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Full-Text Articles in Engineering
Application Of Artificial Neural Networks In The Drilling Processes: Can Equivalent Circulation Density Be Estimated Prior To Drilling?, Husam Hasan Alkinani, Abo Taleb Al-Hameedi, Shari Dunn-Norman, David Lian
Application Of Artificial Neural Networks In The Drilling Processes: Can Equivalent Circulation Density Be Estimated Prior To Drilling?, Husam Hasan Alkinani, Abo Taleb Al-Hameedi, Shari Dunn-Norman, David Lian
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
As the drilling environment became more challenging nowadays, managing equivalent circulating density (ECD) is a key factor to minimize non-productive time (NPT) due to many drilling obstacles such as stuck pipe, formation fracturing, and lost circulation. The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). Once ECD is recognized, the crucial drilling variables impact ECD can be modified to control ECD within the acceptable ranges. Data from over 2000 wells collected worldwide were used in this study to create an ANN to predict ECD prior to drilling. Into training, validation, and …
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Electrical and Computer Engineering Faculty Research & Creative Works
While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Comparative Analysis Of Feature Selection Methods To Identify Biomarkers In A Stroke-Related Dataset, Thomas Clifford, Justin Bruce, Tayo Obafemi-Ajayi, John Matta
Electrical and Computer Engineering Faculty Research & Creative Works
This paper applies machine learning feature selection techniques to the REGARDS stroke-related dataset to identify health-related biomarkers. A data-driven methodological framework is presented to evaluate multiple feature selection methods. In applying the framework, three classifiers are chosen in conjunction with two wrappers, and their performance with diverse classification targets such as Current Smoker, Current Alcohol Use, and Deceased is evaluated. The performance across logistic regression, random forest and naïve Bayes classifier methods, as quantified by the ROC Area Under Curve metric and selected features, was similar. However, significant differences were observed in running time. Performance of the selected features was …
Mud Loss Estimation Using Machine Learning Approach, Abo Taleb T. Al-Hameedi, Husam H. Alkinani, Shari Dunn-Norman, Ralph E. Flori, Steven Austin Hilgedick, Ahmed S. Amer, Mortadha Alsaba
Mud Loss Estimation Using Machine Learning Approach, Abo Taleb T. Al-Hameedi, Husam H. Alkinani, Shari Dunn-Norman, Ralph E. Flori, Steven Austin Hilgedick, Ahmed S. Amer, Mortadha Alsaba
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Lost circulation costs are a significant expense in drilling oil and gas wells. Drilling anywhere in the Rumaila field, one the world's largest oilfields, requires penetrating the Dammam formation, which is notorious for lost circulation issues and thus a great source of information on lost circulation events. This paper presents a new, more precise model to predict lost circulation volumes, equivalent circulation density (ECD), and rate of penetration (ROP) in the Dammam formation. A larger data set, more systematic statistical approach, and a machine-learning algorithm have produced statistical models that give a better prediction of the lost circulation volumes, ECD, …
Data-Driven Integral Reinforcement Learning For Continuous-Time Non-Zero-Sum Games, Yongliang Yang, Liming Wang, Hamidreza Modares, Dawei Ding, Yixin Yin, Donald C. Wunsch
Data-Driven Integral Reinforcement Learning For Continuous-Time Non-Zero-Sum Games, Yongliang Yang, Liming Wang, Hamidreza Modares, Dawei Ding, Yixin Yin, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper develops an integral value iteration (VI) method to efficiently find online the Nash equilibrium solution of two-player non-zero-sum (NZS) differential games for linear systems with partially unknown dynamics. To guarantee the closed-loop stability about the Nash equilibrium, the explicit upper bound for the discounted factor is given. To show the efficacy of the presented online model-free solution, the integral VI method is compared with the model-based off-line policy iteration method. Moreover, the theoretical analysis of the integral VI algorithm in terms of three aspects, i.e., positive definiteness properties of the updated cost functions, the stability of the closed-loop …
Source Reconstruction Method Based On Machine Learning Algorithms, He Ming Yao, Lijun Jiang, Wei E.I. Sha
Source Reconstruction Method Based On Machine Learning Algorithms, He Ming Yao, Lijun Jiang, Wei E.I. Sha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a new source reconstruction method (SRM) based on deep learning. The conventional SRM usually requires oversampled measurements data to ensure higher accuracy. Thus, conventional SRM numerical system is usually highly singular. A deep convolutional neural network (ConvNet) is proposed to reconstruct the equivalent sources of the target to overcome difficulty. The deep ConvNet allows us to employ less data samples. Besides, the ill-conditioned numerical system can be effectively avoided. Numerical examples are presented to demonstrate the feasibility and accuracy of the proposed method. Its performance is also compared with the traditional neural network and interpolation method. Moreover, …
Applying Deep Learning Approach To The Far-Field Subwavelength Imaging Based On Near-Field Resonant Metalens At Microwave Frequencies, He Ming Yao, Min Li, Lijun Jiang
Applying Deep Learning Approach To The Far-Field Subwavelength Imaging Based On Near-Field Resonant Metalens At Microwave Frequencies, He Ming Yao, Min Li, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added …
Machine-Learning-Based Pml For The Fdtd Method, He Ming Yao, Lijun Jiang
Machine-Learning-Based Pml For The Fdtd Method, He Ming Yao, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
In this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark …