Wireless power transfer via magnetically resonant coupling is a new technology to deliver power over a relatively long distance. Here, we present a mat-based design to wirelessly power moving targets based on this tec...
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Wireless power transfer via magnetically resonant coupling is a new technology to deliver power over a relatively long distance. Here, we present a mat-based design to wirelessly power moving targets based on this technology. Our design is specifically applied to transcutaneously power medical implants within free-moving laboratory animals. Our system comprises a driver coil array, a hexagonally packed transmitter mat, a receiver coil, and a load coil, and generates a nearly flat magnetic distribution over a defined area to produce an approximately constant power output independent of the location of the receiver coil. This paper also describes a novel power receiver coil design of the same shape as the exterior of the implant, allowing for maximum magnetic coupling, eliminating the space restrictions due to the coil within the implant, and matching the resonant frequencies of the implant and the transmitter coil. Our new transmitter and receiver designs significantly reduce the size of a biomedical implant and may provide a lifetime power supply to implanted circuits without the need for an internal battery. Our designs are also useful in various other applications involving moving targets, such as part of a robot or a vehicle.
This paper is concerned with the mean-square stability of the Split-Step Backward Euler method for stochastic delayed Hopfield neural networks. The sufficient conditions to guarantee the mean-square stability of the S...
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This paper is concerned with the mean-square stability of the Split-Step Backward Euler method for stochastic delayed Hopfield neural networks. The sufficient conditions to guarantee the mean-square stability of the Split-Step Backward Euler method are given. Moreover, an example of the comparison of our method with the Euler-Maruyama method is used to show the superiority of our method.
A new method to calculate the corridor of Reentry Launch Vehicles is proposed, based on their dynamic properties. The upper boundary of the corridor can be calculated by the skipping trajectories from the lower bounda...
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A new method to calculate the corridor of Reentry Launch Vehicles is proposed, based on their dynamic properties. The upper boundary of the corridor can be calculated by the skipping trajectories from the lower boundary of the corridor. By combination with a specific kinematic equations and a numerical simulation, the reliability and optimality of the method is analyzed. The results also show that the traditional reentry corridor estimated by quasi-equilibrium gliding conditions has its conservativeness, the reentry corridor that the new method calculated can better reflect the RLV's maneuvers abilities. Moreover, compared to the general methods, the direct and indirect methods to calculate the corridor, which are usually used to solve optimization problems, the proposed method has a smaller amount of calculation, shorter calculation time, stronger convergence, thus has stronger practicability.
This paper investigates the problem of leader-following consensus of a linear multi-agent system on a switching network. The input of each agent is subject to saturation. Low gain feedback based distributed consensus ...
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This paper investigates the problem of leader-following consensus of a linear multi-agent system on a switching network. The input of each agent is subject to saturation. Low gain feedback based distributed consensus protocols are developed. It is established that, under the assumptions that each agent is asymptotically null controllable with bounded controls and that the network is connected or jointly connected, semi-global leader-following consensus of the multi-agent system can be achieved. Numerical examples are presented to illustrate this result.
This paper develops a routing method to control the picker congestion that challenges the traditional assumption regarding the narrow-aisle order picking system. We proposes a new routing algorithm based on Ant Colony...
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This paper develops a routing method to control the picker congestion that challenges the traditional assumption regarding the narrow-aisle order picking system. We proposes a new routing algorithm based on Ant Colony Optimization (ACO) for two order pickers (A-TOP) with congestion consideration. Using two extended dedicated heuristics with congestion consideration as reference group, a comprehensive simulation study is conducted to evaluate the effectiveness of A-TOP. The simulation proves that A-TOP achieves the shortest total picking time in most instances and performs well in dealing with the congestion. The impacts of warehouse layout, order size, and pick:walk-time ratio on A-TOP and system performance are analyzed as well. A-TOP can adapt to different warehouse configurations, meanwhile, it can be easily extended to the situation with more than two order pickers.
This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed ev...
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This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.
Estimation of on-off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods s...
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Estimation of on-off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods still rely on the experience of the operator to manually establish a threshold level. In this paper, a novel approach to address this issue is presented. Based on the generalized likelihood ratio test, the maximum likelihood (ML) method is improved with an adaptive threshold technique based on the signal-to-noise ratio (SNR) estimate in the initial time before accurate sEMG analyses. The dependence of optimal threshold on SNR is determined by minimizing the onset/offset estimate error on a large set of simulated signals with well-known signal parameters. Accuracy and precision of the algorithm were assessed by using a set of simulated signals and real sEMG signals recorded from two healthy subjects during elbow flexion-extension movements with and without workload. Comparison with traditional algorithms shows that with amoderate increase in the computational effort the ML algorithm performs well even for low levels of EMG activity, while the proposed adaptive method is most robust with respect to variations in SNRs. Also, we discuss the results of analyzing the sEMG recordings from the selected proximal muscles of the upper limb in two hemiparetic subjects. The detection algorithm is automatic and user-independent, managing the detection of both onset and offset activation, and is applicable in presence of noise allowing use by skilled and unskilled operators alike.
This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the mem...
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This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based RNNs is established. Taking into account of the information of the neuron activation functions and the involved time-varying delays, several improved results with less computational burden and conservatism have been obtained in the sense of Filippov solutions. A numerical example is presented to show the effectiveness of the obtained results. (C) 2013 The Franklin Institute, Published by Elsevier Ltd. All rights reserved.
This Letter is concerned with the problem of fuzzy modeling and synchronization of memristor-based Lorenz circuits with memristor-based Chua's circuits. In this Letter, a memristor-based Lorenz circuit is set up, ...
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This Letter is concerned with the problem of fuzzy modeling and synchronization of memristor-based Lorenz circuits with memristor-based Chua's circuits. In this Letter, a memristor-based Lorenz circuit is set up, and illustrated by phase portraits and Lyapunov exponents. Furthermore, a new fuzzy model of memristor-based Lorenz circuit is presented to simulate and synchronize with the memristor-based Chua's circuit. Through this new fuzzy model, two main advantages can be obtained as: (1) only two linear subsystems are needed;(2) fuzzy synchronization of these two different chaotic circuits with different numbers of nonlinear terms can be achieved with only two sets of gain K. Finally, numerical simulations are used to illustrate the effectiveness of these obtained results. (C) 2013 Elsevier B.V. All rights reserved.
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