scholarly journals Peri-urban peak hour travel behaviour study using a weigh-in-motion data processing application software

2015 ◽  
Author(s):  
N. Ciont ◽  
R. D. Cadar ◽  
M. Iliescu
Keyword(s):  
Data Processing ◽  
Travel Behaviour ◽  
Application Software ◽  
Processing Application ◽  
Motion Data ◽  
Weigh In Motion ◽  
Behaviour Study

scholarly journals Basketball Action Data Processing Method Based on Mode Symmetric Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 560 ◽  
Author(s):  
Fei Zhang ◽  
Yi Jiang
Keyword(s):  
Data Processing ◽  
High Efficiency ◽  
Least Squares Method ◽  
Detection Algorithm ◽  
Processing Method ◽  
Target Image ◽  
Modal Data ◽  
Motion Data ◽  
Mode Decomposition ◽  
Data Processing Method

In the course of basketball training, a large number of basketball action data are generated according to the athletes’ body movements. Due to the low precision of the basketball action data processed by the traditional method in basketball technical training, basketball action processing is not in place. The basketball motion data processing method, based on the mode symmetric algorithm was studied. The basketball motion detection algorithm based on symmetric difference and background reduction was used to remove the background influence of basketball movement and obtain the binary basketball action target image containing the data. On this basis, the pole symmetric mode decomposition (ESMD) method was used to modally decompose the binary basketball action target image containing the data, and the least squares method was used to optimize the elliptic (AGM) curve to realize the screening of basketball action modal data. Through the cleaning and integration of basketball action modal data, integration and data reduction basketball action modal data, the data was processed efficiently. The experimental results showed that the proposed method was a high precision and high efficiency basketball action data processing method.

scholarly journals Changing Mobility Lifestyle

2021 ◽  
Vol 10 (2) ◽  
pp. 66-79
Author(s):  
Vít Pászto ◽  
Jaroslav Burian ◽  
Karel Macků
Keyword(s):  
Data Processing ◽  
Data Protection ◽  
Personal Data ◽  
Detailed Data ◽  
Real Motion ◽  
The Real ◽  
Location Service ◽  
Motion Data ◽  
Personal Data Protection ◽  
Analytical Processing

The article is focused on a detailed micro-study describing changes in the behaviour of the authors in three months before and during the COVID-19 pandemic. The study is based on data from Google Location Service. Despite the fact it evaluates only three people and the study cannot be sufficiently representative, it is a unique example of possible data processing at such a level of accuracy. The most significant changes in the behaviour of authors before and during the COVID-19 quarantine are described and interpreted in detail. Another purpose of the article is to point out the possibilities of analytical processing of Google Location while being aware of personal data protection issues. The authors recognize that by visualizing the real motion data, one partially discloses their privacy, but one considers it very valuable to show how detailed data Google collects about the population and how such data can be used effectively.

scholarly journals Use of regression trees to predict overweight trucks from historical weigh-in-motion data

2020 ◽  
Vol 7 (6) ◽  
pp. 843-859
Author(s):  
Mariana Bosso ◽  
Kamilla L. Vasconcelos ◽  
Linda Lee Ho ◽  
Liedi L.B. Bernucci
Keyword(s):  
Regression Trees ◽  
Motion Data ◽  
Weigh In Motion

Multiple optimized support vector regression for multi-sensor data fusion of weigh-in-motion system

2020 ◽  
Vol 234 (12) ◽  
pp. 2807-2821
Author(s):  
Xiaofeng Liu ◽  
Zhimin Feng ◽  
Yuehua Chen ◽  
Hongwei Li
Keyword(s):  
Data Fusion ◽  
Support Vector Regression ◽  
Test Sample ◽  
Sensor Data ◽  
Support Vector ◽  
Motion Data ◽  
The Third ◽  
Operation Speed ◽  
Weigh In Motion ◽  
Improved Algorithm

Weigh-in-motion is an efficient way to manage overload vehicles, and usually utilizes multi-sensor to measure vehicle weight at present. To increase generalization and accuracy of support vector regression (SVR) applied in multi-sensor weigh-in-motion data fusion, three improved algorithms are presented in this paper. The first improved algorithm divides train samples into two sets to construct SVR1 and SVR2, respectively, and then test samples are distributed to SVR1 or SVR2 based on the nearest distance principle. The second improved algorithm calculates the theoretical biases of two training samples closeted to one test sample, and then obtains the bias of the test sample by linear interpolation method. The third improved algorithm utilizes the second improved algorithm to realize adaptive adjustment of biases for SVR1 and SVR2. Five vehicles were selected to conduct multi-sensor weigh-in-motion experiments on the built test platform. According to the obtained experiment data, fusion tests of SVR and three improved algorithms are performed, respectively. The results show that three improved algorithms gradually increase accuracy of SVR with fast operation speed, and the third improved algorithm exhibits the best application prospect in multi-sensor weigh-in-motion data fusion.

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