This paper deals with the delay-dependent stabilization problem for singular systems with Markovian jump parameters and time delays. A delay-dependent condition is established for the considered system to be regular, ...
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This paper deals with the delay-dependent stabilization problem for singular systems with Markovian jump parameters and time delays. A delay-dependent condition is established for the considered system to be regular, impulse free and stochastically stable. Based on the condition, a design algorithm of the desired state feedback controller which guarantees the resultant closed-loop system to be regular, impulse free and stochastically stable is proposed in terms of a set of strict linear matrix inequalities （LMIs）. Numerical examples show the effectiveness of the proposed methods.
This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lac...
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An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong ...
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An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.
In this article,an approach for economic performance assessment of model predictive control(MPC) system is *** method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG)...
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In this article,an approach for economic performance assessment of model predictive control(MPC) system is *** method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in *** LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability *** the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization *** proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.
Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the den...
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Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the dendritic spines. In this paper, we propose a new multi scale approach for detecting dendritic spines in a 2D Maximum Intensity Projection (MIP) image of the 3D neuron data stacks collected from a 2-photon laser scanning confocal microscope. The proposed method utilizes the curvilinear structure detector in conjunction with the multi scale spine detection algorithm which automatically and accurately extracts and segments the spines with variational sizes along the dendrite. In addition, a slice-based spine detection algorithm is also proposed to detect spines which are hidden from the MIP image within the dendrite area. Experimental results show that our proposed method is effective for automatic spine detection and is able to accurately segment dendrite.
To find global frequent itemsets in a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time, and little-memory-cost algorithm should be used. Based on the max-frequency window model,...
Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance con...
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Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
A method of topology synthesis based on graph theory and mechanism combination theory was applied to the configuration design of locomotion systems of lunar exploration rovers(LER).Through topology combination of whee...
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A method of topology synthesis based on graph theory and mechanism combination theory was applied to the configuration design of locomotion systems of lunar exploration rovers(LER).Through topology combination of wheel structural unit,suspension unit,and connecting device unit between suspension and load platform,some new locomotion system configurations were proposed and the metrics and indexes to evaluate the performance of the new locomotion system were *** evaluation and comparison between two LER with locomotion systems of different configurations were *** analysis results indicate that the new locomotion system configuration has good trafficability performance.
Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples acco...
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Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.
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