This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitab...
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This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme.
The true-fake detection of Chinese liquors can be considered as a one-class classification problem. Using only positive sample in training process brings about difficulties in determining the optimal parameters of one...
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ISBN:
(纸本)9781467384155
The true-fake detection of Chinese liquors can be considered as a one-class classification problem. Using only positive sample in training process brings about difficulties in determining the optimal parameters of one-class SVM and its kernel. And one-class SVM optimized with conventional grid search method has a low recognition rate of positive test samples. Hence, we proposed a new idea for recognition, namely boosting-based one-class SVM. Six different kinds of liquors were sampled by a self-designed electronic nose system. After the preprocessing and feature extraction of sample data, 18 groups of optimum parameters of one-class SVM were chosen from specified parameter range, each group was used to train a classifier with training algorithm. Then the true-fake decisions were made for test samples with boosting integration rules. Finally the recognition rates of Hongjinjiu and Lanjinjiu positive sample reached 95.24% and 100%, and the recognition rates of both positive and negative sample reached 97.04% and 94.83%, respectively.
We designed a portable electronic nose (e-nose) for rapid detection of Chinese liquors. The e-nose had an evaporation chamber wrapped with silicone heating band, which could vaporize the liquor samples in 1 minute. Th...
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ISBN:
(纸本)9781467384155
We designed a portable electronic nose (e-nose) for rapid detection of Chinese liquors. The e-nose had an evaporation chamber wrapped with silicone heating band, which could vaporize the liquor samples in 1 minute. The sequential sampling and clean-out lasted 100 seconds and 3 minutes, respectively. Hence, one fast detection process could be finished within 3 minutes without regard to clean-out. In order to acquire enough information from array's sampling curves for classification, we chose 60 features (including 6 features per sensor) according to the response curves, the derivative of the response curves and the relative change rate curves of conductivity. Then the dimension of features was reduced by principle component analysis (PCA). Finally, classification of Chinese liquors was performed using C-Support Vector Machine (C-SVM) and the classification rate was 96.7%.
The paper presents an optimisation based method to parametrize input shapers with time delays of piece-wise-equal distribution. The design respects the two key requirements on the shaper performance that are the promp...
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Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. Thi...
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Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.
This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetit...
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This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetitive processes is used to develop formulas for gain matrices design, together with convergent conditions in terms of linear matrix inequalities. An extension to deal with model uncertainty of the polytopic or norm bounded form is also developed and an illustrative example is given.
The advantages of Variable Step Search algorithm - a simple local search-based method of MLP training is that it does not require differentiable error functions, has better convergence properties than backpropagation ...
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