Improved process control of an industrial sludge centrifuge-dryer installation through binary logistic regression modeling of the fouling issues

2012 ◽  
Vol 22 (7) ◽  
pp. 1387-1396 ◽  
Author(s):  
Bart Peeters ◽  
Raf Dewil ◽  
Ilse Y. Smets
Keyword(s):  
Logistic Regression ◽  
Process Control ◽  
Binary Logistic Regression ◽  
Regression Modeling ◽  
Industrial Sludge ◽  
Logistic Regression Modeling

scholarly journals Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

2020 ◽  
Vol 12 (23) ◽  
pp. 9888
Author(s):  
Jiajun Shen ◽  
Guangchuan Yang
Keyword(s):  
Logistic Regression ◽  
Traffic Flow ◽  
Binary Logistic Regression ◽  
Regression Modeling ◽  
Mitigation Strategies ◽  
Heterogeneous Traffic ◽  
Crash Risk ◽  
Flow Data ◽  
Speed Variation ◽  
Logistic Regression Modeling

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

scholarly journals Binary logistic regression modeling with TensorFlow™

2019 ◽  
Vol 7 (20) ◽  
pp. 591-591 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Lei Mo ◽  
Chen Huang ◽  
Ping Xu
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Regression Modeling ◽  
Logistic Regression Modeling

scholarly journals Binary Logistic Regression Modeling of Idle CO Emissions in Order to Estimate Predictors Influences in Old Vehicle Park

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Branimir Milosavljević ◽  
Radivoje Pešić ◽  
Predrag Dašić
Keyword(s):  
Logistic Regression ◽  
Low Power ◽  
Representative Sample ◽  
Binary Logistic Regression ◽  
Spark Ignition ◽  
Regression Modeling ◽  
Spark Ignition Engine ◽  
Research Results ◽  
Logistic Regression Modeling

This paper determines, by experiments, the CO emissions at idle running with 1,785 vehicles powered by spark ignition engine, in order to verify the correctness of emissions values with a representative sample of vehicles in Serbia. The permissible emissions limits were considered for three (3) fitted binary logistic regression (BLR) models, and the key reason for such analysis is finding the predictors that can have a crucial influence on the accuracy of the estimation whether such vehicles have correct emissions or not. Having summarized the research results, we found out that vehicles produced in Serbia (hereinafter referred to as “domestic vehicles”) cause more pollution than imported cars (hereinafter referred to as “foreign vehicles”), although domestic vehicles are of lower average age and mileage. Another trend was observed: low-power vehicles and vehicles produced before 1992 are potentially more serious polluters.

Logistic Regression Modeling of Cropping Systems To Predict Fumonisin Contamination in Maize

2008 ◽  
Vol 56 (21) ◽  
pp. 10433-10438 ◽  
Author(s):  
Paola Battilani ◽  
Amedeo Pietri ◽  
Carlo Barbano ◽  
Andrea Scandolara ◽  
Terenzio Bertuzzi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cropping Systems ◽  
Regression Modeling ◽  
Logistic Regression Modeling
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