Prediction on Vehicle Crash Acceleration Based on Circle of Constant Acceleration Method

2013 ◽  
Vol 380-384 ◽  
pp. 51-54
Author(s):  
Zhi Xin Liu ◽  
Yue Zhang ◽  
Ming Jiang Wei
Keyword(s):  
Passive Safety ◽  
Prediction Algorithm ◽  
Constant Acceleration ◽  
Sled Test ◽  
Vehicle Crash ◽  
Vehicle Acceleration ◽  
Acceleration Method ◽  
Cae Analysis ◽  
Impact Data ◽  
Selection Of

Vehicle crash acceleration was essential for automotive passive safety analysis. Especially in CAE analysis and sled test, B-pillar lower accelerations are considered as vehicle acceleration and often used as crash pulse to reproduce the real crash conditions. But for frontal offset impact, the magnitudes and directions of the B-pillars lower accelerations were significantly different,which made the selection of vehicle acceleration very complicate. In this paper,a prediction algorithm based on Circle of Constant Acceleration method that can calculate the acceleration of any point on vehicle was presented. And then it is verified with the full scale impact data. The results show that both the trend of the acceleration curves and the curve peak of the calculated value have a strong consistency with the measured value.

scholarly journals Motor Vehicle Crash-Related Subdural Hematoma from Real-World Head Impact Data

2012 ◽  
Vol 29 (18) ◽  
pp. 2774-2781 ◽  
Author(s):  
Jillian E. Urban ◽  
Christopher T. Whitlow ◽  
Colston A. Edgerton ◽  
Alexander K. Powers ◽  
Joseph A. Maldjian ◽  
...  
Keyword(s):  
Real World ◽  
Subdural Hematoma ◽  
Motor Vehicle ◽  
Head Impact ◽  
Motor Vehicle Crash ◽  
Vehicle Crash ◽  
Impact Data

Exploring Implicit Relationships between Pavement Surface Friction and Vehicle Crash Severity Using Interpretable Extreme Gradient Boosting Method

2021 ◽  
Author(s):  
Guangyuan Zhao ◽  
Yi Jiang ◽  
Shuo Li ◽  
Susan Tighe
Keyword(s):  
Traffic Safety ◽  
Model Performance ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Crash Severity ◽  
Pavement Surface ◽  
Vehicle Crash ◽  
Extreme Gradient Boosting ◽  
Boosting Method ◽  
Pavement Friction

Pavement friction has been identified as crucial in traffic safety. Since the Highway Safety Manual prediction algorithm is often based on crash frequency, the crash severity distribution might be assumed unchanged before and after the countermeasure. However, pavement surface treatments can improve the friction to different levels, by which crash severity outcomes may vary greatly. To explore the implicit effects of pavement friction on vehicle crash severity, this paper first validates the extreme gradient boosting model performance and then the Shapley additive explanations interaction values are employed to interpret individual features and the nonlinear interactions among predictors. Under various scenarios, the XGBoost output probability is utilized to convert into dynamic crash severity distributions. Results also indicate that friction becomes more significant when the friction number is less than 38, and immediate corrective actions are needed when the friction number is below 20.

A Personalized Approach to Course Recommendation in Higher Education

2021 ◽  
Vol 17 (2) ◽  
pp. 100-114
Author(s):  
Gina George ◽  
Anisha M. Lal
Keyword(s):  
Higher Education ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Pattern Mining ◽  
Education Level ◽  
Prediction Algorithm ◽  
Personal Choice ◽  
Sequence Prediction ◽  
Personalized Approach ◽  
Selection Of

The selection of elective courses, which best fits the student's personal choice, becomes a challenge, considering the variety of courses available at the higher education level. The traditional recommendation approach often uses collaborative filtering along with sequential pattern mining. Existing recommender systems also use ontology. However, these approaches have several limitations, including lack of availability of ratings at higher education level and lack of personalization based on student attributes. The proposed system intends to overcome these limitations by firstly extracting student personality and profile attributes and thereby generating a set of similar users by utilizing the versatile ontology. Secondly, it predicts courses based on a well-performing sequence prediction algorithm, the compact prediction tree (CPT). The results show that the proposed approach increases the accuracy in terms of precision to a tune of 0.97 and F1 measure to a tune of 0.58 when compared with existing systems which makes the proposed method more suitable for recommending courses.

scholarly journals Predicting Seagoing Ship Energy Efficiency from the Operational Data

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2832
Author(s):  
Aleksandar Vorkapić ◽  
Radoslav Radonja ◽  
Sanda Martinčić-Ipšić
Keyword(s):  
Energy Efficiency ◽  
Measurement Data ◽  
Absolute Error ◽  
Management Plan ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Automation System ◽  
Liquefied Petroleum Gas ◽  
Modeling Framework ◽  
Selection Of

This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measurement data, noon logbook reports, and related meteorological and oceanographic data. The model is tested with generalized linear model (GLM) regression, multilayer preceptor (MLP), support vector machine (SVM), and random forest (RF). Upon verification of modeling framework and analyzing the results to improve the prediction accuracy, the best numeric prediction algorithm is selected based on standard evaluation metrics for regression, i.e., primarily root mean square error (RMSE) and relative absolute error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant measurement data, RF exhibits the lowest RMSE of 17.2632 and RAE 2.304%. Furthermore, this paper elaborates the selection of measurement data, the analysis of input parameters, and their significance in building the prediction model and selection of suitable output variables by the ship’s energy efficiency management plan (SEEMP). In addition, discretization was introduced to allow the end user to interpret the prediction results, placing them in the context of the actual ship operations. The results presented in this research can assist in setting up a decision support system whenever energy consumption savings in a marine transport are at stake.

scholarly journals Classification of Crash Pattern Based on Vehicle Acceleration and Prediction Algorithm for Occupant Injury

2013 ◽  
Vol 4 (1) ◽  
pp. 9-16
Author(s):  
Maika Katagiri ◽  
Hiroyuki Tsubouchi ◽  
Sadayuki Ujihashi ◽  
Takashi Fukaya ◽  
Masahiro Awano ◽  
...  
Keyword(s):  
Prediction Algorithm ◽  
Vehicle Acceleration ◽  
Occupant Injury
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