This note generalizes the stability analysis for a high frequency networked control system. The high-frequency networked control system is described by a delta operator system with a high frequency constraint. Stabili...
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This note generalizes the stability analysis for a high frequency networked control system. The high-frequency networked control system is described by a delta operator system with a high frequency constraint. Stability conditions are given for the high frequency delta operator system. Furthermore, by developing the generalized Kalman-Yakubovic-Popov lemma, improved stability conditions are also presented in terms of linear matrix inequalities. Some experiment results are presented to illustrate the effectiveness of the developed techniques.
To further enhance emergency management skills of an organisation's emergency response personnel, emergency response training, especially 3D emergency drill, is currently becoming more and more important in the pe...
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ISBN:
(纸本)9781467313971
To further enhance emergency management skills of an organisation's emergency response personnel, emergency response training, especially 3D emergency drill, is currently becoming more and more important in the petrochemical sector. So, a novel 3D emergency drills system is designed and developed based on ACP approach, which can be used for mocking emergency response plan drills and evaluating the plan. A case study reveals that the performance of the system is good and the system can meet the needs of emergency response training and optimizing emergency response plan in petrochemical plants.
To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learn...
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To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learning into the Adaboost learning framework and at the same time leverages the advantages of Co-training algorithm for performance enhancement. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of the convergence rate, the accuracy, and the steady performance as compared to the original AdaBoost algorithm, without relying on redundant and sufficient feature sets. As a algorithm application in software engineering, the Embedded Co-AdaBoost has been applied to the classification of software document relations to improve the quality of the architecture design documents and the reusability of design knowledge.
Many nonlinear systems can be modeled by or converted to strict-feedback forms, whose stabilization problem without modeling uncertainties has been extensively discussed in the literature. However, in practice, inevit...
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ISBN:
(纸本)9781457710957
Many nonlinear systems can be modeled by or converted to strict-feedback forms, whose stabilization problem without modeling uncertainties has been extensively discussed in the literature. However, in practice, inevitable modeling uncertainties, especially co-existence of parametric uncertainties and nonparametric uncertainties, will make it very difficult and challenging to achieve desirable control performance. In this background, we consider a challenging adaptive control problem, aiming at asymptotic tracking to any bounded reference signal, in the presence of periodic uncertainties of both parametric and nonparametric types, i.e., periodic time-varying parameters and periodic unknown time-delays in the uncertain nonlinearities, for a class of strict-feedback discrete-time nonlinear systems due to the lack of related research and the increasing demand of digital control in current epoch. To deal with the co-existing various uncertainties, which bring highly-nonlinear coupled effects to the closed-loop system, a novel nearest-neighbor previous instant compensation technique, combined with the lifting approach and the future states prediction, is introduced in this paper to asymptotically fully compensate the effects of uncertainties. With such elaborately constructed adaptive estimation mechanism, the developed discrete-time backstepping adaptive controller can guarantee the closed-loop stability as well as asymptotic output tracking, which have been rigorously established and testified by extensive simulations.
Calibration of a functional structural plant model is a challenging task because of the complexity of model structure. Parameter estimation through gradient-based optimization technique was highly dependent on initial...
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ISBN:
(纸本)9781467304290
Calibration of a functional structural plant model is a challenging task because of the complexity of model structure. Parameter estimation through gradient-based optimization technique was highly dependent on initial parameter values. This motivated the use of global sensitivity analysis technique to choose parameter subset in fitting the data sequence. Global sensitivity indices were computed using the source sink ratio as the output of interest, which regulates all organ growth. By fitting on chrysanthemum data from nine sampling dates, it is shown that sensitivity analysts method helps to identify the influential parameters for a given sampling date. As a result, fitting process is less dependent on the initial parameter values. Current work provides a new method of calibrating a plant growth model with multiple outputs.
The present work focuses on the node deployment algorithm of Wireless Sensor Networks. The Central Voronoi Tessellation algorithm is employed to optimize the node position. The energy consumption of the whole sensor n...
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Multiple-target tracking in complex scenes is one of the most complicated problems in computer vision. Handling the occlusion between objects is the key issue in multiple target tracking. This paper presents an occlus...
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In order to solve the challenging problem of diagnosis for sensor bias and drift faults, a method of sensor fault diagnosis based on the least squares support vector machine (LS-SVM) online prediction is proposed. In ...
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Fault diagnosis based on the wavelet packet decomposition, one-against-one support vector machine (SVM) and genetic algorithm (GA) is proposed in order to realize the real-time sensor fault diagnosis accurately. The i...
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Chemical industry is complex and continuous process industry, and the control and management of the long-term safe operation involves a great deal of information and data on the staffs, management, equipment and techn...
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