The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

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

  • Robust Linear Multiuser Receiver With Worst-Case Probability Guarantee
    IEEE Transactions on Vehicular Technology, 2008
    Co-Authors: Jianhui Peng, Zhongfu Ye, Xu Xu
    Abstract:

    A novel robust linear receiver with worst-case probability guarantee is proposed in this correspondence. Based on multivariate Chebyshev Inequality, deterministic expressions of the probabilistic constraints are derived, and then, an iterative second-order cone programming is developed to obtain the robust solutions. Compared to the existing receivers, our proposed receiver improves performance by allowing a small outage probability and a more general uncertainty model. Simulations illustrate the improvement of our proposed approach.

Seyed Hossein Razavi Hajiagha - One of the best experts on this subject based on the ideXlab platform.

  • Bi-objective mean–variance method based on Chebyshev Inequality bounds for multi-objective stochastic problems
    Rairo-operations Research, 2018
    Co-Authors: Hannan Amoozad Mahdiraji, Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Edmundas Kazimieras Zavadskas
    Abstract:

    Multi-objective programming became more and more popular in real world decision making problems in recent decades. There is an underlying and fundamental uncertainty in almost all of these problems. Among different frameworks of dealing with uncertainty, probability and statistic-based schemes are well-known. In this paper, a method is developed to find some efficient solutions of a multi-objective stochastic programming problem. The method composed a process of transforming the stochastic multi-objective problem to a bi-objective equivalent using the concept of Chebyshev Inequality bounds and then solving the obtained problem with a fuzzy set based approach. Application of the proposed method is examined on two numerical examples and the results are compared with different methods. These comparisons illustrated that the results are satisfying.

  • bi objective mean variance method based on Chebyshev Inequality bounds for multi objective stochastic problems
    Rairo-operations Research, 2018
    Co-Authors: Hannan Amoozad Mahdiraji, Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Edmundas Kazimieras Zavadskas
    Abstract:

    Multi-objective programming became more and more popular in real world decision making problems in recent decades. There is an underlying and fundamental uncertainty in almost all of these problems. Among different frameworks of dealing with uncertainty, probability and statistic-based schemes are well-known. In this paper, a method is developed to find some efficient solutions of a multi-objective stochastic programming problem. The method composed a process of transforming the stochastic multi-objective problem to a bi-objective equivalent using the concept of Chebyshev Inequality bounds and then solving the obtained problem with a fuzzy set based approach. Application of the proposed method is examined on two numerical examples and the results are compared with different methods. These comparisons illustrated that the results are satisfying.

  • multi period data envelopment analysis based on Chebyshev Inequality bounds
    Expert Systems With Applications, 2015
    Co-Authors: Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Hannan Amoozad Mahdiraji, Jamshid Azaddel
    Abstract:

    Proposing an algorithm for appraising relative efficiency of DMUs over several time periods.Providing an interval efficiency approximation of DMUs by considering inputs and outputs inexactness.Considering the variance of inputs and outputs in efficiency appraisal. Data envelopment analysis is a cross-sectional approach to evaluate the relative efficiency of a set of homogeneous units in a single time point; nonetheless, organizational units have been performing continuously over a period of time; hence, their performances are considered within this period. Cumulating inputs and outputs over the time periods provide an unnecessary compensating impact, making the efficiency appraisal unrealistic. To avoid this negative impact of data accumulation, a two-stage approach on the basis of Chebyshev Inequality bounds is proposed to find interval efficiency of decision making units (henceforth DMUs). The proposed method is applied in a real case encompassing 115 bank branches over 6 periods of time. This application indicated the significant cautious approach of the proposed method in multi-period data envelopment analysis (hereafter DEA).

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

  • Estimating the Probability of Infeasible Real-Time Dispatch Without Exact Distributions of Stochastic Wind Generations
    IEEE Transactions on Power Systems, 2016
    Co-Authors: Na Li, Jianhui Wang
    Abstract:

    This paper proposes a data-driven and convex optimization based method to quantify the probability of infeasible real-time dispatch (RTD) of power systems with volatile wind energy integrations. The required information about wind power is a finite sequence of moments, instead of the exact probability distribution function (PDF). The candidate PDFs are restricted in a functional set subject to moment constraints. By assuming the dispatchable region of nodal wind power injection is available, we propose a semi-definite programming (SDP) based method and a linear programming (LP) based method to estimate the probability of infeasibility in the worst wind power distribution. We also suggest two alternative methods based on the emerging generalized Chebyshev Inequality (GCI) and generalized Gauss Inequality (GGI), which only utilize the first and second order moments, and boil down to solving SDPs. We compare the performances of all the discussed methods on the moderately sized IEEE 118-bus system. Experimental results demonstrate that our method can offer monotonically better estimation when higher order moments are provided and is competitive with GCI and GGI.

Hannan Amoozad Mahdiraji - One of the best experts on this subject based on the ideXlab platform.

  • Bi-objective mean–variance method based on Chebyshev Inequality bounds for multi-objective stochastic problems
    Rairo-operations Research, 2018
    Co-Authors: Hannan Amoozad Mahdiraji, Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Edmundas Kazimieras Zavadskas
    Abstract:

    Multi-objective programming became more and more popular in real world decision making problems in recent decades. There is an underlying and fundamental uncertainty in almost all of these problems. Among different frameworks of dealing with uncertainty, probability and statistic-based schemes are well-known. In this paper, a method is developed to find some efficient solutions of a multi-objective stochastic programming problem. The method composed a process of transforming the stochastic multi-objective problem to a bi-objective equivalent using the concept of Chebyshev Inequality bounds and then solving the obtained problem with a fuzzy set based approach. Application of the proposed method is examined on two numerical examples and the results are compared with different methods. These comparisons illustrated that the results are satisfying.

  • bi objective mean variance method based on Chebyshev Inequality bounds for multi objective stochastic problems
    Rairo-operations Research, 2018
    Co-Authors: Hannan Amoozad Mahdiraji, Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Edmundas Kazimieras Zavadskas
    Abstract:

    Multi-objective programming became more and more popular in real world decision making problems in recent decades. There is an underlying and fundamental uncertainty in almost all of these problems. Among different frameworks of dealing with uncertainty, probability and statistic-based schemes are well-known. In this paper, a method is developed to find some efficient solutions of a multi-objective stochastic programming problem. The method composed a process of transforming the stochastic multi-objective problem to a bi-objective equivalent using the concept of Chebyshev Inequality bounds and then solving the obtained problem with a fuzzy set based approach. Application of the proposed method is examined on two numerical examples and the results are compared with different methods. These comparisons illustrated that the results are satisfying.

  • multi period data envelopment analysis based on Chebyshev Inequality bounds
    Expert Systems With Applications, 2015
    Co-Authors: Seyed Hossein Razavi Hajiagha, Shide Sadat Hashemi, Hannan Amoozad Mahdiraji, Jamshid Azaddel
    Abstract:

    Proposing an algorithm for appraising relative efficiency of DMUs over several time periods.Providing an interval efficiency approximation of DMUs by considering inputs and outputs inexactness.Considering the variance of inputs and outputs in efficiency appraisal. Data envelopment analysis is a cross-sectional approach to evaluate the relative efficiency of a set of homogeneous units in a single time point; nonetheless, organizational units have been performing continuously over a period of time; hence, their performances are considered within this period. Cumulating inputs and outputs over the time periods provide an unnecessary compensating impact, making the efficiency appraisal unrealistic. To avoid this negative impact of data accumulation, a two-stage approach on the basis of Chebyshev Inequality bounds is proposed to find interval efficiency of decision making units (henceforth DMUs). The proposed method is applied in a real case encompassing 115 bank branches over 6 periods of time. This application indicated the significant cautious approach of the proposed method in multi-period data envelopment analysis (hereafter DEA).

Timothy N. Davidson - One of the best experts on this subject based on the ideXlab platform.

  • Power allocation for orthogonal AF relay systems with outage-based QOS constraints
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Rooholah Hasanizadeh, Timothy N. Davidson
    Abstract:

    We consider the problem of minimizing the cost of the power required to achieve a specified level of quality-of-service (QoS) on a point-to-point link that may be assisted by an orthogonal amplify-and-forward (AF) relay. We consider a scenario in which only the distribution of the channel states is available for the design, and the QoS is specified in terms of a target rate that is to be achievable with a specified probability of outage. We assign prices to the power expended by the source and the relay, and we seek to minimize the cost of the power required to achieve the specified QoS. This chance-constrained problem appears to be difficult to solve in its direct form. Instead, we employ the Chebyshev Inequality to obtain a deterministic formulation whose solution is guaranteed to provide the required QoS. Although that problem is non-convex, its structure enables the development of an effective algorithm. We apply this design methodology to a system with imperfect channel estimation and illustrate its performance.