Closed-loop subspace identification methods have enjoyed tremendous development in last decade. This paper presents a novel method combined with KPLS, aiming at the situation without persistence of excitation. In this...
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Closed-loop subspace identification methods have enjoyed tremendous development in last decade. This paper presents a novel method combined with KPLS, aiming at the situation without persistence of excitation. In this method, KPLS is utilized to obtain Markov parameters, then, state sequence is estimated by SVD decomposition. Based on the estimated state sequence, the model parameters are estimated by linear regression. 30 Monte Carlo simulation examples are presented in the end of the paper, the results are shown to be competitive and robust in the situation without persistence of excitation in MIMO system, and the new method is more applicable for process industry.
The problem of controllability and control strategy for a class of Networked controlsystems (NCSs) with packets loss are studied in this paper. A probabilistic controllability conception is proposed for these systems...
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The problem of controllability and control strategy for a class of Networked controlsystems (NCSs) with packets loss are studied in this paper. A probabilistic controllability conception is proposed for these systems. In order to analyze the probabilistic controllability and relevant properties, a representation of the initial state by a group of constructive base of linear space is introduced. Based on this representation the affects to control system caused by packets loss can be expressed by system substates. A control strategy is developed to guarantee, the controllability of NCSs, and the minimum control steps property of this control strategy is discussed as well. Finally, a necessary and sufficient condition of probabilistic controllability for NCSs with packets loss is represented.
The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose lin...
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The metapopulation framework is adopted in a wide array of disciplines to describe systems of well separated yet connected subpopulations. The subgroups or patches are often represented as nodes in a network whose links represent the migration routes among them. The connections have been so far mostly considered as static, but in general evolve in time. Here we address this case by investigating simple contagion processes on time-varying metapopulation networks. We focus on the SIR process and determine analytically the mobility threshold for the onset of an epidemic spreading in the framework of activity-driven network models. We find profound differences from the case of static networks. The threshold is entirely described by the dynamical parameters defining the average number of instantaneously migrating individuals and does not depend on the properties of the static network representation. Remarkably, the diffusion and contagion processes are slower in time-varying graphs than in their aggregated static counterparts, the mobility threshold being even two orders of magnitude larger in the first case. The presented results confirm the importance of considering the time-varying nature of complex networks.
Clustering plays an important role in data analysis. The classic k-means method is not well scaled to large datasets and its performance is sensitive to the initial seeds. In this paper we present the development of a...
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Clustering plays an important role in data analysis. The classic k-means method is not well scaled to large datasets and its performance is sensitive to the initial seeds. In this paper we present the development of a novel hybrid k-means which proceeds on the result of the self-organizing map(SOM) training and initializes its seeds based on the trained SOM grid. Theoretical analysis suggests this method is time-efficient to the massive datasets. And in addition, the numerical results show the grid-based seed initialization has faster convergence characteristics and competitive accuracy on both computer-generated and real-world test cases compared to its randomized seed initialization counterpart.
This paper presents several notes on the robust control method proposed in [Dong et al (2013), Sampled-data design for robust control of a single qubit. IEEE Transactions on Automatic control, in press] and furthermor...
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This paper presents several notes on the robust control method proposed in [Dong et al (2013), Sampled-data design for robust control of a single qubit. IEEE Transactions on Automatic control, in press] and furthermore improves the original sampling periods so that this control method can be better and more easily realized. For the case of amplitude damping decoherence, a larger sampling period is presented when the upper bound of the probability of failure is small enough. For the case of phase damping decoherence, a larger sampling period is given when the lower bound of the target coherence is large enough. Furthermore, we provide improved sampling periods for both of the above two cases under the same assumption as that in [Dong et al (2013), Sampled-data design for robust control of a single qubit. IEEE Transactions on Automatic control, in press].
As iron and steel products demand becomes increasingly diversified and batch reduced, make-to-order production has gradually satisfied the demand of modern iron and steel enterprises. It is significant for optimizatio...
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As iron and steel products demand becomes increasingly diversified and batch reduced, make-to-order production has gradually satisfied the demand of modern iron and steel enterprises. It is significant for optimization of the make-to-order production planning to guarantee punctual delivery considering inventory, production capacity, processes and product specification. A multi-objective mixed integer nonlinear programming (MINLP) model is formulated to optimize the production, and a hybrid particle swarm optimization algorithm is developed to solve the model. A case study based on the order data of a steel and iron enterprise is presented. Compared with the optimization strategy upon particle swarm optimization and manual scheduling, the proposed optimization model and strategy effectively reduce losses caused by back order, inventory storage and unbalanced production capacity, and provide a reference and guidance for the operation.
Detection and compression of the environmental information incrementally is useful when the mobile robot needs to continuously update its perception database online. In this paper, we propose an incremental subspace f...
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Tracking initiation issue under bearing-only sensor networks is studied. A mechanism for tracking initiation based on probability is proposed, aiming at solving problem under measuring environment with false alarm and...
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This paper shows that the adaptive output error identifier for linear time-invariant continuous-time systems proposed by (Betser and Zeheb [1995]) is robust vis{-a{vis measurement noise. More precisely, it is proven t...
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Based on the image processing of the liquid droplets at the outlet of a single micro pipe, a novel low cost micro flow measurement technology for micro-chemical process is introduced. The connected component labeling ...
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