Results 31 to 40 of about 846,629 (324)

Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Applied Sciences, 2019
Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in
Junjie Lu, Jinquan Huang, Feng Lu
doaj   +1 more source

Intelligent Fault Identification for Rolling Element Bearings in Impulsive Noise Environments Based on Cyclic Correntropy Spectra and LSSVM

IEEE Access, 2020
Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures.
Xuejun Zhao, Yong Qin, Changbo He, Limin Jia   +3 more
doaj   +1 more source

Learning Isometric Separation Maps

, 2009
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional spaces, often revealing the true intrinsic dimension.
Anderson, David V., Gray, Alexander G., Vasiloglou, Nikolaos   +2 more
core   +1 more source

A Kernel-Based Calibration Algorithm for Chromatic Confocal Line Sensors

Sensors
In chromatic confocal line sensors, calibration is usually divided into peak extraction and wavelength calibration. In previous research, the focus was mainly on peak extraction. In this paper, a kernel-based algorithm is proposed to deal with wavelength
Ming Qin, Xiao Xiong, Enqiao Xiao, Min Xia, Yimeng Gao, Hucheng Xie, Hui Luo, Wenhao Zhao   +7 more
doaj   +1 more source

Exact heat kernel on a hypersphere and its applications in kernel SVM

, 2017
Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to ...
Song, Jun S., Zhao, Chenchao
core   +1 more source

Multivariate Time Series Clustering with State Space Dynamical Modeling and Grassmann Manifold Learning: A Systematic Review on Human Motion Data

Applied Sciences
Multivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging ...
Sebin Heo, Andrew Beng Jin Teoh, Sunjin Yu, Beom-Seok Oh   +3 more
doaj   +1 more source

Semiparametric Survival Analysis of 30-Day Hospital Readmissions with Bayesian Additive Regression Kernel Model

Stats, 2022
In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with ...
Sounak Chakraborty, Peng Zhao, Yilun Huang, Tanujit Dey   +3 more
doaj   +1 more source

Systemic T Cell Receptor Profiling Reveals Adaptive Immune Activation and Potential Immune Signatures of Diagnosis and Brain Atrophy in Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy is increasingly associated with immune dysregulation and inflammation. The T cell receptor (TCR), a key mediator of adaptive immunity, shows repertoire alterations in various immune‐mediated diseases. The unique TCR sequence serves as a molecular barcode for T cells, and clonal expansion accompanied by reduced overall TCR ...
Yong‐Won Shin, Sang Bin Hong, Yong Woo Shin, Inpyeong Hwang, Jaeseong Oh, Jihyeon Choi, Narae Kim, Jangsup Moon, Keun‐Hwa Jung, Kyung‐Il Park, Ki‐Young Jung, Kon Chu, Sang Kun Lee   +12 more
wiley   +1 more source

Spatio-Functional Nadaraya–Watson Estimator of the Expectile Shortfall Regression

Axioms
The main aim of this paper is to consider a new risk metric that permits taking into account the spatial interactions of data. The considered risk metric explores the spatial tail-expectation of the data.
Mohammed B. Alamari, Fatimah A. Almulhim, Zoulikha Kaid, Ali Laksaci   +3 more
doaj   +1 more source

Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Sensors, 2018
The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation
Xiaobo Chen, Cheng Chen, Yingfeng Cai, Hai Wang, Qiaolin Ye   +4 more
doaj   +1 more source