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The adaptive kernel-based extreme learning machine for state of charge estimation

IONICS 2023年第5期29卷 1863-1872页

作者： Zhang, Yanxin Zhang, Zili Chen, Jing Liao, Cuicui Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Sci Technol Near Surface Detect Lab Wuxi 214122 Peoples R China

The state of charge (SOC) is a key factor in the battery management, and the accuracy of its estimation plays an important role in battery-life prediction. This paper develops a kernel-based extreme learning machine for SOC estimation. The extreme learning machine is a single hidden layer feedforward neural network with a randomly initialized weight matrix and a bias vector. Unlike the traditional neural network, it does not need to update the network parameters. Then, the kernel-based method is combined with the extreme learning machine to avoid overfitting in parameter estimation. In the example, three kernel-based extreme learning machine methods and the regularized extreme learning machine method are used to train the model. The simulation results show the effectiveness of the proposed methods.

关键词： Lithium battery State of charge kernel-based method Adaptive kernel Extreme learning machine

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Change point detection for high dimensional data via kernel measure with application to human aging brain data

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STATISTICS IN MEDICINE 2023年第25期42卷 4644-4663页

作者： Wang, Jinjuan Li, Na Meng, Zhen Li, Qizhai Beijing Inst Technol Sch Math & Stat Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Appl Sci Beijing Peoples R China Capital Univ Econ & Business Sch Stat Beijing Peoples R China Chinese Acad Sci LSC Acad Math & Syst Sci Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China

Identifying the existence and locations of change points has been a broadly encountered task in many statistical application areas. The existing change point detection methods may produce unsatisfactory results for high-dimensional data since certain distributional assumptions are made on data, which are hard to verify in practice. Moreover, some parameters (such as the number of change points) need to be estimated beforehand for some methods, making their powers sensitive to these values. Here, we propose a kernel-based U-statistic to identify change points (KUCP) for high dimensional data, which is free of distributional assumptions and sup-parameter estimations. Specifically, we employ a kernel function to describe similarities among the subjects and construct a U-statistic to test the existence of change point for a given location. The asymptotic properties of the U-statistic are deduced. We also develop a procedure to locate the change points sequentially via a dichotomy algorithm. Extensive simulations demonstrate that KUCP has higher sensitivity in identifying existence of change points and higher accuracy in locating these change points than its counterparts. We further illustrate its practical utility by analyzing a gene expression data of human brain to detect the time point when gene expression profiles begin to change, which has been reported to be closely related with aging brain.

关键词： change point detection gene expression profile high dimensional data kernel-based method U-statistic

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A Novel Mean-Shift Algorithm for Data Clustering

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IEEE ACCESS 2022年 10卷 14575-14585页

作者： Cariou, Claude Le Moan, Steven Chehdi, Kacem IETR CNRS UMR 6164 Univ Rennes 1 F-22300 Lannion France Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway

We propose a novel Mean-Shift method for data clustering, called Robust Mean-Shift (RMS). A new update equation for point iterates is proposed, mixing the ones of the standard Mean-Shift (MS) and the Blurring Mean-Shift (BMS). Despite its simplicity, the proposed method has not been studied so far. RMS can be set up in both a kernel-based and a nearest-neighbor (NN)-based fashion. Since the update rule of RMS is closer to BMS, the convergence of point iterates is conjectured based on the Chen's BMS convergence theorem. Experimental results on synthetic and real datasets show that RMS in several cases outperforms MS and BMS in the clustering task. In addition, RMS exhibits larger attraction basins than MS and BMS for identical parametrization;consequently, its kernel variant requires a lower aperture of the kernel function, and its NN variant a lower number of nearest neighbors compared to MS or BMS, to achieve optimal clustering results. In addition, the NN version of RMS does not need to specify a convergence threshold to stop the iterations, contrarily to the NN-BMS algorithm.

关键词： Convergence kernel Bandwidth Clustering algorithms Task analysis Standards Sensitivity Data mining data clustering mean-shift kernel-based method nearest neighbors

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A kernel-based identification approach for a class of nonlinear systems with quantized output data

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DIGITAL SIGNAL PROCESSING 2022年 128卷

作者： Ding, Jiling Zhang, Weihai Zhao, Junsheng Shandong Univ Sci & Technol Coll Elect Engn & Automation Qingdao 266590 Peoples R China Jining Univ Coll Math & Comp Applicat Technol Jining 273155 Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252059 Peoples R China

In this paper, we consider the problem of identification for a class of nonlinear systems with Hammerstein structure, which is a combination of some basis functions for the static nonlinearity and finite impulse response models for the linear block under quantized output data. Following the empirical Bayes method, we model the impulse response of the system using a Gaussian process whose covariance matrix depends on some suitable kernels. Then, we compute the minimum mean-square error estimation of the impulse response via a special case of Markov Chain Monte Carlo sampling approach, namely, Gibbs sampler. The estimation of the impulse response depends on the kernel hyperparameters, the noise variance and the coefficients of the static nonlinearity. These unknowns are estimated by the marginal likelihood maximization, which is solved via the expectation-conditional maximization algorithm combining the Gibbs sampler. The expectation-conditional maximization method is a variation of the standard expectation-maximization method for solving maximum-likelihood problems. Numerical simulations show the effectiveness of the proposed scheme. (C) 2022 Elsevier Inc. All rights reserved.

关键词： System identification Nonlinear systems kernel-based method Quantized data Expectation maximization

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Advanced general particle dynamics with nonlocal foundation for fracture analysis

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FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES 2022年第10期45卷 2794-2810页

作者： Zhou, Xiao-Ping Yao, Wu-Wen Berto, Filippo Northeastern Univ Coll Resources & Civil Engn Shenyang Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Norwegian Univ Sci & Technol Dept Mech Engn Trondheim Norway

The general particle dynamics (GPD), which is based on the kernel approximation method and Navier-Stokes equation, was developed from smoothed-particle hydrodynamics to simulate the fracture behaviors by using the collective of damage variable to describe the damage evolution behaviors. In this paper, an advanced GPD with a nonlocal foundation is proposed for better description of the solid mechanics. The nonlocal vector calculus and microscopic constitutive model are employed to derive the governing equations in the proposed theory. The novel GPD with a nonlocal core is a generalization, which permits the inclusion of the microscopic constitutive model and the multi-scale modeling compared with the previous GPD. To accomplish this generalization, the relationship between GPD and the previous kernel-based theory is established. The novel GPD is superior in the fracture problems and multi-scale modeling. Some numerical experiments are launched to verify the ability of the novel GPD.

关键词： fracture general particle dynamics kernel-based method nonlocal vector calculus solid mechanics

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The disturbance of urban mobility in the context of COVID-19 pandemic

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CITIES 2022年 128卷 103821页

作者： You, Geonhwa Kyung Hee Univ Dept Geog Seoul 02447 South Korea

Since the COVID-19 outbreaks, extensive studies have focused on mobility changes to demonstrate the pandemic effect;some studies identified remarkable mobility declines and revealed a negative relationship between mobility and the number of COVID-19 cases. However, counter-arguments have been raised, exemplifying insignificant variations, recuperated travel frequency, and transitory decline effect. This paper copes with this contentious issue, analyzing time series mobility data in comprehensive timelines. The assessment of the pandemic effect builds on significant change rate (SCR) ceilings and the density of the semantic outliers derived from the kernel-based approach. The comparison between pre-and post-pandemic periods indicated that mobility decline pervaded Australia, Europe, New York, New Zealand, and Seoul. However, the degree of the effect was alleviated over time, showing decreased/increased SCR ceilings of negative/positive outliers. The changes in resulting outlier density and SCR ceilings corroborated that the pandemic outbreaks did not lead to persistent mobility decline. The findings provide useful insights for predicting epidemics and setting appropriate restrictions and transportation systems in urban areas.

关键词： COVID-19 effects Intercity mobility International mobility kernel-based method Time series pattern detection

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Data-driven software reliability evaluation under incomplete knowledge on fault count distribution

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QUALITY ENGINEERING 2020年第3期32卷 421-433页

作者： Dohi, Tadashi Zheng, Junjun Okamura, Hiroyuki Hiroshima Univ Dept Informat Engn Grad Sch Engn Higashihiroshima Japan Ritsumeikan Univ Dept Informat Sci & Engn Kusatsu Japan

In this article, we consider data-driven approaches for software reliability evaluation without specifying the fault count distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive non-parametric method based on the kernel estimation is provided with several kernel functions and bandwidth estimations, in addition to the non-parametric bootstrap. The resulting data-driven methodologies can give useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault count distribution.

关键词： Bootstrap confidence region data-driven approach kernel-based method non-homogeneous Poisson processes non-parametric estimation software reliability

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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

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IEEE ACCESS 2020年 8卷 157482-157492页

作者： Mello-Roman, Jorge Daniel Hernandez, Adolfo Univ Complutense Madrid Fac Math Sci Madrid 28040 Spain Univ Complutense Madrid Financial & Actuarial Econ & Stat Dept Madrid 28223 Spain

kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the nature-inspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristic algorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression.

关键词： kernel Optimization Genetic algorithms Prediction algorithms Particle swarm optimization Sociology Statistics Partial least squares regression kernel-based method cross-validation method nature-inspired metaheuristic algorithms

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method for classification of PQ events based on discrete Gabor transform with FIR window and T2FK-based SVM and its experimental verification

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第1期11卷 133-141页

作者： Naderian, Sobhan Salemnia, Ahmad Shahid Beheshti Univ Abbaspour Sch Engn Tehran Iran

This paper aims to develop a new method for identification of Power Quality (PQ) events based on Discrete Gabor Transform (DGT) with a Finite Impulse Response Window (FIR-DGT) and Type-2 Fuzzy kernel-based Support Vector Machine (T2FK-SVM). The FIR-DGT extracted features from the input signals and the T2FK-SVM classified them. Using proper window function for DGT is essential. Iterated sine window was used as window function to extract the events' features. The iterated sine window's function is four times faster than the default window function of DGT. kernel design is a main part of many kernel-based methods such as SVM, so by using T2FK, the total accuracy of classification is enhanced. In the present work, use of this hybrid approach decreased the extracted features size, so the execution time and total required memory were optimized for the classification section. The simulation results revealed accurate classification and execution in the detection and classification of nine types of PQ events. The overall accuracy of the proposed method was comparable to other methods and the accuracy evaluated under noisy conditions. Hardware platform was developed for evaluating the proposed method based on ARM LPC 1768 microcontroller to assess accuracy of the method in real conditions.

关键词： power engineering computing iterative methods feature extraction fuzzy reasoning power supply quality discrete transforms support vector machines PQ event detection kernel-based method ARMLPC1768 microcontroller feature size extraction kernel-based methods iterated sine window type-2 fuzzy kernel-based support vector machine power quality even identification FIR-DGT finite impulse response window T2FK-based SVM FIR window discrete Gabor transform PQ event classification

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On estimation of peakedness-ordered distributions

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COMMUNICATIONS IN STATISTICS-THEORY AND methodS 2017年第10期46卷 4855-4869页

作者： El Barmi, Hammou Wu, Rongning CUNY Baruch Coll New York NY 10021 USA

A random variable X is said to be less peaked about a point than a random variable Y about a point , denoted by X?Y-pkd(,Y- ), if This paper develops new consistent estimators of the distribution functions of two continuous random variables X and Y when X is less peaked than Y. Through simulation studies, we show that our estimators outperform in terms of mean-squared error the estimators proposed in ElBarmi and Mukerjee(2012) for the same problem. To illustrate the theory, an empirical example is presented.

关键词： Density estimation kernel-based method peakedness ordering

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