Search Results - "S. Qin"

Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions by Liu, Ruonan, Wang, Fei, Yang, Boyuan, Qin, S. Joe

“…Motor fault diagnosis is imperative to enhance the reliability and security of industrial systems. However, since motors are often operated under nonstationary…”
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Spectrum of Light- and Heavy-Baryons by Qin, S.-X., Roberts, C. D., Schmidt, S. M.

“…A symmetry-preserving truncation of the strong-interaction bound-state equations is used to calculate the spectrum of ground-state J = 1 / 2 + , 3 / 2 + ( q q…”
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Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures by Qin, S. Joe, Zheng, Yingying

“…This paper proposes a new concurrent projection to latent structures is proposed in this paper for the monitoring of output‐relevant faults that affect the…”
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Latent vector autoregressive modeling and feature analysis of high dimensional and noisy data from dynamic systems by Qin, S. Joe

“…In this article, a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective is proposed to estimate…”
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Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data by Dong, Yining, Liu, Yingxiang, Joe Qin, S.

“…Dynamic-inner canonical correlation analysis (DiCCA) extracts dynamic latent variables from high-dimensional time series data with a descending order of…”
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Dynamic latent variable regression for inferential sensor modeling and monitoring by Zhu, Qinqin, Joe Qin, S., Dong, Yining

“…Canonical correlation analysis (CCA) and projection to latent structures (PLS) are popular statistical approaches for process modeling and monitoring. CCA…”
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UALCAN: An update to the integrated cancer data analysis platform by Chandrashekar, Darshan Shimoga, Karthikeyan, Santhosh Kumar, Korla, Praveen Kumar, Patel, Henalben, Shovon, Ahmedur Rahman, Athar, Mohammad, Netto, George J., Qin, Zhaohui S., Kumar, Sidharth, Manne, Upender, Creighton, Chad J., Varambally, Sooryanarayana

“…Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and…”
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Semi-Supervised Dynamic Latent Variable Regression for Prediction and Quality-Relevant Fault Monitoring by Liu, Qiang, Yang, Chao, Qin, S. Joe

“…Supervised latent variable regression methods such as partial least squares (PLS) and dynamic PLS have found wide applications in data analytics, quality…”
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Partial least squares, steepest descent, and conjugate gradient for regularized predictive modeling by Qin, S. Joe, Liu, Yiren, Tang, Shiqin

“…In this article, we explore the connection of partial least squares (PLS) to other regularized regression algorithms including the Lasso and ridge regression,…”
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New Dynamic Predictive Monitoring Schemes Based on Dynamic Latent Variable Models by Dong, Yining, Qin, S. Joe

“…In this paper, dynamic predictive monitoring schemes based on dynamic latent variable models are proposed. We consider the most typical case in industrial data…”
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Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA by Fan, Jicong, Qin, S. Joe, Wang, Youqing

“…In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis–principal component analysis (FKICA–PCA),…”
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Total projection to latent structures for process monitoring by Zhou, Donghua, Li, Gang, Qin, S. Joe

“…Partial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component…”
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Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring by Yu, Jie, Qin, S. Joe

“…A novel batch process monitoring approach is proposed in this article to handle batch processes with multiple operation phases. The basic idea is to combine…”
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Geometric properties of partial least squares for process monitoring by Li, Gang, Qin, S. Joe, Zhou, Donghua

“…Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on input X, while principal component analysis (PCA)…”
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Statistical process monitoring: basics and beyond by Joe Qin, S.

“…This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault…”
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Multiblock Concurrent PLS for Decentralized Monitoring of Continuous Annealing Processes by Qiang Liu, Qin, S. Joe, Tianyou Chai

“…In this paper, a data-driven multiblock concurrent projection to latent structures (CPLS) method is proposed for monitoring large-scale manufacturing lines,…”
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Knowledge-informed Sparse Learning for Relevant Feature Selection and Optimal Quality Prediction by Liu, Yiren, Qin, S. Joe

“…Industrial data are usually collinear, which can cause pure data-driven sparse learning to deselect physically relevant variables and select collinear…”
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Gene Density, Transcription, and Insulators Contribute to the Partition of the Drosophila Genome into Physical Domains by Hou, Chunhui, Li, Li, Qin, Zhaohui S., Corces, Victor G.

“…The mechanisms responsible for the establishment of physical domains in metazoan chromosomes are poorly understood. Here we find that physical domains in…”
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Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models by Yu, Jie, Qin, S. Joe

“…For complex industrial processes with multiple operating conditions, the traditional multivariate process monitoring techniques such as principal component…”
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Soil moisture modifies the response of soil respiration to temperature in a desert shrub ecosystem by Wang, B, Zha, T. S, Jia, X, Wu, B, Zhang, Y. Q, Qin, S. G

“…The current understanding of the responses of soil respiration (Rs) to soil temperature (Ts) and soil moisture is limited for desert ecosystems. Soil CO2…”
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