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FEM-coupled simulations and genetic algorithm model applied to reduce the torque ripple of a 2-phase SR motor drive

JOURNAL OF ENGINEERING-JOE 2019年第17期2019卷 3705-3708页

作者： Correa, Daniel A. P. Nabeta, Silvio, I Pereira, Fabio H. da Silva, Jorge A. da Silva, Wanderlei M. Univ Sao Paulo Escola Politecn Sao Paulo Brazil Ctr Tecnol Marinha SP Sao Paulo Brazil Univ Nove Julho Sao Paulo Brazil

In order to reduce the torque ripple of SR motor drive, this work proposes a new methodology based on the converter, controller, and motor modelling, associated with a genetic algorithm optimisation model which uses the electromagnetic vibration data to calculate the best firing angles for the inverter power switches. To implement the power electronic motor drive model and the optimisation procedure, it was used, respectively, with coupled FEM simulations resources and a MATLAB Script. In this work, they were tested with two inverter commutation strategies whose values were validated through the comparison with the experimental results. The simulated and experimental values converge in the same direction, validating the proposed methodology.

关键词： power convertors machine control finite element analysis reluctance motor drives genetic algorithms machine theory torque control invertors motor drives commutation FEM-coupled simulations genetic algorithm model torque ripple 2-phase SR motor drive motor modelling genetic algorithm optimisation model electromagnetic vibration data firing angles inverter power switches power electronic motor drive model optimisation procedure coupled FEM simulations resources inverter commutation strategies whose values simulated values experimental values

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Ground Handling Process Optimization model Linked to Flight Delay Prediction Results

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IEEE ACCESS 2024年 12卷 114838-114857页

作者： Xu, Zhen-Teng Li, Yan-Jun Zuo, Hong-Fu Xu, Teng-Zhou Wang, Qi Yu, Wang-Wang Yan, Hong-Sheng Liu, Bing Chen, Tao Zhou, Mao-Hui Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211100 Peoples R China Nanjing Vocat Univ Ind Technol Sch Aeronaut Engn Nanjing 210046 Peoples R China Aeronaut Intelligent Mfg & Digital Hlth Management Nanjing 210046 Peoples R China Shanghai Airport Grp Co Ltd Shanghai 201300 Peoples R China Nanjing Vocat Univ Ind Technol Sch Mech Engn Nanjing 210046 Peoples R China Nanjing Vocat Univ Ind Technol Dept Publ Fdn Courses Nanjing 210046 Peoples R China

In response to the challenge of mitigating flight delays, this study introduces an innovative solution that encompasses the prediction of delay durations for existing flights and the subsequent optimization of ground service processes based on these predictions. The indirect forecasting of flight delays is achieved through the construction of a random forest model, exhibiting a remarkable 100% accuracy when considering a 15-minute standard for flight delays. In light of the delay prediction outcomes, distinct delay coefficients are assigned to individual flights, facilitating the development of a ground service optimization model through the application of a genetic algorithm. Within the genetic algorithm optimization framework, significant enhancements have been implemented in the gene encoding of the initial population, incorporating a segmented encoding approach. Employing this refined model to optimize the service sequence and duration of ground service vehicles for all flights culminates in the notable accomplishment of achieving zero delays for the entire set of flights.

关键词： Delays Airports Aircraft Random forests Predictive models Atmospheric modeling Optimization genetic algorithms Flight delay prediction ground service optimization random forest model genetic algorithm model

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An Improved genetic algorithm Approach on Mechanism Kinematic Structure Enumeration with Intelligent Manufacturing

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2018年第3-4期89卷 343-350页

作者： Liu, Hao Shi, Shunyi Yang, Ping Yang, Jianming Jiangsu Univ Sch Mech Engn Zhenjiang 212013 Peoples R China Mem Univ Fac Engn & Appl Sci St John NF A1B 3X5 Canada

During the process of mechanism kinematic structure enumeration, isomorphism identification of graphs is an important and complicated problem. The problem is known to be a NP-complete problem. In this paper, according to the mechanism kinematic chain isomorphism identification criteria, a highly efficient hybrid genetic algorithm model is proposed for isomorphism identification. The model method is coupled with genetic algorithm, optimal choice, and optimal crossover operation. It shows a quick convergence rate of the late operation and can avoid convergence to local optimum. Simulation results show that the hybrid algorithm is more rapid and effective compared with simple genetic algorithm and the improved neural network algorithm.

关键词： Isomorphism identification genetic algorithm model Optimal choice Optimal crossover Simulation

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Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 182-186页

作者： Kim, Byungwhan Kwon, Sanghee Kim, Dong Hwan Sejong Univ Dept Elect Engn Seoul 143747 South Korea Seoul Natl Univ Technol Sch Mech Design & Automat Engn Seoul 139743 South Korea

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. in this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens I and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box-Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96 x 10(-12) for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Scanning electron microscope Resolution Generalized regression neural network genetic algorithm model Statistical experiment Lens Statistical regression model

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genetic algorithm quality of service design in resilient dense wavelength division multiplexing optical networks

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IET COMMUNICATIONS 2008年第4期2卷 505-513页

作者： Kavian, Y. S. Rashvand, H. F. Ren, W. Naderi, M. Leeson, M. S. Hines, E. L. Iran Univ Sci & Technol Dept Elect Engn Tehran 16844 Iran Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England

The important role of quality of service (QoS) in deployment of a resilient dense wavelength division multiplexing (DWDM) backbone for global networks requires critical design-phase planning optimisation. The design issues of resilient DWDM networks for bandwidth and delay sensitive applications of dedicated path protection are addressed. A genetic algorithm (GA) model has been developed to solve the routing and wavelength assignment problem using binary variable-length chromosome encoding under two different schemes of bandwidth optimisation (BOS) and delay optimisation (DOS). The performance of the new GA-based resiliency model has been evaluated for four benchmark networks: PAN EUROPEAN, COST239, NSFNET and ARPA2. Simulation results show a superior capability and efficiency for the model to solve this complex, multi-constraint and nondeterministic polynomial-hard problem for BOS and DOS. The nonlinear nature of this process reveals a significant sensitivity for optical layer network topology on the optimum-design QoS. The results also demonstrate that the PAN EUROPEAN network shows the highest flexibility for primary path design, NSFNET for the secondary path and ARPA2 comes with the lowest design flexibility for both primary and secondary paths.

关键词： resilient DWDM optical network bandwidth optimisation design-phase planning optimisation network topology PAN EUROPEAN network quality of service binary codes dense wavelength division multiplexing optical fibre networks telecommunication network topology binary variable-length chromosome encoding QoS wavelength division multiplexing genetic algorithm model telecommunication network planning variable length codes delay sensitive application genetic algorithms

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The spatial distribution of foragers and food patches can influence antipredator vigilance

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BEHAVIORAL ECOLOGY 2017年第1期28卷 304-311页

作者： Beauchamp, Guy 1 Independent Researcher Canada

Antipredator vigilance is a major component of defenses against predators for many prey species. For group foragers, such vigilance is predicted by models to decrease with group size reflecting better predator detection ability and risk dilution in larger groups. Influential vigilance models for group foragers have made simplifying and often quite restrictive assumptions. Prey species, for instance, are expected to search for resources in groups of fixed sizes although frequent changes in group sizes often occur while foraging. Groups of prey in the same area are also assumed to be attacked independently, but predators could sequentially target several local groups after a failed attempt. I propose a framework in which prey animals can form groups by joining feeding neighbors and also adjust their vigilance in these groups of varying sizes. Predators can attack one of the many groups that occur in the same area and can also target groups of specific sizes. I used a genetic algorithm approach to simultaneously tackle joining and vigilance choices by prey individuals. I show that joining tendencies and the effect of group size on vigilance can vary with forager population size, the spatial distribution of resources, and predator attack tactics. The modeling framework adopted here generates several novel predictions about vigilance and joining tendencies for group foragers, and highlights the importance of considering the availability and vulnerability of prey groups in the same habitat when predicting antipredator vigilance.

关键词： antipredator vigilance fission-fusion dynamics genetic algorithm model group size predation

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Performance of a genetic algorithm for mass spectrometry proteomics

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BMC BIOINFORMATICS 2004年第1期5卷 180-180页

作者： Jeffries, NO NINDS Off Clin Director Bethesda MD 20892 USA

Background: Recently, mass spectrometry data have been mined using a genetic algorithm to produce discriminatory models that distinguish healthy individuals from those with cancer. This algorithm is the basis for claims of 100% sensitivity and specificity in two related publicly available datasets. To date, no detailed attempts have been made to explore the properties of this genetic algorithm within proteomic applications. Here the algorithm's performance on these datasets is evaluated relative to other methods. Results: In reproducing the method, some modifications of the algorithm as it is described are necessary to get good performance. After modification, a cross-validation approach to model selection is used. The overall classification accuracy is comparable though not superior to other approaches considered. Also, some aspects of the process rely upon random sampling and thus for a fixed dataset the algorithm can produce many different models. This raises questions about how to choose among competing models. How this choice is made is important for interpreting sensitivity and specificity results as merely choosing the model with lowest test set error rate leads to overestimates of model performance. Conclusions: The algorithm needs to be modified to reduce variability and care must be taken in how to choose among competing models. Results derived from this algorithm must be accompanied by a full description of model selection procedures to give confidence that the reported accuracy is not overstated.

关键词： genetic algorithm Fitness Function genetic algorithm model Resolution Dataset Fixed Dataset

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Optimal Search Plan model On The Sea

Optimal Search Plan Model On The Sea

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2015 International Conference on Materials Engineering and Information Technology Applications(MEITA 2015)

作者： Luyao Zhu China University of Political Science and Law

ISBN: (纸本)9781510812055

In this passage, we intend to determinate the specific searching plan for lost aircraft on the basis of big data application. First, we cope with Maximum Flow Problem by BFS, in order to the determination of cruise route. Then, in the determination of numbers of search planes and warships along each path, we can use the genetic algorithm model.

关键词： Maximum Flow Problem BFS genetic algorithm model

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