Safety is an important aim in designing safe-critical systems.To design such systems,many policy iterative algorithms are introduced to find safe optimal controllers.Due to the fact that in most practical systems,find...Safety is an important aim in designing safe-critical systems.To design such systems,many policy iterative algorithms are introduced to find safe optimal controllers.Due to the fact that in most practical systems,finding accurate information from the system is rather impossible,a new online training method is presented in this paper to perform an iterative reinforcement learning based algorithm using real data instead of identifying system dynamics.Also,in this paper the impact of model uncertainty is examined on control Lyapunov functions(CLF)and control barrier functions(CBF)dynamic limitations.The Sum of Square program is used to iteratively find an optimal safe control solution.The simulation results which are applied on a quarter car model show the efficiency of the proposed method in the fields of optimality and robustness.展开更多
The world’s elderly population is growing every year.It is easy to say that the fall is one of the major dangers that threaten them.This paper offers a Trained Model for fall detection to help the older people live c...The world’s elderly population is growing every year.It is easy to say that the fall is one of the major dangers that threaten them.This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home.The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body.Several machine learning technologies have been proposed for automatic fall detection.The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera.The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities.These features are based on motion,changes in human shape,and oval diameters around the human and temporal head position.The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection.Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81%that have been tested with UR Fall Detection dataset.展开更多
文摘Safety is an important aim in designing safe-critical systems.To design such systems,many policy iterative algorithms are introduced to find safe optimal controllers.Due to the fact that in most practical systems,finding accurate information from the system is rather impossible,a new online training method is presented in this paper to perform an iterative reinforcement learning based algorithm using real data instead of identifying system dynamics.Also,in this paper the impact of model uncertainty is examined on control Lyapunov functions(CLF)and control barrier functions(CBF)dynamic limitations.The Sum of Square program is used to iteratively find an optimal safe control solution.The simulation results which are applied on a quarter car model show the efficiency of the proposed method in the fields of optimality and robustness.
文摘The world’s elderly population is growing every year.It is easy to say that the fall is one of the major dangers that threaten them.This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home.The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body.Several machine learning technologies have been proposed for automatic fall detection.The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera.The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities.These features are based on motion,changes in human shape,and oval diameters around the human and temporal head position.The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection.Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81%that have been tested with UR Fall Detection dataset.
