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Automatic Transmission Shift Strategy Based on greedy algorithm Using Predicted Velocity

INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2020年第1期21卷 159-168页

作者： Jeoung, Dongwon Min, Kyunghan Sunwoo, Myoungho Hyundai Motor Co Auto Transmiss Test Team 150 Hyundaiyeonguso Ro Hwaseong Si 18280 Gyeonggi South Korea Hanyang Univ Dept Automot Engn Seoul 04763 South Korea

Control strategies for the vehicle equipped with an automatic transmission greatly affects the fuel economy and drivability. In general, the gear shift of automatic transmission is controlled based on the two-dimensional lookup tables. The lookup tables are calibrated based on the experimental results at a steady state condition. However, this method has a limitation on improving the fuel efficiency in a dynamic driving environment like an urban condition. In order to improve the fuel efficiency, this study proposes an optimal gear shift strategy based on the greedy control method using the predicted velocity. Since future driving conditions can be estimated using predicted velocity, optimal gear shifting is searched using a greedy algorithm based on the predicted velocity. A PI-type driver model and powertrain model are designed to calculate the forecasting vehicle states after gear shifting with predicted velocity. The proposed strategy was validated through the simulation of the urban driving cycle using various time period predicted velocity. Results show fuel efficiency was improved by up to 1.6% while shiftbusyness is prevented compared with the shift pattern which focused on fuel economy. As a result, the proposed strategy is affordable for improving not only the fuel economy but also the drivability in the dynamic driving environment.

关键词： Automatic transmission Gear shift strategy Powertrain model greedy algorithm Predicted velocity

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An adaptive sparse kernel technique in greedy algorithm framework to simulate an anomalous solute transport model

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2020年 121卷 243-254页

作者： Raei, Marzieh Ghehsareh, Hadi Roohani Galletti, Ardelio Malek Ashtar Univ Technol Fac Appl Sci Dept Math Tehran Iran Univ Napoli Dept Sci & Technol Parthenope Italy

In the current work, an efficient and powerful computational technique is implemented to simulate an anomalous mobile-immobile solute transport process. The process is mathematically modelled as a time-fractional mobile-immobile diffusion equation in the sense of Riemann-Liouville derivative. Firstly, an implicit time integration procedure is used to semi-discretize the model in the time direction. The unconditional stability of the proposed time discretization scheme has been proven. Then an adaptive sparse meshless method has been formulated and implemented to fully discretize the model. In this approach, a kernel-based collocation method is equipped with a greedy sparse approximation procedure to discretize the governing problem on a convenient neighborhood of each data point with acceptable accuracy. Therefore, it leads to a sparse and well-conditioned algebraic system. Some test problems on regular and irregular computational domains are presented to verify the validity, efficiency, and accuracy of the method.

关键词： Meshfree approximation methods Sparse matrix efficient assembly greedy algorithm Mobile-immobile solute transport model

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Multi-criteria ranking based greedy algorithm for physical resource block allocation in multi-carrier wireless communication systems

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SIGNAL PROCESSING 2012年第11期92卷 2706-2717页

作者： Nwamadi, Obilor Zhu, Xu Nandi, Asoke K. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

A computationally efficient algorithm, referred to as the multi-criteria ranking based greedy (MCRG) algorithm, is proposed for physical resource block (PRB) allocation in multi-carrier wireless communications systems, where the users utilities are ranked with multiple criteria. The MCRG algorithm not only outperforms the previous single criterion ranking based greedy algorithm in terms of throughput and outage probability, but also provides a near optimal performance, irrespective of whether the channel frequency response (CFR) or bit error rate (BER) or throughput optimisation utilities are used. In particular, when the MCRG algorithm is used to optimise the CFR utility, the overall computational complexity is kept at a very low level, without sacrificing the performance. To further reduce the overall computational complexity, a selectivity ratio is included in the MCRG algorithm, where greedy PRB allocation is applied only to a selection of users with lower multi-criteria ranking. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Resource allocation Physical resource block greedy algorithm Multi-criteria ranking Multi-carrier

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Fast identification of critical nodes in complex network based on improved greedy algorithm

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PHYSICA SCRIPTA 2024年第12期99卷 125282-125282页

作者： Sun, Yang Guo, Sijia Chen, Lei Li, Shengquan Shi, Dongdong Ding, Yipei Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225127 Peoples R China

Over the past decades, many critical and complex systems, such as power grid, transportation network, and information network, have been effectively modeled using complex network. However, these networks are susceptible to cascading failure, triggered by minor failure, leading to partial or total collapse. Preventing cascading failure necessitates the protection of critical nodes within the network, making the identification of these nodes particularly crucial. In this paper, we introduce an Improved greedy algorithm (IGA), inspired by the traditional greedy algorithm and the relationship between the propagation mechanism of cascading failure and N-K failure. This algorithm gets rid of the shortcomings of traditional recognition algorithms for dealing with large-scale networks with long time and low accuracy, and evaluates the critical degree of nodes based on network connectivity and overload rate. The simulation is carried out in Barabsi-Albert (BA) network and IEEE 39-, 118-bus systems, and make comparisons with other different algorithms. The results show that IGA not only has low computational complexity, but also has high accuracy in identifying critical nodes in complex networks.

关键词： complex network cascading failure greedy algorithm N-K contingency

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Total tardiness minimization in permutation flow shops: a simple approach based on a variable greedy algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第22期46卷 6479-6498页

作者： Framinan, J. M. Leisten, R. Univ Seville Sch Engn E-41092 Seville Spain Univ Duisburg Essen Mercator Sch Management Dept Technol & Operat Management D-47057 Duisburg Germany

This paper addresses the problem of scheduling jobs in a permutation flowshop with the objective of minimizing the total tardiness of jobs. To tackle this problem, it is suggested that a procedure based on a greedy algorithm is employed that successively iterates over an increasing number of candidate solutions. The computational experiments carried out show this algorithm outperforms the best existing one for the problem under consideration. In addition, out some tests are carried out to analyse the efficiency of the adopted design.

关键词： scheduling flow shop due dates greedy algorithm

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Adaptive greedy algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-74页

作者： Nguyen, Minh-Lien Jeanne Lacour, Claire Rivoirard, Vincent Leiden Univ Math Inst Niels Bohrweg 1 NL-2333 CA Leiden Netherlands Univ Paris Est Creteil Univ Gustave Eiffel CNRS LAMA UMR8050 F-77447 Marne La Vallee France PSL Univ CEREMADE CNRS Univ Paris DauphineUMR 7534 F-75016 Paris France

This paper studies the estimation of the conditional density f (x, center dot) of Yi given Xi = x, from the observation of an i.i.d. sample (Xi, Yi) E Rd, i E {1, ... , n}. We assume that f depends only on r unknown components with typically r << d. We provide an adaptive fully-nonparametric strategy based on kernel rules to estimate f. To select the bandwidth of our kernel rule, we propose a new fast iterative algorithm inspired by the Rodeo algorithm (Wasserman and Lafferty, 2006) to detect the sparsity structure of f. More precisely, in the minimax setting, our pointwise estimator, which is adaptive to both the regularity and the sparsity, achieves the quasi-optimal rate of convergence. Our results also hold for (unconditional) density estimation. The computational complexity of our method is only O (dn log n). A deep numerical study shows nice performances of our approach.

关键词： Conditional density Sparsity Minimax rates Kernel density estimators greedy algorithm

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Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

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RENEWABLE ENERGY 2016年第PartA期96卷 676-686页

作者： Chen, K. Song, M. X. Zhang, X. Wang, S. F. South China Univ Technol Sch Chem & Chem Engn Minist Educ Key Lab Enhanced Heat Transfer & Energy Conservat Guangzhou 510640 Guangdong Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tsinghua Univ Dept Engn Mech Minist Educ Key Lab Thermal Sci & Power Engn Beijing 100084 Peoples R China

Wind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using greedy algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional greedy algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed greedy algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed greedy algorithm than the one using genetic algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional greedy algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wind farm Layout optimization Multiple hub heights greedy algorithm Complex terrain

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A PRIORI CONVERGENCE OF THE greedy algorithm FOR THE PARAMETRIZED REDUCED BASIS METHOD

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ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION MATHEMATIQUE ET ANALYSE NUMERIQUE 2012年第3期46卷 595-603页

作者： Buffa, Annalisa Maday, Yvon Patera, Anthony T. Prud'homme, Christophe Turinici, Gabriel CNR Inst Matemat Appl & Tecnol Informat I-27100 Pavia Italy Univ Paris 06 UPMC Lab Jacques Louis Lions UMR 7598 F-75005 Paris France Brown Univ Div Appl Math Providence RI 02912 USA MIT Dept Mech Engn Cambridge MA 02139 USA Univ Grenoble 1 Joseph Fourier Lab Jean Kuntzmann F-38041 Grenoble 9 France Univ Paris 09 CEREMADE F-75016 Paris France

The convergence and efficiency of the reduced basis method used for the approximation of the solutions to a class of problems written as a parametrized PDE depends heavily on the choice of the elements that constitute the "reduced basis". The purpose of this paper is to analyze the a priori convergence for one of the approaches used for the selection of these elements, the greedy algorithm. Under natural hypothesis on the set of all solutions to the problem obtained when the parameter varies, we prove that three greedy algorithms converge;the last algorithm, based on the use of an a posteriori estimator, is the approach actually employed in the calculations.

关键词： greedy algorithm reduced basis approximations a priori analysis best fit analysis

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CONVERGENCE OF A greedy algorithm FOR HIGH-DIMENSIONAL CONVEX NONLINEAR PROBLEMS

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MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES 2011年第12期21卷 2433-2467页

作者： Cances, Eric Ehrlacher, Virginie Lelievre, Tony Univ Paris Est Ecole Ponts Paristech CERMICS F-77455 Marne La Vallee 2 France

In this paper, we present a greedy algorithm based on a tensor product decomposition, whose aim is to compute the global minimum of a strongly convex energy functional. We prove the convergence of our method provided that the gradient of the energy is Lipschitz on bounded sets. The main interest of this method is that it can be used for high-dimensional nonlinear convex problems. We illustrate this method on a prototypical example for uncertainty propagation on the obstacle problem.

关键词： greedy algorithm high dimension obstacle problem uncertainty quantification

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An efficient radial basis functions mesh deformation with greedy algorithm based on recurrence Choleskey decomposition and parallel computing

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JOURNAL OF COMPUTATIONAL PHYSICS 2019年 377卷 183-199页

作者： Fang, Hong Hu, Yikun Yu, Caihui Tie, Ming Liu, Jie Gong, Chunye Sci & Technol Space Phys Lab Beijing 100076 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Univ Def Technol Comp Sch Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

The mesh deformation method based on radial basis functions (RBF) has many advantages and is widely used. RBF based mesh deformation method mainly has two steps: data reduction and displacement interpolation. The data reduction step includes solving interpolation weight coefficients and searching for the node with the maximum interpolation error. The data reduction schemes based on greedy algorithm is used to select an optimum reduced set of surface mesh nodes. In this paper, a parallel mesh deformation method based on parallel data reduction and displacement interpolation is proposed. The proposed recurrence Choleskey decomposition method (RCDM) can decrease the computational cost of solving interpolation weight coefficients from O (N-c(4)) to O (N-c(3)), where N-c denotes the number of support nodes. The technology of parallel computing is used to accelerate the searching for the node with the maximum interpolation error and displacement interpolation. The combination of parallel data reduction and parallel interpolation can greatly improve the efficiency of mesh deformation. Two typical deformation problems of the ONERA M6 and DLR-F6 wing-body-Nacelle-Pylon configuration are taken as the test cases to validate the proposed approach and can get up to 19.57 times performance improvement with the proposed approach. Finally, the aeroelastic response of HIRENASD wing-body configuration is used to verify the efficiency and robustness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Mesh deformation Recurrence Choleskey decomposition method Radial basis functions greedy algorithm Data reduction Parallel computing

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