The Experts below are selected from a list of 12876 Experts worldwide ranked by ideXlab platform

Huai Sun - One of the best experts on this subject based on the ideXlab platform.

  • A Transferrable Coarse-Grained Force Field for Simulations of Polyethers and Polyether Blends
    Macromolecules, 2018
    Co-Authors: Hao Huang, Huiming Xiong, Huai Sun
    Abstract:

    We developed a transferable coarse-grained (CG) force field to cover polyethers and polyether blends in this work. On the basis of the perturbation theory of liquids and high-temperature expansion, we examined the theoretical foundation of using a temperature-dependent function to represent the nonbonded interactions. Using a Mapping Rule that balances molecular representation with the degree of coarse-graining, we defined seven CG beads that represent common polyethers. A hybrid parametrization workflow combining bottom-up and top-down approaches was applied to derive the force field parameters. The large ratio between the number of training data and the number of adjustable parameters enables the resulting force field to accurately predict many polymer properties, including cohesive energies, structural parameters, glass-transition temperatures, and surface tensions. In addition, the force field is capable of simulating polyether blends using modified Lorentz–Berthelot combination Rules. As a preliminar...

  • A Transferrable Coarse-Grained Force Field for Simulations of Polyethers and Polyether Blends
    2018
    Co-Authors: Hao Huang, Huiming Xiong, Huai Sun
    Abstract:

    We developed a transferable coarse-grained (CG) force field to cover polyethers and polyether blends in this work. On the basis of the perturbation theory of liquids and high-temperature expansion, we examined the theoretical foundation of using a temperature-dependent function to represent the nonbonded interactions. Using a Mapping Rule that balances molecular representation with the degree of coarse-graining, we defined seven CG beads that represent common polyethers. A hybrid parametrization workflow combining bottom-up and top-down approaches was applied to derive the force field parameters. The large ratio between the number of training data and the number of adjustable parameters enables the resulting force field to accurately predict many polymer properties, including cohesive energies, structural parameters, glass-transition temperatures, and surface tensions. In addition, the force field is capable of simulating polyether blends using modified Lorentz–Berthelot combination Rules. As a preliminary application, we have investigated the phase behavior of three polyether blends. For immiscible polymers, two glass transitions are identified, which are shown to be correlated to the chain dynamics of each component

Ying Tan - One of the best experts on this subject based on the ideXlab platform.

  • which Mapping Rule in the fireworks algorithm is better for large scale optimization
    Congress on Evolutionary Computation, 2018
    Co-Authors: Xuemei Yet, Ying Tan
    Abstract:

    Fireworks algorithm(FWA), which is proposed for global optimization of complex function, becomes a hot spot in optimization field recently, caused by its competitive performance. Boundary handling for FWA, which maps the out-of-bound sparks into feasible space, is critical for its convergence efficiency. However, random Mapping Rule, which is widely used for boundary handling, always caused computing resource waste, especially for high-dimensional optimization. In this paper, we propose three novel Mapping Rules to speed up large scale optimization of FWA. Meanwhile, to evaluate the effectiveness of the new Rules, we compare them by representative nine benchmark functions on different dimensionality scale. Experimental results indicate that the mirror Rule which we proposed, achieve superior performance for most optimization functions.

  • CEC - Which Mapping Rule in the Fireworks Algorithm is Better for Large Scale Optimization
    2018 IEEE Congress on Evolutionary Computation (CEC), 2018
    Co-Authors: Xuemei Yet, Ying Tan
    Abstract:

    Fireworks algorithm(FWA), which is proposed for global optimization of complex function, becomes a hot spot in optimization field recently, caused by its competitive performance. Boundary handling for FWA, which maps the out-of-bound sparks into feasible space, is critical for its convergence efficiency. However, random Mapping Rule, which is widely used for boundary handling, always caused computing resource waste, especially for high-dimensional optimization. In this paper, we propose three novel Mapping Rules to speed up large scale optimization of FWA. Meanwhile, to evaluate the effectiveness of the new Rules, we compare them by representative nine benchmark functions on different dimensionality scale. Experimental results indicate that the mirror Rule which we proposed, achieve superior performance for most optimization functions.

Hao Huang - One of the best experts on this subject based on the ideXlab platform.

  • A Transferrable Coarse-Grained Force Field for Simulations of Polyethers and Polyether Blends
    Macromolecules, 2018
    Co-Authors: Hao Huang, Huiming Xiong, Huai Sun
    Abstract:

    We developed a transferable coarse-grained (CG) force field to cover polyethers and polyether blends in this work. On the basis of the perturbation theory of liquids and high-temperature expansion, we examined the theoretical foundation of using a temperature-dependent function to represent the nonbonded interactions. Using a Mapping Rule that balances molecular representation with the degree of coarse-graining, we defined seven CG beads that represent common polyethers. A hybrid parametrization workflow combining bottom-up and top-down approaches was applied to derive the force field parameters. The large ratio between the number of training data and the number of adjustable parameters enables the resulting force field to accurately predict many polymer properties, including cohesive energies, structural parameters, glass-transition temperatures, and surface tensions. In addition, the force field is capable of simulating polyether blends using modified Lorentz–Berthelot combination Rules. As a preliminar...

  • A Transferrable Coarse-Grained Force Field for Simulations of Polyethers and Polyether Blends
    2018
    Co-Authors: Hao Huang, Huiming Xiong, Huai Sun
    Abstract:

    We developed a transferable coarse-grained (CG) force field to cover polyethers and polyether blends in this work. On the basis of the perturbation theory of liquids and high-temperature expansion, we examined the theoretical foundation of using a temperature-dependent function to represent the nonbonded interactions. Using a Mapping Rule that balances molecular representation with the degree of coarse-graining, we defined seven CG beads that represent common polyethers. A hybrid parametrization workflow combining bottom-up and top-down approaches was applied to derive the force field parameters. The large ratio between the number of training data and the number of adjustable parameters enables the resulting force field to accurately predict many polymer properties, including cohesive energies, structural parameters, glass-transition temperatures, and surface tensions. In addition, the force field is capable of simulating polyether blends using modified Lorentz–Berthelot combination Rules. As a preliminary application, we have investigated the phase behavior of three polyether blends. For immiscible polymers, two glass transitions are identified, which are shown to be correlated to the chain dynamics of each component

Fan Wei - One of the best experts on this subject based on the ideXlab platform.

  • ack feedback based ue to ctu Mapping Rule for scma uplink grant free transmission
    International Conference on Wireless Communications and Signal Processing, 2017
    Co-Authors: Jiali Shen, Wen Chen, Fan Wei
    Abstract:

    Massive machine type communication (mMTC) is one of the three significant scenarios in 5G, which is characterized by massive connection and low energy consumption. To meet these requirements, the contention based grant-free sparse code multiple access (SCMA) transmission which can support a large number of devices and reduce the cost of signaling overhead caused by massive connection is proposed. It allows user equipment (UE) to transmit in the preconfigured radio resources shared by multiple UEs, which is called contention transmission unit (CTU). Since several UEs can be mapped to the same CTU in the transmission, collision may occur. In the conventional set-up, the UE is allocated to a CTU by the fixed Mapping Rule, which brings the problem of unfairness among the UEs and the probability that UE may collide again in retransmission. In this paper, we propose a new Mapping Rule, which utilizes the Acknowledgement (ACK) feedback to indicate the radio resource allocation. The theoretical deduction confirms the superiority of the proposed method. The simulation results show that collision probability is reduced by around 20%.

  • WCSP - ACK feedback based UE-to-CTU Mapping Rule for SCMA uplink grant-free transmission
    2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017
    Co-Authors: Jiali Shen, Wen Chen, Fan Wei
    Abstract:

    Massive machine type communication (mMTC) is one of the three significant scenarios in 5G, which is characterized by massive connection and low energy consumption. To meet these requirements, the contention based grant-free sparse code multiple access (SCMA) transmission which can support a large number of devices and reduce the cost of signaling overhead caused by massive connection is proposed. It allows user equipment (UE) to transmit in the preconfigured radio resources shared by multiple UEs, which is called contention transmission unit (CTU). Since several UEs can be mapped to the same CTU in the transmission, collision may occur. In the conventional set-up, the UE is allocated to a CTU by the fixed Mapping Rule, which brings the problem of unfairness among the UEs and the probability that UE may collide again in retransmission. In this paper, we propose a new Mapping Rule, which utilizes the Acknowledgement (ACK) feedback to indicate the radio resource allocation. The theoretical deduction confirms the superiority of the proposed method. The simulation results show that collision probability is reduced by around 20%.

Xuemei Yet - One of the best experts on this subject based on the ideXlab platform.

  • which Mapping Rule in the fireworks algorithm is better for large scale optimization
    Congress on Evolutionary Computation, 2018
    Co-Authors: Xuemei Yet, Ying Tan
    Abstract:

    Fireworks algorithm(FWA), which is proposed for global optimization of complex function, becomes a hot spot in optimization field recently, caused by its competitive performance. Boundary handling for FWA, which maps the out-of-bound sparks into feasible space, is critical for its convergence efficiency. However, random Mapping Rule, which is widely used for boundary handling, always caused computing resource waste, especially for high-dimensional optimization. In this paper, we propose three novel Mapping Rules to speed up large scale optimization of FWA. Meanwhile, to evaluate the effectiveness of the new Rules, we compare them by representative nine benchmark functions on different dimensionality scale. Experimental results indicate that the mirror Rule which we proposed, achieve superior performance for most optimization functions.

  • CEC - Which Mapping Rule in the Fireworks Algorithm is Better for Large Scale Optimization
    2018 IEEE Congress on Evolutionary Computation (CEC), 2018
    Co-Authors: Xuemei Yet, Ying Tan
    Abstract:

    Fireworks algorithm(FWA), which is proposed for global optimization of complex function, becomes a hot spot in optimization field recently, caused by its competitive performance. Boundary handling for FWA, which maps the out-of-bound sparks into feasible space, is critical for its convergence efficiency. However, random Mapping Rule, which is widely used for boundary handling, always caused computing resource waste, especially for high-dimensional optimization. In this paper, we propose three novel Mapping Rules to speed up large scale optimization of FWA. Meanwhile, to evaluate the effectiveness of the new Rules, we compare them by representative nine benchmark functions on different dimensionality scale. Experimental results indicate that the mirror Rule which we proposed, achieve superior performance for most optimization functions.