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Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water

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JOURNAL OF HYDROLOGY 2020年 591卷 125574-125574页

作者： Hong, Huachang Zhang, Zhiying Guo, Aidi Shen, Liguo Sun, Hongjie Liang, Yan Wu, Fuyong Lin, Hongjun Zhejiang Normal Univ Coll Geog & Environm Sci Jinhua 321004 Zhejiang Peoples R China Environm Monitoring Ctr Hangzhou Yuhang Dist Hangzhou 311100 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Northwest A&F Univ Coll Nat Resources & Environm Yangling 712100 Shaanxi Peoples R China

Many models have been developed in previous studies for predicting the formation of disinfection by-products (DBPs) in drinking water. However, most of them were linear or log-linear regression models, and generated based on simulated disinfection of source water or treated water in a laboratory other than real tap water, which shows low application potential in practice. In this study, a radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis (GRA) was proposed to predict trihalomethanes (THMs) levels in real distribution systems. A total of 64 sets of data including THM5 levels (trichloromethane (TCM), bromodichloromethane (BDCM) and total-THMs (T-THMs)) and 8 water quality parameters (temperature, pH, UV absorbance at 254 (UVA(254)), dissolved organic carbon, bromide, residual free chlorine, nitrite and ammonia) were used to train and verify the proposed model. As compared to linear and log-linear regression models (r(p) = 0.254-0.659;N-25 = 46-78%), RBF ANN5 for THM5 (TCM, BDCM and T-THMs) prediction consistently show higher regression coefficients (r(p) = 0.760-0.925) and prediction accuracy (N-25 = 92-98%), which indicates the high capability of RBF ANN to learn the complex non-linear relationships involved THM5 formation. Further analysis shows that RBF ANN5 using fewer water quality parameters based on GRA still make excellent performance in THM5 prediction (r(p) = 0.760-0.946;N-25 = 92-98%). This result demonstrates that GRA can be an effective technique to facilitate the generation of sound RBF ANN models with fewer factors.

关键词： Trihalomethanes Tap water linear regression model radial basis function artificial neural network (RBF ANN) grey relational analysis (GRA)

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A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2020年 204卷 187-205页

作者： Ohishi, Mineaki Yanagihara, Hirokazu Fujikoshi, Yasunori Hiroshima Univ Grad Sch Sci Dept Math 1-3-1 Kagamiyama Higashihiroshima Hiroshima 7398526 Japan

In this paper, we deal with the optimization method of ridge parameters in a generalized ridge regression by minimizing a model selection criterion (MSC). The optimization methods based on minimizations of generalized C p criterion and GCV criterion are fast because the minimizers of the two criteria can be derived as closed forms. Although the methods are fast, they do not work well when the number of explanatory variables is larger than the sample size. Methods based on minimizations of other MSCs may work well even if the number of explanatory variables is larger than the sample size and are not fast because the minimizers of the MSCs have not been derived as closed forms. Even though a minimization problem of MSC cannot be solved analytically, we propose a fast optimization algorithm to minimize MSC by specifying the small number of minimizer candidates. By conducting numerical examinations, we compare performances of the optimization methods based on the minimizations of MSCs. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Generalized ridge regression linear regression model model selection criterion Ridge parameter Optimization of ridge parameters

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Morphologic and radiologic parameters correlating to shoulder function at diagnosis for patients with rotator cuff tear

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JOURNAL OF SHOULDER AND ELBOW SURGERY 2020年第11期29卷 2272-2281页

作者： Cauchon, Anne-Marie Tetreault, Patrice Bascans, Clarisse Skalli, Wafa Hagemeister, Nicola Univ Montreal Montreal PQ Canada Ecole Technol Super Montreal PQ Canada Arts & Metiers ParisTech Paris France

Background: The magnetic resonance imaging (MRI) parameters used to diagnose rotator cuff tears are weakly correlated to shoulder function. Our hypothesis was that adding 3-dimensional morphologic parameters resulting from biplanar radiographs (3DXR parameters) to the MRI parameters would improve this correlation. Methods: We assessed 52 patients with rotator cuff tears with an EOS Imaging radiographic examination, MRI study, and clinical evaluation of the shoulder, as well as the Constant score. The bones of the 52 shoulders were reconstructed 3-dimensionally, and eleven 3DXR parameters were automatically extracted. First, the trueness and reliability of these parameters were evaluated. Then, bivariate correlations between each parameter and the Constant score were made. A linear regression model was subsequently built to correlate the 11 parameters and 5 MRI findings with shoulder function at diagnosis, as assessed by the Constant score. Results: The parameters showed good trueness and reliability of most 3DXR parameters. Supraspinatus tear extension, muscle atrophy, and the distance between the greater and deltoid tuberosities were the only parameters with a statistically significant correlation to a lower Constant score ( P <.05) in the bivariate study. These correlations were either weak or negligible. A regression model was successfully built with one MRI parameter and four 3DXR parameters. Correlation to function increased from 16.7% to 43.3% with this model. Conclusion: For patients with rotator cuff tears, the combination of MRI and 3DXR parameters of the shoulder in a linear regression model improves the correlation with the Constant score (shoulder function) at diagnosis. (C) 2020 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved.

关键词： Rotator cuff tear shoulder morphology 3D reconstruction of shoulder shoulder function linear regression model radiology of shoulder

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Two-sample empirical likelihood method for difference between coefficients in linear regression model

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STATISTICAL PAPERS 2012年第1期53卷 83-93页

作者： Zi, Xuemin Zou, Changliang Liu, Yukun Nankai Univ Dept Stat Sch Math Sci Tianjin 300071 Peoples R China Tianjin Univ Technol & Educ Dept Math Tianjin 300222 Peoples R China E China Normal Univ Sch Finance & Stat Shanghai 200062 Peoples R China

The empirical likelihood method is proposed to construct the confidence regions for the difference in value between coefficients of two-sample linear regression model. Unlike existing empirical likelihood procedures for one-sample linear regression models, as the empirical likelihood ratio function is not concave, the usual maximum empirical likelihood estimation cannot be obtained directly. To overcome this problem, we propose to incorporate a natural and well-explained restriction into likelihood function and obtain a restricted empirical likelihood ratio statistic (RELR). It is shown that RELR has an asymptotic chi-squared distribution. Furthermore, to improve the coverage accuracy of the confidence regions, a Bartlett correction is applied. The effectiveness of the proposed approach is demonstrated by a simulation study.

关键词： Bartlett correction Coverage accuracy Empirical likelihood linear regression model Two-sample problem

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COVID-19 pandemic, socioeconomic crisis and human stress in resource-limited settings: A case from Bangladesh

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HELIYON 2020年第5期6卷 e04063页

作者： Shammi, Mashura Bodrud-Doza, Md Islam, Abu Reza Md Towfiqul Rahman, Md Mostafizur Jahangirnagar Univ Dept Environm Sci Dhaka 1342 Bangladesh BRAC Climate Change Programme Dhaka Bangladesh Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh

Considering the population density, healthcare capacity, limited resources and existing poverty, environmental factors, social structure, cultural norms, and already more than 18,863 people infected, the community transmission of COVID-19 is happening fast. These exacerbated a complex fear among the public. The aim of this article is, therefore, to understand the public perception of socioeconomic crisis and human stress in resource-limited settings of Bangladesh during the COVID-19 outbreak. The sample comprised of 1066 Bangladeshi participants. Principal component analysis (PCA) was considered to design a standardized scale to measure the mental stress and socioeconomic crisis, one-way ANOVA and t-test were conducted to perceive different demographic risk groups;multiple linear regression was applied to estimate the statistically significant association between each component, and classical test theory (CTT) analysis was applied to examine the reliability of each item according to the components to develop a composite score. Without safeguarding the fundamental needs for the vulnerable ultra-poor group can undeniably cause the socioeconomic crisis and mental stress due to the COVID-19 lockdown. It has further created unemployment, deprivation, hunger, and social conflicts. The weak governance in the fragile healthcare system exacerbates the general public's anxiety as the COVID-19 testing facilities are centered around in the urban areas, a long serial to be tested, minimum or no treatment facilities in the dedicated hospital units for COVID-19 patients are the chief observations hampered along with the disruption of other critical healthcare services. One-way ANOVA and t-test confirmed food and nutritional deficiency among the vulnerable poorest section due to loss of livelihood. Also, different emergency service provider professions such as doctors, healthcare staff, police forces, volunteer organizations at the frontline, and bankers are at higher risk of infec

关键词： Psychology COVID-19 Perception-based questionnaire Principal component analysis (PCA) linear regression model Social panic Social conflict

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Confidence sets in a linear regression model for interval data

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2012年第6期142卷 1320-1329页

作者： Blanco-Fernandez, Angela Colubi, Ana Gonzalez-Rodriguez, Gil Univ Oviedo Oviedo 33007 Spain

The construction of confidence sets for the parameters of a flexible simple linear regression model for interval-valued random sets is addressed. For that purpose, the asymptotic distribution of the least-squares estimators is analyzed. A simulation study is conducted to investigate the performance of those confidence sets. In particular, the empirical coverages are examined for various interval linear models. The applicability of the procedure is illustrated by means of a real-life case study. (C) 2011 Elsevier B.V. All rights reserved.

关键词： linear regression model Interval data Confidence set Asymptotic distribution

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Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal regression model for Grout Defect Identification in a Precast Structure

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SENSORS 2020年第11期20卷 3264.-3264.页

作者： Zhang, Xuan Tang, Hesheng Zhou, Deyuan Chen, Shanshan Zhao, Taotao Xue, Songtao Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam-column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model;furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

关键词： local defect identification linear regression model damage indicator beam-column connection concrete frame structure

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Machine-Learning modeling of Asphalt Crack Treatment Effectiveness

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JOURNAL OF TRANSPORTATION ENGINEERING PART B-PAVEMENTS 2021年第2期147卷

作者： Huang, Zhenhua Manzo, Maurizio Cai, Liping Univ North Texas Dept Mech Engn Denton TX 76207 USA Univ North Texas Dept Mech Engn Photon Microdevices Fabricat Lab Denton TX 76207 USA

Although many research projects have been completed to investigate asphalt crack treatment/repair effectiveness, all of them reported local investigation results with small sample sizes, incomplete time durations, and restricted types of sealants and/or treatment methods. Most importantly, machine-learning techniques have not been used to compare treatment methods and predict treatment effectiveness. In this study, all reported data regarding asphalt crack treatments were collected, and the large sample size data were comprehensively analyzed by statistical measures and machine-learning techniques to find the optimal crack treatment method. The variables including service time, environment temperature, type of sealant, treatment method, and traffic conditions were used as inputs, and the effectiveness of the asphalt crack treatment was selected as output information to compare the contribution of different variables for the treatment effectiveness and to provide prediction models. Chi-square test indicated that except for the variable "Traffic condition," all other variables (Type of sealants, Treatment method, Service time, and Environmental temperature) had a significant relationship with the target variable (crack treatment effectiveness). The decision tree analysis results showed that the relative significance of the input variables to the target variable was ordered as Service time > Environmental temperature > Type of sealants > Treatment method > Traffic condition. The asphalt crack treatment effectiveness (%) can be predicted according to known variable values using the developed linear regression and artificial neural network (ANN) models. ANN model prediction has better performance than that of the linear regression model.

关键词： Machine learning Crack treatment effectiveness Chi-square test linear regression model Artificial neural network

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Pollution gas correction method based on linear regression

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第12期7卷 256-264页

作者： Jiewen Wei Siewei Wang

In the field of atmospheric monitoring, people often use high-precision equipment such as electrochemical sensors to collect data on harmful gas content in the atmosphere. However, the sensor is easily affected by temperature and humidity, resulting in deviation of monitoring data. In this paper, a method of correcting the monitoring values by establishing a linear regression model is proposed, and a fast and universal method of sensor calibration is established to make the sensor meet the data quality requirements of scientific research. By constructing the fitted model, the problem of the inability to unify calibration caused by different environmental factors and different individual performance factors of the sensor is perfectly solved. And with 1300 CO2 and temperature and humidity real values of Beijing Wanliu monitoring station in 2017, the experimental data were modified by three different sensor calibration methods, namely point gradient drop method, rectangular gradient drop method and two-way method, and the difference of loss function and running time of three calibration methods were determined, At the last of this essay, our experimental data were corrected by the simple gradient drop method from Wanliu monitoring station.

关键词： Electrochemical sensor effect of temperature and humidity atmospheric CO2 detection styling calibration of data linear regression model

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Asphalt Binder linear Amplitude Sweep Test: Contribution Related to the α-Value Estimation

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JOURNAL OF MATERIALS IN CIVIL ENGINEERING 2021年第2期33卷 p1-9页

作者： Costa Meneses, Joao Paulo Vasconcelos, Kamilla Ho, Linda Lee Kuchiishi, Andre Kazuo Bernucci, Liedi L. B. Univ Sao Paulo Dept Transportat Engn Av Prof Almeida PradoTravessa 283 Cidade Univ BR-05508070 Sao Paulo SP Brazil Univ Sao Paulo Dept Prod Engn Av Prof Luciano Gualberto 1380Cidade Univ BR-05508070 Sao Paulo SP Brazil North Carolina State Univ Dept Civil Construct & Environm Engn 2501 Stinson Dr Raleigh NC 27607 USA

This paper calls attention to important issues that have not been considered regarding linear amplitude sweep (LAS) test in previous publications: Is the relationship between the storage modulus logarithm [log G'(omega)] and frequency logarithm [log(omega)] linear in the whole range of experimental frequencies? If not, what is the influence on the fatigue life of asphalt binders calculated using the AASHTO protocol? In which range of frequencies is the relation between the storage modulus logarithm [log G'(omega)] and frequency logarithm [log(omega)] linear in the whole range of experimental frequencies? Does fatigue life follow a normal distribution? Several tests were conducted using three different types of asphalt binder [neat, crumb rubber-modified (CRM), and styrene-butadiene-styrene (SBS)-modified asphalt]. Residual analysis showed a lack of fit, indicating the inadequacy of the linear model in the whole range of experimental frequencies. This inadequacy may suggest that the protocol produces an overestimation of the binder fatigue life. In addition, a sequential search reveals that the relationship between log G'(omega) and log(omega) is linear in a limited subset of frequencies. The empirical distribution of the fatigue life is built by an extensive Monte Carlo simulation. (C) 2020 American Society of Civil Engineers.

关键词： linear amplitude test (LAS) Fatigue linear regression model Storage modulus Residual analysis Monte Carlo simulation

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