The absorbing boundary is the key in numerical simulation of borehole radar.Perfect match layer(PML) was chosen as the absorbing boundary in numerical simulation of GPR.But CPML(convolutional perfect match layer) appr...The absorbing boundary is the key in numerical simulation of borehole radar.Perfect match layer(PML) was chosen as the absorbing boundary in numerical simulation of GPR.But CPML(convolutional perfect match layer) approach that we have chosen has the advantage of being media independent.Beginning with the Maxwell equations in a two-dimensional structure,numerical formulas of finite-difference time-domain(FDTD) method with CPML boundary condition for transverse electric(TE) or transverse magnetic(TM) wave are presented in details.Also,there are three models for borehole-GPR simulation.By analyzing the simulation results,the features of targets in GPR are obtained,which can provide a better interpretation of real radar data.The results show that CPML is well suited for the simulation of borehole-GPR.展开更多
Based on conformal construction of physical model in a three-dimensional Cartesian grid,an integral-based conformal convolutional perfectly matched layer（CPML） is given for solving the truncation problem of the open...Based on conformal construction of physical model in a three-dimensional Cartesian grid,an integral-based conformal convolutional perfectly matched layer（CPML） is given for solving the truncation problem of the open port when the enlarged cell technique conformal finite-difference time-domain（ECT-CFDTD） method is used to simulate the wave propagation inside a perfect electric conductor（PEC） waveguide.The algorithm has the same numerical stability as the ECT-CFDTD method.For the long-time propagation problems of an evanescent wave in a waveguide,several numerical simulations are performed to analyze the reflection error by sweeping the constitutive parameters of the integral-based conformal CPML.Our numerical results show that the integral-based conformal CPML can be used to efficiently truncate the open port of the waveguide.展开更多
A convolution perfectly matched layer(CPML)can efficiently absorb boundary reflection in numerical simulation.However,the CPML is suitable for the first-order elastic wave equation and is difficult to apply directly t...A convolution perfectly matched layer(CPML)can efficiently absorb boundary reflection in numerical simulation.However,the CPML is suitable for the first-order elastic wave equation and is difficult to apply directly to the second-order elastic wave equation.In view of this,based on the first-order CPML absorbing boundary condition,we propose a new CPML(NCPML)boundary which can be directly applied to the second-order wave equation.We first systematically extend the first-order CPML technique into second-order wave equations,neglecting the space-varying characteristics of the partial damping coefficient in the complex-frequency domain,avoiding the generation of convolution in the time domain.We then transform the technique back to the time domain through the inverse Fourier transform.Numerical simulation indicates that the space-varying characteristics of the attenuation factor have little influence on the absorption effect and increase the memory at the same time.A number of numerical examples show that the NCPML proposed in this study is effective in simulating elastic wave propagation,and this algorithm is more efficient and requires less memory allocation than the conventional PML absorbing boundary.展开更多
Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics.Therefore,more attention has been paid to the forensics research of median filtering.In this paper,...Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics.Therefore,more attention has been paid to the forensics research of median filtering.In this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is proposed.The median filtering residuals(MFR)are used to preprocess the images.Then the output of MFR is expanded to four channels and used as the input of QCNN.In QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional methods.The quaternion pooling layer is designed to evaluate the result of quaternion convolution.QCNN is proposed to features well combine the three-channel information of color image and fully extract forensics features.Experiments show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.展开更多
A new computational algorithm is introduced for solving scattering problem in periodic structure. The PML technique is used to deal with the difficulty on truncating the unbounded domain while the DSC algorithm is uti...A new computational algorithm is introduced for solving scattering problem in periodic structure. The PML technique is used to deal with the difficulty on truncating the unbounded domain while the DSC algorithm is utilized for the spatial discretization. The present study reveals that the method is efficient for solving the problem.展开更多
The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place i...The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.展开更多
Unlocking task-related EEG spectra is crucial for neuroscience.Traditional convolutional neural networks(CNNs)effectively extract these features but face limitations like overfitting due to small datasets.To address t...Unlocking task-related EEG spectra is crucial for neuroscience.Traditional convolutional neural networks(CNNs)effectively extract these features but face limitations like overfitting due to small datasets.To address this issue,we propose a lightweight CNN and assess its interpretability through the fully connected layer(FCL).Initially tested with two tasks(Task 1:open vs closed eyes,Task 2:interictal vs ictal stage),the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2,aligning with established neurophysiological characteristics.Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity(around 1.55 Hz)and hand movement,with consistent results across pericentral electroencephalogram(EEG)channels.Compared to recent research,our method stands out by delivering task-related spectral features through FCL,resulting in significantly fewer trainable parameters while maintaining comparable interpretability.This indicates its potential suitability for a wider array of EEG decoding scenarios.展开更多
In this study,an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process.Drug-layered pellets were produced by coating microcrystalline c...In this study,an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process.Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved.Drug content increases with pellet size;therefore,particle size monitoring can ensure product safety and quality.The direct imaging system,consisting of a rigid endoscope,a light source,and a high-speed camera,provides real-time information about pellet size and layer uniformity,enabling timely intervention in the case of out-of-spec products.A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus,ensuring that pellet size could be accurately determined despite the dense flow of the particles.After training the model,the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250 e850 mm size range.The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores.The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods,demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capaci...There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.展开更多
基金Project(41174061) supported by the National Natural Science Foundation of ChinaProject(2011QNZT011) supported by the Free Exploration Program of Central South University,China
文摘The absorbing boundary is the key in numerical simulation of borehole radar.Perfect match layer(PML) was chosen as the absorbing boundary in numerical simulation of GPR.But CPML(convolutional perfect match layer) approach that we have chosen has the advantage of being media independent.Beginning with the Maxwell equations in a two-dimensional structure,numerical formulas of finite-difference time-domain(FDTD) method with CPML boundary condition for transverse electric(TE) or transverse magnetic(TM) wave are presented in details.Also,there are three models for borehole-GPR simulation.By analyzing the simulation results,the features of targets in GPR are obtained,which can provide a better interpretation of real radar data.The results show that CPML is well suited for the simulation of borehole-GPR.
基金supported by the National Natural Science Foundation of China(Grant No.61231003)
文摘Based on conformal construction of physical model in a three-dimensional Cartesian grid,an integral-based conformal convolutional perfectly matched layer（CPML） is given for solving the truncation problem of the open port when the enlarged cell technique conformal finite-difference time-domain（ECT-CFDTD） method is used to simulate the wave propagation inside a perfect electric conductor（PEC） waveguide.The algorithm has the same numerical stability as the ECT-CFDTD method.For the long-time propagation problems of an evanescent wave in a waveguide,several numerical simulations are performed to analyze the reflection error by sweeping the constitutive parameters of the integral-based conformal CPML.Our numerical results show that the integral-based conformal CPML can be used to efficiently truncate the open port of the waveguide.
基金supported by the National Science and Technology Major Special Sub-project of China(No.2016ZX05024-001-008)the National Natural Science Foundation Joint Fund Prcject of China(No.U1562215).
文摘A convolution perfectly matched layer(CPML)can efficiently absorb boundary reflection in numerical simulation.However,the CPML is suitable for the first-order elastic wave equation and is difficult to apply directly to the second-order elastic wave equation.In view of this,based on the first-order CPML absorbing boundary condition,we propose a new CPML(NCPML)boundary which can be directly applied to the second-order wave equation.We first systematically extend the first-order CPML technique into second-order wave equations,neglecting the space-varying characteristics of the partial damping coefficient in the complex-frequency domain,avoiding the generation of convolution in the time domain.We then transform the technique back to the time domain through the inverse Fourier transform.Numerical simulation indicates that the space-varying characteristics of the attenuation factor have little influence on the absorption effect and increase the memory at the same time.A number of numerical examples show that the NCPML proposed in this study is effective in simulating elastic wave propagation,and this algorithm is more efficient and requires less memory allocation than the conventional PML absorbing boundary.
基金This work was supported in part by the Natural Science Foundation of China under Grants(Nos.61702235,61772281,U1636219,U1636117,61702235,61502241,61272421,61232016,61402235 and 61572258)in part by the National Key R\&D Program of China(Grant Nos.2016YFB0801303 and 2016QY 01W0105)+2 种基金in part by the plan for Scientific Talent of Henan Province(Grant No.2018JR0018)in part by the Natural Science Foundation of Jiangsu Province,China under Grant BK20141006in part by the Natural Science Foundation of the Universities in Jiangsu Province under Grant 14KJB520024,the PAPD fund and the CICAEET fund.
文摘Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics.Therefore,more attention has been paid to the forensics research of median filtering.In this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is proposed.The median filtering residuals(MFR)are used to preprocess the images.Then the output of MFR is expanded to four channels and used as the input of QCNN.In QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional methods.The quaternion pooling layer is designed to evaluate the result of quaternion convolution.QCNN is proposed to features well combine the three-channel information of color image and fully extract forensics features.Experiments show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.
基金Supported by the NNSF of China(10626017)the Science Foundation of the Education Committee of Heilongjiang Province(11511276)the Foundation of Heilongjiang Province(LBH-Q05114).
文摘A new computational algorithm is introduced for solving scattering problem in periodic structure. The PML technique is used to deal with the difficulty on truncating the unbounded domain while the DSC algorithm is utilized for the spatial discretization. The present study reveals that the method is efficient for solving the problem.
文摘The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.
基金supported by grants from the National Key R&D Program of China(2022YFC2503802)Science and Technology Commission of Shanghai Municipality(201409002000)the Lin Gang Laboratory(LG202106-04).
文摘Unlocking task-related EEG spectra is crucial for neuroscience.Traditional convolutional neural networks(CNNs)effectively extract these features but face limitations like overfitting due to small datasets.To address this issue,we propose a lightweight CNN and assess its interpretability through the fully connected layer(FCL).Initially tested with two tasks(Task 1:open vs closed eyes,Task 2:interictal vs ictal stage),the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2,aligning with established neurophysiological characteristics.Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity(around 1.55 Hz)and hand movement,with consistent results across pericentral electroencephalogram(EEG)channels.Compared to recent research,our method stands out by delivering task-related spectral features through FCL,resulting in significantly fewer trainable parameters while maintaining comparable interpretability.This indicates its potential suitability for a wider array of EEG decoding scenarios.
基金Project no.RRF-2.3.1-21-2022-00015 has been implemented with the support provided by the European Unionsupported by the Agency for Credits and Study Grants coordinated by the Romanian Ministry of National Education from the source of the research grant established through the Government Decision no.118/2023+1 种基金supported by the EKÖP-24-3-BME-103 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National,Research,Development and Innovation Fundsupported by the Doctoral Excellence Fellowship Programme(DCEP)is funded by the National Research Development and Innovation Fund of the Ministry of Culture and Innovation and the Budapest University of Technology and Economics,under a grant agreement with the National Research,Development and Innovation Office.
文摘In this study,an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process.Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved.Drug content increases with pellet size;therefore,particle size monitoring can ensure product safety and quality.The direct imaging system,consisting of a rigid endoscope,a light source,and a high-speed camera,provides real-time information about pellet size and layer uniformity,enabling timely intervention in the case of out-of-spec products.A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus,ensuring that pellet size could be accurately determined despite the dense flow of the particles.After training the model,the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250 e850 mm size range.The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores.The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods,demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金supported by State Grid Corporation Limited Science and Technology Project Funding(Contract No.SGCQSQ00YJJS2200380).
文摘There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.
