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State of Charge Estimation Model Based on Genetic Algorithms and multivariate linear regression with Applications in Electric Vehicles

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SENSORS 2023年第6期23卷 2924页

作者： Manriquez-Padilla, Carlos Gustavo Cueva-Perez, Isaias Dominguez-Gonzalez, Aurelio Elvira-Ortiz, David Alejandro Perez-Cruz, Angel Saucedo-Dorantes, Juan Jose Univ Autonoma Queretaro Engn Fac Campus San Juan del RioAve Rio Moctezuma 249 San Juan Del Rio 76807 Queretaro Mexico

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%;thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

关键词： electric-vehicle battery state-of-charge multivariate linear regression genetic algorithms estimation model

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Estimation of dynamic properties of sandstones based on index properties using artificial neural network and multivariate linear regression methods (Jan, 10.1007/s11600-021-00705-3, 2022)

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ACTA GEOPHYSICA 2022年第1期70卷 505-505页

作者： Alizadeh, Seyed Mehdi Iraji, Amin Tabasi, Somayeh Ahmed, Alim Al Ayub Motahari, Mohammad Reza Australian Coll Kuwait Dept Petr Engn West Mishref Kuwait Kuwait Urmia Univ Technol Engn Fac Khoy Orumiyeh Iran Univ Sistan & Baluchestan Fac Ind & Min Khash Zahedan Iran Jiujiang Univ Sch Accounting 551 Qianjindonglu Jiujiang Jiangxi Peoples R China Arak Univ Dept Civil Engn Fac Engn Arak Iran

The dynamic properties of the rock are very important for the design of geotechnical structures and the modeling of deep drilling. In the present study, the velocity of compressional and shear waves (Vp and Vs) and the dynamic elastic modulus (Ed) of sandstones were estimated based on index tests using artificial neural network (ANN) and multivariate linear regression analysis (MVLRA) methods. For this purpose, petrographic, physical, mechanical and dynamic tests were performed on 54 specimens. Petrographic results showed that the samples were classified as feldspathic litharenite. The results showed that the Vp/Vs ratio was equal to 1.78. Also, the effect of mineralogy on mechanical properties was more than dynamic properties and the effect of quartz on dynamic properties was more than other minerals. The presented relationships were evaluated using R-squared (R-2), root-mean-square error (RMSE), mean absolute relative prediction error (MARPE), variance account for (VAF) and performance index (PI). The results of the ANN to estimate the Ed, Vp and Vs showed that it is possible to estimate these parameters based on inputs with high accuracy. The accuracy of the ANN was higher than the MVLRA. Estimation of Vs, Vp and Ed by ANN showed correlation coefficients of 0.97, 0.86 and 0.92 and RMSE of 0.10, 0.31, and 3.98, respectively. The ANN was also conservative in predicting these variables, while MVLRA was conservative only in estimating the Vs and Ed of the studied sandstones.

关键词： Dynamic elastic properties Physical properties Mechanical properties Petrography Artificial neural network multivariate linear regression

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Moderate deviation principle for likelihood ratio test in multivariate linear regression model

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JOURNAL OF multivariate ANALYSIS 2023年 194卷

作者： Bai, Yansong Zhang, Yong Liu, Congmin Jilin Univ Sch Math Changchun 130012 Peoples R China

Consider a multivariate linear regression model where the sample size is n and the dimensions of the predictors and the responses are p and m, respectively. We know that the limiting distribution of the likelihood ratio test (LRT) in multivariate linear regressions is different in the case of finite and high dimensions. In traditional multivariate analysis, when the dimension parameters (p, m) are fixed, the limiting distribution of the LRT is a chi 2 distribution. However, in the high-dimensional setting, the chi 2 approximation to the LRT may be invalid. In this paper, based on He et al. (2021), we give the moderate deviation principle (MDP) results for the LRT in a high dimensional setting, where the dimension parameters (p, m) are allowed to increase with the sample size n. The performance of the numerical simulation confirms our results. (c) 2022 Elsevier Inc. All rights reserved.

关键词： High -dimensional data Likelihood ratio test Moderate deviation principle multivariate linear regression regression coefficient matrix

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Coordinate Descent Algorithm for Normal-Likelihood-Based Group Lasso in multivariate linear regression 13th

Coordinate Descent Algorithm for Normal-Likelihood-Based Gro...

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13th International KES Conference on Intelligent Decision Technologies (KES-IDT)

作者： Yanagihara, Hirokazu Oda, Ryoya Hiroshima Univ Math Program Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima 7398526 Japan Hiroshima Univ Sch Informat & Data Sci Higashihiroshima Hiroshima 7398526 Japan

ISBN: (纸本)9789811627651;9789811627644

We focus on an optimization algorithm for a normal-likelihood-based group Lasso in multivariate linear regression. A negative multivariate normal log-likelihood function with a block-norm penalty is used as the objective function. A solution for the minimization problem of a quadratic form with a norm penalty is given without using the Karush-Kuhn-Tucker condition. In special cases, the minimization problem can be solved without solving simultaneous equations of the first derivatives. We derive update equations of a coordinate descent algorithm for minimizing the objective function. Further, by using the result of the special case, we also derive update equations of an iterative thresholding algorithm for minimizing the objective function.

关键词： Adaptive group Lasso Block-norm regularization multivariate linear regression Negative normal log-likelihood function

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A Consistent Likelihood-Based Variable Selection Method in Normal multivariate linear regression 13th

A Consistent Likelihood-Based Variable Selection Method in N...

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13th International KES Conference on Intelligent Decision Technologies (KES-IDT)

作者： Oda, Ryoya Yanagihara, Hirokazu Hiroshima Univ Sch Informat & Data Sci 1-3-1 Kagamiyama Hiroshima Japan Hiroshima Univ Grad Sch Adv Sci & Engn 1-3-1 Kagamiyama Hiroshima Japan

ISBN: (纸本)9789811627651;9789811627644

We propose a likelihood-based variable selection method for selecting explanatory variables in normality-assumed multivariate linear regression contexts. The proposed method is reasonably fast in terms of run-time, and it has a selection consistencywhen the sample size always tends to infinity, but the number of response and explanatory variables does not necessarily have to tend to infinity. It can be expected that the probability of selecting the true subset by the proposed method is high under a moderate sample size.

关键词： High-dimension multivariate linear regression Selection consistency

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multivariate linear regression Informs Ligand Design to Improve the Synthesis of a KRAS Inhibitor

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Synfacts 2023年第2期19卷 0189-0189页

作者： Antonia F. Stepan Nadine Kuhl

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Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size

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HIROSHIMA MATHEMATICAL JOURNAL 2020年第3期50卷 339-374页

作者： Oda, Ryoya Hiroshima Univ Grad Sch Sci Dept Math Higashihiroshima 7398526 Japan

This paper is concerned with the selection of explanatory variables in multivariate linear regression. The Akaike's information criterion and the C-p criterion cannot perform in high-dimensional situations such that the dimension of a vector stacked with response variables exceeds the sample size. To overcome this, we consider two variable selection criteria based on an L-2 squared distance with a weighted matrix, namely the scalar-type generalized C-p criterion and the ridge-type generalized C-p criterion. We clarify conditions for their consistency under a hybrid-ultra-high-dimensional asymptotic framework such that the sample size always goes to infinity but the number of response variables may not go to infinity. Numerical experiments show that the probabilities of selecting the true subset by criteria satisfying consistency conditions are high even when the dimension is larger than the sample size. Finally, we illuminate the practical utility of these criteria using empirical data.

关键词： Hybrid-ultra-high-dimensional asymptotic framework multivariate linear regression Non-normality Selection consistency Variable selection criterion

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A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables

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ELECTRONIC JOURNAL OF STATISTICS 2020年第1期14卷 1386-1412页

作者： Oda, Ryoya Yanagihara, Hirokazu Hiroshima Univ Grad Sch Sci Dept Math 1-3-1 Kagamiyama Higashihiroshima Hiroshima 7398526 Japan

We put forward a variable selection method for selecting explanatory variables in a normality-assumed multivariate linear regression. It is cumbersome to calculate variable selection criteria for all subsets of explanatory variables when the number of explanatory variables is large. Therefore, we propose a fast and consistent variable selection method based on a generalized C-p criterion. The consistency of the method is provided by a high-dimensional asymptotic framework such that the sample size and the sum of the dimensions of response vectors and explanatory vectors divided by the sample size tend to infinity and some positive constant which are less than one, respectively. Through numerical simulations, it is shown that the proposed method has a high probability of selecting the true subset of explanatory variables and is fast under a moderate sample size even when the number of dimensions is large.

关键词： Consistency high-dimensional asymptotic framework multivariate linear regression variable selection

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Boolean and fuzzy logic operators and multivariate linear regression applied to airborne gamma-ray spectrometry data for regolith mapping in granite-greenstone terrain in Midwest Brazil

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JOURNAL OF SOUTH AMERICAN EARTH SCIENCES 2021年第Part1期112卷 103562-103562页

作者： Peixoto, Sanclever Freire Horbe, Adriana Maria Coimbra Soares, Tulio Marques Freitas, Caroline Araujo Sousa, Endel Muller Dalat de Iza, Edgar Romeo Herrera de Figueiredo Univ Brasilia Inst Geociencias Campus Univ Darcy Ribeiro BR-70910900 Brasilia DF Brazil Inst Nat Tocantins NATURATINS Q 302Norte Alameda 1S-N Lote 3 BR-77006336 Palmas TO Brazil Serv Geol Brasil CPRM Salvador BA Brazil

Recent studies have proposed several possibilities of combining K, Th and U airborne gamma-ray spectrometry channels to generate predictive algorithms maps. These algorithms can be used for mapping regolith in deeply weathered terrains with residual, erosion, and deposition surfaces helping to developed strategies to better understand the regolith landscape and to improve geomorphology interpretation, and to identify mineral exploration target sites for primary (bedrock or saprolite) or supergene (hosted in lateritic duricrust) ore deposits. With the goal to easily map the regolith, two mathematical procedures were used on airborne gamma-ray spectrometry data in GIS software, validated by fieldwork on granite-greenstone belts in Midwest Brazil: 1. airborne gamma-ray spectrometry and altimetric data integrated in Boolean and fuzzy logic allowed segregating the areas with ferruginous and manganese residual lateritic duricrusts from erosional surface with rocks and saprolite with 90% of accuracy (kappa Boolean = 0.69 and kappa FAPO = 0.66) and 2. airborne gamma-ray spectrometry and altimetric data integrated with weathering stages in multivariate linear regression (basic statistic) helped establish the regional weathering intensity index, with acceptable error (r2 adjusted 0.6 and p-value < 5%). These two modeling techniques provide useful, accurate, rapidly and complementary regolith maps and can be applied in large regions for preliminary interpretations.

关键词： Ferruginous and manganese lateritic duricrusts Fuzzy logic Boolean logic multivariate linear regression Map algebra Weathering intensity index

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multivariate linear regression analysis on online image study for IoT

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COGNITIVE SYSTEMS RESEARCH 2018年 52卷 312-316页

作者： Zhu Xuanxuan Shanghai Int Studies Univ Sch Journalism & Commun Shanghai Peoples R China

To improve the effectiveness of national image study on the "Belt and Road" countries in Central Asia, a national image study method based on the multivariate linear is proposed in this paper. First, the national competitiveness database of International Institute for Management Development (IMD) and World Economic Forum is adopted to construct the evaluation index system of the "Belt and Road" countries;second, the multivariate linear regression analysis method is introduced to analyze the evaluation index system model of the "Belt and Road" countries mentioned above;third, the effectiveness of the proposed algorithm is verified through the simulation experiment. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Central Asian countries The Belt and Road National image multivariate linear regression Index system

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