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Advanced Forecasting and Inventory Modeling
Published in Adedeji B. Badiru, Project Management, 2019
The primary function of regression analysis is to develop a model that expresses the relationship between a dependent variable and one or more independent variables. It is sometimes called line fitting or curve fitting. Regression analysis is an important statistical tool that can be applied to many prediction problems and forecasting problems in the project environment. The utility of a regression model is often tested by analysis of variance (ANOVA), which is a technique for breaking down the variance in a statistical sample into components that can be attributed to each factor affecting that sample. One major purpose of ANOVA is testing of the model. Model testing is important because of the serious consequences of erroneously concluding that a regression model is good when, in fact, it has little or no significance to the data. Model inadequacy often implies an error in the assumed relationships between the variables, poor data, or both. A validated regression model can be used for the following purposes: Prediction/forecastingDescriptionControl
Linear Regression and Interactive Models
Published in Nicholas P. Cheremisinoff, Practical Statistics for Engineers and Scientists, 2020
The U.S. production of paper in millions of pounds during the years of 1976-1983 is given below. Prepare a time plot of the data.Prepare an equation of a least square line fitting the data.Estimate the production during the year 1984.
Examples and applications
Published in John P. D’Angelo, Linear and Complex Analysis for Applications, 2017
Exercise 1.1. Find the least squares line fitting the data (0,−3),(1,3.1),(2,8.9),(3,16),(4,20.8).
Lane Detection Based on Instance Segmentation of BiSeNet V2 Backbone Network
Published in Applied Artificial Intelligence, 2022
For the case where the curvature of the lane line is slight, the lane line information obtained by the straight-line fitting model can meet the needs of lane line detection, but the straight-line fitting model cannot be applied to the lane line area with significant curvature. In this study, the straight-line fitting model is only used for the lane line fitting in the A area, and the curve model is designed for the B area with more complex linear features. Standard curve fitting models include Bayesian fitting (Denison et al. 1998), B-spline curve fitting (Zheng et al. 2012), and least-squares curve fitting (Kaibo, Ning, and Peishou 2015). When performing curve fitting at the junction of the straight line and the curve of the lane line, the commonly used model does not have the adaptive ability. On this basis, this study uses a third-order linear equation to fit the model, and the specific formula is as follows.
Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter
Published in Applied Artificial Intelligence, 2021
Hui Ma, Wenhao Lv, Yu Li, Yilun Liu
In a lane detection system, the detection of the lane line is generally divided into three steps: 1) The establishment of a region of interest (ROI). Previous reports (Tan, Yin, and Ouyang et al. 2015; Vanquang, Heungsuk, and Seochang et al. 2018) adopted a fixed ROI detection method, and Sonet al. (Son et al. 2015) selected the vanishing point as the ROI, which effectively reduces the amount of calculation. 2) Lane line edge extraction, including color information, edge, and gradient; 3) Lane line fitting. To quickly detect the lane line, Bajiwaet al.(Bajwa and Kaur, 2013) used the Hough transformto fit the line on a compressed JPEG image. According to (Tan, Yin, and Ouyang et al. 2015) and (Wang, Teoh, and Shen 2004), the least squaresand b-shape methods were used to fit the curve line. The selection of a polynomial fit lane line is consistent with the actual situation.For sections with straighter roads, lane lines in the ROI can be approximately treated as straight lines.
Passive radial mechanism of a bogie with the auxiliary steering device for the straddle monorail vehicle
Published in Vehicle System Dynamics, 2021
Yuanjin Ji, Lihui Ren, Youpei Huang
When the least square method was used to estimate the parameters, the weighted sum of squared deviation of the observed value f was required to be the minimum. For line fitting, the following relationship was obtained: where N is the number of curve radius; the optimisation only needed the pre-pressure of the oil-gas spring, assuming (the pre-pressure of the hydropneumatic spring did not change dramatically with the change in the curve radius,, and hence this assumption was made).