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检索条件"主题词=convolutional autoencoder"

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Making Noise - Improving Seismocardiography Based Heart Analysis With Denoising autoencoders 23

Making Noise - Improving Seismocardiography Based Heart Anal...

引用

8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (IWOAR)

作者： Burian, Jonas Toedtmann, Helmut Haescher, Marian Aehnelt, Mario Kuijper, Arjan Fraunhofer IGD Rostock Germany Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9798400708169

Seismocardiography is a method commonly used to monitor and prevent cardiovascular diseases. However, noise and artifacts in the signals often interfere with the assessment of cardiac health and the analysis of the signal morphology. Therefore, this work presents a new approach to denoise seismocardiography signals by applying fully convolutional denoising autoencoders. In order to investigate the suitability and robustness of this approach, a series of experiments have been carried out with respect to the optimal configuration for the denoising task and a comparison with wavelet denoising as a traditional approach. Furthermore, the practical applicability of the method is tested with the use case of transforming noisy seismocardiography signals into electrocardiography signals. Our approach using autoencoders outperforms the commonly used wavelet denoising. Additionally, we demonstrate that the widespread usage of Butterworth filters may not only be unnecessary but even detrimental. Finally, the generalizability of the method is verified on unseen data. With those combined improvements in noise reduction, the assessment of cardiac health using seismocardiography in the presence of noise may be facilitated in the future.

关键词： seismocardiography cardiac health assessment signal denoising convolutional autoencoder signal processing

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A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced Deep Neural Networks 19th

A Cycle-GAN Based Image Encoding Scheme for Privacy Enhanced...

引用

19th International Conference on Information Systems Security (ICISS)

作者： Rodriguez, David Krishnan, Ram Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

ISBN: (纸本)9783031490989;9783031490996

Deep learning model training on cloud platforms typically require users to upload raw input data. However, uploading raw image data to cloud service providers raises serious privacy concerns. To address this problem, we propose a Cycle-Gan based-image transformation scheme that leverages convolutional autoencoder image encoding for domain translation. Our Cycle-GAN based image transformation scheme enhances privacy of deep neural networks while preserving model utility. In this paper, we demonstrate that our Cycle-GAN based image transformation scheme protects visual feature information of sensitive image data. We evaluate the effectiveness of our proposed method to preserve model utility using classification accuracy and robustness against reconstruction attacks using structural similarity index measure (SSIM). The classification accuracy of encoded images using our proposed method is 92.48, 91.05, 90.37 for Chest X-ray, Dermoscopy and OCT datasets, respectively. The SSIM scores for reconstruction attacks where the attacker only has access to the encoded data and corresponding labels are 0.1002, 0.0995 and 0.0329 for Chest X-ray, Dermoscopy and OCT datasets, respectively. Our results demonstrate that the Cycle GAN based encoding scheme effectively enhance privacy while preserving model utility.

关键词： Cycle-GAN Deep Neural Networks convolutional autoencoder Privacy Utility

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An Energy-Efficient Reconfigurable autoencoder Implementation on FPGA 1

引用

Intelligent Systems Conference (IntelliSys)

作者： Isik, Murat Oldland, Matthew Zhou, Lifeng Drexel Univ Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

autoencoders are unsupervised neural networks that are used to process and compress input data and then reconstruct the data back to the original data size. This allows autoencoders to be used for different processing applications such as data compression, image classification, image noise reduction, and image coloring. Hardware-wise, re-configurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations from several domains because of their unique combination of flexibility, performance, and power efficiency. In this paper, we look at the different autoencoders available and use the convolutional autoencoder in both FPGA and GPU-based implementations to process noisy static MNIST images. We compare the different results achieved with the FPGA and GPU-based implementations and then discuss the pros and cons of each implementation. The evaluation of the proposed design achieved 80% accuracy and our experimental results show that the proposed accelerator achieves a throughput of 21.12 Giga-Operations Per Second (GOP/s) with a 5.93 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency and design flexibility. We also discuss future work that can be done with the use of our proposed accelerator.

关键词： convolutional autoencoder Reconfigurable architecture FPGA Energy efficiency

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Performance Degradation Evaluation Model of Rolling Bearing Based on CAE-SVDD

Performance Degradation Evaluation Model of Rolling Bearing ...

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International Conference of The Efficiency and Performance Engineering Network (TEPEN)

作者： Dong, Xinyang Cao, Yunpeng Li, Hui Han, Xiaoyu Feng, Weixing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Peoples R China China State Shipbldg Corp Ltd Res Inst 703 Harbin 150001 Peoples R China

ISBN: (纸本)9783031261954;9783031261930;9783031261923

Rolling bearing is one of the core components of mechanical equipment, and the degradation state of its performance can directly affect the stability of the entire mechanical equipment, so the evaluation of the degradation state of rolling bearing is of great significance. This paper proposes a performance degradation evaluation model for rolling bearings based on the combination of convolutional autoencoder (CAE) and support vector data description (SVDD). CAE is used to process the extracted temporal features to realize dimensionality reduction of data features and re-extraction of depth features. The depth features are fed into the optimized SVDD evaluation model as training samples to achieve performance degradation evaluation. Finally, the multi-method verification is carried out using the full life data samples of bearings, and a residual life prediction method using a hybrid prediction model combining LSTM neural network and AR model is proposed. The experimental results show that the method can obtain more accurate evaluation of degradation state and prediction of remaining life.

关键词： Rolling bearings State evaluation convolutional autoencoder Support vector data description Deep feature extraction Hybrid predictive models

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Deep Learning Anomaly Detection methods to passively detect COVID-19 from Audio

Deep Learning Anomaly Detection methods to passively detect ...

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IEEE International Conference on Digital Health (ICDH) / IEEE World Congress on Services (SERVICES)

作者： Murthy, Shreesha Narasimha Agu, Emmanuel Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781665416856

The world has been severely affected by COVID-19, an infectious disease caused by the SARS-Cov-2 coronavirus. COVID-19 incubates in a patient for 7 days before symptoms manifest. The identification of the presence of COVID-19 is challenging as its symptoms are similar to influenza symptoms such as cough, cold, runny nose, and chills. COVID-19 affects human speech sub-systems involved in respiration, phonation, and articulation. We propose a deep anomaly detection framework for passive, speech-based detection of COVID-related anomalies in voice samples of COVID-19 affected individuals. The low percentage of positive cases and extreme imbalance in available COVID audio datasets present a challenge to machine learning classifiers but create an opportunity to utilize anomaly detection techniques. We investigate COVID detection from audio using various types of deep anomaly detectors and convolutional autoencoders. Contrastive loss methods are also explored to force our models to learn discrepancies between COVID and non-COVID cough data representations. In contrast with prior work that controlled data collection, our work focuses on crowdsourced datasets that are true representatives of the general population. In rigorous evaluation, the variational autoencoder with the elliptic envelope as the anomaly detector analyzing Mel Filterbanks audio representations performed best with an AUC of 0.65, outperforming the state of the art.

关键词： convolutional autoencoder Variational autoencoder Contrastive Learning COVID-19 Anomaly detection Cough

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Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning

Direct Prediction of Cardiovascular Mortality from Low-dose ...

引用

Conference on Medical Imaging: Image Processing

作者： van Velzen, Sanne G. M. Zreik, Majd Lessmann, Nikolas Viergever, Max A. de Jong, Pim A. Verkooijen, Helena M. Isgum, Ivana Univ Med Ctr Utrecht Image Sci Inst Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiol Utrecht Netherlands Univ Utrecht Univ Med Ctr Utrecht Imaging Div Utrecht Netherlands

ISBN: (纸本)9781510625464

Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD. Existing methods analyzing CT images from lung cancer screening for prediction of CVD events or mortality use engineered features extracted from the images combined with patient information. In this work we propose a method that automatically predicts 5-year cardiovascular mortality directly from chest CT scans without the need for hand-crafting image features. A set of 1,583 participants of the National Lung Screening Trial was included (1,188 survivors, 395 non survivors). Low-dose chest CT images acquired at baseline were analyzed and the follow-up time was 5 years. To limit the analysis to the heart region, the heart was first localized by our previously developed algorithm for organ localization exploiting convolutional neural networks. Thereafter, a convolutional autoencoder was used to encode the identified heart region. Finally, based on the extracted encodings subjects were classified into survivors or non-survivors using a neural network. The performance of the method was assessed in eight cross-validation experiments with 1,433 images used for training, 50 for validation and 100 for testing. The method achieved a performance with an area under the ROC curve of 0.73. The results demonstrate that prediction of cardiovascular mortality directly from low-dose screening chest CT scans, without hand-crafted features, is feasible, allowing identification of subjects at risk of fatal CVD events.

关键词： Cardiovascular disease mortality prediction convolutional autoencoder lung screening low-dose CT deep learning

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Acoustic Anomaly Detection of Machinery using autoencoder based Deep Learning 32

Acoustic Anomaly Detection of Machinery using Autoencoder ba...

引用

32nd Southern African Universities Power Engineering Conference (SAUPEC)

作者： Chinnasamy, Mark Damien Sumbwanyambe, Mbuyu Hlalele, Tlotlollo Sidwell Univ South Africa Dept Elect Engn Johannesburg South Africa

ISBN: (纸本)9798350371345

This research paper focuses on the domain of Acoustic Anomaly Detection (AAD) in industrial machinery using Deep Learning techniques. The primary objective of this study is to develop a reliable system for detecting anomalies in the acoustic patterns of industrial machines. In large-scale industrial environments, the early detection of faults and anomalies is critical to ensure the uninterrupted operation of machinery, minimize downtime, and optimize maintenance efforts. The datasets used in this research encompass a range of Signal-to-Noise Ratios (SNR) to simulate diverse operating conditions for industrial fans and pumps. After preprocessing each audio sample was transformed into 9 segments of shape 128 x 32 log mel-spectrograms. The study encompasses a comprehensive analysis of convolutional autoencoders (CAE) and Dense autoencoders (DAE). These models are trained and evaluated against real-world industrial datasets (MIMII dataset), and their performance is meticulously compared. The results for the DAE and CAE had shown over 0.80 at the 6 dB SNR level and decaying results as the SNR level worsens. This research confirms some of the trends pointed out by the literature and provides detailed insight into how the autoencoders are developed and their properties could be used in order to detect anomalous behavior in audio data.

关键词： autoencoder Acoustic Detection convolutional autoencoder Dense autoencoder ML Unsupervised Anomaly Detection

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AI-Based Temperature Monitoring System for Hydro Generators 23

AI-Based Temperature Monitoring System for Hydro Generators

引用

23rd International Symposium INFOTEH-JAHORINA (INFOTEH)

作者： Milic, Sasa D. Kozicic, Misa Univ Belgrade Elect Engn Inst Nikola Tesla Belgrade Serbia HPPs Derdap EPS JSC Belgrade Kladovo Serbia

ISBN: (纸本)9798350329940;9798350307368

Hydrogenerators operate in challenging environments, and temperature variations can significantly impact their performance. Temperature monitoring systems often rely on remote infrared and contact real-time temperature measurement of the generator. AI-based temperature monitoring systems offer a state-of-the-art solution, providing continuous, automated, and predictive maintenance capabilities. The paper presents a complex AI-based fuzzy decision-making algorithm for anomaly (overheated rotor pole) detection and failure prediction. Time-based analysis of operational parameters from SCADA systems is essential for failure prediction. The use of recurrent neural networks for time-series analysis and convolutional autoencoders for anomaly detection is especially emphasized. The paper also presents a fuzzy decision-making method that uses fuzzy logic to make decisions after analyzing the output of the AI models.

关键词： temperature monitoring system hydro generator fuzzy decision-making LSTM convolutional autoencoder

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A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing 17

A Deep Learning-based Approach to Anomaly Detection with 2-D...

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17th IEEE International Conference on Industrial Informatics (INDIN)

作者： Maggipinto, Marco Beghi, Alessandro Susto, Gian Antonio Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781728129273

In modern manufacturing scenarios, detecting anomalies in production systems is pivotal to keep high-quality standards and reduce costs. Even in the Industry 4.0 context, real-world monitoring systems are often simple and based on the use of multiple univariate control charts. Data-driven technologies offer a whole range of tools to perform multivariate data analysis that allow to implement more effective monitoring procedures. However, when dealing with complex data, common data-driven methods cannot be directly used, and a feature extraction phase must be employed. Feature extraction is a particularly critical operation, especially in anomaly detection tasks, and it is generally associated with information loss and low scalability. In this paper we consider the task of Anomaly Detection with two-dimensional, image-like input data, by adopting a Deep Learning-based monitoring procedure, that makes use of convolutional autoencoders. The procedure is tested on real Optical Emission Spectroscopy data, typical of semiconductor manufacturing. The results show that the proposed approach outperforms classical feature extraction procedures.

关键词： Anomaly detection convolutional autoencoder Deep Neural Networks Industry 4.0 Machine Learning Semiconductor Manufacturing

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R-CAE-Informer Based Short-Term Load Forecasting by Enhancing Feature in Smart Grids 20th

R-CAE-Informer Based Short-Term Load Forecasting by Enhancin...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Yiying Liu, Ke Dong, Yanping Li, Siwei Li, Wenjing Tianjin Univ Sci & Technol Tianjin 300457 Peoples R China Beijing Fibrlink Commun Co LTD Beijing 100070 Peoples R China State GridInformat & Telecommun Co Beijing 100192 Peoples R China

ISBN: (纸本)9789819756650;9789819756667

As renewable energy usage increases, power systems become more intricate and demand fluctuations intensify. Accurate short-term load forecasting (STLF) is vital for balancing energy supply and demand. Traditional models often struggle with long input sequences, risking critical feature loss due to inadequate capture of long-period characteristics. To address these challenges, we introduce the R-VAE-Informer, a novel forecasting approach that combines Deep Residual Networks (DRN) and convolutional autoencoders (CAE). This model leverages the ProbSparse self-attention mechanism and attention distillation techniques to manage the quadratic complexity of long load sequences more efficiently. It also incorporates a Residual convolutional autoencoder (R-CAE) to produce two-dimensional representations of load data, thereby enhancing feature representation and mitigating potential feature loss. Furthermore, the TempoLocode submodule integrates time and positional data, effectively capturing long-term dependencies and periodic variations in time series. Tests on two public datasets demonstrate that our model achieves a Mean Absolute Percentage Error (MAPE) as low as 0.97%, confirming its strong generalization capabilities. Overall, this model markedly improves the precision and sophistication of STLF.

关键词： short-term load forecasting convolutional autoencoder Residual Network deep learning R-VAE-Informer

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