In this paper, we present a novel approach based on bayesian expectation maximization-maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional an...
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In this paper, we present a novel approach based on bayesian expectation maximization-maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional and nonlinear optimization problems, BEMM offers a robust framework that combines the benefits of bayesian inference with the flexibility of expectationmaximization and maximization techniques. By iteratively updating parameter estimates based on observed data and maximizing the likelihood of the model, BEMM effectively navigates the solution space to converge on accurate estimates of the unspecified variables in Proton Exchange Membrane Fuel Cells (PEMFCs)models. Through extensive experimentation and comparison with other metaheuristic techniques, including Arithmetic Optimization Algorithm (AOA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), and Biogeography-Based Optimization (BBO), we demonstrate the superior performance of our BEMM approach. Our results show that BEMM outperforms these alternative methods in terms of precision, convergence speed, and stability across various scenarios involving different numbers of unspecified variables. The implications of our findings are significant for both researchers and practitioners in the field of PEMFC modeling and optimization. By providing a more efficient and reliable method for estimating model parameters, our approach can facilitate more accurate predictions of PEMFC performance, leading to better-informed decision-making in the design, operation, and optimization of PEMFC systems. Furthermore, the robustness and versatility of BEMM make it well-suited for a wide range of optimization problems beyond PEMFC modeling, highlighting its potential impact across various domains of engineering and scientific research. In the sensitivity analysis, as the population size increases from 10 to 40, there is a significant improvement in solution quality by approximately 100%. However, b
We develop new and optimal algorithms for distributed detection in sensor networks over fading channels with multiple receive antennas at the Fusion Centre (FC). Sensors observe a hidden physical phenomenon over fadin...
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We develop new and optimal algorithms for distributed detection in sensor networks over fading channels with multiple receive antennas at the Fusion Centre (FC). Sensors observe a hidden physical phenomenon over fading channels and transmit their observations using the amplify-and-forward scheme over fading channels to the FC which is equipped with multiple antennas. We derive the optimal decision rules and the associated probabilities of detection and false alarm for three scenarios of Channel State Information (CSI) availability. For the most difficult case of unknown CSI, we develop two new algorithms to derive the optimal decision rule. The first is based on a Gaussian approximation method where we quantify the approximation error and its rate of convergence (to a true Normal distribution) via a multivariate version of the Berry-Esseen bound. The second is based on a multivariate Saddle-point (Laplace) approximation which is obtained via a non-convex optimisation problem which is solved efficiently via bayesianexpectation-Maximisation method. We show under which system configuration which algorithm is suitable and should be used. For cases where the distribution of the optimal decision rule can not be derived exactly, we develop a Laguerre series expansion to approximate the resulting distribution. The performance of the proposed algorithms is evaluated via analytic bounds and numerical simulations. We show that the detection performance of the proposed algorithms is significantly superior to a local vote decision fusion based algorithms.
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