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The Experts below are selected from a list of 130116 Experts worldwide ranked by ideXlab platform

Zhendong Li - One of the best experts on this subject based on the ideXlab platform.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

Ke Jiang - One of the best experts on this subject based on the ideXlab platform.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

Ryan Merrill - One of the best experts on this subject based on the ideXlab platform.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

  • optimal control for transboundary pollution under ecological compensation a stochastic differential game approach
    Journal of Cleaner Production, 2019
    Co-Authors: Ke Jiang, Ryan Merrill, Zhendong Li
    Abstract:

    Abstract To account for previously ignored, yet widely observed uncertainty in nature's capability to replenish the natural environment in ways that should inform Ideal Design of ecological compensation (EC) regimes, this study constructs a stochastic differential game (SDG) model to analyze transboundary pollution control options between a compensating and compensated region. Equilibrium strategies in the stochastic, two player game inform optimal control theory and reveal a welfare distribution mechanism to form the basis of an improved cooperative game contract. A case-based numerical example serves to verify the theoretical results and supports three key insights. First, accounting for various random disturbance factors, the probabilistic pollutant stock in Stackelberg non-cooperative game exceeds that of a cooperative game situation. Second, the EC mechanism provides long-term, effective incentives only when the marginal losses of environmental damage in the compensating region are more than twice that of the compensated region. Achieving a Pareto optimal equilibrium relies upon the attainment of a dynamic allocation ratio derived from the analysis of a robust welfare allocation mechanism. Third, cross-region cooperation reliably outperforms Stackelberg non-cooperation due to either overwhelming incumbent economic interests or high abatement costs. This study illuminates the importance of balancing both parties' interests within an EC agreement while reducing uncertainty around unobserved environmental factors during ex-ante negotiations.

Liang Gao - One of the best experts on this subject based on the ideXlab platform.

  • an ensemble fruit fly optimization algorithm for solving range image registration to improve quality inspection of free form surface parts
    Information Sciences, 2016
    Co-Authors: Liang Gao, Quanke Pan
    Abstract:

    Free-form surface part inspection can be conducted by comparing an Ideal Design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the Design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective.

  • cuckoo search based range image registration for free form surface inspection
    Computer Supported Cooperative Work in Design, 2015
    Co-Authors: Liang Gao, Yuewei Bai
    Abstract:

    3D parts inspection can be conducted by comparing the Ideal Design geometry with the real measurement points. Since the Design coordinate system is different from the measurement coordinate system, these measurement points should be registered to the Design coordinate system first. In this research area, iterative closest point (ICP) is the best-known algorithm, however, in order to converge to the global minima, ICP needs the good initial parameter which is hard to get in the actual inspection process. In this research, a hybrid Cuckoo Search (CS) method is proposed to solve the registration problem and two different optimizing strategies based on CS are described. The proposed algorithm seems much superior to other algorithms in terms of accuracy and robustness. Experiment results show that the proposed algorithm is effective.

Yuewei Bai - One of the best experts on this subject based on the ideXlab platform.

  • cuckoo search based range image registration for free form surface inspection
    Computer Supported Cooperative Work in Design, 2015
    Co-Authors: Liang Gao, Yuewei Bai
    Abstract:

    3D parts inspection can be conducted by comparing the Ideal Design geometry with the real measurement points. Since the Design coordinate system is different from the measurement coordinate system, these measurement points should be registered to the Design coordinate system first. In this research area, iterative closest point (ICP) is the best-known algorithm, however, in order to converge to the global minima, ICP needs the good initial parameter which is hard to get in the actual inspection process. In this research, a hybrid Cuckoo Search (CS) method is proposed to solve the registration problem and two different optimizing strategies based on CS are described. The proposed algorithm seems much superior to other algorithms in terms of accuracy and robustness. Experiment results show that the proposed algorithm is effective.