scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Design and research on the fatigue detection system of ship bridge duty based on image processing

2021 ◽  
Vol 2131 (3) ◽  
pp. 032119
Author(s):  
Yonggang Zong ◽  
Xiandong Zhao ◽  
Zhongfeng Ba
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Early Warning ◽  
Performance Indicators ◽  
Large Scale ◽  
Detection System ◽  
Warning System ◽  
Technical Standards ◽  
Eyelid Closure ◽  
Fatigue Driving

Abstract With the development of the marine economy, the number of ships is increasing day by day, and is developing towards large-scale, diversified and professional development, and marine accidents caused by driver fatigue have attracted more and more attention. In order to reduce marine traffic accidents caused by fatigue driving of ship drivers and ensure the safety of life and property at sea, it is very necessary and important to study effective methods to detect the fatigue state of ship drivers in real time. This article mainly studies the early warning of ship fatigue driving. In view of the difficulties of the ship fatigue driving detection technology, reasonable performance indicators of the ship anti-fatigue driving image processing and early warning system are proposed; according to the system performance indicators, the HOG+SVM method is determined to automatically track the human face, and the human eye detection and tracking method is designed. Improved the method of eyelid closure to determine fatigue. In order to determine the eye opening and closing state or blinking frequency. The PERCLOS method is used to determine whether the driver is tired, and a warning is given when the ship’s watch driver is tired. The system has the characteristics of non-contact, real-time, etc. and complies with the relevant technical standards of the International Maritime Organization (IMO) on the ship bridge fatigue warning system (BNWAS).

scholarly journals A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6619
Author(s):  
Yongbo Wu ◽  
Ruiqing Niu ◽  
Yi Wang ◽  
Tao Chen
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Prediction Method ◽  
Surface Displacement ◽  
Warning System ◽  
Early Warning Systems ◽  
Support Vector ◽  
Time Prediction ◽  
Svm Model

Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Baige landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Baige landslide with high accuracy.

scholarly journals Research on intelligent early warning system of rail transit line trip based on pscada

2021 ◽  
Vol 2113 (1) ◽  
pp. 012079
Author(s):  
Yuqing Zhang ◽  
Xiaohong Zhang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Weather Data ◽  
Support Vector ◽  
Rail Transit ◽  
Good Prediction ◽  
Data Set ◽  
The Real

Abstract In this paper, an intelligent early warning scheme for rail transit line trip based on PSCADA system is proposed. The scheme takes into account the defects of low prediction accuracy and real-time prediction caused by the lack of power data in the traditional line trip prediction method. At the same time, a large number of power data generated by PSCADA system in the long-term application process are ignored in the field of rail transit[1]. Based on this situation, the prediction data set is constructed by combining the historical power data collected by PSCADA system in rail transit and the lightning weather data in traditional prediction methods. On this basis, the lightgbm machine learning intelligent algorithm is used to compare the similar support vector machine (SVM) and logistic regression algorithm to obtain a model with good prediction effect. In practical application, the real-time data set is constructed by using the real-time power data and real-time weather data collected by PSCADA system to predict, and an intelligent early warning system with the dual advantages of real-time and high accuracy is obtained.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

scholarly journals Development of an early warning system for loose automobile tires

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042094088
Author(s):  
Huibo Wu ◽  
Fei Song ◽  
Kainan Wu ◽  
Cheng Chen ◽  
Xiaohua Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Traffic Accidents ◽  
Warning System ◽  
Working Environment ◽  
Vehicle Body ◽  
Economic Losses ◽  
Control Module ◽  
Alarm Signal ◽  
Monitoring Module

The looseness of tires or even falling off from cars will lead to serious traffic accidents. Once it occurs, it will bring casualties and huge economic losses to society, seriously affecting the traffic safety. To mitigate such possible safety concerns, an early loosening warning system is developed in this article. The system consists of the tire monitoring module and the working control module. The tire monitoring module is installed on the tire and is designed with no power supply. The control module is installed in the vehicle body. Signal transmission between the two modules is achieved through wireless radio frequency. In the driving, once the tire is loosened, the monitoring device will send out the alarm signal automatically and wirelessly. After the driver gets the alarm signal, he can immediately perform the emergency processing, parking, and inspection, which can avoid traffic accidents caused by it. This article introduces the detailed structure, working principle, and operation process of the system. This early warning system has simple structure, high reliability, and is easy to use. It can be used in the common working environment of automobiles. Meanwhile, it is also the foundation of intelligent connected vehicle.

A Reliability-Based Early Warning System for Real-Time Monitoring on RC Buildings

2012 ◽  
Vol 446-449 ◽  
pp. 3422-3427
Author(s):  
Wang Sheng Liu ◽  
Ming Zhao
Keyword(s):  
Probability Distribution ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Limit State ◽  
Warning System ◽  
State Function ◽  
Real Time Monitoring ◽  
Rc Buildings ◽  
Load Effect

Today there is an urgent need for effective monitoring whether for old buildings or new ones. While conventional early warning system for real-time monitoring is based on safety factor, this paper proposes a new reliability-based framework to monitor the safety of RC buildings probabilistically. The framework includes modeling resistance, predicting probability distribution of load effect, calculating reliability and setting reliability index threshold. The in-situ test data enables to update the resistance model through a Bayesian process. Meanwhile, the observed monitoring data predicts the probability distribution of load effect. FORM is used to calculate the reliability because the limit state function for real-time monitoring is linear and simple. This study shows that the reliability-based early warning system is of more scientific sense in quantifying the safety and may be applied to many engineering fields.

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