This paper proposes a method of hierarchical WSN deployment and routing for fast localization. Position-known anchor nodes form a static upper-layer network organization and several mobile nodes constitute the lower-l...
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This paper proposes a method of hierarchical WSN deployment and routing for fast localization. Position-known anchor nodes form a static upper-layer network organization and several mobile nodes constitute the lower-layer network which need dynamic message exchange with the upper network. In the upper static network, near-optimal routing table is allocated to each anchor node based on their known-position and the strength of signal for communication during network initialization. Mobile nodes in the lower-layer can access to the upper network dynamically and at the same time report the RSSI value of nearby anchors to the control-center. At the center, the computer will calculate the real-time location of the target based on the reported RSSI and node-IDs. Algorithm presented in this paper is low cast, easy to implement and can be used for fast location estimation and motion tracking indoors and outdoors.
One of the most difficult challenges in automatic face recognition is computing facial similarity between two images captured in different modalities, called heterogeneous face recognition. In this paper, we propose a...
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Human Flesh Search is an explosive Web phenomenon these years in China, especially when new media, such as Weibo, appeared. In this research, we present the empirical studies about growing patterns of participated Hum...
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A vast amount of complex spatio-temporal brain data, such as EEG-, have been accumulated. Technological advances in many disciplines rely on the proper analysis, understanding and utilisation of these data. In order t...
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In order to investigate the feasibility of integrating functional electrical stimulation (FES) with robot-based rehabilitation training, this paper proposes an FES-assisted training strategy combined with impedance co...
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ISBN:
(纸本)9781479927456
In order to investigate the feasibility of integrating functional electrical stimulation (FES) with robot-based rehabilitation training, this paper proposes an FES-assisted training strategy combined with impedance control for our self-made exoskeleton lower limb rehabilitation robot. This control strategy is carried out in a leg press task. Through impedance control, an active compliance of the robot is established, and the patient's voluntary effort to accomplish the task is inspired. During the training process, the patient's related muscles are applied with FES which provides an extra assistance to the patient. The intensity of the FES is properly chosen aiming to induce a desired active torque which is proportional to the voluntary effort of the patient. This kind of enhancement serves as a positive feedback which reminds the patient of the correct attempt to fulfill the desired motion. FES control is conducted by a combination of neural network-based feedforward controller and a PD feedback controller. The feasibility of this control strategy has been verified in Matlab.
One of the most important issues among active rehabilitation technique is how to extract the voluntary intention of patient through bio-signals, especially EEG signal. This pilot study investigates the feasibility of ...
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In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm per...
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In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm permits an arbitrary admissible control law to initialize the iterative algorithm. It is the first time that the properties of the policy iterative ADP are established for the discrete-time situation. It proves that the iterative performance index function is non-increasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. It also proves that any of the iterative control policy can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method.
In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is...
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In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is mathematically equivalent to the quasi-Newton's iteration in a Banach space. The implementation using neural networks is given, where a critic neural network is used to learn its value function, and an action neural network sharing the same parameters with the corresponding critic neural network is used to learn its optimal control policy for each player. All the critic and action neural networks are updated online in real-time and continuously. A simulation example is presented to demonstrate the effectiveness of the developed scheme.
This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify...
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This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify the unknown system dynamics with stability proof. Then, two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal value, respectively. By using this architecture, the action NN and the critic NN are tuned simultaneously, without the requirement of the knowledge of system dynamics. In addition, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded based on Lyapunov's direct method. A simulation example is provided to verify the effectiveness of the developed theoretical results.
This paper develops a novel neural-network-based direct adaptive control scheme for a class of multi-input-multi-output uncertain nonlinear discrete-time (DT) systems in the presence of unknown bounded disturbances. B...
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This paper develops a novel neural-network-based direct adaptive control scheme for a class of multi-input-multi-output uncertain nonlinear discrete-time (DT) systems in the presence of unknown bounded disturbances. By employing feedback linearization methods, neural network (NN) approximation can cancel the nonlinearity of the DT systems. Meanwhile, the weights of NNs are directly updated online instead of preliminary offline training. In addition, unlike most literatures, the condition for persistent excitation is removed. Based on Lyapunov's direct method, both tracking errors and weight estimates are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the proposed approach.
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