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Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Survey of modelling methods for wind turbine wakes and wind farms
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A survey on unsupervised outlier detection in high‐dimensional numerical data
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Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm
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Imbalanced dataset-based echo state networks for anomaly detection
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A Survey of Outlier Detection Methodologies
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Wind-Turbine and Wind-Farm Flows: A Review
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An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
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What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle
inferredcites →https://doi.org/10.1016/j.asoc.2019.105716openalex-snapshot
Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms
inferredcites →https://doi.org/10.1016/j.cosrev.2009.03.005openalex-snapshot
Reservoir computing approaches to recurrent neural network training
inferredcites →https://doi.org/10.1016/j.dib.2021.107538openalex-snapshot
Synthetic image dataset of shaft junctions inside wind turbines in presence or absence of oil leaks
inferredcites →https://doi.org/10.1016/j.engappai.2019.06.011openalex-snapshot
Online learning control with Echo State Networks of an oil production platform
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Utilizing online learning based on echo-state networks for the control of a hydraulic excavator
inferredcites →https://doi.org/10.1016/j.neucom.2005.12.126openalex-snapshot
Extreme learning machine: Theory and applications
inferredcites →https://doi.org/10.1016/j.neucom.2016.12.089openalex-snapshot
Deep reservoir computing: A critical experimental analysis
inferredcites →https://doi.org/10.1016/j.neunet.2011.02.002openalex-snapshot
Architectural and Markovian factors of echo state networks
inferredcites →https://doi.org/10.1016/j.oceaneng.2020.107381openalex-snapshot
Review of the current status, technology and future trends of offshore wind farms
inferredcites →https://doi.org/10.1016/j.patcog.2020.107281openalex-snapshot
Binary neural networks: A survey
inferredcites →https://doi.org/10.1016/j.renene.2018.05.024openalex-snapshot
Anomaly detection and fault analysis of wind turbine components based on deep learning network
inferredcites →https://doi.org/10.1016/j.renene.2018.10.047openalex-snapshot
Machine learning methods for wind turbine condition monitoring: A review
inferredcites →https://doi.org/10.1016/j.renene.2020.02.004openalex-snapshot
Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels
inferredcites →https://doi.org/10.1016/j.rse.2004.11.015openalex-snapshot
Oil spill detection by satellite remote sensing
inferredcites →https://doi.org/10.1023/a:1007452223027openalex-snapshot
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
inferredcites →https://doi.org/10.1080/14786451.2021.1890736openalex-snapshot
Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management
inferredcites →https://doi.org/10.1109/36.774704openalex-snapshot
Automatic detection of oil spills in ERS SAR images
inferredcites →https://doi.org/10.1109/access.2018.2816564openalex-snapshot
CoDetect: Financial Fraud Detection With Anomaly Feature Detection
inferredcites →https://doi.org/10.1109/access.2018.2840086openalex-snapshot
Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach
inferredcites →https://doi.org/10.1109/access.2018.2870151openalex-snapshot
An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset
inferredcites →https://doi.org/10.1109/access.2019.2911358openalex-snapshot
Automated Product Boundary Defect Detection Based on Image Moment Feature Anomaly
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SCueU-Net: Efficient Damage Detection Method for Railway Rail
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A Machine Vision-based Realtime Anomaly Detection Method for Industrial Products Using Deep Learning
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Latent Space Autoregression for Novelty Detection
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Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
inferredcites →https://doi.org/10.1109/ijcnn.2010.5596537openalex-snapshot
Change detection tests using the ICI rule
inferredcites →https://doi.org/10.1109/ijcnn.2016.7727309openalex-snapshot
Echo-state conditional variational autoencoder for anomaly detection
inferredcites →https://doi.org/10.1109/ijcnn.2018.8489464openalex-snapshot
Deep Tree Echo State Networks
inferredcites →https://doi.org/10.1109/ipsn.2008.35openalex-snapshot
Using Echo State Networks for Anomaly Detection in Underground Coal Mines
inferredcites →https://doi.org/10.1109/jestie.2021.3056692openalex-snapshot
Package Leak Detection Based on Aperiodic Anomaly Extraction
inferredcites →https://doi.org/10.1109/jsen.2017.2704584openalex-snapshot
Anomaly detection and visual perception for landslide monitoring based on a heterogeneous sensor network
inferredcites →https://doi.org/10.1109/lsens.2020.3007880openalex-snapshot
Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures
inferredcites →https://doi.org/10.1109/mmsp.2017.8122281openalex-snapshot
Block-based compressed sensing of images via deep learning
inferredcites →https://doi.org/10.1109/ssiai.2014.6806033openalex-snapshot
Removing JPEG blocking artifacts using machine learning
inferredcites →https://doi.org/10.1109/tgrs.2020.3005924openalex-snapshot
Hyperspectral Anomaly Detection via Image Super-Resolution Processing and Spatial Correlation
inferredcites →https://doi.org/10.1109/tie.2018.2844805openalex-snapshot
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
inferredcites →https://doi.org/10.1109/tii.2019.2952917openalex-snapshot
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
inferredcites →https://doi.org/10.1109/tim.2021.3059321openalex-snapshot
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A Model-Driven Scheme to Compensate the Strain-Based Non-Intrusive Dynamic Pressure Measurement for Hydraulic Pipe
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Anomaly Monitoring Framework in Lane Detection With a Generative Adversarial Network
inferredcites →https://doi.org/10.1109/tkde.2013.184openalex-snapshot
Outlier Detection for Temporal Data: A Survey
inferredcites →https://doi.org/10.1109/tmi.2016.2521442openalex-snapshot
Multiple-Instance Learning for Anomaly Detection in Digital Mammography
inferredcites →https://doi.org/10.1109/tnn.2006.875977openalex-snapshot
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
inferredcites →https://doi.org/10.1109/tnnls.2012.2208655openalex-snapshot
Feedback Control by Online Learning an Inverse Model
inferredcites →https://doi.org/10.1109/tnsre.2019.2948798openalex-snapshot
Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
inferredcites →https://doi.org/10.1109/tpwrs.2017.2656939openalex-snapshot
Data Improving in Time Series Using ARX and ANN Models
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1109/tsmcb.2011.2168604openalex-snapshot
Extreme Learning Machine for Regression and Multiclass Classification
inferredcites →https://doi.org/10.1109/tsmcc.2013.2261984openalex-snapshot
A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences
inferredcites →https://doi.org/10.1109/tsusc.2019.2906657openalex-snapshot
An Integrated Framework for Privacy-Preserving Based Anomaly Detection for Cyber-Physical Systems
inferredcites →https://doi.org/10.1109/wts.2018.8363930openalex-snapshot
Autoencoder-based network anomaly detection
inferredcites →https://doi.org/10.2352/issn.2470-1173.2017.7.mwsf-329openalex-snapshot
Image Recapture Detection with Convolutional and Recurrent Neural Networks
inferredcites →https://doi.org/10.3390/app10155191openalex-snapshot
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
inferredcites →https://doi.org/10.3390/en11102561openalex-snapshot
Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System
inferredcites →https://doi.org/10.3390/en12183411openalex-snapshot
A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network
inferredcites →https://doi.org/10.3390/en13133414openalex-snapshot
Analysis of the Gearbox Oil Maintenance Procedures in Wind Energy
inferredcites →https://doi.org/10.3390/rs12061015openalex-snapshot
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
inferredcites →https://doi.org/10.4028/www.scientific.net/amr.744.87openalex-snapshot
The Influence of Gearbox Oil-Leak to the Bolt Joint in Wind Turbine Tower
inferredcites →https://doi.org/10.48550/arxiv.1904.09245openalex-snapshot
Deep Pattern of Time Series and Its Applications in Estimation, Forecasting, Fault Diagnosis and Target Tracking
inferredcites →https://doi.org/10.5201/ipol.2019.263openalex-snapshot
How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise
inferredcites →https://doi.org/10.5962/bhl.title.68934openalex-snapshot
Ridge, a computer program for calculating ridge regression estimates /
inferredcites →https://doi.org/10.7717/peerj.453openalex-snapshot
scikit-image: image processing in Python
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market
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Direct Power Control Based on Modified Sliding Mode Controller for a Variable-Speed Multi-Rotor Wind Turbine System Using PWM Strategy
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