A Discrete Geese Swarm Algorithm for Spectrum Assignment of Cognitive Radio

In order to solve spectrum assignment problem, this paper proposes a discrete geese swarm algorithm (DGSA) based on particle swarm optimization and quantum particle swarm optimization, and we evaluate the performance of the DGSA through some classical benchmark functions. The proposed DGSA algorithm applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization. We also use it to solve cognitive radio spectrum assignment problem. The new spectrum allocation method has the ability to search global optimal solution under different network utility functions. Simulation results for cognitive radio system are provided to show that the designed spectrum allocation algorithm is superior to some previous spectrum allocation algorithms

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Usabilidade pedagógica: um fator determinante na adoção do e-Learning no ensino superior

O artigo que apresentamos neste simpósio doutoral surge no âmbito do Curso de Doutoramento em Educação, especialidade em Tecnologias de Informação e Comunicação na Educação, do Instituto de Educação da Universidade de Lisboa. O estudo tem como objetivo principal propor e testar um modelo que permita explicar a intenção comportamental dos docentes do Ensino Superior aquando da adoção e uso continuado das plataformas de e-Learning. Para o efeito procura-se compreender o contributo da usabilidade pedagógica como fator determinante no processo de adoção da tecnologia

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Bowdoin Orient v.133, no.1-24 (2003-2004)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1004/thumbnail.jp