Tikhonov Regularization Techniques in Simulated Brain Electrical Tomography

Abstract In this paper, a Cauchy problem for the Laplace equation is considered. We develop a modified Tikhonov regularization method based on Hermite expansion to deal with the ill posed-ness of the problem. The regularization parameter is determined by a discrepancy principle. For various smoothness conditions, the solution process of the method is uniform and the convergence rate can be obtained self-adaptively. Numerical tests are also carried out to verify the effectiveness of the method.

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Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks

IEEE/ACM Transactions on Audio Speech and Language Processing ◽

10.1109/taslp.2021.3082307 ◽

2021 ◽

pp. 1-1

Author(s):

Khaled Koutini ◽

Hamid Eghbal-zadeh ◽

Gerhard Widmer

Keyword(s):

Neural Networks ◽

Receptive Field ◽

Convolutional Neural Networks ◽

Audio Classification ◽

Deep Convolutional Neural Networks ◽

Regularization Techniques

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Microwave thermometry with potential application in non-invasive monitoring of hyperthermia

Journal of Inverse and Ill-Posed Problems ◽

10.1515/jiip-2020-0102 ◽

2020 ◽

Vol 28 (5) ◽

pp. 739-750

Author(s):

Morteza Ghaderi Aram ◽

Larisa Beilina ◽

Hana Dobsicek Trefna

Keyword(s):

Least Squares Method ◽

Regularization Parameter ◽

Region Of Interest ◽

Optimal Choice ◽

Adaptive Finite Element Method ◽

Adaptive Finite Element ◽

Non Invasive ◽

Full Wave ◽

Reconstruction Quality ◽

Regularization Techniques

AbstractIntegration of an adaptive finite element method (AFEM) with a conventional least squares method has been presented. As a 3D full-wave forward solver, CST Microwave Studio has been used to model and extract both electric field distribution in the region of interest (ROI) and S-parameters of a circular array consisting of 16 monopole antennas. The data has then been fed into a differential inversion scheme to get a qualitative indicator of how the temperature distribution evolves over a course of the cooling process of a heated object. Different regularization techniques within the Tikhonov framework are also discussed, and a balancing principle for optimal choice of the regularization parameter was used to improve the image reconstruction quality of every 2D slice of the final image. Targets are successfully imaged via proposed numerical methods.

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Sparse damage detection via the elastic net method using modal data

Structural Health Monitoring ◽

10.1177/14759217211021938 ◽

2021 ◽

pp. 147592172110219

Author(s):

Rongrong Hou ◽

Xiaoyou Wang ◽

Yong Xia

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Damage Identification ◽

Measurement Data ◽

Elastic Net ◽

Sensitivity Matrix ◽

Regularization Technique ◽

Modal Data ◽

Regularization Techniques ◽

Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

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Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Risks ◽

10.3390/risks9010005 ◽

2020 ◽

Vol 9 (1) ◽

pp. 5

Author(s):

Karim Barigou ◽

Stéphane Loisel ◽

Yahia Salhi

Keyword(s):

Life Insurance ◽

Cross Validation ◽

Cohort Effect ◽

Information Criterion ◽

Mortality Forecasting ◽

Hand Model ◽

Regularization Techniques ◽

Bayes Information Criterion ◽

The One ◽

Regularized Model

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

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Comparison between electrical resistivity tomography and tunnel seismic prediction 303 methods for detecting the water zone ahead of the tunnel face: A case study

Open Geosciences ◽

10.1515/geo-2020-0193 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1094-1104

Author(s):

Nima Dastanboo ◽

Xiao-Qing Li ◽

Hamed Gharibdoost

Keyword(s):

Electrical Resistivity ◽

Electrical Resistivity Tomography ◽

Geological Structure ◽

Rock Properties ◽

Electrical Tomography ◽

Tunnel Face ◽

Resistivity Tomography ◽

Geological Conditions ◽

Seismic Prediction

AbstractIn deep tunnels with hydro-geological conditions, it is paramount to investigate the geological structure of the region before excavating a tunnel; otherwise, unanticipated accidents may cause serious damage and delay the project. The purpose of this study is to investigate the geological properties ahead of a tunnel face using electrical resistivity tomography (ERT) and tunnel seismic prediction (TSP) methods. During construction of the Nosoud Tunnel located in western Iran, ERT and TSP 303 methods were employed to predict geological conditions ahead of the tunnel face. In this article, the results of applying these methods are discussed. In this case, we have compared the results of the ERT method with those of the TSP 303 method. This work utilizes seismic methods and electrical tomography as two geophysical techniques are able to detect rock properties ahead of a tunnel face. This study shows that although the results of these two methods are in good agreement with each other, the results of TSP 303 are more accurate and higher quality. Also, we believe that using another geophysical method, in addition to TSP 303, could be helpful in making decisions in support of excavation, especially in complicated geological conditions.

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