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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Science China(Technological Sciences) 2022年第7期65卷 1470-1481页

作者： LI ZhuoXuan SHI XinLi CAO JinDe WANG XuDong HUANG Wei School of Mathematics Southeast UniversityNanjing 210096China School of Cyber Science&Engineering Southeast UniversityNanjing 210096China Yonsei Frontier Lab Yonsei UniversitySeoul 120-749Korea Research Institute of Highway Ministry of TransportBeijing 100088China Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China

The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and *** experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

关键词： FWD pavement management systems RIOHTrack deflection basin area CPSO XGBoost segmented regression model

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A Robust segmented Mixed Effect regression model for Baseline Electricity Consumption Forecasting

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 71-80页

作者： Xiaoyang Zhou Yuanqi Gao Weixin Yao Nanpeng Yu Department of Statistics University of CaliforniaRiversideCAUSA Department of Electrical and Computer Engineering University of CaliforniaRiversideUSA

Renewable energy production has been surging around the world in recent *** mitigate the increasing uncertainty and intermittency of the renewable generation,proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system *** of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources *** this paper,we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California,USA,by combining the ideas of random effect regression model,segmented regression model,and the least trimmed squares *** the log-likelihood of the considered model is not differentiable at breakpoints,we propose a new backfitting algorithm to estimate the unknown *** estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

关键词： segmented regression model mixed effects trimmed maximum likelihood demand response electric load

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chngpt: threshold regression model estimation and inference

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BMC BIOINFORMATICS 2017年第1期18卷 1-7页

作者： Fong, Youyi Huang, Ying Gilbert, Peter B. Permar, Sallie R. Fred Hutchinson Canc Res Ctr Vaccine & Infect Dis Div Dept Biostat Bioinformat & Epidemiol 1100 Fairview Ave N Seattle WA 98104 USA Duke Univ Human Vaccine Inst Med Ctr 2 Genome Court Durham NC 27706 USA

Background: Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor. Results: The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses. Conclusion: chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.

关键词： segmented regression model regression kink Jump-type Change point

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Evaluation of Fitting and Adjustment Methods for Taper and Volume Prediction of Black Pine in Turkey

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FOREST SCIENCE 2017年第4期63卷 349-355页

作者： Ozcelik, Ramazan Cao, Quang V. Suleyman Demirel Univ Fac Forestry TR-32260 Isparta Turkey Louisiana State Univ Ctr Agr Sch Renewable Nat Resources Baton Rouge LA 70803 USA

A modified form (Cao, Q.V. South. J. Appl. For. 33[2]: 58-61, 2009) of the taper model of T.A. Max and H.E. Burkhart (For. Sci. 22[3]: 283-289, 1976) was used in this study to predict taper and volume of black pine. A total of 16 methods were evaluated, including 4 fitting methods (optimized for taper, cumulative volume, taper and cumulative volume, and taper and total volume) and 4 adjustment strategies (unadjusted, adjusted to match dbh, predicted total volume, and dbh and predicted total volume). Results showed that adjustment did not improve the performance of the taper model. Also for this data set, the model with parameters optimized for taper and either total or cumulative volume performed better than the other fitting methods in predicting both taper and cumulative volume.

关键词： taper volume segmented regression model optimization black pine

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Calibrating fixed- and mixed-effects taper equations

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FOREST ECOLOGY AND MANAGEMENT 2011年第4期262卷 671-673页

作者： Cao, Quang V. Wang, Jing Louisiana State Univ Ctr Agr Sch Renewable Nat Resources Baton Rouge LA 70803 USA Louisiana State Univ Ctr Agr Dept Expt Stat Baton Rouge LA 70803 USA

Accurate and affordable measurements of upper-stem diameters are now possible thanks to recent advances in laser technology. Measurement of the midpoint upper-stem diameter can be employed to improve the accuracy of diameter predictions along the tree bole. Felled-tree data from a loblolly pine (Pinus taeda L.) plantation was used to evaluate two approaches: (1) calibrating a segmented taper equation by constraining a parameter, and (2) localizing the taper model by predicting the random effects for each tree. The calibration technique is much simpler and produced less-biased prediction of diameters and is therefore recommended. Calibration results were similar for both fixed- and mixed-effects taper models, even though a slight gain in accuracy and precision was attained with the mixed-effects model. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Pinus taeda Loblolly pine Localizing segmented regression model

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Association Between Osteoporosis and Adiposity Index Reveals Nonlinearity Among Postmenopausal Women and Linearity Among Men Aged over 50 Years

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JOURNAL OF EPIDEMIOLOGY AND GLOBAL HEALTH 2024年第3期14卷 1202-1218页

作者： Chen, Po-Ju Lu, Yueh-Chien Lu, Sheng-Nan Liang, Fu-Wen Chuang, Hung-Yi Kaohsiung Chang Gung Mem Hosp Dept Occupat Med Kaohsiung Taiwan Kaohsiung Chang Gung Mem Hosp Dept Family Med Kaohsiung Taiwan Chang Gung Univ Coll Med Taoyuan Taiwan Kaohsiung Chang Gung Mem Hosp Dept Internal Med Div Hepatogastroenterol Kaohsiung Taiwan Kaohsiung Med Univ Coll Hlth Sci Dept Publ Hlth Kaohsiung Taiwan Kaohsiung Med Univ Kaohsiung Med Univ Hosp Dept Med Res Kaohsiung Taiwan Kaohsiung Med Univ Ctr Big Data Res Kaohsiung Taiwan Kaohsiung Med Univ Hosp Dept Community Med 100 Shi Chuan 1st Rd Kaohsiung 80708 Taiwan Kaohsiung Med Univ Coll Med PhD Program Environm & Occupat Med Kaohsiung Taiwan Kaohsiung Med Univ Res Ctr Precis Environm Med Kaohsiung Taiwan

PurposePrevious research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density *** 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression *** study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse *** impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.

关键词： Obesity Osteoporosis Abdominal volume index Visceral adiposity index Generalized additive model segmented regression model

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Calibrating a segmented Taper Equation with Two Diameter Measurements

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SOUTHERN JOURNAL OF APPLIED FORESTRY 2009年第2期33卷 58-61页

作者： Cao, Quang V. Louisiana State Univ Sch Renewable Nat Resources Baton Rouge LA 70803 USA

Recent advances in laser technology help make possible accurate and affordable measurements of upper-stem diameters. These measurements (an be used to calibrate results from a taper equation to improve the accuracy of diameter predictions along the tree bole. Felled-tree data from a loblolly pine (Pinus taeda L.) plantation were used to evaluate two methods for calibrating outputs from a segmented taper equation with parameters either obtained from the data in this study or originally published by Max and Burkhart (1976, segmented polynomial regression applied to taper equations, For. Sci. 22:283-289). For outside-bark diameters, although a simple calibration for dbh gave desirable results, a better calibration involving both dbh and an upper-stem diameter provided significant improvements in predicting tree taper. Results varied depending on where the diameter was measured, with optimum gains obtained when the upper-stem diameter was measured of the midpoint between breast height and the tree tip. For inside-bark diameters, the calibration for inside-bark dbh actually produced inferior predictions, whereas the calibration based on both dbh and an upper-stem diameter offered only modest improvements over the unadjusted predictions.

关键词： Pinus taeda loblolly pine segmented regression model

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Estimating age conditional probability of developing disease from surveillance data

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Population Health Metrics 2004年第1期2卷 1-9页

作者： Fay, Michael P. National Cancer Institute Bethesda MD 20892-8317 6116 Executive Blvd. United States Natl. Inst. of Allergy/Infect. Dis. Bethesda MD 20892-7609 6700 B Rockledge Drive United States

Fay, Pfeiffer, Cronin, Le, and Feuer (Statistics in Medicine, 2003: 22;1837-1848) developed a formula to calculate the age-conditional probability of developing a disease for the first time (ACPDvD) for a hypothetical cohort. The novelty of the formula of Fay et al (2003) is that one need not know the rates of first incidence of disease per person-years alive and disease-free, but may input the rates of first incidence per person-years alive only. Similarly the formula uses rates of death from disease and death from other causes per person-years alive. The rates per person-years alive are much easier to estimate than per person-years alive and disease-free. Fay et al. (2003) used simple piecewise constant models for all three rate functions which have constant rates within each age group. In this paper, we detail a method for estimating rate functions which does not have jumps at the beginning of age groupings, and need not be constant within age groupings. We call this method the mid-age group joinpoint (MAJ) model for the rates. The drawback of the MAJ model is that numerical integration must be used to estimate the resulting ACPDvD. To increase computational speed, we offer a piecewise approximation to the MAJ model, which we call the piecewise mid-age group joinpoint (PMAJ) model. The PMAJ model for the rates input into the formula for ACPDvD described in Fay et al. (2003) is the current method used in the freely available DevCan software made available by the National Cancer Institute. © 2004 Fay, licensee BioMed Central Ltd.

关键词： Female Breast Cancer Estimate Rate Function Piecewise Constant model segmented regression model Invasive Cancer Incidence

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The h index research output measurement: Two approaches to enhance its accuracy

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JOURNAL OF INFORMETRICS 2010年第3期4卷 407-414页

作者： Bornmann, Lutz Mutz, Ruediger Daniel, Hans-Dieter ETH CH-8092 Zurich Switzerland Univ Zurich Evaluat Off CH-8001 Zurich Switzerland

The h index is a widely used indicator to quantify an individual's scientific research output. But it has been criticized for its insufficient accuracy-the ability to discriminate reliably between meaningful amounts of research output. As a single measure it cannot capture the complete information on the citation distribution over a scientist's publication list. An extensive data set with bibliometric data on scientists working in the field of molecular biology is taken as an example to introduce two approaches providing additional information to the h index: (1) h(2) lower, h(2) center, and h(2) upper are proposed, which allow quantification of three areas within a scientist's citation distribution: the low impact area (h(2) lower), the area captured by the h index (h(2) center), and the area of publications with the highest visibility (h(2) upper). (2) Given the existence of different areas in the citation distribution, the segmented regression model (sRM) is proposed as a method to statistically estimate the number of papers in a scientist's publication list with the highest visibility. However, such sRM values should be compared across individuals with great care. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： h index Research output Accuracy h(2) lower h(2) center h(2) upper segmented regression model sRM value

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