In this paper, we explore aggregative games over networks of multi-integrator agents with coupled constraints. To reach the general Nash equilibrium of an aggregative game, a distributed strategy-updating rule is prop...
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This paper introduces an innovative approach for determining the dynamic stability margin of a power system. The method employs a Genetic Algorithm (GA) to optimize the parameters of a Conventional Power System Stabil...
This paper introduces an innovative approach for determining the dynamic stability margin of a power system. The method employs a Genetic Algorithm (GA) to optimize the parameters of a Conventional Power System Stabilizer (CPSS) linked with the Automatic Voltage Regulator (AVR) in a multi-machine power system. The objective is to tune the stabilizers in a way that effectively shifts the lightly damped and undamped electromechanical modes of all plants to a specified region in the s-plane. To achieve this, a multiobjective problem is formulated, optimizing a composite set of objective functions that include the damping factor of the lightly damped electromechanical modes. The dynamic behavior of the system is assessed using small signal stability analysis, while the stability of the system is evaluated by monitoring the variations of eigenvalues under line-to-ground (LG) fault conditions. The proposed method is then demonstrated on a 3-machine 9-bus system, and the effectiveness and robustness of the suggested Power System Stabilizers (PSSs) are showcased by incorporating them into a conventional AVR-PSS model. This allows the proposed approach to efficiently damp low-frequency oscillations in the power system.
In this paper, a distributed non-model based seeking algorithm which combines the extremum seeking control (ESC) jointly with learning algorithms is proposed to seek a generalized Nash equilibrium (GNE) for a class of...
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作者：
Yuefei WangJianghong HanQingxin HuSchool of Computer and Information
School of Mechanical and Automobile Engineering Engineering Research Center of Measurement and Control in Safety Critical Industry Ministry of Education Hefei University of Technology Hefei Anhui China School of Computer and Information
Engineering Research Center of Measurement and Control in Safety Critical Industry Ministry of Education Hefei University of Technology Hefei Anhui China
In terms of the design of SCDCS (Safety Critical Distributed control System), MATI (Maximum Allowable Transfer Interval) is one of the important design reference parameters that influences message transmission period ...
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ISBN:
(纸本)9781424425020
In terms of the design of SCDCS (Safety Critical Distributed control System), MATI (Maximum Allowable Transfer Interval) is one of the important design reference parameters that influences message transmission period and network scheduling strategy. To obtain its upper bound, Lyapunov theory and matrix measure are applied to analyze SCDCS which is networked only in its feedback path, and a sufficient condition is induced which can guarantee that the SCDCS is asymptotically stable. Furthermore, an explicit and simple method of obtaining MATI for SCDCS is derived on the base of that stable condition. The simulation validates the method and shows that the method is much less conservative than the existing methods.
This paper proposes a boundary feedback control design for open canal networks using the linearization of boundary conditions. For open canal networks with any types of cross-sections, which can be modelled by the Sai...
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The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye’s visual mechanism, unartful 3D scene structure de...
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In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field *** from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree...
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In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field *** from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree,our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery ***,we analytically study the basic reproductive number and the final epidemic *** with numerical simulations,it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are *** the mean of these two distributions is identical,increasing the variance of susceptibility may block the spread of epidemics,while the corresponding increase in the variance of disease course has little effect on the final epidemic *** is also shown that positive correlations between individual susceptibility,course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence,whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.
A method for the constrained estimation of the physical system parameters of linear systems from measured data is described which uses a two-stage procedure: step response estimation via the frequency-sampled filter a...
A method for the constrained estimation of the physical system parameters of linear systems from measured data is described which uses a two-stage procedure: step response estimation via the frequency-sampled filter approach followed by a non-quadratic bounded optimisation to obtain the physical parameters. The method is illustrated using simulated data and evaluated using experimental data.
In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is strongly convex and the regularized n...
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In many cases, protein mass-spectrometry data are imbalanced, i.e. the number of positive examples is much less than that of negative ones, which generally degrade the performance of classifiers used for protein recog...
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In many cases, protein mass-spectrometry data are imbalanced, i.e. the number of positive examples is much less than that of negative ones, which generally degrade the performance of classifiers used for protein recognition. Despite its importance, few works have been conducted to handle this problem. In this paper, we present a new method that utilizes the EasyEnsemble algorithm to cope with the imbalance problem in mass-spectrometry data. Furthermore, two feature selection algorithms, namely PREE (Prediction Risk based feature selection for EasyEnsemble) and PRIEE (Prediction Risk based feature selection for Individuals of EasyEnsemble), are proposed to select informative features and improve the performance of the EasyEnsemble classifier. Experimental results on three mass spectra data sets demonstrate that the proposed methods outperform two existing filter feature selection methods, which prove the effectiveness of the proposed methods.
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