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Classifier Design by a Multi-objective genetic algorithm Approach for GPR Automatic Target Detection

IFAC-PapersOnLine 2018年第10期51卷 187-192页

作者： Harkat, H. Ruano, A. Ruano, M.G. Bennani, S.D. Faculty of Science and Technology University of Algarve Faro Portugal Faculty of Sciences and Technologies University of Sidi Mohamed Ben Abdellah Fez Morocco IDMEC Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal CISUC-University of Coimbra Portugal

GPR is an electromagnetic remote sensing technique, used for detection of relatively small objects in high noise environments. Data inversion requires a fitting procedure of hyperbola signatures, which represent the target reflections, sometimes producing bad results due to high resolution of GPR images. The idea proposed in this paper consists of narrowing down the position of hyperbolas to small regions, using a machine learning approach. A Multi-objective genetic Approach (MOGA) is used to design a Radial Basis Function classifier. High order statistic cumulants are employed as features to this framework. Due to the complexity of the formulated problem, feature selection can be done in two ways: either by MOGA alone, or acting on a reduced subset obtained using a mutual information approach. The chosen classifier was tested on experimental data, the results outperforming the one presented in literature, or achieving similar results with models of much lower complexity. © 2016

关键词： Classification (of information) Complex networks Feature extraction genetic algorithms Geological surveys Geophysical prospecting Ground penetrating radar systems Learning systems Neural networks Object detection Radial basis function networks Remote sensing Automatic target detection Ground penetrating radar (GPR) High noise environments High order statistics Machine learning approaches Multi objective genetic algorithm Radial basis function classifiers Remote sensing techniques

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Multi-objective genetic algorithm for an automatic transmission gear shift map

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IFAC-PapersOnLine 2016年第3期49卷 123-128页

作者： Fofana, Adama Haas, Olivier Ersanilli, Vince Burnham, Keith Mahtani, Joe Woolley, Chris Vithanage, Karen Adama Fofana is with Coventry University United Kingdom Olivier Haas is with Coventry University United Kingdom Vince Ersanilli is with Coventry University United Kingdom Keith Burnham is with Coventry University United Kingdom Joe Mahtani is with Coventry University United Kingdom Chris Woolley is with SAIC MOTOR UK Technical Centre Limited Lowhill Lane BirminghamB31 2BQ United Kingdom Karen Vithanage is Director of KJV Consultancy United Kingdom

Reductions in engine emissions including carbon dioxide (CO2) can be achieved by analysing the Brake Specific Fuel consumption (BSFC) map and selecting a set of gear ratio and associated gear shift map which will move the engine operating point into the most efficient region. This paper formulates the determination of the most appropriate gear shift map for a Dual Clutch Transmission as a multi objective optimisation problem. A multi-objective genetic algorithm (MOGA) is complemented by a new repair mechanism to enforce acceptable driveability in the solutions produced as well as a new problem specific local search operator exploiting engineering knowledge to determine feasible gear shift maps that result in reduced emissions. The solutions are evaluated against standard as well as novel objective formulations that take into account CO2, driveability, gearbox durability and other practical implementation considerations. A correlation analysis, combined with Pareto analysis, is performed to identify and visualise related objectives that can be combined using a traditional weighted sum approach and those that should be handled separately using a Pareto front approach to facilitate the trade-offs identification and resolutions. The first set of objectives aim to reduce CO2, the second to maintain driveabil-ity, and the third to consider durability. The novel approach was used to identify trade-off solutions focusing on reducing CO2emission. These solutions were validated on a prototype vehicle on a rolling road using the New European Driving Cycle, confirming the expected CO2savings whilst conserving good driveability characteristics. © 2016

关键词： Carbon dioxide Automobile transmissions Durability Economic and social effects Engines Fuel consumption genetic algorithms Multiobjective optimization Automatic transmission Brake specific fuel consumption Dual clutch transmission Local search operators Multi objective genetic algorithm New european driving cycles Reducing co2 emissions Weighted sum approaches

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A New Convex Hull, Sliding Window Based Online Adaptation Method

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IFAC-PapersOnLine 2018年第10期51卷 211-216页

作者： Khosravani, H. Ruano, A. Ferreira, P.M. Faculty of Science and Technology University of Algarve Faro Portugal IDMEC Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal LaSIGE Faculdade de Ciências Universidade de Lisboa Portugal LAETA Portugal

In any online adaptation scheme, two important phenomena should be taken into consideration; parameter shadowing and parameter interference. To alleviate these problems, in this paper a convex hull, sliding window based online adaptation method for fixed-structure Neural Networks is proposed. The method is capable of dealing with the two phenomena, presenting better results than known alternatives. An analysis of the real-time run time and memory consumption of the algorithm demonstrates that it can be used for real-time applications. © 2016

关键词： Radial basis function networks Computational geometry genetic algorithms Convex hull Multi objective genetic algorithm On line adaptation Radial basis function neural networks Time series models

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Efficient Generation of Models of Fed-Batch Fermentations for Process Design and Control

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IFAC-PapersOnLine 2016年第7期49卷 621-626页

作者： Hebing, Lukas Neymann, Tobias Thüte, Tobias Jockwer, Alexander Engell, Sebastian Department of Biochemical and Chemical Engineering TU Dortmund DortmundD-44221 Germany Bayer Technology Services LeverkusenD-51368 Germany Bayer HealthCare AG WuppertalD-42113 Germany

The paper presents a fast and efficient approach to the generation of process models for fed-batch fermentation processes. Elementary Modes (EM) with kinetic terms are used as macro-react ions in the model. A method based upon dynamic metabolic flux analysis (DMFA) is used to find an optimal selection of EM based on concentration measurements instead of reaction rates that must be calculated from the measurements. A multi-objective genetic algorithm is used to investigate the tradeoff between the number of EM and the difference of the estimated concentration profiles from the available measurements. Thus the number of macro-reactions for the model is kept small but the measured data are described sufficiently well by the selected EM. The influence of the process conditions on each macro-reaction rate are identified by a statistical analysis and kinetic expressions are fitted to the estimated evolutions by solving multiple simple optimization problems instead of a multi-dimensional large-scale nonlinear parameter optimization problem. The resulting process model provides a good fit of the measurement data. Provided that the data is generated by a systematic approach, e.g., using a design of experiments strategy, the modelling procedure provides models of adequate complexity that are valid within the so-called design space of the process. © 2016

关键词： Parameter estimation Design of experiments Fermentation genetic algorithms Metabolism Reaction rates Concentration Measurement Concentration profiles Elementary mode Fed batch fermentation Macroscopic modelling Metabolic flux analysis Multi objective genetic algorithm Process design and control

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PVM-based intelligent predictive control of HVAC systems

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IFAC-PapersOnLine 2016年第5期49卷 371-376页

作者： Ruano, A. Pesteh, S. Silva, S. Duarte, H. Mestre, G. Ferreira, P.M. Khosravani, H. Horta, R. Faculty of Science and Technology University of Algarve Faro Portugal and IDMEC Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Science and Technology University of Algarve Faro Portugal Easy Sensing - Intelligent Systems Centro Empresarial de Gambelas University of Algarve Faro8005-139 Portugal LaSIGE Faculdade de Ciências Universidade de Lisboa Portugal Rolear SA Faro8001-906 Portugal

This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in term... 详细信息

关键词： Wireless sensor networks genetic algorithms Model predictive control Neural networks Thermal comfort Hvac models HVAC system Intelligent predictive control Multi objective genetic algorithm objective functions Predicted mean vote Real time Weather stations

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