Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete(UHPC).This study detects crack evolution using a novel dynamic mode d...Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete(UHPC).This study detects crack evolution using a novel dynamic mode decomposition(DMD)method.In this method,the sparse matrix‘determined’from images is used to reconstruct the foreground that contains cracks,and the global threshold method is adopted to extract the crack patterns.The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy.Accordingly,the geometric features,including the area and its projection in two major directions,are evaluated over time.The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed.Due to the irregular shape of cracks in the spatial domain,the cracks are then transformed into the Fourier domain to assess their development.Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position.Moreover,the power spectral density of cracks exhibits an increasing trend over time.The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.展开更多
Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain a...Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.展开更多
Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of ...Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.展开更多
基金The first author would like to acknowledge the support from 2022 Open Project of Failure Mechanics and Engineering Disaster Prevention,Key Laboratory of Sichuan Province,No.FMEDP202204The authors acknowledge the financial support from the National Natural Science Foundation of China(Grant Nos.52108379 and 51908504)+3 种基金Youth Top Talent Program,Education Department of Hebei Province(No.BJK2022047)Natural Science Foundation of Hebei Province(No.E2021210002)Scientific Research Foundation for the Returned Overseas Scholars,Hebei Province(No.C20210307)Innovation Research Group Program of Natural Science,Hebei Province(No.E2021210099).
文摘Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete(UHPC).This study detects crack evolution using a novel dynamic mode decomposition(DMD)method.In this method,the sparse matrix‘determined’from images is used to reconstruct the foreground that contains cracks,and the global threshold method is adopted to extract the crack patterns.The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy.Accordingly,the geometric features,including the area and its projection in two major directions,are evaluated over time.The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed.Due to the irregular shape of cracks in the spatial domain,the cracks are then transformed into the Fourier domain to assess their development.Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position.Moreover,the power spectral density of cracks exhibits an increasing trend over time.The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R236),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.
文摘Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.
