A stall inception model for transonic fan/compressors is presented in this paper. It can be shown that under some assumptions the solution of unsteady flow field consists of pressure wave which propagates upstream or ...A stall inception model for transonic fan/compressors is presented in this paper. It can be shown that under some assumptions the solution of unsteady flow field consists of pressure wave which propagates upstream or downstream, vortex wave and entropy wave convected with the mean flow speed. By further using the mode-matching technique and applying the conservation law and conditions reflecting the loss characteristics of a compressor in the inlet and outlet of the rotor or stator blade rows, a group of homogeneous equations can be obtained from which the stability equation can be derived. Based on the analysis of the unsteady phenomenon caused by casing treatments, the function of casing treatments has been modeled by a wall impedance condition which has been included in the stability model through the eigenvalues and the corresponding eigenfunctions of the system. Besides, the effect of shock waves in cascade channel on the stability prediction is also considered in the stall inception model. Finally, some numerical analysis and experimental investigation are also conducted with emphasis on the mutual comparison.展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at an...Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.展开更多
In this paper,a high-order distortion model is proposed for analyzing the rotating stall inception process induced by inlet distortion in axial compressors.A distortion-generating screen in the compressor inlet is con...In this paper,a high-order distortion model is proposed for analyzing the rotating stall inception process induced by inlet distortion in axial compressors.A distortion-generating screen in the compressor inlet is considered.By assuming a quadratic function for the local flow total pressure-drop,the existing Mansoux model is extended to include the effects of static inlet distortion,and a new high-order distortion model is derived.To illustrate the effectiveness of the distortion model,numerical simulations are performed on an eighteenth-order model.It is demonstrated that long length-scale disturbances emerge out of the distorted background flow,and further induce the onset of rotating stall in advance.In addition,the circumferential non-uniform distribution and time evolution of the axial flow are also shown to be consistent with the existing features.It is thus shown that the high-order distortion model is capable of describing the transient behavior of stall inception and will contribute further to stall detection under inlet distortion.展开更多
In the network security field,the network intrusion detection system(NIDS)is considered one of the critical issues in the detection accuracy andmissed detection rate.In this paper,amethod of two-step network intrusion...In the network security field,the network intrusion detection system(NIDS)is considered one of the critical issues in the detection accuracy andmissed detection rate.In this paper,amethod of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks(CNNs)models is proposed.The proposed method used the GoogLeNet Inception model to identify the network packets’binary problem.Subsequently,the characteristics of the packets’raw data and the traffic features are extracted.The CNNs model is also used to identify the multiclass intrusions by the network packets’features.In the experimental results,the proposed method shows an improvement in the identification accuracy,where it achieves up to 99.63%.In addition,the missed detection rate is reduced to be 0.1%.The results prove the high performance of the proposed method in enhancing the NIDS’s reliability.展开更多
Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological i...Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.展开更多
基金National Natural Science Foundation of China (50736007, 51010007)
文摘A stall inception model for transonic fan/compressors is presented in this paper. It can be shown that under some assumptions the solution of unsteady flow field consists of pressure wave which propagates upstream or downstream, vortex wave and entropy wave convected with the mean flow speed. By further using the mode-matching technique and applying the conservation law and conditions reflecting the loss characteristics of a compressor in the inlet and outlet of the rotor or stator blade rows, a group of homogeneous equations can be obtained from which the stability equation can be derived. Based on the analysis of the unsteady phenomenon caused by casing treatments, the function of casing treatments has been modeled by a wall impedance condition which has been included in the stability model through the eigenvalues and the corresponding eigenfunctions of the system. Besides, the effect of shock waves in cascade channel on the stability prediction is also considered in the stall inception model. Finally, some numerical analysis and experimental investigation are also conducted with emphasis on the mutual comparison.
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR11).
文摘Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.
基金co-supported by the National Major Scientific Instruments Development Project of China(No.61527811)the National Science Fund for Distinguished Young Scholars of China(No.61225014)+3 种基金the Guangdong Inovative Project(No.2013KJCX0009)the Guangdong Provice Natural Science Foundation(No.2014A030312005)the Guangdong Provice Key Laboratory of Biomedical Engineeringthe Space Intelligent Control Key Laboratory of Science and Technology for National Defense
文摘In this paper,a high-order distortion model is proposed for analyzing the rotating stall inception process induced by inlet distortion in axial compressors.A distortion-generating screen in the compressor inlet is considered.By assuming a quadratic function for the local flow total pressure-drop,the existing Mansoux model is extended to include the effects of static inlet distortion,and a new high-order distortion model is derived.To illustrate the effectiveness of the distortion model,numerical simulations are performed on an eighteenth-order model.It is demonstrated that long length-scale disturbances emerge out of the distorted background flow,and further induce the onset of rotating stall in advance.In addition,the circumferential non-uniform distribution and time evolution of the axial flow are also shown to be consistent with the existing features.It is thus shown that the high-order distortion model is capable of describing the transient behavior of stall inception and will contribute further to stall detection under inlet distortion.
基金This work was supported by the Education Department of Jilin Province(No.JJKH20180518KJ)Science and Technology Research Project of Jilin Business and Technology College(No.kz2018002).
文摘In the network security field,the network intrusion detection system(NIDS)is considered one of the critical issues in the detection accuracy andmissed detection rate.In this paper,amethod of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks(CNNs)models is proposed.The proposed method used the GoogLeNet Inception model to identify the network packets’binary problem.Subsequently,the characteristics of the packets’raw data and the traffic features are extracted.The CNNs model is also used to identify the multiclass intrusions by the network packets’features.In the experimental results,the proposed method shows an improvement in the identification accuracy,where it achieves up to 99.63%.In addition,the missed detection rate is reduced to be 0.1%.The results prove the high performance of the proposed method in enhancing the NIDS’s reliability.
基金Supporting this research through Taif University Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.
