scholarly journals A Study of the Behavior and Responsibility of Slovak Drivers, Especially in Case of Fatigue

2021 ◽  
Vol 11 (17) ◽  
pp. 8249
Author(s):  
Adrian Hajducik ◽  
Stefan Medvecky ◽  
Slavomir Hrcek ◽  
Jaromir Klarak
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Driving Performance ◽  
Older Drivers ◽  
Optimum Temperature ◽  
Driver Fatigue ◽  
Online Questionnaire ◽  
Professional Drivers ◽  
Young Drivers ◽  
Lower Temperature

Driver fatigue can be manifested by various highly dangerous direct and indirect symptoms, for example, inattention or lack of concentration. The aim of the study was to compare the behavior of young drivers, older drivers and professional drivers, particularly in situations where they feel fatigued. In the online questionnaire, drivers answered various questions which analysed their responsibility of driving a car during fatigue, the optimum temperature in the car, or experience with microsleep. The sample of drivers consisted of 507 women and 951 men in Slovakia. Young drivers are more responsible when driving during fatigue, while professional drivers take risks, break the law, and drive tired more often. A total of 25% of all drivers experience fatigue more than once a week. Adverse results were found in connection with driving and fatigue, where more than 42% of respondents stated that their duties require them to drive even when they are tired. A total of 27% of drivers have had microsleep while driving. The survey showed that drivers are aware that thermoneutral temperature in a car interior can improve driving performance and a lower temperature can positively affect a person’s attention. The regulation of the temperature in the car was helpful for 75% of all drivers when they felt tired, and more than 97% of the drivers lowered the temperature in the interior of the vehicle in order to achieve a better concentration. In addition to standard statistical methods, a neural network was used for the evaluation of the questionnaire, which sought for individual connections and subsequent explanations for the hypotheses. The applied neural network was able to determine parameters such as the age of the driver and the annual raid as the riskiest and closely associated with the occurrence of microsleep between drivers.

scholarly journals Acceptance of Transportation Technologies by Aging Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 555-555
Author(s):  
Neil Charness ◽  
Dustin Souders ◽  
Ryan Best ◽  
Nelson Roque ◽  
JongSung Yoon ◽  
...  
Keyword(s):  
Older Adults ◽  
Older Adult ◽  
Detection System ◽  
Blind Spot ◽  
Driving Performance ◽  
Older Drivers ◽  
Crash Risk ◽  
Warning Systems ◽  
Risk Of Death ◽  
Spot Detection

Abstract Older adults are at greater risk of death and serious injury in transportation crashes which have been increasing in older adult cohorts relative to younger cohorts. Can technology provide a safer road environment? Even if technology can mitigate crash risk, is it acceptable to older road users? We outline the results from several studies that tested 1) whether advanced driver assistance systems (ADAS) can improve older adult driving performance, 2) older adults’ acceptance of ADAS and Autonomous Vehicle (AV) systems, and 3) perceptions of value for ADAS systems, particularly for blind-spot detection systems. We found that collision avoidance warning systems improved older adult simulator driving performance, but not lane departure warning systems. In a young to middle-aged sample the factor “concern with AV” showed age effects with older drivers less favorable. Older drivers, however, valued an active blind spot detection system more than younger drivers.

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

Study of the low cycle fatigue of 12Cr18Ni10Ti steel based on fractal analysis and artificial intelligence approaches

2021 ◽  
Vol 87 (9) ◽  
pp. 59-67
Author(s):  
A. A. Khlybov ◽  
Yu. G. Kabaldin ◽  
M. S. Anosov ◽  
D. A. Ryabov ◽  
D. A. Shatagin
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Fractal Dimension ◽  
Low Temperatures ◽  
Main Crack ◽  
Fatigue Loading ◽  
Wide Range ◽  
Lower Temperature ◽  
Number Of Cycles ◽  
Dimension Index

The evolution of the structure and assessment of the age limit of steel 12Cr18Ni10Ti upon fatigue loading is considered using neural network modeling and approaches of fractal analysis of the microstructure. An algorithm for processing images of the microstructures has been developed to improve their quality. An indicator of the fractal dimension of the image is used as a quantitative indicator for assessing the evolution of the microstructure of the surface metal layer. A quantitative assessment of the structures at different stress amplitudes is carried out in a wide range of low temperatures using the fractal dimension index. Correlation of the fractal dimension index with the run of the sample material is shown. The appearance of the main crack was observed in the range of 0.7 - 0.8 from the number of cycles to failure, after which the crack growth rate increased. At a lower temperature, the main crack is formed later, but further loading results in a higher crack growth rate. Formation of the secondary phases in austenitic steel at a lower temperature occurred at earlier stages than that at a temperature of t = +20°C, which led to hardening of the material. An artificial neural network (ANN) has been developed and trained for assessing structural changes in metal proceeding from the fractal dimensionality of the microstructure images at different stages of fatigue loading. The developed neural network made it possible to estimate with a sufficiently high accuracy the number of cycles before damage of the sample and the residual life of the material. Thus, the developed ANN can be used to assess the current state of the material in a wide range of low temperatures.

scholarly journals Impact of Age-Related Vision Changes on Driving

2020 ◽  
Vol 17 (20) ◽  
pp. 7416
Author(s):  
Sonia Ortiz-Peregrina ◽  
Carolina Ortiz ◽  
Miriam Casares-López ◽  
José J. Castro-Torres ◽  
Luis Jiménez del Barco ◽  
...  
Keyword(s):  
Linear Regression ◽  
Visual Function ◽  
Driving Simulator ◽  
Multiple Linear Regression Model ◽  
Driving Performance ◽  
Older Drivers ◽  
Linear Regression Models ◽  
Simulated Driving ◽  
Age Related ◽  
Intraocular Straylight

Aging leads to impaired visual function, which can affect driving—a very visually demanding task—and has a direct impact on an individual’s quality of life if their license is withdrawn. This study examined the associations between age-related vision changes and simulated driving performance. To this end, we attempted to determine the most significant visual parameters in terms of evaluating elderly drivers’ eyesight. Twenty-one younger drivers (aged 25–40) were compared to 21 older drivers (aged 56–71). Study participants were assessed for visual acuity, contrast sensitivity, halos, and intraocular straylight, which causes veiling luminance on the retina and degrades vision. Driving performance was evaluated using a driving simulator. The relationships between simulated driving performance and the visual parameters tested were examined with correlation analyses and linear regression models. Older drivers presented impairment in most visual parameters (p < 0.05), with straylight being the most significantly affected (we also measured the associated effect size). Older drivers performed significantly worse (p < 0.05) in the simulator test, with a markedly lower performance in lane stability. The results of the multiple linear regression model evidenced that intraocular straylight is the best visual parameter for predicting simulated driving performance (R2 = 0.513). Older drivers have shown significantly poorer results in several aspects of visual function, as well as difficulties in driving simulator performance. Our results suggest that the non-standardized straylight evaluation could be significant in driver assessments, especially at the onset of age-related vision changes.

Experiences of living with a sexually transmitted disease: A cross-sectional descriptive survey

2018 ◽  
Vol 39 (2) ◽  
pp. 107-116
Author(s):  
Toni Haapa ◽  
Tarja Suominen ◽  
Anna-Maija Koivisto ◽  
Jari Kylmä
Keyword(s):  
Everyday Life ◽  
Statistical Methods ◽  
Sexually Transmitted Disease ◽  
Transmitted Disease ◽  
Previous Literature ◽  
Online Questionnaire ◽  
Cross Sectional ◽  
Sexually Transmitted ◽  
Responsible Attitude ◽  
Descriptive Survey

Some dimensions such as stigmatization have been identified in the previous literature regarding experiences of living with a sexually transmitted disease (STD). However, relatively little is known about the generic experiences of those infected. The aim of this study was to describe the experiences of living with an STD, as evaluated by those infected. Data ( n = 213) were collected via an online questionnaire and analysed using statistical methods. The experiences of living with an STD varied overall. The immutability of everyday life, a thirst for knowledge, a responsible attitude towards treatment of an STD and the dilemma of disclosing the STD were considered to best describe the experiences of living with an STD. The most recently diagnosed STD and its phase were most often statistically significantly associated with the experiences. We conclude that the diversity of these experiences should be recognized in healthcare, and used in the prevention of STDs.

Statistical Methods and Artificial Neural Networks Techniques in Electromyography

2012 ◽  
Vol 1 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Ahmad Taher Azar ◽  
Valentina E. Balas
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Muscle Activity ◽  
Masseter Muscle ◽  
Discrete Wavelet ◽  
Neural Network Classifier ◽  
Denture Base ◽  
Feature Vectors ◽  
Significant Difference ◽  
Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

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