For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown varian...
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This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal pe...
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A balanced-to-single-ended (BTSE) power divider is proposed in this paper. Its input is a differential pair while both the outputs are single-ended ports. It can replace the cascade of a balun and a conventional power...
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Optimal signal timing is an efficient and effective method to mitigate traffic congestion in urban road traffic networks. In this paper, we propose a new method for signal-timing optimization of urban arterial roads. ...
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Optimal signal timing is an efficient and effective method to mitigate traffic congestion in urban road traffic networks. In this paper, we propose a new method for signal-timing optimization of urban arterial roads. The main idea to the method is to design a bi-direction green wave band for arterial roads. In order to reduce delay and stops, an arterial road signal coordination approach is developed. In addition, the arterial signal coordination approach has been expanded to deal with the problem of coordination for urban traffic networks coordination control. Finally, simulation experiments are given to illustrate the effectiveness of the proposed method.
In this work, compressive sensing (CS) is applied to facilitate efficient wireless information transmission over lossy communication links. Inherently sparse data packets are transmitted without compression or error p...
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ISBN:
(纸本)9781479960378
In this work, compressive sensing (CS) is applied to facilitate efficient wireless information transmission over lossy communication links. Inherently sparse data packets are transmitted without compression or error protection. The packet loss during transmission is modeled as a random sampling process of the transmitted data. The original signal then is reconstructed based on correctly received data packets using CS-based reconstruction method. No computations for source, channel coding or random measurement sampling will be required at the transmitter side. Thus, this method is suitable for applications where transmitters have extreme low power constraints such as wireless sensor networks. Compared with traditional error protection technique(automatic repeat request, data interleaving and interpolation), the proposed method delivers higher quality of sparse signal while significantly reducing energy consumption at transmitter as well as transmission latency.
This paper considers the problem of synthesizing output-feedback control laws for a class of discrete-time hybrid systems in order for the trajectories of the system to satisfy certain high-level specifications expres...
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ISBN:
(纸本)9781479932757
This paper considers the problem of synthesizing output-feedback control laws for a class of discrete-time hybrid systems in order for the trajectories of the system to satisfy certain high-level specifications expressed in linear temporal logic. By leveraging ideas from robust interpretation of temporal logic formulas and bounded-error estimation, we identify a subclass of systems for which it is possible to reduce the problem to a state-feedback form. In particular, we use locally superstable hybrid observers to resolve the partial information at the continuous level. This allows us to use recent results in temporal logic planning to synthesize the desired controllers based on two-player perfect-information games. The overall control architecture consists of a hybrid observer, a high-level switching protocol and a low-level continuous controller. We demonstrate the proposed framework in a case study on designing control protocols for an aircraft air management system.
The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, elect...
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ISBN:
(纸本)9781424479276
The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, electroencephalographic (EEG) changes in sensorimotor a-band at central electrodes, which desynchronizes both during execution and observation of goal-directed actions (i.e., μ suppression), have been considered an analog to MNS function. However, methodological and developmental issues, as well as the nature of generalized μ suppression to imagined, observed, and performed actions, have yet to provide a mechanistic relationship between EEG μ-rhythm and MNS function, and the extent to which EEG can be used to infer intent during MNS tasks remains unknown. In this study we present a novel methodology using active EEG and inertial sensors to record brain activity and behavioral actions from freely-behaving infants during exploration, imitation, attentive rest, pointing, reaching and grasping, and interaction with an actor. We used δ-band (1-4Hz) EEG as input to a dimensionality reduction algorithm (locality-preserving Fisher's discriminant analysis, LFDA) followed by a neural classifier (Gaussian mixture models, GMMs) to decode the each MNS task performed by freely-behaving 6-24 month old infants during interaction with an adult actor. Here, we present results from a 20-month male infant to illustrate our approach and show the feasibility of EEG-based classification of freely occurring MNS behaviors displayed by an infant. These results, which provide an alternative to the μ-rhythm theory of MNS function, indicate the informative nature of EEG in relation to intentionality (goal) for MNS tasks which may support action-understanding and thus bear implications for advancing the understanding of MNS function.
By using the Karush-Kuhn-Tucker (KKT) conditions in a multi-hop hierarchical tree structure we obtained the optimal placement of the underwater acoustic sensor nodes with respect to the capacity of the wireless links ...
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By using the Karush-Kuhn-Tucker (KKT) conditions in a multi-hop hierarchical tree structure we obtained the optimal placement of the underwater acoustic sensor nodes with respect to the capacity of the wireless links between the nodes. We assumed that the energy consumption of each sensor node is constant. We were able to calculate the vertical and horizontal distances between each sensor nodes and also between any levels of interest. On the same tree topology we focused on the energy efficient transmission in underwater sensor networks by providing the optimal transmitting energies for the nodes with fixed locations.
Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of ...
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ISBN:
(纸本)9781479938414
Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.
Research is done from the point of flight mechanics on Titan Aerobot. Consdering there are some errors in exstiong literatures on atmospheric temperature and density models for Titan, atmospheric environment model was...
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ISBN:
(纸本)9781624102578
Research is done from the point of flight mechanics on Titan Aerobot. Consdering there are some errors in exstiong literatures on atmospheric temperature and density models for Titan, atmospheric environment model was built by polynomial fitting including temperature, density, pressure, viscosity and wind field models. The kinetic model of the airship was derived considering dynamic buoyancy effect, wind field disturbance and time varying mass characteristics. Also, repetitive calculation of Munk moment was avoided. Finally, after determining mass, inertia matrix, added mass tensor and apparent inertia tensor of LRC Titan Aerobot, the dynamic process is simulated by Matlab. Results shows that inertia aerodynamic force has significant influence on the airship motion and that repetitive calculation of Munk moment can produce wrong results.
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