Fuzzy-rough set and fuzzy ID3 decision approaches to knowledge discovery in datasets

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets mainly concentrate on the construction of approximation operators. Less effort has been put on the knowledge discovery in datasets with fuzzy rough sets. This paper mainly focuses on knowledge discovery in datasets with fuzzy rough sets. After analyzing the previous works on knowledge discovery with fuzzy rough sets, we introduce formal concepts of attribute reduction with fuzzy rough sets and completely study the structure of attribute reduction

Experimental and numerical analysis of imbibition processes in a corrugated capillary tube

Spontaneous imbibition is a capillary-driven flow phenomenon that exists widely in nature and is important for several industries. Recently, Tolman length has been introduced to improve the classical Lucas-Washburn imbibition model, in order to alleviate the deviations in calculating the capillary pressure. However, imbibition experiments to measure Tolman length have been scarce. In addition, the fluid-wall friction has a considerable impact on the imbibition process, while it is often ignored. In this work, imbibition experiments under specific conditions are carried out to measure the values of Tolman length, and the fluid-wall friction is taken into consideration in the equilibrium equation. The water uptake model in  fractures is adopted to make corrections to the rise of water level. The experimental results show that Tolman length decreases first and then rises with the increasing curvature radius of liquid-gas interface. The data reveal that the Tolman length-based model can better describe the real imbibition processes than the classical Lucas-Washburn model.Cited as: Wang, J., Salama, A., Kou, J. Experimental and numerical analysis of imbibition processes in a corrugated capillary tube. Capillarity, 2022, 5(5): 83-90. https://doi.org/10.46690/capi.2022.05.0

Upscaling of Permeability Field of Fractured Rock System: Numerical Examples

When the permeability field of a given porous medium domain is heterogeneous by the existence of randomly distributed fractures such that numerical investigation becomes cumbersome, another level of upscaling may be required. That is such complex permeability field could be relaxed (i.e., smoothed) by constructing an effective permeability field. The effective permeability field is an approximation to the real permeability field that preserves certain quantities and provides an overall acceptable description of the flow field. In this work, the effective permeability for a fractured rock system is obtained for different coarsening scenarios starting from very coarse mesh all the way towards the fine mesh simulation. In all these scenarios, the effective permeability as well as the pressure at each cell is obtained. The total flux at the exit boundary is calculated in all these cases, and very good agreement is obtained

Compressive Sensing Based Estimation of Direction of Arrival in Antenna Arrays

This thesis is concerned with the development of new compressive sensing (CS) techniques both in element space and beamspace for estimating the direction of arrival of various types of sources, including moving sources as well as fluctuating sources, using one-dimensional antenna arrays. The problem of estimating the angle of arrival of a plane electromagnetic wave is referred to as the direction of arrival (DOA) estimation problem. Such algorithms for estimating DOA in antenna arrays are often used in wireless communication network to increase their capacity and throughput. DOA techniques can be used to design and adapt the directivity of the array antennas. For example, an antenna array can be designed to detect a number of incoming signals and accept signals from certain directions only, while rejecting signals that are declared as interference. This spatio-temporal estimation and filtering capability can be exploited for multiplexing co-channel users and rejecting harmful co-channel interference that may occur because of jamming or multipath effects. In this study, three CS-based DOA estimation methods are proposed, one in the element space (ES), and the other two in the beamspace (BS). The proposed techniques do not require a priori knowledge of the number of sources to be estimated. Further, all these techniques are capable of handling both non-fluctuating and fluctuating source signals as well as moving signals. The virtual array concept is utilized in order to be able to identify more number of sources than the number of the sensors used. In element space, an extended version of the least absolute shrinkage and selection operator (LASSO) algorithm, the adaptable LASSO (A-LASSO), is presented. A-LASSO is utilized to solve the DOA problem in compressive sensing framework. It is shown through extensive simulations that the proposed algorithm outperforms the classical DOA estimation techniques as well as LASSO using a small number of snapshots. Furthermore, it is able to estimate coherent as well as spatially-close sources. This technique is then extended to the case of DOA estimation of the sources in unknown noise fields. In beamspace, two compressive sensing techniques are proposed for DOA estimation, one in full beamspace and the other in multiple beam beamspace. Both these techniques are able to estimate correlated source signals as well as spatially-close sources using a small number of snapshots. Furthermore, it is shown that the computational complexity of the two beamspace-based techniques is much less than that of the element-space based technique. It is shown through simulations that the performance of the DOA estimation techniques in multiple beam beamspace is superior to that of the other two techniques proposed in this thesis, in addition to having the lowest computational complexity. Finally, the feasibility for real-time implementation of the proposed CS-based DOA estimation techniques, both in the element-space and the beamspace, is examined. It is shown that the execution time of the proposed algorithms on Raspberry Pi board are compatible for real-time implementation

An Efficient Method of Reweighting and Reconstructing Monte Carlo Molecular Simulation Data for Extrapolation to Different Temperature and Density Conditions

AbstractThis paper introduces an efficient technique to generate new molecular simulation Markov chains for different temperature and density conditions, which allow for rapid extrapolation of canonical ensemble averages at a range of temperatures and densities different from the original conditions where a single simulation is conducted. Obtained information from the original simulation are reweighted and even reconstructed in order to extrapolate our knowledge to the new conditions. Our technique allows not only the extrapolation to a new temperature or density, but also the double extrapolation to both new temperature and density. The method was implemented for Lennard-Jones fluid with structureless particles in single-gas phase region. Extrapolation behaviors as functions of extrapolation ranges were studied. Limits of extrapolation ranges showed a remarkable capability especially along isochors where only reweighting is required. Various factors that could affect the limits of extrapolation ranges were investigated and compared. In particular, these limits were shown to be sensitive to the number of particles used and starting point where the simulation was originally conducted

Numerical treatment of two-phase flow in porous media including specific interfacial area

In this work, we present a numerical treatment for the model of two-phase flow in porous media including specific interfacial area. For numerical discretization we use the cell-centered finite difference (CCFD) method based on the shifting-matrices method which can reduce the time-consuming operations. A new iterative implicit algorithm has been developed to solve the problem under consideration. All advection and advection-like terms that appear in saturation equation and interfacial area equation are treated using upwind schemes. Selected simulation results such as p(c) - S-w - a(wn) surface, capillary pressure, saturation and specific interfacial area with various values of model parameters have been introduced. The simulation results show a good agreement with those in the literature using either pore network modeling or Darcy scale modeling

Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls. Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the Radiology Society of North America (RSNA), and the Kaggle Chest X-ray Database for COVID-19 (n = 240), Viral Pneumonia (n = 274), and Normal Controls (n = 267). Images were resized, normalized, without any augmentation, and arranged in m batches of 16 images before supervised training, testing, and cross-validation of the DCNN classifier. Results: The ResNet-34 had a diagnostic accuracy as of the receiver operating characteristic (ROC) curves of the true-positive rate versus the false-positive rate with the area under the curve (AUC) of 1.00, 0.99, and 0.99, for COVID-19 and Viral Pneumonia patient and Normal control CXR images; respectively. This accuracy implied identical high sensitivity and specificity values of 100, 99, and 99% for the three groups, respectively. ResNet-34 achieved a success rate of 100%, 99.6%, and 98.9% for classifying CXR images of the three groups, with an overall accuracy of 99.5% for the testing subset for diagnosis/prognosis. Conclusions: Based on this high classification precision, we believe the output activation map of the final layer of the ResNet-34 is a powerful tool for the accurate diagnosis of COVID-19 infection from CXR images

Adaptive time-splitting scheme for two-phase flow in heterogeneous porous media

In the present paper, an adaptive time-splitting scheme is introduced to investigate the problem of two-phase flow in heterogeneous porous media. The pressure and saturation equations are coupled by the capillary pressure which is linearized in terms of saturation. An IMplicit Pressure Explicit Saturation scheme is used to solve the problem under consideration. We use the time schemes for the pressure and saturation equations. The external time interval is divided into two levels, the first level is for the pressure, the second one is for the saturation. This method can reduce the computational cost arisen from the implicit solution of the pressure equation and the rapid changes in saturation. The time-step size for saturation equation is adaptive under computing and satisfying the Courant–Friedrichs–Lewy (CFL<1) condition. In order to show the well performance of the suggested scheme, we introduce a numerical example of a highly heterogeneous porous medium. The adaptive time step-size is shown in graphs as well as the water saturation is shown in contours.Cited as: El-Amin, M., Kou, J., Sun, S., et al. Adaptive time-splitting scheme for two-phase flow in heterogeneous porous media. Advances in Geo-Energy Research, 2017, 1(3): 182-189, doi: 10.26804/ager.2017.03.0