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8th Conference on Oxide-Based Materials and Devices VIII

作者： Pour, Elnaz Afshari Shafai, Cyrus Univ Manitoba Winnipeg Dept Elect & Comp Engn 66 Chancellors Cir Winnipeg MB R3T 2N2 Canada

ISBN: (数字)9781510606524

ISBN: (纸本)9781510606517;9781510606524

The variation of oxygen concentration in the Indium Tin Oxide (ITO) structure highly impacts its electrical and optical characteristics. In this work, we investigated the effect of oxygen partial flow (O-2/O-2+Ar) and deposition pressure (p) on the refractive index (n) of reactive sputtered ITO thin films. A statistical study with a genetic algorithm (GA) optimization was implemented to find optimal deposition conditions for obtaining particular refractive indices. Several samples of ITO thin films with refractive indices ranging from 1.69 - 2.1 were deposited by DC sputtering technique at various oxygen concentrations and deposition pressures, in order to develop the statistical database. A linear polynomial surface was locally fitted to the data of O-2/O-2+Ar, p, and n of deposited films. This surface was then used as the fitness function of the GA. By defining the desired n as the objective value of the GA, the optimized deposition conditions can be found. Two cases were experimentally demonstrated, with the GA determining the needed process parameters to deposit ITO with n=2.2 and n=1.6. Measured results were very close to desired values, with n=2.25 and n=1.62, demonstrating the effectiveness of this method for predicting needed reactive sputtering conditions to enable arbitrary refractive indices.

关键词： Indium-Tin oxide refractive index of ITO genetic algorithm optimization ITO reactive DC sputtering

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Hybrid genetic algorithm optimization of Vehicle Major Reducer

Hybrid Genetic Algorithm Optimization of Vehicle Major Reduc...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Song Yandong Nanjing Inst Ind Technol Dept Mech Engn Nanjing 210046 Peoples R China

ISBN: (纸本)9780769538044

The function of vehicle major reducer is to increase the input torque and decrease rotational speed correspondingly. The performance parameters of major reducer can greatly affect the dynamics and economy of vehicle, therefore it is very important to optimize the vehicle major reducer. Considered the boundary and performance constraints, the objective function is specified to create optimization model of vehicle major reducer. Global algorithms are known for their slower convergence to the true global optimum once the optimum region is found. This drawback of the genetic algorithm can be overcome by combining it with local gradient-based algorithms, which are known for their faster convergence. The results demonstrate that the hybrid approach is an effective tool to deal with the uncertainties present in design optimization and can provide more realistic solutions. So that the optimization process is simplified and global optimum is acquired reliably.

关键词： genetic algorithm optimization major reducer neural network

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Research On Dynamics Modeling And genetic algorithm optimization Of Automobile Steer-By-Wire System

Research On Dynamics Modeling And Genetic Algorithm Optimiza...

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2nd International Conference on Information and Computing Science

作者： Yu Lei-yan Yun Ping-li Liu Feng China Univ Petr Coll Mech & Elect Engn Dongying Peoples R China

ISBN: (纸本)9780769536347

Automobile Steering by Wire (SBW) system is an advanced steering system, which cuts off the mechanical connection between steering wheel and steered wheels. It controls the steering motor to steer front wheels actively so as to improve handling and stabilities and active safety, and controls steering wheel motor to supply the driver with ideal road feel. Driver-vehicle-road closed loop models of SBW are built based on Matlab/Simulink software platform, including steering wheel assembly and driver model. Model parameters are optimized using genetic algorithm. Real car test proves that the model fits experiments well.

关键词： automobile Steer-by-Wire (SBW) system dynamics modeling genetic algorithm optimization

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The Application of genetic algorithm optimization in Broadband Microstrip antenna Design

The Application of Genetic Algorithm Optimization in Broadba...

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2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Sun, Siyang Lv, Yinghua Zhang, Jinling Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

ISBN: (纸本)9781424449682

A novel broadband microstrip antenna with 50 % bandwidth is proposed in this paper. An optimization project based on the combination of genetic algorithm with HFSS is created to facilitate the antenna design. The operating principle of the proposed design can be explained in terms of a combination of dual-mode operation and capacitance compensation.

关键词： HFSS broadband antennas broadband microstrip antenna design capacitance capacitance compensation dual mode operation genetic algorithm optimization genetic algorithms microstrip antennas

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AC Position Servo System Controller of Aircraft Simulator Based on genetic algorithm optimization

AC Position Servo System Controller of Aircraft Simulator Ba...

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International Conference on Advanced Materials Design and Mechanics (ICAMDM 2012)

作者： Liu, Baolong Zheng, Xiulin Li, Xiaoqi Aviat Univ AF Mil Simulat Technol Inst Changchun Jilin Peoples R China

ISBN: (纸本)9783037854808

This paper analyzes nonlinear and uncertainties factors caused by the friction and clearance of ac servo system. a Fuzzy-PI) controller based on genetic algorithm was designed. Adjust the three parameters of PID controller based on the error and error change rate;Adjust the various parameters of the fuzzy-PID controller using genetic algorithm optimization;This method has better control effect on the AC of Meter systems than traditional PID controller. And both dynamic and steady performances were improved evidently.

关键词： Aircraft Simulator AC Position Servo System Fuzzy PID Controller genetic algorithm optimization

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Production Scheduling using Augmented Lagrangian Relaxation and genetic algorithm optimization 24

Production Scheduling using Augmented Lagrangian Relaxation ...

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24th INTERNATIONAL MINING CONGRESS OF TURKEY

作者： Moosavi, E. Gholamnejad, J. Ataee-pour, M. Khorram, E. Islamic Azad Univ South Tehran Branch Dept Min Engn Tehran Iran Yazd Univ Dept Min & Met Engn Yazd Iran Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Amirkabir Univ Technol Dept Appl Math & Comp Sci Tehran Iran

ISBN: (纸本)9786050107050

To improve the quality of decision making in the mining operation, it is essential to find global optimum in problems with large dimensional scales. It is widely accepted that Long-term production scheduling (LTPS) problem is playing key role in mining projects to improve their performance by considering availability constraints while maximizing the project total profits during the period. Since production scheduling problems are NP-hard, there is need of improving scheduling methodologies to get good solution. This paper presents a hybrid model between augmented Lagrangian relaxation (ALR) and genetic algorithm (GA) to solve the LTPS problem. We propose to apply the ALR method on the LTPS problem which to improve its performance speeding up the convergence and also, GA is used to update the Lagrangian multipliers. The results from synthetic data set show that the ALR method is effective in solving large-scale problem and generation a feasible solution then the traditional linearization method. Furthermore, the proposed hybrid strategy based on GA showed better performance in comparison to the available methods.

关键词： Augmented Lagrangian Relaxation genetic algorithm optimization

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Parameters Identification for Nonlinear Dynamic Systems Via genetic algorithm optimization

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2009年第4期4卷 41002-41002页

作者： Gondhalekar, A. C. Petrov, E. P. Imregun, M. Univ London Imperial Coll Sci Technol & Med Dept Mech Engn London SW7 2AZ England

This paper presents a frequency domain method for the location, characterization, and identification of localized nonlinearities in mechanical systems. The nonlinearities are determined by recovering nonlinear restoring forces, computed at each degree-of-freedom (DOF). Nonzero values of the nonlinear force indicate nonlinearity at the corresponding DOFs and the variation in the nonlinear force with frequency (force footprint) characterizes the type of nonlinearity. A library of nonlinear force footprints is obtained for various types of individual and combined nonlinearities. Once the location and the type of nonlinearity are determined, a genetic algorithm based optimization is used to extract the actual values of the nonlinear parameters. The method developed allows simultaneous identification of one or more types of nonlinearity at any given DOF. Parametric identification is possible even if the type of nonlinearity is not known in advance, a very useful feature when the type characterization is difficult. The proposed method is tested on simulated response data. Different combinations of localized cubic stiffness nonlinearity, clearance nonlinearity, and frictional nonlinearity are considered to explore the method's capabilities. Finally, the response data are polluted with random noise to examine the performance of the method in the presence of measurement noise. [DOI: 10.1115/1.3187213]

关键词： nonlinear parametric identification genetic algorithm optimization nonlinearity location and characterization nonlinearity footprint

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Kinetic parameters and heat of reaction for forest fuels based on genetic algorithm optimization

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THERMOCHIMICA ACTA 2022年 713卷

作者： Filho, G. C. Krieger Costa, Franklin Maria, G. F. Torraga Bufacchi, Paulo Trubachev, Stanislav Shundrina, Inna Korobeinichev, Oleg Univ Sao Paulo Sao Paulo Brazil Russian Acad Sci Siberian Branch Voevodsky Inst Chem Kinet & Combust Novosibirsk Russia Novosibirsk State Univ Novosibirsk Russia Russian Acad Sci Siberian Branch NN Vorozhtsov Novosibirsk Inst Organ Chem Novosibirsk Russia

Environmental issues and climate change are playing a central role in our society. Forest and peatland fires can prejudice the environment and population health. Therefore, it is vital to study this phenomenon. The main goal of this work is to determine the kinetic parameters and heat of reaction for different forest fuels. Pine needles and sphagnum peat are typical fuels from the Northern Hemisphere, while Amazonian leaves and high-density peat are from the Southern Hemisphere. The DTG curves for pine needles and Amazonian leaves clearly show two peaks of mass loss, while for sphagnum peat a third mass-loss peak occurs for higher temperatures. For high density peat degradation, there is only one significant peak of mass loss, which happens at a higher temperature. Also, the DSC curves for pine needles, Amazonian leaves, and high-density peat show two exothermic peaks along with the mass-loss peaks. However, the overall pattern is not the same. The maximum heat released for pine needles and high-density peat occurs at the second degradation stage. There are two exothermic peaks of almost the same magnitude for Amazonian leaves. Finally, three exothermic peaks match the peaks of mass loss for sphagnum peat. DTG and DSC curves patterns for all forest fuels are independent of the heating rate and atmospheric composition. In this work, the proposed pseudo reaction mechanism for pyrolysis and oxidation of the forest fuels contains five (for Amazonian leaves, high-density peat, and sphagnum peat) and seven (for pine needle) steps. The genetic algorithm optimization process compares the instantaneous recorded data of TGA, DTG, and DSC with the calculated ones. The optimized kinetic reactions parameters for the forest fuels are the activation energy, the pre-exponential factor, the global order of reaction, the stoichiometric coefficients, and the heat of reaction. The overall performance of the proposed mechanism is evaluated taking the error or the experimental data into

关键词： Forest fuels Kinetics parameters Heat of reaction TGA DSC genetic algorithm optimization

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Enhanced ride performance of electric vehicle suspension system based on genetic algorithm optimization 20

Enhanced ride performance of electric vehicle suspension sys...

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20th IEEE International Conference on Electrical Machines and Systems (ICEMS)

作者： Shao, Xinxin Naghdy, Fazel Du, Haiping Univ Wollongong Sch Elect Comp & Telecommun Engn Northfields Ave Wollongong NSW 2522 Australia

ISBN: (纸本)9781538632468

Parameter optimization of active suspension in in-wheel motor driven electric vehicle using genetic algorithm (GA) is presented. In such vehicles, placing the motors in the wheel results in an increase in the unsprung mass, which greatly deteriorates the suspension ride comfort performance and road holding ability. Structures with suspended shaftless direct-drive motors have the potential to improve the road holding capability and ride performance. The GA is applied to obtain the optimal parameters under different road profiles. Parameters of the motor suspension (damping and stiffness coefficients), vehicle suspension and active controller are optimized based on quarter vehicle model. The optimization process aims to minimize the vertical acceleration of sprung mass and motor, dynamic force transmitted to the motor as well as suspension working space and road holding capability. The performance of the vehicle with passive suspension, active suspension with unoptimized parameters and optimized parameters are compared. The results show that active suspension with optimized parameters significantly outperforms other suspensions in motor ride performance.

关键词： genetic algorithm optimization motor parameters optimization ride performance

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Automatic design of pixelated near-zero refractive index metamaterials based on elite-preserving genetic algorithm optimization

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RESULTS IN PHYSICS 2023年 48卷

作者： Sun, Shuo Jiang, Yannan Wang, Jiao Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

Near-zero refractive index metamaterials (NZIM) have drawn a lot of attention recently for the development of high directivity antennas. However, their application has been constrained by the tedious and time-consuming manual design process. In this work, we offer an automated optimization design technique for NZIM that combines a modeling process based on pixelated metamaterials with an optimization process based on heuristic algorithms. The interactive CST-Python simulation is utilized for accomplishing the above automated design. In detail, the elite-preserving genetic algorithm (EGA) is specifically employed because of its improved capacity in locating the superior unit, and the fitness function proposed guarantees the performance of bandwidth and optimization process based on the principles of proportionate integration and penalty-like functions, respectively. Utilizing the suggested technique, we designed a NZIM unit that operates between 8.0 and 8.52 GHz. In order to validate the performance of the NZIM, a rectangular microstrip patch antenna (MPA) resonating at 8.2 GHz was employed as the radiation source for studies. According to the simulation results, the developed NZIM has a clearly beam focusing effect which causes the MPA's spherical waves to converge into quasi-plane waves. Furthermore, discussing the NZIM's application in high directivity antennas designing, the prototypes of the NZIM and MPA were fabricated and tested. The results revealed that the MPA's gain enhancement achieves 5.3 dB on average and 6.05 dB on maximum. Due to the automated optimization and design, this work is promised to advance NZIM's further applicability in high directivity antennas.

关键词： Near-zero refractive index metamaterial Pixelated metamaterial genetic algorithm optimization Automatic design High directivity antenna

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