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7th International Conference on Automation Electronics and Electrical Engineering

作者： Yan, Mingkai Zhang, Lixiong Li, Yujia Wu, Hanbing Li, Yuran CATARC Software Testing Tianjin Co Ltd Tianjin Peoples R China China Automot Technol & Res Ctr Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798350377040;9798350377033

Side-channel data compression techniques are designed to reduce the dimensionality of input data while preserving critical information, thereby improving the efficiency of side-channel analysis. Traditional methods of side-channel analysis can be broadly classified into two categories: peak extraction techniques and data integration techniques, based on the scale and principles of the input data. In this study, we present a novel side-channel data compression technique utilizing convolutional autoencoder networks. We apply this method to power-side channel data collected from an SM4 encryption circuit implemented on a secure chip. Experimental results show that our proposed technique surpasses conventional methods in Test Vector Leakage Assessment (TVLA) analysis and achieves a 35% improvement in efficiency in Measurements to Disclosure (MTD) analysis, while maintaining the same compression rate. These results underscore the effectiveness and potential of our approach in enhancing side-channel analysis.

关键词： Side Channel Analysis Data Compression autoencoder Security Chip

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Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising autoencoder Architecture

Aircraft Radar Altimeter Interference Mitigation Through a C...

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2024 Military Communications Conference

作者： Brown, Samuel B. Young, Stephen Wagenknecht, Adam Jakubisin, Daniel Thornton, Charles E. Orndorff, Aaron Headley, William C. Virginia Tech Natl Secur Inst Blacksburg VA 24061 USA Boeing Co Crystal City VA USA

ISBN: (纸本)9798350374247;9798350374230

Denoising autoencoders for signal processing applications have been shown to experience significant difficulty in learning to reconstruct radio frequency communication signals, particularly in the large sample regime. In communication systems, this challenge is primarily due to the need to reconstruct the modulated data stream which is generally highly stochastic in nature. In this work, we take advantage of this limitation by using the denoising autoencoder to instead remove interfering radio frequency communication signals while reconstructing highly structured FMCW radar signals. More specifically, in this work we show that a CNN-layer only autoencoder architecture can be utilized to improve the accuracy of a radar altimeter's ranging estimate even in severe interference environments consisting of a multitude of interference signals. This is demonstrated through comprehensive performance analysis of an end-to-end FMCW radar altimeter simulation with and without the convolutional layer-only autoencoder. The proposed approach significantly improves interference mitigation in the presence of both narrow-band tone interference as well as wideband QPSK interference in terms of range RMS error, number of false altitude reports, and the peak-to-sidelobe ratio of the resulting range profile. FMCW radar signals of up to 40,000 IQ samples can be reliably reconstructed.

关键词： interference mitigation radar signal processing radio frequency machine learning autoencoder

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Hybrid Method of Non-invasive Intracranial Pressure Measurement Using autoencoder Neural Network Algorithm

Hybrid Method of Non-invasive Intracranial Pressure Measurem...

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21st International Conference on Global Research and Education (Inter-Academia)

作者： Bohdanowicz, M. Cardim, D. Schmidt, B. Wadehn, F. Nalecz, M. Rupniewski, M. Kim, D. -J. Czosnyka, M. Warsaw Univ Technol Inst Elect Syst Nowowiejska 15-19 PL-00665 Warsaw Poland Univ Texas Southwestern Med Ctr Dallas Dept Neurol 5323 Harry Hines Blvd Dallas TX 75390 USA Neurol Klinikum Chemnitz Dresdner Str 178 D-09131 Chemnitz Germany Zurich Univ Appl Sci Inst Signal Proc & Wireless Commun Tech Str 9 CH-8401 Winterthur Switzerland Korea Univ Dept Brain & Cognit Engn 145 Anam Ro Seoul 02841 South Korea Korea Univ Coll Med Anam Hosp Dept Neurol 73 Goryeodae Ro Seoul 02841 South Korea Univ Cambridge Addenbrookes Hosp Dept Clin Neurosci Div Neurosurg Cambridge CB2 0QQ England

Both short-term and long-term intracranial pressure (ICP) monitoring is indicated for a number of neurological pathologies. The clinical gold standard for ICP monitoring is invasive and involves inserting a pressure sensor into the brain tissue or cerebral spinal fluid space. Such sensors can only be used for a limited time due to the risk of infection and sensor degradation. Our aim was to develop a method for long-term non-invasive ICP monitoring after the removal of invasive ICP sensor. Arterial blood pressure (ABP) and cerebral blood flow velocity (FV) signals were used as inputs to an artificial autoencoder neural network. The network was trained with invasively measured ICP. Following the training phase, the network's outputs were used for estimating ICP based on ABP and FV only. The method was verified on clinical data from 98 traumatic brain injury patients. The proposed procedure managed to recover ICP using FV and ABP measurements. The median value of the Pearson correlation between the recovered and the reference ICP signals was 0.7, and the root mean square error was 3.9 mmHg with an interquartile range of less than 5 mmHg. An additional feature of our algorithm is that it not only outputs an ICP estimate, but also provides a confidence level.

关键词： artificial neural networks (ANN) autoencoder biomedical signal processing intracranial pressure

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Intelligent bearing condition monitoring for electrical machines through vibration signal based on autoencoder and almost-parameter-free classifiers

Intelligent bearing condition monitoring for electrical mach...

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27th International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

作者： Lehmann, Michel Moeckel, Andreas Tech Univ Ilmenau Small Elect Drives Grp Ilmenau Germany

ISBN: (纸本)9798350387605;9798350387599

Feature extraction is a critical task in the design of intelligent condition monitoring (CM) systems. In most cases, expert knowledge is required. This study investigates feature extraction for bearing condition monitoring using artificial intelligence (At). A hierarchical extreme learning machine (HELM) is used to design a condition monitoring system (CMS) for bearings that can be used for fault classification and anomaly detection. Acceleration signals are processed using an autoencoder to extract features and anomalies were detected or faults were classified depending on the task of the CMS. Almost-parameter-free machine learning-based classifiers have been used to solve the classification task. In general, the study employed a (minimum) distance classifier (MiDC), and one -class k-nearest neighbors (OC-k-NN). This was done to show that deep learning -based feature extraction provides excellent spatial separation and facilitates classification with minimal training effort. The study compared the distance based classifiers with the performance of several artificial intelligence-based approaches, such as support vector machine (SVM) one-class support vector machine (DC-SVM), isolation forests (IF), and multi -layer perceptron (MLP). The proposed method was evaluated using both the CWRU dataset and an own dataset. The proposed method achieved Fl scores above 0.98 for both anomaly detection and fault classification with the CWRU dataset and an own daset.

关键词： Intelligent Bearing Condition Monitoring Artificial Intelligence CWRU Dataset autoencoder IIELM Fault Classification Anomaly Detection Distance-based classification

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Comparing Optical Transfer Learning and autoencoder Pretraining for Radar Bird-Drone Discrimination

Comparing Optical Transfer Learning and Autoencoder Pretrain...

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IEEE Radar Conference (RadarConf)

作者： White, Daniel Jahangir, Mohammed Baker, Chris Antoniou, Michail Thiyagalingam, Jeyan Univ Birmingham Microwave Integrated Syst Lab MISL Birmingham W Midlands England Rutherford Appleton Lab Sci & Technol Facil Council Didcot Oxon England

ISBN: (纸本)9798350329216;9798350329209

Drone surveillance radars are dependent on reliable classification of targets for useful operation. Machine learning-based approaches, such as those based on deep learning, have been helpful for advancing such sensors' operation in real unpredictable environments. However, with labelled data being scarce resource in radar domain, machine learning-based approaches inherently carry several performance uncertainties. This is true even with approaches that involve domain translation or transfer learning, for example, with models evolved from optically trained models. In this paper, a comparison of the classification effectiveness of two different types of machine learning based approaches is performaned, namely, a convolutional neural network (CNN) pretrained with optical data, and an autoencoder-based model trained with real-world radar-only data. This comparison unlike others in the literature compares supervised and unsupervised pretraining techniques. Our results show that the unsupervised approach can outperform the resourcedemanding supervised approach based on transfer learning. Furthermore, autoencoder pretraining repeated with synthetic micro-Doppler data yielded near identical classification results, which paves the possibility to utilize greater amounts of synthetic data for pretraining deep learning models. A brief inspection of the latent distribution of the simple symmetric, unregularized autoencoder confirms minor preservation of features in the learned representation.

关键词： Classification Drones CNN autoencoder Pretraining Transfer Learning

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Model Order Reduction for Waste Heat Recovery: An Integrated autoencoder-LSTM Neural Network Approach

Model Order Reduction for Waste Heat Recovery: An Integrated...

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International Workshop on Artificial Intelligence and Machine Learning for Energy Transformation (AIE)

作者： Toghranegar, Sina Behdadnia, Tohid Deconinck, Geert Sabariego, Ruth V. Katholieke Univ Leuven Energyville Dept Elect Engn ESAT B-3001 Leuven Belgium

ISBN: (纸本)9798350364965;9798350364972

An evaporator is a critical component in waste heat recovery systems, particularly within organic Rankine cycle technologies. In this paper, an approach for the simulation of two-phase evaporators is presented. This method aims at dimension reduction and prediction of temporal measurements, leveraging an autoencoder integrated with long short-term memory neural networks. The autoencoder-LSTM integration effectively reduces dimensions by compressing data representations and enhances temporal predictions, capturing dynamic system behaviors efficiently. The methodology effectiveness in predicting system dynamics is demonstrated, including a case study on high-fidelity data generation. In the absence of explicit system equations and with only snapshot matrices available, this method is preferred over projection-based model order reduction methods for its enhanced capability to capture and predict complex, non-linear temporal dynamics directly from data. The potential of integrating deep learning techniques with conventional engineering simulations to address the challenges of complex energy systems is highlighted. The results indicate that architectures with varying numbers of neurons in the examined bottleneck layer can accurately reconstruct and predict the original system's transient and steady-state behaviors with an error rate below 8%.

关键词： Reduced-order model autoencoder long-short term memory organic Rankine cycle evaporator

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Comparison of autoencoder architectures for fault detection in industrial processes

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DIGITAL CHEMICAL ENGINEERING 2024年 12卷

作者： Spina, Deris Eduardo Campos, Luiz Felipe de O. de Arruda, Wallthynay F. Melo, Afranio Alves, Marcelo F. de S. Rabello, Gildeir Lima Anzai, Thiago K. Pinto, Jose Carlos Univ Fed Rio De Janeiro Programa Engn Quim COPPE BR-21941972 Rio De Janeiro RJ Brazil Univ Fed Rio De Janeiro Programa Pos Grad Engn Proc Quim & Bioquim EPQB BR-21941909 Rio De Janeiro RJ Brazil Petrobras Petr Brasileiro SA Ctr Pesquisas Leopoldo Americo Miguez Mello CENPES BR-21941915 Rio De Janeiro RJ Brazil

Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data -driven techniques. autoencoders are a particular type of unsupervised Artificial Neural Networks that can be suitable for fault detection applications. Diverse architectures might be used for autoencoders, resulting in different fault detection performances, which are usually compared by means of Fault Detection Rates for a fixed threshold of the False Alarm Rate, limiting the conclusions to particular cases. To improve the comparability, the present work uses the area under the receiver operating characteristic curve to compare autoencoder architectures for a range of false alarm rates using the Tennessee Eastman Process benchmark. Performances obtained for shallow and deep autoencoders were compared with those of the denoising and variational autoencoders for undercomplete and sparse structures. Overall, the results indicate better performances for sparse structures, especially for the variational autoencoder and the deep denoising autoencoder, with area under the curve of 98.35%.

关键词： autoencoder Denoising Variational Fault detection

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A One-Class autoencoder for Porcelain Art Authentication 8

A One-Class Autoencoder for Porcelain Art Authentication

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IEEE 8th International Conference on Signal and Image Processing Applications (IEEE ICSIPA)

作者： Ugail, Hassan Edwards, Howell Omar, Zaid Univ Bradford Ctr Visual Comp Bradford W Yorkshire England Univ Bradford Fac Life Sci Bradford W Yorkshire England Univ Teknol Malaysia Fac Elect Engn Johor Baharu Malaysia

ISBN: (纸本)9798350352368

This preliminary study explores the application of machine learning techniques, specifically autoencoders, for the authentication of porcelain artworks. By focusing on the works of William Billingsley, we demonstrate how autoencoders can accurately reconstruct normal artworks and identify deviations in potentially misattributed pieces through higher reconstruction errors. We trained the chosen autoencoder by using five handcrafted image features-entropy, energy, contrast, homogeneity, and Hausdorff-Besicovitch dimension. The preliminary experimental results indicate that the autoencoder architecture selected and trained effectively distinguishes genuine Billingsley works from forgeries, providing a reliable tool for porcelain art attribution and authentication. This approach can potentially create a complementary tool for porcelain art authentication, adding reliability and objectivity to the world of art authentication, traditionally dominated by expert opinions as opposed to independent objectivity.

关键词： Porcelain Art Recognition autoencoder Machine Learning Digital Heritage

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COMB: Interconnected Transformers-Based autoencoder for Multi-Perspective Business Process Anomaly Detection

COMB: Interconnected Transformers-Based Autoencoder for Mult...

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IEEE International Conference on Web Services (IEEE ICWS)

作者： Guan, Wei Cao, Jian Yao, Yan Gu, Yang Qian, Shiyou SJTU Dept Comp Sci & Engn Rizhao Peoples R China QFNU Sch Comp Sci Rizhao Peoples R China

ISBN: (纸本)9798350368567;9798350368550

In business processes, anomalies are prevalent, arising from diverse factors, such as software malfunctions and operator errors. Detecting these anomalies is imperative, as it significantly influences not only the financial well-being of a business but also the dependability of event logs for subsequent analysis. However, existing deep business process anomaly detection approaches either lack effective temporal dependency modeling between events or encounter challenges associated with gradient vanishing. In this paper, we introduce COMB, which stands for an interConnected transfOrmers-based autoencoder for Multi-perspective Business process anomaly detection. Considering the interdependent nature of multi-perspectives, COMB leverages multiple parallel and interconnected transformers, facilitated by the inclusion of aggregation layers. These layers serve as integration points for information from various perspectives. Notably, considering how control flow influences other perspectives, COMB incorporates innovative mask adapters to enhance its detection performance. Furthermore, we propose a novel method for calculating anomaly scores, which effectively mitigates the influence of varying numbers of potential attribute values. Our extensive experimental evaluation encompasses both synthetic and real-life logs, and the results clearly demonstrate that COMB outperforms state-of-the-art methods in both trace-level and attribute-level anomaly detection.

关键词： Business process management process mining anomaly detection deep learning autoencoder event log

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Unveiling Irregular Heart Rhythms: Utilizing a Pretrained ResNet50 Convolutional Neural Network autoencoder Strategy for Detecting Anomalies in ECG Signals 2

Unveiling Irregular Heart Rhythms: Utilizing a Pretrained Re...

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2nd IEEE World Conference on Communication and Computing (WCONF)

作者： Agarwal, Muskan Gill, Kanwarpartap Singh Aggarwal, Priyanshi Rawat, Ramesh Singh Chitkara Univ Inst Engn & Technol Rajpura Punjab India Graph Era Hill Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Graph Era Deemed Be Univ Comp Sci & Engn Dehra Dun 248002 Uttarakhand India

ISBN: (纸本)9798350395334;9798350395327

This study examines the application of ResNet50 (CNN) autoencoders for identifying abnormalities in electrocardiogram (ECG) data using the PTB Diagnostic ECG Database. The dataset comprises 14,552 samples categorized as normal heartbeats and heartbeats affected by cardiac disorders. Utilizing Transposed Convolution has significantly improved the model's performance. The study emphasizes the vital role of immediately detecting cardiac irregularities and presents a CNN autoencoder model designed to efficiently encode and decode ECG data, aiding in the detection of aberrant patterns. The approach involves building a robust autoencoder with encoder and decoder components that are trained to minimize errors in reconstruction. The evaluation metrics show the model's outstanding performance with an accuracy of 76.93%, precision of 55.23%, recall of 89.81%, and F1 score of 65.40%. This work highlights the need of utilizing deep learning techniques to detect abnormalities in ECG data at an early stage. This method demonstrates significant promise in increasing diagnostic abilities and enhancing patient results.

关键词： Artificial Intelligence Deep Learning Anomaly detection Model Training autoencoder

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