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An Early Warning Model for Student Status Based on genetic algorithm-Optimized Radial Basis Kernel Support Vector Machine

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第2期20卷 263-272页

作者： Li, Hui Huang, Qixuan Wang, Chao Handan Univ Sch Informat Engn Handan Peoples R China Hebei key Lab Opt Fiber Biosensing & Commun Device Handan Peoples R China Handan Univ Sch Software Handan Peoples R China

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

关键词： Early Warning Model genetic algorithm optimization Radial Basis Kernel Support Vector Machine

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Calibration of a hypoplastic model using genetic algorithms

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ACTA GEOTECHNICA 2021年第7期16卷 2031-2047页

作者： Mendez, Francisco Jose Pasculli, Antonio Mendez, Miguel Alfonso Sciarra, Nicola Univ G DAnnunzio Dept Engn Geol INGEO I-66013 Chieti Italy Von Karman Inst Fluid Dynam Dept Environm & Appl Fluid Dynam B-1640 Rhode St Genese Belgium Univ G DAnnunzio INDAM Res Grp GNCS Natl Inst Adv MathNatl Grp Sci Comp Chieti Italy

This article proposes an optimization framework, based on genetic algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law, known as Sand Hypoplasticity (SH), allows for robust and accurate modelling of the soil behaviour but requires a complex calibration involving eight parameters. The proposed optimization can automatically fit these parameters from the results of an oedometric and a triaxial drained compression test, by combining the GA with a numerical solver that integrates the SH in the test conditions. By repeating the same calibration several times, the stochastic nature of the optimizer enables the uncertainty quantification of the calibration parameters and allows studying their relative importance on the model prediction. After validating the numerical solver on the ExCalibre-Laboratory software from the SoilModels' website, the GA calibration is tested on a synthetic dataset to analyse the convergence and the statistics of the results. In particular, a correlation analysis reveals that two couples of the eight model parameters are strongly correlated. Finally, the calibration procedure is tested on the results from von Wolffersdorff, 1996, and Herle and Gudehus, 1999, on the Hochstetten sand. The model parameters identified by the GA optimization improves the matching with the experimental data and hence lead to a better calibration.

关键词： genetic algorithm optimization Hypoplasticity model calibration Nonlinear regression

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Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2014年第3期2卷 659-677页

作者： Ahmed, Ryan El Sayed, Mohammed Arasaratnam, Ienkaran Tjong, Jimi Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada Powertrain Engn Res & Dev Ctr Windsor ON N8Y 1W2 Canada

The current phase in our transportation system represents a paradigm shift from conventional, fossil-fuel-based vehicles into the second-generation electric and hybrid vehicles. Electric vehicles (EVs) provide numerous advantages compared with conventional vehicles because they are more efficient, sustainable, greener, and cleaner. The commercial market penetration and success of EVs depend on the efficiency, safety, cost, and lifetime of the traction battery pack. One of the current key electrification challenges is to accurately estimate the battery pack state of charge (SOC) and state of health (SOH), and therefore provide an estimate of the remaining driving range at various battery states of life. To estimate the battery SOC, a high-fidelity battery model along with a robust, accurate estimation strategy is necessary. This paper provides three main contributions: 1) introducing a new SOC parameterization strategy and employing it in setting up optimizer constraints to estimate battery parameters;2) identification of the full-set of the reduced-order electrochemical battery model parameters by using noninvasive genetic algorithm optimization on a fresh battery;and 3) model validation by using real-world driving cycles. Extensive tests have been conducted on lithium iron phosphate-based cells widely used in high-power automotive applications. Models can be effectively used onboard of battery management system.

关键词： Battery parameters identification electric vehicles (EVs) electrochemical battery model (ECM) genetic algorithm optimization lithium-ion batteries

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Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia

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BMC MEDICAL GENOMICS 2013年第1期6卷 1-10页

作者： Tejera, Eduardo Bernardes, Joao Rebelo, Irene Univ Porto Inst Mol & Cell Biol IBMC P-4100 Oporto Portugal Univ Porto Fac Med Ctr Res Hlth Technol & Informat Syst CINTESIS P-4100 Oporto Portugal Sao Joao Hosp Porto INEB Inst Biomed Engn Dept Obstet & Gynecol Oporto Portugal Univ Porto Fac Pharm Dept Biol Sci Lab Biochem P-4100 Oporto Portugal

Background: In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively. Methods: After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods. Results: Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia. Conclusions: Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied. Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.

关键词： Preeclampsia Pathway Enrichment Analysis genetic algorithm optimization Gene Prioritization Weighted Adjacency Matrix

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System modeling and identification the two-link pneumatic artificial muscle (PAM) manipulator optimized with genetic algorithms

System modeling and identification the two-link pneumatic ar...

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SICE-ICASE International Joint Conference

作者： Ahn, Kyoung Kwan Anh, Ho Pham Huy Univ Ulsan Sch Mech & Automot Engn Ulsan South Korea Univ Ulsan Grad Sch Mech & Automot Engn Ulsan South Korea

ISBN: (纸本)9788995003848

In this paper, the application of modified genetic algorithms (MGA) in the parameterization of the 2-link Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new algorithm is proposed from the conventional genetic algorithm (SGA) with some additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of the prototype 2-link Pneumatic Artificial Muscle (PAM) manipulator described by an ARX model in the presence of white noise and this result will be validated by comparing with the simple genetic algorithm (SGA) and LMS (Least mean-squares) method as well. The parameters of the hysteresis as well as other nonlinear disturbances existing intuitively in the 2-link Pneumatic Artificial Muscle (PAM) manipulator are estimated in a single identification experiment. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the system modeling and identification of the PAM manipulator. These results can be applied to model and identify other nonlinear systems as well.

关键词： genetic algorithm optimization modified genetic algorithm (MGA) system identification ARX model Pneumatic Artificial Muscle (PAM) 2-link PAM manipulator

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Thermodynamic performance of a cryogenic energy storage system based on natural gas liquefaction

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Energy Storage and Saving 2024年第1期3卷 23-29页

作者： Xiaoqiao Qin Hongbo Tan Wei Shen Na Wen Yu Sun Department of Refrigeration and Cryogenic Engineering School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an710049China Yingde Gases(Shanghai)Group Company Limited Shanghai210035China Technical Institute of Physics and Chemistry CASBeijing100190China

Cryogenic energy storage(CES)is a viable method for grid-scale electrical energy *** the high energy density and mature application of liquefied natural gas(LNG),we proposed an LNG cryogenic energy storage(LNGES)system.A steady-state process model of the LNGES system was established using Aspen *** effects of the natural gas composition and key operating parameters such as the charging pressure,discharging pressure,throttling temperature,and liquid storage pressure on the system performance were investigated.A multi-parameter genetic algorithm model built using the MATLAB software was used to optimize the LNGES system to optimize the round-trip efficiency(RTE).Then,an exergy analysis of the optimal configuration was *** results suggested that the LNGES system could achieve optimal RTE and exergy efficiency values of 60.14%and 71.64%,*** destruction mainly occurred during the compression,throttling,expansion,and heat *** proposed LNGES system could be a promising candidate for the large-scale application of CES technology in power grids and gas networks.

关键词： Cryogenic energy storage Liquefied natural gas genetic algorithm optimization Round-trip efficiency Exergy analysis

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An Improved genetic algorithm based Power System Stabilizer for power system stabilization

An Improved Genetic Algorithm based Power System Stabilizer ...

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IEEE AFRICON Conference - Powering Africa s Sustainable Energy for AD Agenda - The Role ofICT and Engineering.

作者： Mustapha, Hamza Buhari, Muhammad Ahmad, Abubakar Sadiq Ahmadu Bello Univ Elect Engn Zaria Nigeria Bayero Univ Elect Engn Kano Nigeria Fed Univ Technol Elect & Elect Engn Minna Nigeria

ISBN: (纸本)9781728132891

This paper considers the damping of the local-mode power system oscillation using Lead-Lag power system stabilizer (PSS). Thus, a simple single-machine infinite-bus power system is used. The Lead-Lag PSS parameters are tuned using genetic algorithm optimization (GAs). Simulation results are presented with and without the proposed controller. Also, a comparison study is presented when using the conventional Lead-Lag PSS, Simple genetic algorithm optimization-based PSS, and when using the Improved genetic algorithm optimization-based PSS. The results obtained show the significance of using improved genetic algorithm optimization as compared to using conventional PSS and Simple genetic algorithm based PSS.

关键词： Power system oscillation genetic algorithm optimization Improved genetic algorithm optimization Lead-Lag Power System Stabilizer (PSS)

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Offline Parameter Identification and SOC Estimation for New and Aged Electric Vehicles Batteries

Offline Parameter Identification and SOC Estimation for New ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ahmed, Ryan Rahimifard, Sara Habibi, Saied McMaster Univ Dept Mech Engn Hamilton ON Canada

ISBN: (纸本)9781538693100

The hybrid ( HEVs) and battery electric vehicles (BEVs) represent a sustainable alternative in compare to conventional, fossil fuel-based vehicles. Battery pack is a major and the most expensive part of electric vehicles. It requires accuracy, real-time monitoring, and control. Parameters such as state of charge (SOC) and state of health (SOH) have to monitor accurately to guarantee battery safety and reliability and avoid overcharge or under-discharge conditions. These conditions can lead to irreversible capacity degradation and power fade. An accurate battery model with robust estimation method is needed for battery condition monitoring. In this paper, a way to estimate battery parameters is proposed for the first order equivalent circuit battery model (OCV-R-RC) using genetic algorithm (GA) optimization at various ages of the battery to track the changes. The smooth variable structure filter (SVSF) strategy has been considered to estimate the state of charge based on the optimized model. An aging test has been conducted over a period of 12 months using real-world driving scenarios. Experimental results are provided to show the efficacy of the proposed approach.

关键词： Lithium-Ion Batteries genetic algorithm optimization equivalent circuit-based battery model parameter identification smooth variable structure filter state of charge

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Inerter-based Passive Structural Control for Barge Floating Offshore Wind Turbines 21st

Inerter-based Passive Structural Control for Barge Floating ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Villoslada, D. Santos, M. Tomas-Rodriguez, M. Univ Complutense Madrid C Prof G Santesmases 9 Madrid 28040 Spain City Univ London London England

Floating offshore wind turbines (FOWT) stand as a promising concept to expand the wind energy generation into the more productive deep-water areas, where conventional bottom-fixed turbines are infeasible. Barge-type floating wind turbines experience an inverted pendulum effect which produces a coupling with the wind turbine response, resulting in large structural loads. In this paper, the authors investigate passive structural control to mitigate the tower fatigue, in the form of a tuned mass damper (TMD) installed in the nacelle. The study focuses on evaluating the benefits of adding a parallel-connected inerter device to the TMD. Based on a reduced dynamics model for the barge-type offshore wind turbine identified using the FAST-SC synthetic reference data, an optimization of the TMD and the inerter parameters is carried out. To that end, genetic algorithms were used taking the tower fatigue as a fitness function, derived from the tower top displacement. The results confirm that the inerter has limitations when installed in a traditional TMD, but show significant benefits when the TMD stroke is constrained by stops. It is found that the improved performance including the inerter is dependent on the stroke limitation with respect to the ideal TMD stroke without stops. Therefore, the use of the inerter is especially useful to enhance performances for both mass and stroke constrained applications. The load reduction for the selected baseline model improved up to 6 % over the TMD with stops and 12 % over the TMD without stops. Copyright (C) 2020 The Authors.

关键词： Barge Offshore Wind Turbine Passive Structural Control Tuned Mass Damper Inerter genetic algorithm optimization Fatigue

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ONE-DIMENSIONAL AND THREE-DIMENSIONAL NUMERICAL optimization AND COMPARISON OF SINGLE-STAGE SUPERSONIC AND DUAL-STAGE TRANSONIC RADIAL INFLOW TURBINES FOR THE ORC

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ASME Power Conference (POWER2016)

作者： Rahbar, K. Mahmoud, S. Al-Dadah, R. K. Moazami, N. Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

ISBN: (纸本)9780791850213

Organic Rankine cycle is one of the most efficient technologies that can utilize low-to-medium grade heat sources and generate useful power. Radial inflow turbine (RIT) is the key component of the ORC and its efficiency has significant effect on the overall cycle performance. Obtaining high cycle thermal efficiency requires large pressure difference (expansion ratio) across the cycle. With the low speed of sound of organic fluids and the high expansion ratios, RIT becomes chocked with supersonic flow regime and shock waves that deteriorate the turbine efficiency and hence reduce the cycle performance. Therefore, developing highly efficient RIT that can both preserve the high expansion ratio requirements of the ORC and maintain the turbine isentropic efficiency is crucial. This paper proposed the complete 1-D and 3D numerical optimization of two different configurations as single stage supersonic and dual-stage transonic RITs. Initially, the integrated 1-D modelling of the ORC with RIT coupled with genetic algorithm optimization technique was conducted to maximize the cycle thermal efficiency. The results showed that the dual-stage RIT exhibited considerably higher turbine efficiency in both stages and hence higher cycle efficiency compared to the single-stage supersonic one. Both configurations were further optimized using the 3-D CFD optimization procedure to maximize the turbine efficiency. The CFD results showed that the optimization of each stage individually was successful as the turbine performance increased significantly. The results revealed that the optimizations were more effective for the dual-stage transonic turbine compared to the single-stage supersonic due to the presence of shock waves. Comparison of the optimized single stage supersonic RIT and complete dual-stage transonic RIT showed that about 15.7%, 10.63kW and 16.08% higher turbine isentropic efficiency, turbine power and cycle thermal efficiency were achieved respectively with the latter config

关键词： Organic Rankine cycle radial turbine genetic algorithm optimization dual-stage CFD supersonic

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