Journal ArticleDOI
Magnetic anomaly detection based on fast convergence wavelet artificial neural network in the aeromagnetic field
TLDR
An OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR and has higher training accuracy and better stability is proposed.About:
This article is published in Measurement.The article was published on 2021-05-01. It has received 10 citations till now. The article focuses on the topics: Wavelet & Detector.read more
Citations
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Journal ArticleDOI
Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest
TL;DR: Convolutional autoencoder networks are utilized to mine latent high-level features for each predictor variable in parallel to prevent the mixing of features and to enhance the feature mapping outcomes of each channel.
Journal ArticleDOI
A Deep learning based malicious module identification using Stacked Sparse Autoencoder Network for VLSI circuit reliability
TL;DR: In this paper , a deep learning-based malicious module identification method is proposed in this work by implementing stacked autoencoder and stacked sparse auto-encoder model, which outperforms the best in detecting the malicious modifications with an average accuracy of 97.53%, true positive rate of 93% and moreover the true negative rate achieved is 98.14%.
Proceedings ArticleDOI
A Glimpse of Research on Underwater Target Intelligent Detection Technology
TL;DR: This article compares the characteristics of different methods, clarifies the problems in the technology, and analyzes the trends to provides a reference for further research on underwater target detection technology.
Journal ArticleDOI
Isolation Forest Based on Minimal Spanning Tree
TL;DR: In this study, a novel efficient approach based on Isolation Forest is proposed, which has proven to produce better results than other compared state-of-the-art methods available in popular data mining programming libraries.
Journal ArticleDOI
Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm
TL;DR: A magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm which can train an effective detection model with only a small number of negative samples and has a higher detection rate under different SNR of Gaussian color noise.
References
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Journal ArticleDOI
Wavelet neural networks: A practical guide
TL;DR: This study presents a complete statistical model identification framework in order to apply WNs in various applications and shows that the proposed algorithms produce stable and robust results indicating that the framework can be applied inVarious applications.
Journal ArticleDOI
Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions
TL;DR: A novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage.
Journal ArticleDOI
Low-Order Dominant Harmonic Estimation Using Adaptive Wavelet Neural Network
TL;DR: The test results confirm that the proposed method accurately estimates the dominant low-order harmonics in pragmatic situations of fundamental frequency deviation, presence of interharmonics, low signal-to-noise ratio, etc.
Journal ArticleDOI
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
Léonard Seydoux,Randall Balestriero,Piero Poli,Maarten V. de Hoop,Michel Campillo,Richard G. Baraniuk +5 more
TL;DR: A new unsupervised machine learning framework that can detect and cluster seismic signals in continuous seismic records is developed and demonstrated the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture.
Journal ArticleDOI
Processing of magnetic scalar gradiometer signals using orthonormalized functions
TL;DR: In this paper, the authors used the Gram-Schmidt algorithm to decompose the magnetic anomaly detector signal into orthogonal functions (an orthonormal basis) constructed with the use of a gradiometer that comprises two scalar sensors and functions as a magnetostatic dipole.
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