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Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges Conference

作者： M.G. Ruano E. Hajimani A. E. Ruano Faculty of Science and Technology University of Algarve Faro Portugal and CISUC University of Coimbra Portugal Faculty of Science and Technology University of Algarve Faro Portugal Faculty of Science and Technology University of Algarve Faro Portugal and IDMEC Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781509024872

A Radial Basis Function Neural Network (RBFNN) based diagnosis system for automatic identification of Cerebral Vascular Accident (CVA) through analysis of Computer Tomographic images (CT) is presented. For the design of a neural network classifier, most published methods just focus on the feature selection aspect and do not consider any approach for determining a model structure that best fits the application at their hand. Moreover, considering the domain of lesion detection from brain tissues, their feature space rarely contains symmetry/asymmetry information with respect to ideal mid-sagittal line. Another issue is how to handle multiple conflicting objectives in the design process, such as the maximization of both specificity and sensitivity, enforcing as well generalization. To deal with these challenges, a multi objective genetic algorithm (MOGA) based approach is used to determine the architecture of the classifier, its corresponding parameters and input features subject to multiple objectives, as well as their corresponding restrictions and priorities.

关键词： Cerebral Vascular Accident Intelligent support systems multi-objective genetic algorithm Neural Networks Symmetry Features

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Optimization of shape and angle of attack of winglet vortex generator in a rectangular channel for heat transfer enhancement

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APPLIED THERMAL ENGINEERING 2015年 81卷 376-387页

作者： Abdollahi, Azita Shams, Mehrzad KN Toosi Univ Technol Dept Mech Engn Tehran Iran

A three dimensional numerical simulation was performed to study the effect of the shape and the angle of attack of the winglet vortex generator on the heat transfer and fluid flow characteristics in a rectangular heat sink. The mass, momentum and energy equations were solved using finite volume method by considering the steady state, laminar and incompressible fluid flow. The average and local Nusselt number and pressure drop were investigated in the presence of the vortex generators at different shapes and angles of attack. To achieve a maximum heat transfer enhancement and a minimum pressure drop, the optimal values of these parameters were calculated using the Pareto optimal strategy. For this purpose, computational fluid dynamics analyses, multi-objective genetic algorithm and artificial neural networks were combined together and used in the optimization process. Finally, the optimal values of these parameters were presented. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Heat transfer enhancement Vortex generator multi-objective genetic algorithm Artificial neural network

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Partially Optimized Cyclic Shift Crossover for multi-objective genetic algorithms for the multi-objective Vehicle Routing Problem with Time-Windows

Partially Optimized Cyclic Shift Crossover for Multi-Objecti...

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2014 IEEE Symposium on Computational Intelligence in multi-Criteria Decision-Making (MCDM)

作者： Pierre, Djamalladine Mahamat Zakaria, Nordin Univ Teknol PETRONAS Ctr High Performance Comp Perak Malaysia

ISBN: (纸本)9781479944675

The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with multi-objective genetic algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.

关键词： multi-objective Vehicle Routing Problem multi-objective genetic algorithm Crossover Mutation

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Proportional integral controller gain tuning using real time digital simulation models and multi-objective optimization based co-simulation

Proportional integral controller gain tuning using real time...

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9th IFAC Symposium on Control of Power and Energy Systems (CPES 2015)

作者： Prabakar, Kumaraguru Li, Fangxing Univ Tennessee Knoxville TN USA

Proportional integral (PI) controller is a widely used controller strategy. It has two gain constants: proportional(k(p)) and integral(k(i)) gain constant. The dynamics of the PI controller depends on the values of the gain constant used. If gains of these control terms are not selected properly, instability Will occur. Gain tutting methodologies are available to tune these controllers to avoid instability. Hut, these gain tuning strategies and algorithms utilize simplified mathematical model of the plant ut order to obtain gain values. This approach might, not provide optimal gain values for models used in real time digital simulator. In the proposed approach, the algorithm utilizes the step response of the system to obtain optimal gain constants. The plant is modeled using the real time digital simulator (RIDS). A multi-objective optimization formulation is used to find the optimal gain constants. Results are presented here to support the effectiveness of proposed multi-objective gain tutting strategy. (C) 2015, IFAC (International Rderation or Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Proportional integral control Gain tuning multi-objective optimization multi-objective genetic algorithm

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Co-ordinated Design of AVR-PSS Using multi objective genetic algorithm

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1st International Conference on Swarm, Evolutionary, and Memetic Computing

作者： Selvabala, B. Devaraj, D. Sri Manakula Vinayagar Engn Coll Dept EEE Pondicherry India kalasalingam Univ krishnankoil Tamil Nadu India

ISBN: (纸本)9783642175626

Automatic Voltage Regulator (AVR) regulates the generator terminal voltage by controlling the amount of current supplied to the generator field winding by the exciter. Power system stabilizer (PSS) is installed with AVR to damp the low frequency oscillations in power system by providing a supplementary signal to the excitation system. Optimal tuning of AVR controller and PSS parameters is necessary for the satisfactory operation of the power system. When applying tuning method to obtain the optimal controller parameters individually, AVR improves the voltage regulation of the system and PSS improves the damping of the system. Simultaneous tuning of AVR and PSS is necessary to obtain better both voltage regulation and oscillation damping in the system. This paper deals with the optimal tuning of AVR controller and PSS parameters in the synchronous machine. The problem of obtaining the optimal controller parameters is formulated as an optimization problem and multi-objective genetic algorithm (MOGA) is applied to solve the optimization problem. The suitability of the proposed approach has been demonstrated through computer simulation in a Single Machine Infinite Bus (SMIB) system.

关键词： Automatic Voltage Regulator Power System Stabilizer multi-objective genetic algorithm Single Machine Infinite Bus system

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Kansei Engineering System for Test Generation Using multi-objective Optimization 2

Kansei Engineering System for Test Generation Using Multi-Ob...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Choudhary, Kavita Shilpa ITM Univ Dept CSE&IT Gurgaon India ITM Univ Gurgaon India

SoftwareQuality plays a critical role in software development. This paper emphasize on involvement of Kansei Engineering in the field of test case generation. multi-objective genetic algorithm under multi-objective op... 详细信息

ISBN: (纸本)9789380544168

关键词： Kansei engineering multi-objective optimization multi-objective genetic algorithm Pareto-optimal Boolean-specific conditions etc

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Wind Turbine Transformer Optimum Design Assuming a 3D Wound Core 30

Wind Turbine Transformer Optimum Design Assuming a 3D Wound ...

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30th International Power System Conference (PSC)

作者： Elhaminia, Pedram Moradnouri, Ahmad Vakilian, Mehdi Sharif Univ Technol Ctr Excellence Power Syst Management & Control Elect Engn Dept Tehran Iran

ISBN: (纸本)9781509027057

A wind turbine transformer (WTT) is designed using a 3D wound core while the transformer's total owning cost (TOC) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. multi-objective genetic algorithm is utilized to derive Pareto optimal solutions. The effects of inrush current improvement on other operating and design parameters of the transformer such as: losses, dimensions, and weights are investigated. An approach is presented to select one design from optimal Pareto solutions based on relative improvement of the inrush current performance. Finally, this multi-objective optimum wind turbine transformer design is compared with an optimum transformer design obtained when just TOC is the objective function.

关键词： wind turbine transformer (WTT) 3D wound core total owning cost (TOC) inrush current multi-objective genetic algorithm Pareto optimal solutions

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Improving a neural networks based HVAC predictive control approach 9

Improving a neural networks based HVAC predictive control ap...

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IEEE 9th International Symposium on Intelligent Signal Processing (WISP)

作者： Ruano, A. E. Silva, S. Pesteh, S. Ferreira, P. M. Duarte, H. Mestre, G. Khosravani, H. Horta, R. Univ Algarve Faro Portugal EasySensing Ltd Coimbra Portugal IST IDMEC Ctr Intelligent Syst Lisbon Portugal Rolear SA Faro Portugal Univ Lisbon Fac Sci LaSIGE P-1699 Lisbon Portugal

ISBN: (纸本)9781479972524

This paper improves an existing Model Based Predictive Control Approach (MBPC), applied for Heating Ventilation and Air Conditioning (HVAC) control in buildings. The existing approach uses the Predictive Mean Vote (PMV) to assess thermal comfort. It has been found that PMV estimation and forecasts deteriorate when the room is occupied. In order to solve this problem, we propose to incorporate measurements of activity inside the room in the predictive models of the inside air temperature. Another improvement to the existing approach is to use an economic cost function, reflecting the money needed for the HVAC control, instead of a cost function related with the consumption of energy.

关键词： HVAC Predictive Control Predicted Mean Vote Neural Networks multi-objective genetic algorithm Thermal Comfort Room Occupancy Economic Cost Function

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A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach

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Journal of Mathematical Modelling and algorithms in Operations Research 2015年第3期14卷 287-312页

作者： Khalili-Damghani, Kaveh Abtahi, Amir-Reza Ghasemi, Amir Department of Industrial Engineering South-Tehran Branch Islamic Azad University Tehran Iran Department of Knowledge Engineering and Decision Sciences Faculty of Management Economics University of Economic Sciences Tehran Iran

Supply, manufacture, and distribution of perishable products are challenging jobs in supply chains. Location of warehouses and routing of vehicles are essential issues to distribute perishable products properly. In this paper, a new bi-objective mixed integer mathematical programming is proposed to reduce the total cost of the supply chain and to balance the workload of distribution centers while the due dates of delivery of perishable product are met, concurrently. The considered properties and constraints of proposed model made it well-posed to illustrate the real life situation. As the proposed model is NP-Hard, an evolutionary algorithm called Non-Dominated Sorting genetic algorithm-II (NSGA-II) is customized to solve the problem. The structure of chromosome and genetic operators are customized for the problem. The performance of proposed NSGA-II and an efficient exact multi-objective method, called ε-constraint, is compared using accuracy and diversity metrics on several benchmark instances. © 2015, Springer Science+Business Media Dordrecht.

关键词： Bi-objective optimization Distribution of perishable products Location-routing problem Mixed-integer programming multi-objective genetic algorithm ε-constraint Method

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Real-time online Compensation the Nonlinearities and Uncertainties in Missile Servo Control System 27

Real-time online Compensation the Nonlinearities and Uncerta...

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27th Chinese Control and Decision Conference (CCDC)

作者： Feng, Zhang Kun Wei, Rao Da Bo, Zhu Jian Hua, Duan Li Luoyang Optoelect Technol Dev Ctr Luoyang 471009 Peoples R China

ISBN: (纸本)9781479970162

To restrain the influences of the nonlinearities and uncertainties in missile servo control systems, the active disturbance rejection control (ADRC) technique is introduced to real-time online estimate and compensate these factors. As to the problem of the difficulties in tuning ADRC parameters, an improved multi-objective genetic algorithm is adopted to solve the complete set of Pareto solutions for the control parameters, then a multi-criteria decision making algorithm based on utility function is chosen to determine the fittest solution. Then a test platform using TMS320C28346 floating-point DSP as control core is built to verify the research results. The experimental results show that the optimal ADRC parameter is appropriate for the servo control system. The system tracks the input commands smoothly and reliably, and has good robustness and adaptability to nonlinearities and parameters drifts. Also the strategy for tuning the ADRC parameters is practical and feasible.

关键词： Nonlinearity and Uncertainty Active Disturbance Rejection Control multi-objective genetic algorithm

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