The Experts below are selected from a list of 192231 Experts worldwide ranked by ideXlab platform

Jiong Wang - One of the best experts on this subject based on the ideXlab platform.

  • a piecewise Parametric Method based on polynomial phase model to compensate ionospheric phase contamination
    International Conference on Acoustics Speech and Signal Processing, 2003
    Co-Authors: Kun Lu, Jiong Wang
    Abstract:

    This paper addresses a Parametric Method based on high-order ambiguity function (HAF) to solve the problem of phase contamination of HF skywave radar signals corrupted by the ionosphere. When signal-to-noise ratio and data sequence available satisfy the predefined conditions, the ionospheric phase contamination may be modeled by the polynomial phase signal. As a new Parametric tool for analyzing polynomial phase signal, HAF is applied to estimate polynomial phase model parameters and reconstruct the disturbance signal. Using the estimated reconstructed signal, compensation can be performed before coherent integration and the original radar return spectrum can be restored. A piecewise scheme is proposed to track rapid variation of the phase contamination in HAF Method, and it can remove the Doppler spread effect caused by the ionosphere nonstationarity. Simulation is used to demonstrate the efficiency of the proposed Method.

Fuyuan Xiao - One of the best experts on this subject based on the ideXlab platform.

  • A Non-Parametric Method to Determine Basic Probability Assignment Based on Kernel Density Estimation
    IEEE Access, 2018
    Co-Authors: Fuyuan Xiao
    Abstract:

    Dempster–Shafer evidence theory has been extensively applied in a variety of fields due to its ability to solve knowledge reasoning and decision-making problem under uncertain environments. Nevertheless, it is still an open issue about how to determine the basic probability assignment (BPA). In this paper, a new non-Parametric Method based on kernel density estimation is proposed to determine BPA. First, the probability density function of each attribute is calculated, which can be regarded as the probability model for the related attribute using the training sample. Then, a nested BPA function is constructed using the intersections point of test sample and probability models. Finally, Dempster’s combination rule is used to combine multiple BPAs to get the final BPA. Some classification experiments are conducted on several datasets. The experimental results demonstrate that the proposed Method is more effective and reasonable in determining BPAs, which has a better classification performance than the existing Method.

Yong Deng - One of the best experts on this subject based on the ideXlab platform.

  • A non-Parametric Method to determine basic probability assignment for classification problems
    Applied Intelligence, 2014
    Co-Authors: Peida Xu, Xiaoyan Su, Chenzhao Li, Yong Deng
    Abstract:

    As an important tool for knowledge representation and decision-making under uncertainty, Dempster-Shafer evidence theory (D-S theory) has been used in many fields. The application of D-S theory is critically dependent on the availability of the basic probability assignment (BPA). The determination of BPA is still an open issue. A non-Parametric Method to obtain BPA is proposed in this paper. This Method can handle multi-attribute datasets in classification problems. Each attribute value of the dataset sample is treated as a stochastic quantity. Its non-Parametric probability density function (PDF) is calculated using the training data, which can be regarded as the probability model for the corresponding attribute. The BPA function is then constructed based on the relationship between the test sample and the probability models. The missing attribute values in datasets are treated as ignorance in the framework of the evidence theory. This Method does not have the assumption of any particular distribution. As a result, it can be flexibly used in many engineering applications. The obtained BPA can avoid high conflict between evidence, which is desired in data fusion. Several benchmark classification problems are used to demonstrate the proposed Method and to compare against existing Methods. The constructed classifier based on the proposed Method compares well to the state-of-the-art algorithms.

Kun Lu - One of the best experts on this subject based on the ideXlab platform.

  • a piecewise Parametric Method based on polynomial phase model to compensate ionospheric phase contamination
    International Conference on Acoustics Speech and Signal Processing, 2003
    Co-Authors: Kun Lu, Jiong Wang
    Abstract:

    This paper addresses a Parametric Method based on high-order ambiguity function (HAF) to solve the problem of phase contamination of HF skywave radar signals corrupted by the ionosphere. When signal-to-noise ratio and data sequence available satisfy the predefined conditions, the ionospheric phase contamination may be modeled by the polynomial phase signal. As a new Parametric tool for analyzing polynomial phase signal, HAF is applied to estimate polynomial phase model parameters and reconstruct the disturbance signal. Using the estimated reconstructed signal, compensation can be performed before coherent integration and the original radar return spectrum can be restored. A piecewise scheme is proposed to track rapid variation of the phase contamination in HAF Method, and it can remove the Doppler spread effect caused by the ionosphere nonstationarity. Simulation is used to demonstrate the efficiency of the proposed Method.

Hans Auer - One of the best experts on this subject based on the ideXlab platform.

  • modeling post liberalized european gas market concentration a game theory perspective
    Forecasting, 2020
    Co-Authors: Hassan Hamie, Anis Hoayek, Hans Auer
    Abstract:

    The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model European gas markets and their effect on prices. In this research, the Parametric and nonParametric game theory Methods are employed to study the effect of the market concentration on gas prices. The Parametric Method takes into account the classical Cournot equilibrium test, with assumptions on cost and demand functions. However, the non-Parametric Method does not make any prior assumptions, a factor that allows greater freedom in modeling. The results of the Parametric Method demonstrate that the gas suppliers’ behavior in Austria and The Netherlands gas markets follows the Nash–Cournot equilibrium, where companies act rationally to maximize their payoffs. The non-Parametric approach validates the fact that suppliers in both markets follow the same behavior even though one market is more liquid than the other. Interestingly, our findings also suggest that some of the gas suppliers maximize their ‘utility function’ not by only relying on profit, but also on some type of non-profit objective, and possibly collusive behavior.