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18th IEEE Sensors Applications Symposium (SAS)

作者： Tran, Thanh Bader, Sebastian Lundgren, Jan Mid Sweden Univ Dept Comp & Elect Engn STC Res Ctr Sundsvall Sweden Assa Abloy Grp Technol Team Stockholm Sweden

ISBN: (纸本)9798350323078

Denoising sound is essential for improving signal quality in various applications such as speech processing, sound event classification, and machine failure detection systems. This paper proposes an autoencoder method to remove two types of noise, Gaussian white noise, and environmental noise from water flow, from induction motor sounds. The method is trained and evaluated on a dataset of 246 sounds from the Machinery Fault Database (MAFAULDA). The denoising effectiveness is measured using the mean square error (MSE), which indicates that both noise types can be significantly reduced with the proposed method. The MSE is below or equal to 0.15 for normal operation sounds and misalignment sounds. This improvement in signal quality can facilitate further processing, such as induction motor operation classification. Overall, this work presents a promising approach for denoising machine sounds using an autoencoder, with potential for application in other industrial settings.

关键词： autoencoder Denoise sound

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Robust Seizure Prediction Based on Riemannian Manifold Enhanced Denoising Adversarial autoencoder

Robust Seizure Prediction Based on Riemannian Manifold Enhan...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Peng, Peizhen Wu, Yansheng Yang, Wanqi Wei, Wenxin Yang, Ming School of Computer and Electronic Information The School of Artificial Intelligence Nanjing Normal University Nanjing China

ISBN: (纸本)9798350368741

The seizure early warning devices based on multichannel EEG signals is one of the most used assisted-living strategies for drug-resistant epileptic patients. One of the challenges in the development of these devices is that existing algorithms cannot avoid the effects of electrode loosening. To alleviate such problem, a seizure prediction model robust to corrupted EEG recordings is proposed in this paper. This method intends to learn a stable feature space adapted to different corruption versions via constructing a jointly-optimized autoencoder. Robust information is captured during the reconstruction process by developing a corruption module, while an adversarial training procedure circumvents overfitting using variational inference. Moreover, a regularization term is designed to minimize the distance among various corruption versions in the embedding space with the Riemannian manifold-based distribution alignment. Both raw EEG data and corrupted EEG data are utilized to evaluate prediction performance. Experimental results indicate that this method can predict seizures effectively and stably under the condition of electrode looseness. © 2025 IEEE.

关键词： autoencoder Electroencephalography(EEG) Riemannian manifold seizure prediction time series classification

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Clark-Park Transformation based autoencoder for 3-Phase Electrical Signals

Clark-Park Transformation based Autoencoder for 3-Phase Elec...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Kummerow, André Alramlawi, Mansour Dirbas, Mohammad Nicolai, Steffen Bretschneider, Peter Cognitive Energy Systems Fraunhofer IOSB IOSB-AST Fraunhofer Center for Machine Learning Ilmenau Germany

ISBN: (纸本)9798350396782

During the past decades, significant progress has been made in the field of artificial neural networks to process images (Convolutional Neural Networks), audio signals (Temporal Convolutional Networks), or textual information (Transformers). However, for electrical three-phase signals processing, these network architectures ignore important characteristics and therefore lack of computational efficiency. This can lead to performance problems and limits the application potential of neural networks for a fast and efficient local analysis of three-phase electrical current or voltage waveforms. To address this issue, a novel autoencoder architecture is proposed in this paper, which incorporates Clark-Park transformation to learn the representations of three-phase electrical signals. Using unbalanced and noisy voltage signals, the Clark-Park based autoencoder shows superior performance and computational efficiency compared to recurrent and convolutional benchmark architectures. © 2023 IEEE.

关键词： autoencoder IEC 61850 Sampled Values Neural Networks Space-Vector Transformation Time Series Analysis

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Utilizing autoencoder to Improve the Robustness of Intrusion Detection Systems against Adversarial Attacks

Utilizing Autoencoder to Improve the Robustness of Intrusion...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Kibenge-MacLeod, Patricia Ye, Qiang Cui, Fangda Dalhousie Univ Dept Comp Sci Halifax NS Canada

ISBN: (纸本)9798350310900

Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learning (ML) and deep learning (DL) based IDSs have gained popularity due to their detection capability and adaptability. However, this type of schemes are susceptible to adversarial attacks, which involve minor perturbations to attack features causing misclassification. autoencoders (AEs) have proven effective in mitigating adversarial attacks in computer vision, but their capacity for enhancing IDSs remains relatively unexplored. In this paper, we focus on the use of AEs to detect adversarial network flows. Specifically, we propose an AE-enhanced IDS (AE-IDS) that leverages the power of AEs to improve the robustness of IDSs against adversarial attacks. Our experimental results indicate that AE-IDS outperforms the baseline schemes under investigation in terms of accuracy and detection rate. We believe that AE-IDS showcases the potential of using AEs to enhance the robustness of IDSs, providing improved security against sophisticated and evolving cyber threats.

关键词： autoencoder Intrusion Detection System Generative Adversarial Network Adversarial Attacks

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Bayesian Optimized autoencoder for Predictive Maintenance of Smart Packaging Machines 6

Bayesian Optimized Autoencoder for Predictive Maintenance of...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Arifeen, Murshedul Petrovski, Andrei Robert Gordon Univ Sch Comp Aberdeen Scotland

ISBN: (纸本)9798350311259

Smart packaging machines incorporate various components (blades, motors, films) to accomplish the packaging process and are involved in almost all types of the manufacturing industry. Proper maintenance and monitoring of the components over time can help industries to maintain a sustainable production environment. On the contrary, a faulty system may degrade production efficiency and increase the cost. Smart packaging machines comprising several sensors can generate time series data and leverage data driven condition monitoring models to overcome faulty conditions. In this work, we have studied the application of autoencoder as a data driven condition monitoring tool for the predictive maintenance of packaging machines. The trained autoencoder on the new system's data can detect worn or degraded components over time. We have also used the Bayesian optimization algorithm to tune the hyper-parameters of the autoencoder for better predictive performance. Moreover, the reconstruction error is analyzed to identify the worn components in the packaging machine.

关键词： autoencoder Bayesian Optimization Predictive Maintenance Packaging Machine Fault Detection

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Tree health assessment from UAV images: Improving object detection and classification using hard negative mining and semi-supervised autoencoder 20

Tree health assessment from UAV images: Improving object det...

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20th Conference on Robots and Vision (CRV)

作者： Jemaa, Hela Bouachir, Wassim Leblon, Brigitte LaRocque, Armand Haddadi, Ata Bouguila, Nizar Univ Quebec TELUQ Data Sci Lab Montreal PQ Canada Concordia Univ Montreal PQ Canada Lakehead Univ Thunder Bay ON Canada Univ New Brunswick Fredericton NB Canada A&L Canada Labs London ON Canada

ISBN: (纸本)9798350341393

Orchard tree inventory has been an essential step to obtain up-to-date information for effective tree treatments and crop insurance purposes. Inventorying trees is often performed manually through fieldwork surveys, which are generally time-consuming, costly, and subject to errors. Motivated by the latest advances in UAV imagery and deep learning, we propose a new framework for individual tree detection and health assessment. We adopt a divide-and-conquer approach to address the problem of orchard trees' health assessment in two stages. First, we build a tree detection model based on a hard negative mining strategy to improve object detection. In the second stage, we address the health classification problem using a new convolutional autoencoder architecture mainly designed to extract relevant features. The performed experiments demonstrate the robustness of the proposed framework for orchard tree health assessment from UAV images. In particular, our framework achieves an F1-score of 86.24% for tree detection and an overall accuracy of 98.06% for tree health assessment. Moreover, our work could be generalized for a wide range of UAV applications involving a detection/classification process.

关键词： Hard Negative Mining (HNM) autoencoder semi-supervised learning UAV YOLO DeepForest

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autoencoder asset pricing models

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JOURNAL OF ECONOMETRICS 2021年第1期222卷 429-450页

作者： Gu, Shihao Kelly, Bryan Xiu, Dacheng Univ Chicago Booth Sch Business Chicago IL 60637 USA Yale Univ AQR Capital Management New Haven CT 06520 USA NBER Cambridge MA 02138 USA

We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of KPS, we model factor exposures as a flexible nonlinear function of covariates. Our model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature - autoencoder neural networks - to incorporate information from covariates along with returns themselves. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Stock returns Conditional asset pricing model Nonlinear factor model Machine learning autoencoder Neural networks Big data

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Comparison of autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences 18th

Comparison of Autoencoder Models for Unsupervised Representa...

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18th International Symposium on Visual Computing (ISVC)

作者： Zaw, Thura Zhou, Jiaxin Komuro, Takashi Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan

ISBN: (纸本)9783031479687;9783031479694

This study comprehensively explores the performance of six distinct feature extraction models on two widely used skeleton datasets, UCLA and NTU 60, with a focus on establishing the superiority of hybrid transformer architectures. The models include autoencoder, convolutional autoencoder, temporal autoencoder (GRU autoencoder), transformer, convolutional transformer, and transformer with a convolutional encoder, representing diverse approaches in skeleton data analysis. The primary objective is to compare the models' effectiveness in extracting meaningful features from the skeleton datasets. Rigorous evaluations and comparisons are conducted using quantitative measures and visualizations to assess each model's discriminative power in capturing relevant information. The findings demonstrate that the transformer model consistently outperforms all other models on both datasets, showcasing its unique capability in extracting meaningful and discriminative features from skeleton data. Additionally, the study investigates hybrid models, combining transformer architectures with other feature extraction techniques, revealing their potential to surpass individual model capabilities. These findings can guide future studies in selecting appropriate models for similar tasks and promoting the development of more accurate and robust action recognition systems based on skeleton data.

关键词： Human Action Recognition (HAR) autoencoder Transformer Convolution Features extraction

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DBERT-ELVA: Discourse-Aware Extractive Text Summarization with autoencoder 35

DBERT-ELVA: Discourse-Aware Extractive Text Summarization wi...

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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Kia, Mahsa Abazari Northeastern Univ London Comp & Data Sci Dept London England

ISBN: (纸本)9798350342734

In recent advancements in text summarization, BERT has gained popularity for encoding documents. However, sentence-based extractive models often lead to redundant or uninformative phrases in the generated summaries. Additionally, BERT, which is pretrained on sentence pairs rather than full documents, struggles to capture long-range dependencies present within a document. To overcome these challenges, we introduce DBERT-ELVA, a discourse-aware neural summarization model. DBERT-ELVA extracts sub-sentential discourse units, offering a more refined granularity for extractive selection, in contrast to traditional sentence-based approaches. To learn a compressed representation of these discourse units while capturing the interdependencies among them, an autoencoder is designed, utilizing Extreme Learning for improved generalization performance. Experimental evaluations on popular summarization benchmarks demonstrate that the proposed model significantly outperforms state-of-the-art methods, including other BERT-based models, by a substantial margin.

关键词： Text summarization autoencoder BERT encoder Extreme learning machine

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A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional autoencoder Long Short-Term Memory Network 24

A Novel Smartphone-Based Human Activity Recognition Approach...

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IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)

作者： Thakur, Dipanwita Roy, Sandip Biswas, Suparna Ho, Edmond S. L. Chattopadhyay, Samiran Shetty, Sachin Banasthali Vidyapith Dept Comp Sci Jaipur 304022 Rajasthan India Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Suffolk VA 23435 USA Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata 700064 India Univ Glasgow Sch Comp Sci Glasgow Scotland

ISBN: (纸本)9798350334586

In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.

关键词： Human activity recognition (HAR) deep Learning CNN smartphone sensors autoencoder LSTM

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