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Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE)

作者： Ruano, M. G. Hajimani, E. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Coimbra CISUC P-3000 Coimbra Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9781509024865

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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Target Systems and Decision-Making to Increase Production Sustainability

Target Systems and Decision-Making to Increase Production Su...

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23rd CIRP Conference on Life Cycle Engineering

作者： Putz, Matthias Goetze, Uwe Stoldt, Johannes Franz, Enrico Fraunhofer Inst Machine Tools & Forming Technol I Reichenhainer Str 88 D-09126 Chemnitz Germany Tech Univ Chemnitz Chair Management Accounting & Control Prof BWL 3 D-09107 Chemnitz Germany

Many industrial companies are trying to improve their operation's sustainability. However, these efforts towards change are usually governed by economic considerations. This serves to neglect or at least diminish the perceivable relevance of ecologic or social consequences for investigated alternatives. This paper discusses the implications of multi-criterial target systems which extend the scope of considerations beyond economics to support the realisation of proactive environmental strategies. A special focus is set on the definition of targets as well as decision-making in brown-field planning projects. The findings are applied in a simulation-based study on the parameterisation of an energy sensitive production control strategy. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Target systems Brown-field planning projects Sustainability Energy-sensitive production strategy multi-objective genetic algorithm NSGA-II

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Construction of Variable-Length Error-Correcting Codes using MOGA 18

Construction of Variable-Length Error-Correcting Codes using...

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18th Mediterranean Electrotechnical Conference (MELECON)

作者： Vella, Paula Buttigieg, Victor Univ Malta Dept Comm & Comp Engn Msida 2080 MSD Malta

ISBN: (纸本)9781509000586

Variable-length codes are classically used for source coding to compress data. However, Variable-Length Error-Correcting (VLEC) codes can also be used for channel coding where they are able to overcome the effects of channel impairments by detecting and correcting bit errors. multi-objective optimization is an area of multiple criteria decision making, where an optimization algorithm attempts to solve a problem with more than one objective. In this paper, VLEC codes are constructed by taking into consideration their two main properties: Average Codeword Length and Free Distance, associated respectively with their source and channel coding aspects. These two properties are taken as objectives in a multi-objective global optimization genetic algorithm. It is shown that better codes can be constructed using this algorithm.

关键词： variable-length error-correcting codes multi-objective genetic algorithm code construction

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Stabilization of nonlinear inverted pendulum system using MOGA and APSO tuned nonlinear PID controller

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COGENT ENGINEERING 2017年第1期4卷 Article: 1357314页

作者： Valluru, Sudarshan K. Singh, Madhusudan Delhi Technol Univ Dept Elect Engn Delhi 110042 India

An inverted pendulum system (IPS) is a highly nonlinear dynamical open loop unstable system, typically used as a benchmark to verify the performance of controllers. The IPS emulates the behaviour of an altitude control of a space booster or rocket on take-off. The problem is to develop suitable controllers to maintain the stabilization and swing up of an inverted pendulum on a cart. This paper presents the evolutionary tuning methods of nonlinear PID (NL-PID) controller for IPS with the multi-objective genetic algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm. The function of NL-PID controllers is to keep the pendulum in an upright position by maintaining the pendulum at same state and angle at zero degrees. The comparison of responses and performance of MOGA tuned NL-PID and APSO tuned NL-PID controllers for an IPS are described. The mathematical modeling and simulation analysis of the IPS is presented in detail to test the effectiveness of controller tuning algorithm. The APSO based tuning of the NL-PID controller has lesser chattering, noise and fast settling time than MOGA based tuning of the controller.

关键词： inverted pendulum system NL-PID controller tuning multi-objective genetic algorithm adaptive particle swarm optimization algorithm

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A Novel scheme in Audio Watermarking using multi-objective genetic algorithm

A Novel scheme in Audio Watermarking using Multi-Objective G...

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8th International Conference on Mechatronics and Information Technology (ICMIT09)

作者： Majid, Abdul Javed, Tahir Choi, Tae-Sun Pakistan Inst Engn & Appl Sci Dept Informat & Comp Sci Islamabad Pakistan Gwangju Inst Sci & Technol Dept Mechatron Gwangju 500712 South Korea Allama Iqbal Open Univ Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9780819478115

In this paper, multi-objective genetic algorithm (MOGA) based novel watermarking scheme of audio signal is proposed. Using this MOGA scheme small size images are embedded efficiently as a digital watermark in the discrete wavelet domain of audio signal. The main advantage of proposed scheme is that it automatically selects the intensity of watermark for embedding in audio signal. As a result an optimal tradeoffs is obtained between two contradicting properties i.e. robustness and imperceptibility. The results obtained using the proposed MOGA technique show the embedded watermark is more robustness against common cropping and Gaussian noise attacks.

关键词： Audio Watermarking multi-objective genetic algorithm DWT Domain

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Schemes research and application of close-to-wheel drive system based on slider-crank mechanism 13

Schemes research and application of close-to-wheel drive sys...

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13th IEEE Vehicle Power and Propulsion Conference (VPPC)

作者： Li, Yan Zhu, Jianfei Gu, Cheng Chen, Xinbo Wang, Wei Wang, Yefeng Tongji Univ Sch Mech Engn Shanghai Peoples R China Tongji Univ Sch Automot Engn Shanghai Peoples R China

ISBN: (纸本)9781509035281

Normal in-wheel drive system with large unsprung mass has negative effects on the ride comfort and wheel ground adhesion. Therefore, three new kinds of close-to-wheel drive concepts were proposed: the motor suspended on car frame, the motor suspended on knuckle and two-stage suspension. Dynamic differential equation with the three concepts were deduced and their three degree-of-freedom(DOF) vibration models of 1/4 suspension were established in MATLAB software respectively. In order to analyze the three concepts' effects of ride comfort and handling stability compared with in-wheel drive system, the multi-objective genetic algorithms was applied to optimize the added stiffness and damping parameters of different concepts based on two objectives of Root-Mean-Square of body acceleration and wheel dynamic load.

关键词： Close-to-wheel drive two-stage suspension multi-objective genetic algorithm ride comfort handling stability

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multi-objective aero acoustic optimization of rear end in a simplified car model by using hybrid Robust Parameter Design, Artificial Neural Networks and genetic algorithm methods

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COMPUTERS & FLUIDS 2014年 90卷 123-132页

作者： Beigmoradi, Sajjad Hajabdollahi, Hassan Ramezani, Asghar SAIPA Automot Ind Res & Innovat Ctr Tehran Iran Vali e Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran Zamyad Co Tehran Iran

In this paper, optimization of rear end of a simplified car model is performed considering aerodynamic and acoustic objectives. Slant angle, rear box angle, boat tail angle, and rear box length are considered as main variables of the rear end. For numerical simulation of flow around the model and studying aerodynamic noise, realizable turbulent model and broad band noise model are used, respectively. Simulation results are validated by the experimental results reported in the literature. To reduce number of simulations to reach optimum values of parameters, Taguchi method has been used. The results of Taguchi are in good agreement with simulation results. Then, the results of Taguchi have been used to obtain a relation between parameters and objectives employing Artificial Neural Networks. Optimization of the model has been conducted by the Neural Network and multi objective genetic algorithm methods. Finally, flow around the optimized model has been studied by numerical simulation and results have been reported. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Computational Fluid Dynamics Drag coefficient Aerodynamic noise Taguchi method Neural Networks multi-objective genetic algorithm

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Comparison of the optimized thermal performance of square and circular ammonia-cooled microchannel heat sink with genetic algorithm

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ENERGY CONVERSION AND MANAGEMENT 2015年 102卷 59-65页

作者： Ghazali-Mohd, Normah Jong-Taek, Oh Nguyen Ba Chien Kwang-Il, Choi Ahmad, Robiah Univ Teknol Malaysia Fac Mech Engn Skudai 81300 Johor Malaysia Chonnam Natl Univ Dept Refrigerat & Aircond Engn Dunduk Dong Yeosu South Korea UTM Kuala Lumpur UTM Razak Sch Engn & Adv Technol Kuala Lumpur Malaysia

Minimization of the thermal resistance and pressure drop of a microchannel heat sink is desirable for efficient heat removal which is becoming a serious challenge due to the demand for continuous miniaturization of such cooling systems with increasing high heat generation rate. However, a reduction in the thermal resistance generally leads to the increase in the pressure drop and vice versa. This paper reports the outcome of optimization of the hydraulic diameter and wall width to channel width ratio of square and circular microchannel heat sink for the simultaneous minimization of the two objectives;thermal resistance and pressure drop. The procedure was completed with multi-objective genetic algorithm (MOGA). Environmentally friendly liquid ammonia was used as the coolant and the thermophysical properties have been obtained based on the average experimental saturation temperatures measured along an ammonia-cooled 3.0 mm internal diameter horizontal microchannel rig. The optimized results showed that with the same hydraulic diameter and pumping power, circular microchannels have lower thermal resistance. Based on the same number of microchannels per square cm, the thermal resistance for the circular channels is lower by 21% at the lowest pumping power and lower by 35% at the highest pumping power than the thermal resistance for the square microchannels. Results obtained at 10 degrees C and 5 degrees C showed no significant difference probably due to the slight difference in properties at these temperatures. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Optimization Microchannel heat sink multi-objective genetic algorithm Ammonia Thermal resistance

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A novel application of genetic algorithm for synthesizing optimal water reuse network with multiple objectives

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2015年第Aug.期100卷 39-56页

作者： Halim, Iskandar Adhitya, Arief Srinivasan, Rajagopalan ASTAR Inst Chem & Engn Sci Singapore 627833 Singapore Indian Inst Technol Gandhinagar Ahmadabad 382424 Gujarat India

The current drive towards water conservation has compelled the process industries to seek various measures to minimize water. One such strategy is to design and synthesize an optimum water network that enables maximum reuse in between operations. Such optimization has been known to be challenging due to the presence of nonlinearities in the formulation that makes it difficult to solve using the standard deterministic techniques. Moreover, most of these techniques have focused on solving single objective problem involving freshwater usage while other decision factors such as treatment costs and network topologies are generally not accounted for as they would introduce extra complexities to the problem. In this paper, we propose to address optimal water reuse network as a multi-objective optimization problem our strategy is to apply a well-known genetic algorithm (NSGA-II) for single- or multi-objective optimization of water reuse network synthesis in a total (integrated) setting. First, a superstructure entailing all reuse opportunities between the water-using processes and wastewater treatment units is formulated. Next, decision variables in the form of various stream flows are encoded as the initial population chromosomes and then subjected to a set of genetic operators (selection, mutation, and crossover) to generate the offspring chromosomes. Coupled with these operators is an elitism strategy to improve the convergence and spread of the solutions. A key benefit of this approach is that it can be equally applied for both single objective and multi-objective cases in a robust manner. To demonstrate the efficacy of the approach, four single-objective case studies are solved with the results compared with the reported global optima. Thereafter, two multi-objective case studies are solved to generate the complete Pareto set. The key advantage of the proposed approach lies in its ability to solve complex optimization problem without resorting to simplifying heuristics

关键词： Water reuse Water network design Wastewater minimization Integrated water network multi-objective genetic algorithm Pareto optima

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Optimization of a high-speed direct-injection diesel engine at low-load operation using computational fluid dynamics with detailed chemistry and a multi-objective genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2010年第D4期224卷 547-563页

作者： Ge, H-W Shi, Y. Reitz, R. D. Willems, W. Univ Wisconsin Engn Res Ctr Madison WI 53706 USA Ford Res Ctr Aachen Germany

A passenger car high-speed direct-injection diesel engine operating at low-load conditions in the modulated kinetic combustion mode was optimized using a multidimensional computational fluid dynamics code and a multi-objective genetic algorithm. Spray targeting, piston bowl geometry, and swirl ratio were optimized. Since the combustion is mainly kinetics controlled, detailed chemistry was considered through a recently developed adaptive multi-grid chemistry (AMC) model. The numerical results from the AMC model, including the pressure and pollutant emissions, were first validated on the baseline engine for a parametric sweep by comparison with the results from the standard KIVA-CHEMKIN model. The AMC model was found to give consistent results with the KIVA-CHEMKIN model, with a computational cost that is less than half that of the KIVA-CHEMKIN model. Optimal designs from the optimization were also validated using the full KIVA-CHEMKIN model and were found to reduce the fuel consumption and/or pollutant emissions. Start-of-injection timing was found to be the primary parameter influencing the fuel consumption and soot emissions for the engine operating in the low-load condition. Later injection benefits the fuel consumption and soot reduction. However, further retardation of the injection timing leads to reduced combustion efficiency and even misfire, and results in higher unburned hydrocarbon emissions. Different piston bowl shapes have different responses to bulk flow motions and the resulting geometry-generated turbulence will affect soot formation and oxidation.

关键词： high-speed direct-injection diesel engine multi-objective genetic algorithm detailed chemistry KIVA-CHEMKIN model adaptive multi-grid chemistry model

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