MODIFIED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR ELECTROMAGNETIC ABSORBER DESIGN

designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade-offs between thickness and reflection coefficient of absorbers. Particle swarm optimization method in comparison with most of optimization algorithms such as genetic algorithms is simple and fast. But the basic form of Multi-objective Particle Swarm Optimization may not obtain the best Pareto. We applied some modifications to make it more efficient in finding optimal Pareto front. Comparison with reported results in previous articles confirms the ability of this algorithm in finding better solutions. 1

Model-based Fault Detection and Isolation using Neural Networks: An Industrial Gas Turbine Case Study

This study proposed a model based fault detection and isolation (FDI) method using multi-layer perceptron (MLP) neural network. Detection and isolation of realistic faults of an industrial gas turbine engine in steady-state conditions is mainly centered. A bank of MLP models which are obtained by nonlinear dynamic system identification is used to generate the residuals, and also simple thresholding is used for the intend of fault detection while another MLP neural network is employed to isolate the faults. The proposed FDI method was tested on a singleshaft industrial gas turbine prototype and it have been evaluated using non-linear simulations based on the real gas turbine data. A brief comparative study with other related works in the literature on this gas turbine benchmark is also provided to show the benefits of proposed FDI method