Unsupervised Seismic Footprint Removal With Physical Prior Augmented Deep Autoencoder

Qian, Feng; Yue, Yuehua; He, Ya‐Ling; Yu, Hongtao; Zhou, Yingjie; Tang, Jinliang; Hu, Guangmin

doi:10.1109/tgrs.2023.3277973

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…Although there are several types of ANN, we have chosen to use the multilayer perceptronbackpropagation, in the present study, since it offers several advantages, namely: It is a compact network with fast computational speed, enabling the training of large input datasets [27]; It provides automatic knowledge generalization, enabling the recognition of data patterns [28]; It exhibits robustness against data obscured by noise; and finally, it minimizes the mean squared error across all training datasets [29]. The formulas (2) and (3) represent the general output and MLP's functions of error [30].…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Energy use and CO2 emissions of the Moroccan transport sector

Oubnaki,

Haouraji,

Mounir

et al. 2024

IJECE

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In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems.

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Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Energy use and CO2 emissions of the Moroccan transport sector

Oubnaki,

Haouraji,

Mounir

et al. 2024

IJECE

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“…In recent years, how to effectively estimate and remove noise errors is a research focus of various fields. [56][57][58] To solve the problems of environmental noise interference and insufficient input information, Ma et al 59 proposed measurement error prediction using improved local outlier factor and kernel support vector regression. Kong et al 60 proposed a remote prediction method for smart meter errors.…”

Section: Components Of Stgnetmentioning

confidence: 99%

“…For these errors, we can reduce them by increasing sampling frequency, using high‐quality measuring equipment, improving the reliability of data transmission and storage system, continuously monitoring and regularly evaluating the data collection process. In recent years, how to effectively estimate and remove noise errors is a research focus of various fields 56–58 . To solve the problems of environmental noise interference and insufficient input information, Ma et al 59 proposed measurement error prediction using improved local outlier factor and kernel support vector regression.…”

Section: Experimental Evaluationsmentioning

confidence: 99%

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

Feng,

Li,

Zhou

et al. 2023

Energy Science & Engineering

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With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches.

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“…Meanwhile, deep neural networks (DNNs) have proven to be particularly effective in processing the complex data obtained from SAR images [3,4]. Their advanced representation capabilities facilitate accurate analysis and have been instrumental in the development of SAR automatic target recognition (SAR-ATR) models [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

Zhang,

Liu,

et al. 2023

Drones

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With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods.

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Unsupervised Seismic Footprint Removal With Physical Prior Augmented Deep Autoencoder

Cited by 16 publications

References 46 publications

Energy use and CO<sub>2</sub> emissions of the Moroccan transport sector

Energy use and CO<sub>2</sub> emissions of the Moroccan transport sector

STGNet: Short‐term residential load forecasting with spatial–temporal gated fusion network

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

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