The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,whi...The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.展开更多
The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void ...The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam,two transmission detectors, and one scattering detector. In this work, artificial neural network(ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.展开更多
Objective Tuberculosis(TB)is among the most frequent causes of infectious-disease-related mortality.Despite being treatable by antibiotics,tuberculosis often goes misdiagnosed and untreated,especially in rural and low...Objective Tuberculosis(TB)is among the most frequent causes of infectious-disease-related mortality.Despite being treatable by antibiotics,tuberculosis often goes misdiagnosed and untreated,especially in rural and low-resource areas.Chest X-rays are frequently used to aid diagnosis;however,this presents additional challenges because of the possibility of abnormal radiological appearance and a lack of radiologists in areas where the infection is most prevalent.Implementing deep-learning-based imaging techniques for computer-aided diagnosis has the potential to enable accurate diagnoses and lessen the burden on medical specialists.In the present work,we aimed to develop deep-learning-based segmentation and classification models for accurate and precise detection of tuberculosis in chest X-ray images,with visualization of infection using gradient-weighted class activation mapping(Grad-CAM)heatmaps.Methods First,we trained the UNet segmentation model using 704 chest X-ray radiographs taken from the Montgomery County and Shenzhen Hospital datasets.Next,we implemented the trained UNet model on 1,400 tuberculosis and control chest X-ray scans to segment the lung region.The images were taken from the National Institute of Allergy and Infectious Diseases(NIAID)TB portal program dataset.Then,we applied the deep learning Xception model to classify the segmented lung region into tuberculosis and normal classes.We further investigated the visualization capabilities of the model using Grad-CAM to view tuberculosis abnormalities in chest X-rays and discuss them from radiological perspectives.Results For segmentation by the UNet model,we achieved accuracy,Jaccard index,Dice coefficient,and area under the curve(AUC)values of 96.35%,90.38%,94.88%,and 0.99,respectively.For classification by the Xception model,we achieved classification accuracy,precision,recall,F1-score,and AUC values of 99.29%,99.30%,99.29%,99.29%,and 0.999,respectively.The Grad-CAM heatmap images from the tuberculosis class showed similar heatmap patterns,where l展开更多
Although lithium iodide(LiI)as a redox mediator(RM)can decrease the overpotential in Li-O_(2)batteries,the stability of the Li anode is still one critical issue due to the redox shuttling.Here,we firstly present a nov...Although lithium iodide(LiI)as a redox mediator(RM)can decrease the overpotential in Li-O_(2)batteries,the stability of the Li anode is still one critical issue due to the redox shuttling.Here,we firstly present a novel approach for generating Ag and LiTFSI enriched Li anode(designated as ALE@Li anode)via a spontaneous substitution between pure Li and bis(trifluoromethanesulfonyl)imide silver,in a LiI-participated Li-O_(2)cell.It can induce the generation of a lithiophilic solid electrolyte interphase(SEI)enriched with Ag,F,and N species(e.g.,Ag_(2)O,Li-Ag alloy,LiF,and Li_(3)N)during cell operation,which contributes to promoting the electrochemical performance through the shuttling inhibition.Compared to a cell with a bare Li anode,the one with as-prepared ALE@Li anode shows an enhanced cyclability,a considerable rate capability,and a good reversibility.In addition,a synchrotron X-ray computed tomography technique is employed to investigate the inhibition mechanism for shuttling effect by monitoring the morphological evolution on the cell interfaces.Therefore,this work highlights the deliberate design in the modified Li anode in an easy-to-operate and cost-effective way as well as providing guidance for the construction of artificial SEI layers to suppress the redox shuttling of RMs in Li-O_(2)batteries.展开更多
The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)an...The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.展开更多
In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specif...In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.展开更多
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imagin...A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.展开更多
An intergeneric artificial hybridization was conducted between Cunninghamia R. Br. and Cryptomeria D.Don The results are as follows:1. A considerable number of hybrid seeds shed from 76 pollinated cones were ...An intergeneric artificial hybridization was conducted between Cunninghamia R. Br. and Cryptomeria D.Don The results are as follows:1. A considerable number of hybrid seeds shed from 76 pollinated cones were empty and a total of 628 looks plump. Soft X ray radiographs showed that, still and all, a majority of the “plump" seeds were embryoless (597, 95.6%) whereas some were partially developed (17,2.7%) and only a few were really full (14, 2.2%). 2. Germination test showed that all of the radiographed hybrid seeds with fully developed embryos were germinable whereas those with partially developed embryos were ungerminable. 3. Physiologically, the growth rate of hypocotyl, the date for shedding of seed coat and spreading of cotyledons, the elongation of epicotyl, and the branching of shoot of the 11 month old seedlings showed a tendency to fall behind those of the female parent; morphologically, the 11 month old hybrid seedlings with linear leaves appeared rather short, slender and weak, whereas the seedlings of the female parents with linear_lanceolate leaves appeared rather tall, stout and strong. 4. It is considered that the hybrid may be true and the crossability reveals a close phylogenetic affinity of Cunninghamia with Cryptomeria.展开更多
The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary.The situation is very complex as the COVID-19 test kits are limited,th...The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary.The situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily identified.Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19.Motivated by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images.However,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several countries.This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN models.The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are luxuries.Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.展开更多
Explosive reactive armor(ERA)is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets.Considering mobility,the...Explosive reactive armor(ERA)is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets.Considering mobility,the aim is to design a protection system with a minimal amount of required mass.The efficiency of an ERA is sensitive to the impact position and the timing of the detonation.Therefore,different designs have to be tested for several impact scenarios to identify the best design.Since analytical models are not predicting the behavior of the ERA accurately enough and experiments,as well as numerical simulations,are too time-consuming,a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here.The ground truth for the artificial neural network(ANN)is numerical simulation results that are validated with experiments.The ANN approximates the plate positions for different materials,plate sizes,and detonation point positions with sufficient accuracy in real-time.In a future investigation,the results from the model can be used to estimate the interaction of the ERA with a given threat.Then,a measure for the effectiveness of an ERA can be calculated.Finally,an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time.展开更多
文摘The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.
基金the financial support of Kermanshah University of Technology for this research under grant number S/P/T/1102
文摘The ability to precisely estimate the void fraction of multiphase flow in a pipe is very important in the petroleum industry. In this paper, an approach based on our previous works is proposed for predicting the void fraction independent of flow regime and liquid phase density changes in gas–liquid two-phase flows. Implemented technique is a combination of dual modality densitometry and multi-beam gamma-ray attenuation techniques. The detection system is comprised of a single energy fan beam,two transmission detectors, and one scattering detector. In this work, artificial neural network(ANN) was also implemented to predict the void fraction percentage independent of the flow regime and liquid phase density changes. Registered counts in three detectors and void fraction percentage were utilized as the inputs and output of ANN, respectively. By applying the proposed methodology, the void fraction was estimated with a mean relative error of less than just 1.2480%.
文摘Objective Tuberculosis(TB)is among the most frequent causes of infectious-disease-related mortality.Despite being treatable by antibiotics,tuberculosis often goes misdiagnosed and untreated,especially in rural and low-resource areas.Chest X-rays are frequently used to aid diagnosis;however,this presents additional challenges because of the possibility of abnormal radiological appearance and a lack of radiologists in areas where the infection is most prevalent.Implementing deep-learning-based imaging techniques for computer-aided diagnosis has the potential to enable accurate diagnoses and lessen the burden on medical specialists.In the present work,we aimed to develop deep-learning-based segmentation and classification models for accurate and precise detection of tuberculosis in chest X-ray images,with visualization of infection using gradient-weighted class activation mapping(Grad-CAM)heatmaps.Methods First,we trained the UNet segmentation model using 704 chest X-ray radiographs taken from the Montgomery County and Shenzhen Hospital datasets.Next,we implemented the trained UNet model on 1,400 tuberculosis and control chest X-ray scans to segment the lung region.The images were taken from the National Institute of Allergy and Infectious Diseases(NIAID)TB portal program dataset.Then,we applied the deep learning Xception model to classify the segmented lung region into tuberculosis and normal classes.We further investigated the visualization capabilities of the model using Grad-CAM to view tuberculosis abnormalities in chest X-rays and discuss them from radiological perspectives.Results For segmentation by the UNet model,we achieved accuracy,Jaccard index,Dice coefficient,and area under the curve(AUC)values of 96.35%,90.38%,94.88%,and 0.99,respectively.For classification by the Xception model,we achieved classification accuracy,precision,recall,F1-score,and AUC values of 99.29%,99.30%,99.29%,99.29%,and 0.999,respectively.The Grad-CAM heatmap images from the tuberculosis class showed similar heatmap patterns,where l
基金supported by Science and Technology Project of Jilin Provincial Education Department(grant no.JJKH20221160KJ)Jilin Province Science and Technology Department(grant no.20230402059GH)+1 种基金The Swedish Foundation for International Cooperation in Research and Higher Education(grant no.KO2017-7351)Swedish Energy Agency(Project no.P2020-90216).
文摘Although lithium iodide(LiI)as a redox mediator(RM)can decrease the overpotential in Li-O_(2)batteries,the stability of the Li anode is still one critical issue due to the redox shuttling.Here,we firstly present a novel approach for generating Ag and LiTFSI enriched Li anode(designated as ALE@Li anode)via a spontaneous substitution between pure Li and bis(trifluoromethanesulfonyl)imide silver,in a LiI-participated Li-O_(2)cell.It can induce the generation of a lithiophilic solid electrolyte interphase(SEI)enriched with Ag,F,and N species(e.g.,Ag_(2)O,Li-Ag alloy,LiF,and Li_(3)N)during cell operation,which contributes to promoting the electrochemical performance through the shuttling inhibition.Compared to a cell with a bare Li anode,the one with as-prepared ALE@Li anode shows an enhanced cyclability,a considerable rate capability,and a good reversibility.In addition,a synchrotron X-ray computed tomography technique is employed to investigate the inhibition mechanism for shuttling effect by monitoring the morphological evolution on the cell interfaces.Therefore,this work highlights the deliberate design in the modified Li anode in an easy-to-operate and cost-effective way as well as providing guidance for the construction of artificial SEI layers to suppress the redox shuttling of RMs in Li-O_(2)batteries.
文摘The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.
文摘In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.
文摘A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.
文摘An intergeneric artificial hybridization was conducted between Cunninghamia R. Br. and Cryptomeria D.Don The results are as follows:1. A considerable number of hybrid seeds shed from 76 pollinated cones were empty and a total of 628 looks plump. Soft X ray radiographs showed that, still and all, a majority of the “plump" seeds were embryoless (597, 95.6%) whereas some were partially developed (17,2.7%) and only a few were really full (14, 2.2%). 2. Germination test showed that all of the radiographed hybrid seeds with fully developed embryos were germinable whereas those with partially developed embryos were ungerminable. 3. Physiologically, the growth rate of hypocotyl, the date for shedding of seed coat and spreading of cotyledons, the elongation of epicotyl, and the branching of shoot of the 11 month old seedlings showed a tendency to fall behind those of the female parent; morphologically, the 11 month old hybrid seedlings with linear leaves appeared rather short, slender and weak, whereas the seedlings of the female parents with linear_lanceolate leaves appeared rather tall, stout and strong. 4. It is considered that the hybrid may be true and the crossability reveals a close phylogenetic affinity of Cunninghamia with Cryptomeria.
文摘The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary.The situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily identified.Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19.Motivated by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images.However,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several countries.This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN models.The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are luxuries.Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.
文摘Explosive reactive armor(ERA)is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets.Considering mobility,the aim is to design a protection system with a minimal amount of required mass.The efficiency of an ERA is sensitive to the impact position and the timing of the detonation.Therefore,different designs have to be tested for several impact scenarios to identify the best design.Since analytical models are not predicting the behavior of the ERA accurately enough and experiments,as well as numerical simulations,are too time-consuming,a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here.The ground truth for the artificial neural network(ANN)is numerical simulation results that are validated with experiments.The ANN approximates the plate positions for different materials,plate sizes,and detonation point positions with sufficient accuracy in real-time.In a future investigation,the results from the model can be used to estimate the interaction of the ERA with a given threat.Then,a measure for the effectiveness of an ERA can be calculated.Finally,an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time.
