المؤلفون: Abhinav Prakash, Rui Tuo, Yu Ding

المصدر: Technometrics. 65:70-82

مصطلحات موضوعية: FOS: Computer and information sciences, Statistics and Probability, Statistics - Machine Learning, Applied Mathematics, Modeling and Simulation, Applications (stat.AP), Machine Learning (stat.ML), Statistics - Applications

الوصف: This paper is concerned with a nonparametric regression problem in which the input variables and the errors are autocorrelated in time. The motivation for the research stems from modeling wind power curves. Using existing model selection methods, like cross validation, results in model overfitting in presence of temporal autocorrelation. This phenomenon is referred to as temporal overfitting, which causes loss of performance while predicting responses for a time domain different from the training time domain. We propose a Gaussian process (GP)-based method to tackle the temporal overfitting problem. Our model is partitioned into two parts -- a time-invariant component and a time-varying component, each of which is modeled through a GP. We modify the inference method to a thinning-based strategy, an idea borrowed from Markov chain Monte Carlo sampling, to overcome temporal overfitting and estimate the time-invariant component. We extensively compare our proposed method with both existing power curve models and available ideas for handling temporal overfitting on real wind turbine datasets. Our approach yields significant improvement when predicting response for a time period different from the training time period. Supplementary material and computer code for this article is available online.
Comment: 30 pages, 6 figures, Supplementary material available in source files as SupplementaryMaterial.pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62d33e27de5b6818f8a9f254b2567aa5Test
https://doi.org/10.1080/00401706.2022.2069158Test

المؤلفون: Ahmed Aziz Ezzat, Arash Pourhabib, Yu Ding

المصدر: Technometrics. 60:286-296

مصطلحات موضوعية: Statistics and Probability, 021103 operations research, Calibration (statistics), Computer science, Applied Mathematics, 0211 other engineering and technologies, Experimental data, 02 engineering and technology, Computer experiment, computer.software_genre, 01 natural sciences, Set (abstract data type), 010104 statistics & probability, Sequential analysis, Simple (abstract algebra), Modeling and Simulation, Econometrics, Data mining, 0101 mathematics, Focus (optics), computer

الوصف: The calibration of computer models using physical experimental data has received a compelling interest in the last decade. Recently, multiple works have addressed the functional calibration of computer models, where the calibration parameters are functions of the observable inputs rather than taking a set of fixed values as traditionally treated in the literature. While much of the recent works on functional calibration was focused on estimation, the issue of sequential design for functional calibration still presents itself as an open question. Addressing the sequential design issue is thus the focus of this paper. We investigate different sequential design approaches and show that the simple separate design approach has its merit in practical use when designing for functional calibration. Analysis is carried out on multiple simulated and real world examples.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0f0536fb50b3c79cd22d4d2a8648ae0dTest
https://doi.org/10.1080/00401706.2017.1377638Test

المؤلفون: Xiaodong Li, Yanjun Qian, Hui Zhang, Ling Dong, Dan Yu, Zhong Zhang, Yu Ding

المصدر: Technometrics. 59:391-403

مصطلحات موضوعية: Statistics and Probability, K-function, Applied Mathematics, Nanoparticle, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, 010104 statistics & probability, Host material, Point distribution model, Agglomerate, Modeling and Simulation, Homogeneity (physics), Statistics, Statistical physics, 0101 mathematics, 0210 nano-technology, Dimensionless quantity, Mathematics

الوصف: Ripley’s K function is commonly used to characterize the homogeneity of spatial point distribution. Not surprisingly, it becomes a favored tool in quantifying the nanoparticles mixing state in composite materials, a parameter that material scientists believe is of close relevance to certain properties of the nanoparticle-embedding material. Ripley’s K function assumes that the spatial points are dimensionless. In reality, the nanoparticles, once mixed in a host material, form clusters or agglomerates of various sizes and shapes. Our analysis shows that using the original K function falls short of ranking or distinguishing the homogeneity of nanoparticle mixing. We therefore propose to revise the K function to account for both particle location and size effects. We apply the revised function to electron microscopy images of material samples and conduct analysis and comparison of nanoparticle mixing. The analysis shows that the revised function is a better index to quantify the mixing states.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6c9a8da1d86a1308afb8efc2aaf4483dTest
https://doi.org/10.1080/00401706.2016.1186563Test

المؤلفون: Arash Pourhabib, Jianhua Z. Huang, Yu Ding

المصدر: Technometrics. 58:138-147

مصطلحات موضوعية: Statistics and Probability, Wind power, Meteorology, business.industry, Astrophysics::High Energy Astrophysical Phenomena, 020209 energy, Applied Mathematics, Probabilistic logic, 02 engineering and technology, Wind direction, 01 natural sciences, Turbine, Wind speed, Term (time), Wind wave model, 010104 statistics & probability, Modeling and Simulation, Physics::Space Physics, 0202 electrical engineering, electronic engineering, information engineering, Astrophysics::Solar and Stellar Astrophysics, Environmental science, 0101 mathematics, business, Physics::Atmospheric and Oceanic Physics, Geostrophic wind

الوصف: Turbine operations in a wind farm benefit from an understanding of the near-ground behavior of wind speeds. This article describes a probabilistic spatial-temporal model for analyzing local wind fields. Our model is constructed based on measurements taken from a large number of turbines in a wind farm, as opposed to aggregating the data into a single time-series. The model incorporates both temporal and spatial characteristics of wind speed data: in addition to using a time epoch mechanism to model temporal nonstationarity, our model identifies an informative neighborhood of turbines that are spatially related, and consequently, constructs an ensemble-like predictor using the data associated with the neighboring turbines. Using actual wind data measured at 200 wind turbines in a wind farm, we found that the two modeling elements benefit short-term wind speed forecasts. We also investigate the use of regime switching to account for the effect of wind direction and the use of geostrophic wind to account for...

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::2a0c26723ac64be0ab43fca3409a96deTest
https://doi.org/10.1080/00401706.2014.988291Test

المؤلفون: Yong Chen, Yu Ding, Jung Jin Cho

المصدر: Technometrics. 51:34-46

مصطلحات موضوعية: Statistics and Probability, Mathematical optimization, Applied Mathematics, Linear model, Design matrix, Robust statistics, Estimator, Robust regression, Robustness (computer science), Modeling and Simulation, Linear regression, Algorithm, Mathematics, Sparse matrix

الوصف: In robust statistics, the concept of breakdown point was introduced to quantify the robustness of an estimator in a linear regression model. Computing the breakdown point is useful in tuning some robust regression estimators (e.g., the least trimmed squares estimator). Computing the breakdown point for a structured linear model (i.e., one with dependencies among some p rows of the n × p design matrix X) can be very demanding. This article presents an algorithm for calculating the maximum breakdown point for sparse linear models, which are a special type of structured linear model whose design matrix has many zero entries. The algorithm decomposes a sparse design matrix into smaller submatrixes on which the computation is performed, thereby leading to substantial savings in computation. An assembly process, along with a few numerical examples, illustrate the application of the algorithm and demonstrate its computational benefits.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::8060b950609df679daa28d11fdc79cf1Test
https://doi.org/10.1198/tech.2009.0004Test

المؤلفون: Yu Ding, Pansoo Kim

المصدر: Technometrics. 47:336-348

مصطلحات موضوعية: Statistics and Probability, Iterative design, Computer science, Applied Mathematics, Computer-automated design, computer.software_genre, Design objective, Surrogate model, Modeling and Simulation, Systems design, Probabilistic design, Data mining, Generative Design, Engineering design process, computer

الوصف: An optimal engineering design problem is challenging because nonlinear objective functions usually need to be evaluated in a high-dimensional design space. This article presents a data-mining–aided optimal design method, that is able to find a competitive design solution with a relatively low computational cost. The method consists of four components: (1) a uniform-coverage selection method, that chooses design representatives from among a large number of original design alternatives for a nonrectangular design space; (2) feature functions, of which evaluation is computationally economical as the surrogate for the design objective function; (3) a clustering method, that generates a design library based on the evaluation of feature functions instead of an objective function; and (4) a classification method to create the design selection rules, eventually leading us to a competitive design. Those components are implemented to facilitate the optimal fixture layout design in a multistation panel assembly proc...

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::b66a2375dfa0b9ac7e51fa328aae9b47Test
https://doi.org/10.1198/004017005000000157Test

المؤلفون: Yu Ding, Jianjun Shi, Yong Chen, Shiyu Zhou

المصدر: Technometrics. 45:312-325

مصطلحات موضوعية: Statistics and Probability, Structure (mathematical logic), Class (computer programming), Data collection, Process (engineering), Computer science, Applied Mathematics, media_common.quotation_subject, Fault (power engineering), Reliability engineering, Modeling and Simulation, Product (mathematics), Quality (business), Aliasing (computing), media_common

الوصف: Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of a minimal diagnosable class is proposed to expose the “aliasing” structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can be used for any general linear input–output system, is illustrated using a panel assembly process and an engine head machining process.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::331a5db491ecca9010fa01abcb68d854Test
https://doi.org/10.1198/004017003000000131Test

المؤلفون: Pansoo Kim¹ pskim@knu.ac.kr, Yu Ding² yuding@iemail.tamu.edu

المصدر: Technometrics. Aug2005, Vol. 47 Issue 3, p336-348. 13p.

مصطلحات موضوعية: *ENGINEERING design, *INDUSTRIAL design, *DATA mining, *DATABASE searching, *DATABASE marketing, COMBINATORIAL optimization

مستخلص: An optimal engineering design problem is challenging because nonlinear objective functions usually need to be evaluated in a high-dimensional design space. This article presents a data-mining-aided optimal design method, that is able to find a competitive design solution with a relatively low computational cost. The method consists of four components: (1) a uniform-coverage selection method, that chooses design representatives from among a large number of original design alternatives for a nonrectangular design space; (2) feature functions, of which evaluation is computationally economical as the surrogate for the design objective function; (3) a clustering method, that generates a design library based on the evaluation of feature functions instead of an objective function; and (4) a classification method to create the design selection rules, eventually leading us to a competitive design. Those components are implemented to facilitate the optimal fixture layout design in a multistation panel assembly process. The benefit of the data-mining-aided optimal design is clearly demonstrated by comparison with both local optimization methods (e.g., simplex search) and random search-based optimizations (e.g., simulated annealing). [ABSTRACT FROM AUTHOR]

: Copyright of Technometrics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Shiyu Zhou¹ szhou@engr.wisc.edu, Yu Ding² yuding@iemail.tamu.edu, Yong Chen³ yong-chen@uiowa.edu, Jianjun Shi⁴ shihang@umich.edu

المصدر: Technometrics. Nov2003, Vol. 45 Issue 4, p312-325. 14p.

مصطلحات موضوعية: *QUALITY control, *STANDARDIZATION, *STATISTICAL sampling, *ANALYSIS of variance, *VARIANCES, MULTILEVEL models

مستخلص: Automatic in-process data collection techniques have been widely Used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the dignosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of minimal diagnosable class is proposed to expose the "aliasing" structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can he used for any general linear input-output system, is illustrated using a panel assembly process and an engine head machining process. [ABSTRACT FROM AUTHOR]

: Copyright of Technometrics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Yu Ding

المصدر: Technometrics. 47:240-240

مصطلحات موضوعية: Statistics and Probability, Computer science, Applied Mathematics, Modeling and Simulation, Mission critical, Reliability (statistics), Reliability engineering

الوصف: (2005). Design and Analysis of Accelerated Tests for Mission-Critical Reliability. Technometrics: Vol. 47, No. 2, pp. 240-240.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::016b337edf5de6f94b02878dc2699bc6Test
https://doi.org/10.1198/tech.2005.s268Test