal coupling. The sub-signals are then augmented using the adversarial autoencoder through adversarial training. The construction of RGB digital portraits (RDPs) is used to enhance the validity of characteristic expressions by converting sub-signals to 2-D images. Finally, the MTCNN is designed to achieve PMG component recognition and quantification using two parallel task channels. The performance of the proposed method is validated using PMG samples, including acetone, ether and acetone and ether. Experimental results show that the VAE-MTCNN approach outperforms current state-of-the-art methods, with a PMG component recognition precision of 96.3% and a concentration quantification root mean square error (RMSE) of 3.50 ppm. Therefore, this method has great potential to improve drug-related crime detection.

关键词： adversarial autoencoder multitask convolutional neural network precursor chemical mixture gases (PMGs) semiconductor gas sensor variational mode decomposition (VMD)

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Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks

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INTERNET OF THINGS 2024年 28卷

作者： Duy, Phan The Hien, Do Thi Thu Luong, Tran Duc Quyen, Nguyen Huu Pham, Van-Hau Univ Informat Technol Informat Secur Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Hochiminh City Vietnam

Intrusion Detection Systems (IDS) have garnered escalating significance in response to the evolving landscape of cyberattacks, driven by the adaptability and versatility of Software Defined Networking (SDN)-based networks in enhancing security orchestration. Although Machine Learning (ML) models have been developed for IDS, they require large amounts of labeled data to achieve high performance. However, acquiring labels for attacks is a time-consuming process and can cause problems in deploying the existing ML models in new systems or lower performance due to a shortage of labeled data on pre-trained datasets. Additionally, such ML based IDS models lack the self-learning function to automatically adapt to new cyberattacks during network operations. To overcome these challenges, our work proposes Fed-Evolver, automated evolving approach for federated IDS that combines Generative adversarial Networks (GANs) with Auto Encoder (AE) and a semi-supervised adversarial autoencoder (SSAAE) for spotting intrusion actions. Our Fed-Evolver leverages supervised and unsupervised learning strategies to build efficient IDS models in the context of labeled data scarcity with the help of Federated Learning (FL). It allows data owners to collaborate for training intrusion detection models to provide the self-evolving capability in SDN-enabled networks. Our proposed framework is evaluated on 6 cyberattack datasets, including CICIDS2018, CIC-ToN-IoT, NFUNSW-NB15, InSDN, InSecLab-IDS2021, DNP3 Intrusion Detection, and it outperforms other ML methods even when trained with only 1% proportion of labeled data, achieving consistently high performance across all metrics on the datasets.

关键词： Federated learning Generative adversarial networks adversarial autoencoder Intrusion detection system Software defined networking

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A3Graph: adversarial Attributed autoencoder for Graph Representation Learning 21

A3Graph: Adversarial Attributed Autoencoder for Graph Repres...

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36th Annual ACM Symposium on Applied Computing (SAC)

作者： Hou, Mingliang Wang, Lei Liu, Jiaying Kong, Xiangjie Xia, Feng Dalian Univ Technol Sch Software Dalian Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Federat Univ Australia Sch Engn IT & Phys Sci Ballarat Vic Australia

ISBN: (纸本)9781450381048

Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph.

关键词： adversarial autoencoder graph learning attributed information social network

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