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.

Research and Application of Real-Time Remote Monitoring and Early Warning System of Source Water Quality

2013 ◽  
Vol 779-780 ◽  
pp. 1408-1413
Author(s):  
Shu Yuan Li ◽  
Jian Hua Tao ◽  
Lei Yu
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Early Warning ◽  
Remote Monitoring ◽  
Early Warning System ◽  
Warning System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
The Real ◽  
Monitoring And Early Warning

Drinking water sources play an important role in assurance of life safety, normal production and social stability. In this paper, a real-time remote water quality monitoring and early warning system has been developed. The paper concentrates on the system architecture and key techniques of the real-time water quality monitoring and early warning. The implementation of the system by advanced water quality sensor techniques, wireless transmission, databases and water quality modeling is retraced in detail. It can be applied to the real-time remote monitoring of water quality and decision support for water pollution incidents.

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.

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.

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

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.

Sign in / Sign up

Export Citation Format

Share Document