Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodie...Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.展开更多
Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing...Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.展开更多
Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in...Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in pattern recognition,the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness.However,prevailing estimation techniques contend with accuracy limitations and substantial computational demands.Kalman estimation technology,celebrated for its ease of implementation and real-time adaptability,finds extensive application across diverse domains.This study introduces a continuous Kalman estimation method,leveraging a system model with sEMG and joint angles as inputs and outputs.Facilitated by model parameter training methods,the approach deduces multiple DOF finger kinematics simultaneously.The method’s efficacy is validated using a publicly accessible database,yielding a correlation coefficient(CC)of 0.73.With over 45,000 windows for training Kalman model parameters,the average computation time remains under 0.01 s.This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.展开更多
Long noncoding RNAs(lncRNAs)are emerging as pivotal regulators in gene expression networks,charac-terized by their structural flexibility and functional versatility.In plants,lncRNAs have gained increasing attention d...Long noncoding RNAs(lncRNAs)are emerging as pivotal regulators in gene expression networks,charac-terized by their structural flexibility and functional versatility.In plants,lncRNAs have gained increasing attention due to accumulating evidence of their roles in modulating developmental plasticity and agro-nomic traits.In this review,we focus on the origin,classification,and mechanisms of action of plant lncRNAs,with a particular emphasis on their involvement in developmental processes.We also compre-hensively analyze the relationship between plant lncRNAs and their responses to environmental stimuli,discussing how environmental cues influence their expressions and regulatory functions.We then highlight the importance of the advanced technologies driving their functional exploration.Finally,we discuss recent discoveries of specific long noncoding transcripts that encode functional small peptides,revealing an additional layer of regulatory complexity to these transcripts.Overall,this review discuss the fascinating relationship between the dynamic transcription of lncRNAs and plant developmental plasticity,as well as environmental responses,and emphasizes the need for further research to uncover the underlying mo-lecular mechanisms and exploit the potential of noncoding transcripts for RNA-based strategies in crop improvementandmolecularbreeding.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.NRF-2022R1I1A1A01069526).
文摘Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.
文摘Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.
基金supported in part by the National Key R&D Program of China(#2020YFC2007900)the National Natural Science Foundation of China(#82161160341,#62271477,and #61901464)+2 种基金the Science and Technology Program of Guangdong Province(#2022A0505090007)“The Belt and Road”Innovative Talent Exchange program for foreign experts(DL2022024002L)Jinan 5150 Program for Talents Introduction.
文摘Deciphering hand motion intention from surface electromyography(sEMG)encounters challenges posed by the requisites of multiple degrees of freedom(DOFs)and adaptability.Unlike discrete action classification grounded in pattern recognition,the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness.However,prevailing estimation techniques contend with accuracy limitations and substantial computational demands.Kalman estimation technology,celebrated for its ease of implementation and real-time adaptability,finds extensive application across diverse domains.This study introduces a continuous Kalman estimation method,leveraging a system model with sEMG and joint angles as inputs and outputs.Facilitated by model parameter training methods,the approach deduces multiple DOF finger kinematics simultaneously.The method’s efficacy is validated using a publicly accessible database,yielding a correlation coefficient(CC)of 0.73.With over 45,000 windows for training Kalman model parameters,the average computation time remains under 0.01 s.This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.
基金supported by the National Natural Science Foundation of China(Nos.U24A20386 to Y.-Q.C.and 32470605 to D.W.)the Jiangxi Province Outstanding Youth Fund Project(20242BAB23062 to D.W.)+3 种基金the National Ten Thousand Talent Program(2022WRQB007 to Y.-C.Z.)Guangdong Province(2022B1515020018 to Y.-C.Z.)Agencia I+D+i,ICGEB,to F.A.and the AXA Research Fund(AXA Chair,2025-2029 to F.A.).
文摘Long noncoding RNAs(lncRNAs)are emerging as pivotal regulators in gene expression networks,charac-terized by their structural flexibility and functional versatility.In plants,lncRNAs have gained increasing attention due to accumulating evidence of their roles in modulating developmental plasticity and agro-nomic traits.In this review,we focus on the origin,classification,and mechanisms of action of plant lncRNAs,with a particular emphasis on their involvement in developmental processes.We also compre-hensively analyze the relationship between plant lncRNAs and their responses to environmental stimuli,discussing how environmental cues influence their expressions and regulatory functions.We then highlight the importance of the advanced technologies driving their functional exploration.Finally,we discuss recent discoveries of specific long noncoding transcripts that encode functional small peptides,revealing an additional layer of regulatory complexity to these transcripts.Overall,this review discuss the fascinating relationship between the dynamic transcription of lncRNAs and plant developmental plasticity,as well as environmental responses,and emphasizes the need for further research to uncover the underlying mo-lecular mechanisms and exploit the potential of noncoding transcripts for RNA-based strategies in crop improvementandmolecularbreeding.
