This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences ...This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence (<img src="Edit_e4627ea9-9a9a-4473-9017-202d04a16377.bmp" alt="" /><span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">11.1%</span><span style="font-family:Verdana;"> (for the negative value which is observed during the increasing phase) and </span><span style="font-family:Verdana;">+16.74%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to </span><span style="font-family:Verdana;">37%</span><span style="font-family:Verdana;"> and for the NC at the maximum phase with a percentage of </span><span style="font-family:Verdana;">54.47%</span><span><span style="font-family:Verdana;">. The maximum gap of occurrence (</span><img src="Edit_20fa141b-ecee-4e06-8024-144ba0969395.bmp" alt="" /></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">17.85%</span><span style="font-family:Verdana;"> (for the negative value which is observed at maximum phase) and </span><span style="font-family:Verdana;">+13.53%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly </span><span style="font-family:Verdana;">37%</span><span><span style="font-family:Verdana;"> for the NC. The maximum gap of occurrence (</span><img src="Edit_eecb8939-783e-4d43-b92c-80c528c1890b.bmp" alt="" /><span style="font-family:Verdana;"></span></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span>10% (for the negative value which is observed during the decreasing phase) and </span><span style="font-family:;" "=""><span style="font-family:Verdana;">+20.11%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.</span></span></span>展开更多
The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the...The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.展开更多
Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classi...Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.展开更多
In healthcare facilities,including hospitals,pathogen transmission can lead to infectious disease outbreaks,highlighting the need for effective disinfection protocols.Although disinfection robots offer a promising sol...In healthcare facilities,including hospitals,pathogen transmission can lead to infectious disease outbreaks,highlighting the need for effective disinfection protocols.Although disinfection robots offer a promising solution,their deployment is often hindered by their inability to accurately recognize human activities within these environments.Although numerous studies have addressed Human Activity Recognition(HAR),few have utilized scene graph features that capture the relationships between objects in a scene.To address this gap,our study proposes a novel hybrid multi-classifier information fusion method that combines scene graph analysis with visual feature extraction for enhanced HAR in healthcare settings.We first extract scene graphs,complete with node and edge attributes,from images and use a graph classifi-cation network with a graph attention mechanism for activity recognition.Concurrently,we employ Swin Transformer and convolutional neural network models to extract visual features from the same images.The outputs from these three models are then integrated using a hybrid information fusion approach based on Dempster-Shafer theory and a weighted majority vote.Our method is evalu-ated on a newly compiled hospital activity data set,consisting of 5,770 images across 25 activity categories.The results demonstrate an accuracy of 90.59%,a recall of 90.16%,and a precision of 90.31%,outperforming existing HAR methods and showing its potential for practical applications in healthcare environments.展开更多
Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and e...Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.展开更多
Trajectories carry rich motion cues and thus have been leveraged to many high-level computer vision tasks. Due to the easy implementation of simple trackers, most previous work on trajectory-based applications utilize...Trajectories carry rich motion cues and thus have been leveraged to many high-level computer vision tasks. Due to the easy implementation of simple trackers, most previous work on trajectory-based applications utilizes raw tracking outputs without explicitly considering tracking errors. Reliable trajectories are prerequisite for modeling and recognizing high-level behaviors. Therefore, this paper tackles such problems by rectifying raw trajectories, which aims to post-process existing trajectories. Our approach firstly splits them into short tracks, and then infers identity ambiguity to remove unqualified detection responses. At last, short tracks are stitched via maximum bipartite graph matching. This post- processing is completely scene-free. Results of trajectory rectification and their benefits are both evaluated on two challenging datasets. Results demonstrate that rectified trajectories are conducive to high-level tasks and the proposed approach is also competitive with state-of-the-art multi-target tracking methods.展开更多
文摘This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence (<img src="Edit_e4627ea9-9a9a-4473-9017-202d04a16377.bmp" alt="" /><span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">11.1%</span><span style="font-family:Verdana;"> (for the negative value which is observed during the increasing phase) and </span><span style="font-family:Verdana;">+16.74%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to </span><span style="font-family:Verdana;">37%</span><span style="font-family:Verdana;"> and for the NC at the maximum phase with a percentage of </span><span style="font-family:Verdana;">54.47%</span><span><span style="font-family:Verdana;">. The maximum gap of occurrence (</span><img src="Edit_20fa141b-ecee-4e06-8024-144ba0969395.bmp" alt="" /></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">17.85%</span><span style="font-family:Verdana;"> (for the negative value which is observed at maximum phase) and </span><span style="font-family:Verdana;">+13.53%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly </span><span style="font-family:Verdana;">37%</span><span><span style="font-family:Verdana;"> for the NC. The maximum gap of occurrence (</span><img src="Edit_eecb8939-783e-4d43-b92c-80c528c1890b.bmp" alt="" /><span style="font-family:Verdana;"></span></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span>10% (for the negative value which is observed during the decreasing phase) and </span><span style="font-family:;" "=""><span style="font-family:Verdana;">+20.11%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.</span></span></span>
基金This research was jointly supported by the National Natural Science Foundation of China(Grant No.42005037)the Liaoning Provincial Natural Science Foundation Project(PhD Start-up Research Fund 2019-BS-214),the Special Scientific Research Project for the Forecaster(Grant No.CMAYBY2018-018)+2 种基金a Key Technical Project of Liaoning Meteorological Bureau(Grant No.LNGJ201903)the National Key Research and Development Project(Grant No.2018YFC1505601)the Open Foundation Project of the Institute of Atmospheric Environment,China Meteorological Administration(Grant Nos.2020SYIAE08 and 2020SYIAEZD5).
文摘The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.
文摘Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.
基金US National Science Foundation(NSF)via Grant number 2038967This research also received support from the Science Alliance at the University of Tennessee Knoxville(UTK)via the Joint Directed Research and Development Program.
文摘In healthcare facilities,including hospitals,pathogen transmission can lead to infectious disease outbreaks,highlighting the need for effective disinfection protocols.Although disinfection robots offer a promising solution,their deployment is often hindered by their inability to accurately recognize human activities within these environments.Although numerous studies have addressed Human Activity Recognition(HAR),few have utilized scene graph features that capture the relationships between objects in a scene.To address this gap,our study proposes a novel hybrid multi-classifier information fusion method that combines scene graph analysis with visual feature extraction for enhanced HAR in healthcare settings.We first extract scene graphs,complete with node and edge attributes,from images and use a graph classifi-cation network with a graph attention mechanism for activity recognition.Concurrently,we employ Swin Transformer and convolutional neural network models to extract visual features from the same images.The outputs from these three models are then integrated using a hybrid information fusion approach based on Dempster-Shafer theory and a weighted majority vote.Our method is evalu-ated on a newly compiled hospital activity data set,consisting of 5,770 images across 25 activity categories.The results demonstrate an accuracy of 90.59%,a recall of 90.16%,and a precision of 90.31%,outperforming existing HAR methods and showing its potential for practical applications in healthcare environments.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.
基金This work was supported by the National Science ＆ Technology Pillar Program of China under Grant No. 2012BAK16B06 and the National Natural Science Foundation of China under Grant No. 61173084.
文摘Trajectories carry rich motion cues and thus have been leveraged to many high-level computer vision tasks. Due to the easy implementation of simple trackers, most previous work on trajectory-based applications utilizes raw tracking outputs without explicitly considering tracking errors. Reliable trajectories are prerequisite for modeling and recognizing high-level behaviors. Therefore, this paper tackles such problems by rectifying raw trajectories, which aims to post-process existing trajectories. Our approach firstly splits them into short tracks, and then infers identity ambiguity to remove unqualified detection responses. At last, short tracks are stitched via maximum bipartite graph matching. This post- processing is completely scene-free. Results of trajectory rectification and their benefits are both evaluated on two challenging datasets. Results demonstrate that rectified trajectories are conducive to high-level tasks and the proposed approach is also competitive with state-of-the-art multi-target tracking methods.
