Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

Wan, Yadong; Li, Tong; Wang, Peng; Duan, Shihong; Zhang, Chao; Li, Na

doi:10.1109/access.2021.3049308

Cited by 16 publications

(6 citation statements)

References 81 publications

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“…Two matrixes were created to observe the correlation between input variables: one based on the Pearson correlation coefficient (Wan et al, 2021 ) and one on the Spearman correlation coefficient (Ghosh et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior

Castillo-Sánchez

Jojoa

Garcia-Zapirain

et al. 2022

Int J Ment Health Addiction

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Suicide was the main source of death from external causes in Spain in 2020, with 3,941 cases. The importance of identifying those mental disorders that influenced hospital readmissions will allow us to manage the health care of suicidal behavior. The feature selection of each hospital in this region was carried out by applying Machine learning (ML) and traditional statistical methods. The results of the characteristics that best explain the readmissions of each hospital after assessment by the psychiatry specialist are presented. Adjustment disorder, alcohol abuse, depressive syndrome, personality disorder, and dysthymic disorder were selected for this region. The most influential methods or characteristics associated with suicide were benzodiazepine poisoning, suicidal ideation, medication poisoning, antipsychotic poisoning, and suicide and/or self-harm by jumping. Suicidal behavior is a concern in our society, so the results are relevant for hospital management and decision-making for its prevention.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior

Castillo-Sánchez

Jojoa

Garcia-Zapirain

et al. 2022

Int J Ment Health Addiction

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“…Machine learning algorithms and electromagnetic pulses including a two-sided decaying exponential, Gaussian, triangular, raised cosine, rectangular, and rectangular chirp, were used to classify metallic objects [83]. Thirty-three classification strategies based on eleven dimensionality reduction methods were used on simulated data of the time domain decay of metallic ellipsoids nearby an EMI system [84]. Accuracy of 99% for material-based and shape-based classification was achieved.…”

Section: Machine Learning Classification Of Metallic Objects Using Em...mentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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“…The depth of metallic objects was estimated using a pulse induction metal detector and a 1D CNN [53]. Simulated data of the time domain decay of metallic ellipsoids using an orthogonal dipole model nearby an EMI system were classified using machine learning techniques [61]. Thirty-three classification strategies based on eleven dimensionality reduction methods were investigated, including artificial neural networks, with the best classification achieving 99% accuracy for material-based and shape-based classification.…”

Section: Introductionmentioning

confidence: 99%

Machine learning classification of metallic objects using pulse induction electromagnetic data

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents the classification of metallic objects using total and scattered pulse induction electromagnetic data, with a classification accuracy greater than 90%. Machine learning classification is applied to raw electromagnetic induction (EMI) data without the use of a physics-based model. The EMI method is applied to 8 metallic objects placed at increasing distances from 10–55 mm to the EMI sensing system. The EMI sensing system consists of two RL circuits placed in close proximity. Metallic objects are classified using linear algorithms including a perceptron and multiclass logistic regression, and nonlinear algorithms including a neural network,a 1D and 2D convolutional neural network (CNN). EMI data was collected using an experiment in an electromagnetically shielded laboratory. Feature maps are presented that explain the salient components of the EMI data used by the 1D and 2D CNN.

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Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

Cited by 16 publications

References 81 publications

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Machine learning classification of metallic objects using pulse induction electromagnetic data

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