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Search Results: 1 - 10 of 28696 matches for " JIANG Nan "
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Periodic Wall Blow/Suction Perturbation Evolution in Turbulent Boundary Layer  [PDF]
Gangli Hao, Nan Jiang
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.39153
Abstract: Time sequence signals of instantaneous longitudinal and normal velocity components at different longitudinal and normal positions in a turbulent boundary layer have been finely measured simultaneously by IFA300 constant temperature anemometer and double-sensor hot-wire probe with sampling resolution higher than the frequency that corresponds to the smallest time scale of Kolmogorov dissipation scale before/after introducing artificial periodic blow/suction perturbation. The period-phase-average technique is applied to extract the periodic waveforms of artificial perturbation from instantaneous time sequence signals of longitudinal and normal turbulence background. Experimental investigation is carried out on the attenuation characteristics of periodic perturbation wave with different frequency along longitudinal direction and normal direction in a turbulent boundary layer. The amplitude distributions of longitudinal and normal disturbing velocity component for different perturbation frequencies are measured at different downstream and normal positions in turbulent boundary layer. The amplitude growth rate of artificial periodic perturbation wave is calculated according to flow instability theory. The experimental results are compared and in consistent with the theoretical and numerical results.
Application of Ant Colony Algorithm to the Analysis of Common Mode EMI Model of DC Motor  [PDF]
Jinfeng Liu, Xudong Wang, Jiang Nan
Energy and Power Engineering (EPE) , 2011, DOI: 10.4236/epe.2011.32013
Abstract: The Electromagnetic Compatibility (EMC) of direct current (DC) motor windings is a system model which is used to reflect the functional characters of the system in the whole EMC specified frequency (150 KHz ~ 30 MHz). For most motor designing process, it is always used to evaluate the inductance of windings in lower or working frequency; however, when analyzing the conducted interference, it is necessary to take some pa-rameters in high frequency into account in building up the EMC model, such as the noticeable capacitance distributed among the windings or between windings and shells. Past research neglected the common-mode current generated by the high frequency interference within motor bearings coupled with shells, since the parasitic capacitance of rotor core comes from armature windings supplied sufficient paths. In EMC model-ing process for DC motor problem, first, test the impedance of windings by experiments; then, generate the equivalent circuit with overall parameters. At present, it is a difficulty that how to choose the parameters. Most researchers preferred to adopt analytical calculation results, however, it could not reflect the essence of the model since it requires many simplification. Based on this point, this paper adopted ant colony algorithm (ACA) with positive feedback to intelligently search and globally optimize the parameters of equivalent cir-cuit. Simulation result showed that the impedance of equivalent circuit calculated by this algorithm was the same as experimental result in the whole EMC frequency. In order to further confirm the validity of ACA, PSPICE circuit simulation was implemented to simulate the spectrum of common mode Electromagnetic Interference (EMI) of equivalent circuit. The simulation result accords well with the experiment result re-ceived by EMI receiver. So it sufficiently demonstrated correctness of ACA in the analysis of high frequency equivalent circuit.
Chaos control in random Boolean networks by reducing mean damage percolation rate
Nan Jiang,Shijian Chen
Physics , 2010, DOI: 10.1088/0256-307X/28/4/040504
Abstract: Chaos control in Random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase. However, controlled nodes are only viewed as passive blocks to prevent perturbation spread. This paper proposes a new control method in which controlled nodes can exert an active impact on the network. Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions. Simulation results show that the number of nodes needed to achieve control is largely reduced compared to previous method. Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.
Stability Bounds on Compact Astrophysical Objects from Information-Entropic Measure
Marcelo Gleiser,Nan Jiang
Physics , 2015, DOI: 10.1103/PhysRevD.92.044046
Abstract: We obtain bounds on the stability of various self-gravitating astrophysical objects using a new measure of shape complexity known as configurational entropy. We apply the method to Newtonian polytropes, neutron stars with an Oppenheimer-Volkoff equation of state, and to self-gravitating configurations of complex scalar field (boson stars) with different self-couplings, showing that the critical stability region of these stellar configurations obtained from traditional perturbation methods correlates well with critical points of the configurational entropy with accuracy of a few percent or better.
Algorithms and Models for Turbulence Not at Statistical Equilibrium
Nan Jiang,William Layton
Mathematics , 2015,
Abstract: Standard eddy viscosity models, while robust, cannot represent backscatter and have severe difficulties with complex turbulence not at statistical equilibrium. This report gives a new derivation of eddy viscosity models from an equation for the evolution of variance in a turbulent flow. The new derivation also shows how to correct eddy viscosity models. The report proves the corrected models preserve important features of the true Reynolds stresses. It gives algorithms for their discretization including a minimally invasive modular step to adapt an eddy viscosity code to the extended models. A numerical test is given with the usual and over diffusive Smagorinsky model. The correction (scaled by $10^{-8}$ ) does successfully exhibit intermittent backscatter.
Doubly Robust Off-policy Evaluation for Reinforcement Learning
Nan Jiang,Lihong Li
Computer Science , 2015,
Abstract: We study the problem of evaluating a policy that is different from the one that generates data. Such a problem, known as off-policy evaluation in reinforcement learning (RL), is encountered whenever one wants to estimate the value of a new solution, based on historical data, before actually deploying it in the real system, which is a critical step of applying RL in most real-world applications. Despite the fundamental importance of the problem, existing general methods either have uncontrolled bias or suffer high variance. In this work, we extend the so-called doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and has low variance, and as a point estimator, it outperforms the most popular importance-sampling estimator and its variants in most occasions. We also provide theoretical results on the hardness of the problem, and show that our estimator can match the asymptotic lower bound in certain scenarios.
High Dimensional Tests Based on U-Statistics for Generalized Linear Models
Gong Zi Jiang Nan
Statistics , 2013,
Abstract: I propose two U-statistics to test coefficients in generalized linear models. One of them is used to deal with global hypothesis and the other one to test with the nuisance parameter. Both the statistics proposed are within high-dimensional setting which means the number of coefficients is much larger than the sample size. The statistics are based on quasi-likelihood function so that they have wilder applications. I theoretically analyze the asymptotic distribution of the statistics under the null hypothesis and the power functions under the local and fixed alternatives. To serve as a comparison, the power functions of the test proposed by Goeman et al. (2011) are also derived. Some simulation studies are carried out and I apply my methods to an empirical study.
Nonlinear Steam Valve Adaptive Controller Design for the Power Systems  [PDF]
Nan Jiang, Xiangyong Chen, Ting Liu, Bin Liu, Yuanwei Jing
Intelligent Control and Automation (ICA) , 2011, DOI: 10.4236/ica.2011.21004
Abstract: Considering generator rotor and valve by external disturbances for turbine regulating system, the nonlinear large disturbance attenuation controller and parameter updating law of turbine speed governor system are designed using backstepping method. The controller not only considers transmission line parameter uncer-tainty, and has attenuated the influences of large external disturbances on system output. The nonlinear con-troller does not have the sensitivity to the influences of external disturbances, but also has strong robustness for system parameters variation, which is because of the transmission line uncertainty being considered in internal disturbances. The simulation results show that the control effect of the large disturbance attenuation controller more advantages by comparing with the control performance of conventional nonlinear robust controller.
Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm  [PDF]
Bin Liu, Jixin Kang, Nan Jiang, Yuanwei Jing
Energy and Power Engineering (EPE) , 2011, DOI: 10.4236/epe.2011.31003
Abstract: This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum, the adjustment cost is minimum based on the ant colony algorithm, and the global optimal solu-tion is obtained. Simulation results show that the improved optimal model can obviously reduce the adjust-ment cost and the designed algorithm is safe and easy to implement.
A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks  [PDF]
Xin LI, Yuanwei JING, Nan JIANG, Siying ZHANG
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2009, DOI: 10.4236/ijcns.2009.21010
Abstract: For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.
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