Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f...Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.展开更多
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident...Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.展开更多
为提高提高多机电力系统的暂态稳定性,该文首先建立了静止无功补偿器(static var compensator,SVC)系统的一个含有时变参数不确定性的二阶非线性动态模型,然后在SVC动态模型的基础上,利用自适应控制技术和鲁棒控制技术设计了SVC系统的...为提高提高多机电力系统的暂态稳定性,该文首先建立了静止无功补偿器(static var compensator,SVC)系统的一个含有时变参数不确定性的二阶非线性动态模型,然后在SVC动态模型的基础上,利用自适应控制技术和鲁棒控制技术设计了SVC系统的控制器。为了验证所设计的控制器的有效性,以一个经典的三机九母线电力系统作为测试系统,对鲁棒自适应SVC控制器与PID SVC控制器和反馈线性化SVC控制器分别进行了比较研究。仿真结果表明,与PID SVC控制器和反馈线性化SVC控制器相比,所提出的鲁棒自适应SVC控制器具有良好的性能。展开更多
在电力系统仿真软件DIgSILENT/PowerFactory中建立了静止无功补偿器(static var compensator,SVC)补偿型定速风电机组的模型,分析了其稳态和暂态特性以及由SVC补偿型风电机组组成的风电场对电网的影响,分别采用上述风电机组模型和用电...在电力系统仿真软件DIgSILENT/PowerFactory中建立了静止无功补偿器(static var compensator,SVC)补偿型定速风电机组的模型,分析了其稳态和暂态特性以及由SVC补偿型风电机组组成的风电场对电网的影响,分别采用上述风电机组模型和用电容器组进行补偿的普通定速风电机组模型进行仿真实验,比较结果表明SVC补偿型风电机组具有快速调节无功功率的能力,当系统故障时,该风电机组可快速恢复系统电压,且风电机组启动过程对系统的冲击较小。展开更多
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R348),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.
文摘Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.
文摘为提高提高多机电力系统的暂态稳定性,该文首先建立了静止无功补偿器(static var compensator,SVC)系统的一个含有时变参数不确定性的二阶非线性动态模型,然后在SVC动态模型的基础上,利用自适应控制技术和鲁棒控制技术设计了SVC系统的控制器。为了验证所设计的控制器的有效性,以一个经典的三机九母线电力系统作为测试系统,对鲁棒自适应SVC控制器与PID SVC控制器和反馈线性化SVC控制器分别进行了比较研究。仿真结果表明,与PID SVC控制器和反馈线性化SVC控制器相比,所提出的鲁棒自适应SVC控制器具有良好的性能。
基金中国风电项目（wind power research and training,CWPP）的资助
文摘在电力系统仿真软件DIgSILENT/PowerFactory中建立了静止无功补偿器(static var compensator,SVC)补偿型定速风电机组的模型,分析了其稳态和暂态特性以及由SVC补偿型风电机组组成的风电场对电网的影响,分别采用上述风电机组模型和用电容器组进行补偿的普通定速风电机组模型进行仿真实验,比较结果表明SVC补偿型风电机组具有快速调节无功功率的能力,当系统故障时,该风电机组可快速恢复系统电压,且风电机组启动过程对系统的冲击较小。
