Occupant's identity and location are important information for lighting control in order to reduce the energy consumption while increasing livelihood. While active RFID system provides occupant's identity, it ...
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Occupant's identity and location are important information for lighting control in order to reduce the energy consumption while increasing livelihood. While active RFID system provides occupant's identity, it is nontrivial to localize the occupant's location in an indoor environment due to the multipath effect, the changing environment, and the unreliable communication link. In this paper, we implement a system with multiple active RFID readers, and develop a localization algorithm based on support vector machine (SVM). The algorithm uses round-robin comparison to localize the occupant to one of the multiple regions in a floor. The geometric relationship among the rooms and the historical localization data are used to further improve the localization accuracy. Numerical results demonstrate a high localization accuracy of this algorithm. We hope this work sheds insight on lighting control for energy saving and an increased livelihood.
It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the foll...
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It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feedforward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 × 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.
There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of ...
There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
The main objective of the paper is to study the approximation and complexity trade-off capabilities of the recently proposed Tensor Product Distributed Compensation (TPDC) based control design framework. The TPDC is t...
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The main objective of the paper is to study the approximation and complexity trade-off capabilities of the recently proposed Tensor Product Distributed Compensation (TPDC) based control design framework. The TPDC is the combination of the TP model transformation and the Parallel Distributed Compensation (PDC) framework. The TP model transformation includes an HOSVD based technique to solve the approximation and complexity trade-off. In this paper we generate TP models with different complexity and approximation properties, and then we derive controllers for them. We analyze how the trade-off effects the model behavior and control performance. All these properties are studied via the state feedback controller design of the Translational Oscillations with an Eccentric Rotational Proof Mass Actuator (TORA) System.
The paper proposes some new methods for traffic controlsystems design. The goal was to find solutions that make possible to give constraints in course of the estimation process of the road traffic parameters and the ...
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The paper proposes some new methods for traffic controlsystems design. The goal was to find solutions that make possible to give constraints in course of the estimation process of the road traffic parameters and the traffic controller system design. The proposed solutions are built on the constrained Moving Horizon Estimation and the Fault Detection Filter technique.
This paper proposes a simple robust model predictive algorithm for discrete time linear parameter varying uncertain systems, which depend nonlinearly on their parameters. The method was derived from the robust MPC alg...
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In this paper, a model predictive controller is developed for controlling the main primary circuit dynamics of pressurized water nuclear power plants during load-change transients. The hybrid model of the plant is suc...
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In the paper the road holding of a heavy vehicle is improved by using unilateral braking. The goal of the control design is to reduce the effect of the lateral acceleration of the vehicle, when it exceeds a predefined...
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The idea of inversion-based direct input reconstruction for robust detection and separation of multiple, possibly simultaneous faults in the presence of external, non-mutually separable disturbances for linear dynamic...
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This paper presents an approach towards learning enhanced motion control of DC motor, suitable for applications involving repeated iterations of motion trajectories. The overall structure of the control consists of a ...
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ISBN:
(纸本)8986510081
This paper presents an approach towards learning enhanced motion control of DC motor, suitable for applications involving repeated iterations of motion trajectories. The overall structure of the control consists of a feedback and a feed-forward components. The model-free learning adaptive feedback control (MFLAC) provides for the main system stabilization and an iterative learning control (ILC) algorithm is proposed to serve as a feedforward compensation to nonlinear and unknown dynamics and disturbances, thereby enhancing the improvement achievable with PID or MFLAC alone. It serves as the basis for simulation study of the proposed control scheme. A comparison of the performance achieved with traditional PID and MFLAC is also provided to highlight the advantages of the additional intelligent feedforward mode.
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