Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
In order to satisfy the requirement of realtime gait programming of humanoid walking with foot rotation,a kind of modified Nonlinear Model Predictive Control (NMPC) scheme was proposed. Based on setting suitable kinet...In order to satisfy the requirement of realtime gait programming of humanoid walking with foot rotation,a kind of modified Nonlinear Model Predictive Control (NMPC) scheme was proposed. Based on setting suitable kinetic and kinematic virtual constraints of Single Support Phase (SSP) and three subphases of Double Support Phase (DSP) ,complex realtime gait programming problem was simplified to four online NMPC dynamic optimization problems. A numerical approach was proposed to transform the dynamical optimization problem to the finite dimensional static optimization problem which can be solved by Sequential Quadratic Programming (SQP) . It can be concluded from simulation that using this method on BIP model can realize online gait programming of dynamic walking with foot rotation and the biped stability can be satisfied such that there is no sliding during walking.展开更多
Transit gait programming is a key problem for a multi-legged robot to climb automatically from the ground up the wall, as well as between wall intersections. In this paper, a new idea is put forward by which the compl...Transit gait programming is a key problem for a multi-legged robot to climb automatically from the ground up the wall, as well as between wall intersections. In this paper, a new idea is put forward by which the complex transit gait is decomposed into a sequence of two relatively simpler parts - single-leg motion and body pitching motion. An algorithm based on the above concept shows its feasibility and effectiveness in the graphic kinematics simulation.展开更多
This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exos...This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.展开更多
The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch ...The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.展开更多
Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization ...Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.展开更多
At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S ...At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S rRNA in six tissues and five developmental stages of the Mandarin fish Siniperca chuatsi were assayed with quantitative real-time PCR （qPCR）. Differences in expression levels were analyzed using geNorm program. The results demonstrate that β-actin is the most stable gene at developmental stages and GAPDH is the most stable in different tissues. While 18S rRNA expression during development is differentially regulated, which indicates it is suitable as an internal control for gene expression normalization at the developmental level. Overall, the data suggest that the two most stable housekeeping genes are enough to accurately calibrate gene expression in S. chuatsi. The significance of this study provided convincing references and methodology for housekeeping gene selection and normalization in gene expression analysis with regular PCR or qPCR.展开更多
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金Sponsored by the National High Technology Research and Development Program of China ( 863 Program) ( Grant No. 2006AA04Z201)
文摘In order to satisfy the requirement of realtime gait programming of humanoid walking with foot rotation,a kind of modified Nonlinear Model Predictive Control (NMPC) scheme was proposed. Based on setting suitable kinetic and kinematic virtual constraints of Single Support Phase (SSP) and three subphases of Double Support Phase (DSP) ,complex realtime gait programming problem was simplified to four online NMPC dynamic optimization problems. A numerical approach was proposed to transform the dynamical optimization problem to the finite dimensional static optimization problem which can be solved by Sequential Quadratic Programming (SQP) . It can be concluded from simulation that using this method on BIP model can realize online gait programming of dynamic walking with foot rotation and the biped stability can be satisfied such that there is no sliding during walking.
文摘Transit gait programming is a key problem for a multi-legged robot to climb automatically from the ground up the wall, as well as between wall intersections. In this paper, a new idea is put forward by which the complex transit gait is decomposed into a sequence of two relatively simpler parts - single-leg motion and body pitching motion. An algorithm based on the above concept shows its feasibility and effectiveness in the graphic kinematics simulation.
基金supported by the Pre-research project in the manned space field.Project Number 020202,China.
文摘This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb exoskeleton.The Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN input.This article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing up.In the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features.To optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried out.In addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and SVM.The results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ect.Finally,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.
基金supported by State Key Laboratory of HVDC under Grant SKLHVDC-2021-KF-09.
文摘The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.
基金funded by China Scholarship Council(2020091-10135).
文摘Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.
基金国家自然科学基金(3077164430972263)Aid Program for Science and Technology Innovative Research Team in Higher Educational Instituions of Hunan Province
文摘At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S rRNA in six tissues and five developmental stages of the Mandarin fish Siniperca chuatsi were assayed with quantitative real-time PCR （qPCR）. Differences in expression levels were analyzed using geNorm program. The results demonstrate that β-actin is the most stable gene at developmental stages and GAPDH is the most stable in different tissues. While 18S rRNA expression during development is differentially regulated, which indicates it is suitable as an internal control for gene expression normalization at the developmental level. Overall, the data suggest that the two most stable housekeeping genes are enough to accurately calibrate gene expression in S. chuatsi. The significance of this study provided convincing references and methodology for housekeeping gene selection and normalization in gene expression analysis with regular PCR or qPCR.
