Thermodynamic Behavior of SMPC

Wei-Ping, Qin; Qin, Guanshi; Ji-shen, Zhang; Changfeng, Wu; Wang, Jiwei; Du, Guotong

doi:10.7498/aps.50.1467

Cited by 2 publications

(2 citation statements)

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“…The problem of Eq.5 may be changed into optimization problem in accordance with the SVM. It can be solved by using particle swarm optimization (PSO hereafter)-based WLS-SVCM [3][4]. In order to overcome the problem of error accumulation of ordinary multi-class classification decision-tree optimization algorithm, improved PSO algorithm is provided by using criterion of maximizing margin between different classifications.…”

Section: Wls-svrm Approximation Can Be Expressed In Eq4 Mapping S Amentioning

confidence: 99%

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Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Wang

Lin

2012

AMM

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In order to achieve efficient management of the dam, the new algorithms such as reinforcement learning, Synergetic, Structural Risk Minimization and Particle Swarm Optimization are used to establish a Cooperative Wavelet Least Squares Support Vector Machine Model. To improve the convergence rate and make full use of knowledge and advice of mechanics and hydraulics of the dam, WLS-SVRM and WLS-SVCM models are used cooperatively. Before the training online, mapping provides training samples for WLS-SVCM. During the course of training online, the numerical simulation and WLS-SVCM will provide knowledge and advices for WLS-SVRM. Case study shows that the model can provide timely information of gate opening and management information of the dam so as to provide decision support for engineering management.

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Section: Wls-svrm Approximation Can Be Expressed In Eq4 Mapping S Amentioning

confidence: 99%

“…Where ini ω is the initial value, usually taken as 1, h is evolution speed factor, s is particle aggregation factor, more concrete formula refers to reference [4].…”

Section: Wls-svrm Approximation Can Be Expressed In Eq4 Mapping S Amentioning

confidence: 99%

Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Wang

Lin

2012

AMM

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Size-dependent thermal stresses in the core—shell nanoparticles

Astefanoaei¹,

Dumitru²,

Al³

2013

Chinese Phys. B

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The thermal stress in a magnetic core—shell nanoparticle during a thermal process is an important parameter to be known and controlled in the magnetization process of the core—shell system. In this paper we analyze the stress that appears in a core—shell nanoparticle subjected to a cooling process. The external surface temperature of the system, considered in equilibrium at room temperature, is instantly reduced to a target temperature. The thermal evolution of the system in time and the induced stress are studied using an analytical model based on a time-dependent heat conduction equation and a differential displacement equation in the formalism of elastic displacements. The source of internal stress is the difference in contraction between core and shell materials due to the temperature change. The thermal stress decreases in time and is minimized when the system reaches the thermal equilibrium. The radial and azimuthal stress components depend on system geometry, material properties, and initial and final temperatures. The magnitude of the stress changes the magnetic state of the core—shell system. For some materials, the values of the thermal stresses are larger than their specific elastic limits and the materials begin to deform plastically in the cooling process. The presence of the induced anisotropy due to the plastic deformation modifies the magnetic domain structure and the magnetic behavior of the system.

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Thermodynamic Behavior of SMPC

Cited by 2 publications

References 0 publications

Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

Size-dependent thermal stresses in the core—shell nanoparticles

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