地震数据的稀疏高斯束分解方法

本文研究了地震数据的稀疏分解问题.提出了一种用高斯束稀疏分解表示地震数据的方法.这是一个拟0范数约束优化问题.在求解拟0范数极小化问题的过程中,通过扫描同相轴的方法实现高斯束稀疏分解,数值实现上提出了使用一种快速单调下降的梯度优化方法.本文提出的稀疏优化方法同时具有去噪的功能,数据模拟试验表明了本方法的可行性和可靠性.

Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection

Diseases of the cardiovascular system are one of the major causes of death worldwide.These diseases could be quickly detected by changes in the sound created by the action of the heart.This dynamic auscultations need extensive professional knowledge and emphasis on listening skills.There is also an unmet requirement for a compact cardiac condition early warning device.In this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods.This system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable form.The electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making system.The decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart sounds.The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to patients.As a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%accuracy.Moreover,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

In the field of stroke imaging, deep learning (DL) has enormousuntapped potential.When clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate categorization.Image Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.

Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis.Unfortunately,at the moment,the models for solving this problem using machine learning methods are far from ideal.In this paper,we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3Dcomputed tomography images.We use the ISLES 2018(Ischemic Stroke Lesion Segmentation Challenge 2018)open dataset to train and test the proposed model.Interpretation of the obtained results,as well as the ideas for further experiments are included in the paper.Our evaluation is performed using the Dice or f1 score coefficient and the Jaccard index.Our architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant hyperparameters.The Dice/f1 score similarity coefficient of our model shown58%and results close to ground truth which is higher than the standard 3D UNet model,demonstrating that our model can accurately segment ischemic stroke.The modified 3D UNet model proposed by us uses an efficient averaging method inside a neural network.Since this set of ISLES is limited in number,using the data augmentation method and neural network regularization methods to prevent overfitting gave the best result.In addition,one of the advantages is the use of the Intersection over Union loss function,which is based on the assessment of the coincidence of the shapes of the recognized zones.

ON SOLVABILITY OF A BOUNDARY VALUE PROBLEM FOR A NONHOMOGENEOUS BIHARMONIC EQUATION WITH A BOUNDARY OPERATOR OF A FRACTIONAL ORDER

This paper is concerned with the solvability of a boundary value problem for a nonhomogeneous biharmonic equation. The boundary data is determined by a differential operator of fractional order in the Riemann-Liouville sense. The considered problem is a generalization of the known Dirichlet and Neumann problems.

Features of Polymethacrylic Acid and Poly-2-Methyl-5-Vinylpyridine Hydrogels Remote Interaction in an Aqueous Medium

The remote interaction of polymethacrylic acid hydrogel with a poly-2-methyl-5-vinylpyridine hydrogel was studied. The aim of work was to study the dependence of the swelling coefficient, the conductivity and the pH of the water solutions of intergel system at different mass ratios from time were studied. The goal was achieved by using following methods： pH-metry, conductometry and gravimetry.

Application of the Fictitious Domain Method for Navier-Stokes Equations

To apply the fictitious domain method and conduct numericalexperiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different valuesof the iterative parameter τ and the small parameter ε are presented. Astudy of the auxiliary problem of the fictitious domain method for NavierStokes equations with continuation into a fictitious subdomain by highercoefficients with a small parameter is carried out. A generalized solutionof the auxiliary problem of the fictitious domain method with continuationby higher coefficients with a small parameter is determined. After all theabove mathematical studies, a computational algorithm has been developedfor the numerical solution of the problem. Two methods were used to solvethe problem numerically. The first variant is the fictitious domain methodassociated with the modification of nonlinear terms in a fictitious subdomain.The model problem shows the effectiveness of using such a modification. Theproposed version of the method is used to solve two problems at once that arisewhile numerically solving systems of Navier-Stokes equations: the problem ofa curved boundary of an arbitrary domain and the problem of absence of aboundary condition for pressure in physical formulation of the internal flowproblem. The main advantage of this method is its universality in developmentof computer programs. The second method used calculation on a uniform gridinside the area. When numerically implementing the solution on a uniformgrid inside the domain, using this method it’s possible to accurately take intoaccount the boundaries of the curved domain and ensure the accuracy of thevalue of the function at the boundaries of the domain. Methodical calculationswere carried out, the results of numerical calculations were obtained. Whenconducting numerical experiments in both cases, quantitative and qualitativeindicators of numerical results coincide.

Indoor Air Quality Control Using Backpropagated Neural Networks

Providing comfortable indoor air quality control in residential construction is an exceedingly important issue.This is due to the structure of the fast response controller of air quality.The presented work shows the breakdown and creation of a mathematical model for an interactive,nonlinear system for the required comfortable air quality.Furthermore,the paper refers to designing traditional proportional integral derivative regulators and proportional,integral,derivative regulators with independent parameters based on a backpropagation neural network.In the end,we perform the experimental outputs of a suggested backpropagation neural network-based proportional,integral,derivative controller and analyze model results by applying the proposed system.The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room.The proposed developed model takes into consideration international Heating,Refrigerating,and air conditioning standards as ASHRAE AND ISO.Based on the findings,we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.

Convective Instability and Appearance of Structured Flows for Diffusion in Multicomponent Gas Mixtures

The main objective of this article is to investigate the behavior of gaseous systems with two and more independent gradients or thermodynamic forces exhibiting complicated behavior,when the convective flows occur.The existence of structural formations in these systems is shown by the schlieren method and the fast-response transducers.The linear analysis of stability can explain reasons of the appearance of convective instability in multicomponent gas mixtures.

A Deep Learning-Based Approach for Road Surface Damage Detection

Timely detection and elimination of damage in areas with excessive vehicle loading can reduce the risk of road accidents.Currently,various methods of photo and video surveillance are used to monitor the condition of the road surface.The manual approach to evaluation and analysis of the received data can take a protracted period of time.Thus,it is necessary to improve the procedures for inspection and assessment of the condition of control objects with the help of computer vision and deep learning techniques.In this paper,we propose a model based on Mask Region-based Convolutional Neural Network(Mask R-CNN)architecture for identifying defects of the road surface in the real-time mode.It shows the process of collecting and the features of the training samples and the deep neural network(DNN)training process,taking into account the specifics of the problems posed.For the software implementation of the proposed architecture,the Python programming language and the TensorFlow framework were utilized.The use of the proposed model is effective even in conditions of a limited amount of source data.Also as a result of experiments,a high degree of repeatability of the results was noted.According to the metrics,Mask R-CNN gave the high detection and segmentation results showing 0.9214,0.9876,0.9571 precision,recall,and F1-score respectively in road damage detection,and Intersection over Union(IoU)-0.3488 and Dice similarity coefficient-0.7381 in segmentation of road damages.