Framework for a Real-Time Autonomous Cascading Failure Prediction Model

2021 ◽  
Author(s):  
Mohamed O. Mahgoub ◽  
S. Mahdi Mazhari ◽  
C.Y. Chung ◽  
Sherif Omar Faried
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Failure Prediction ◽  
Cascading Failure ◽  
Time Autonomous

A road traffic accidents prediction model for traffic service robot

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chaohui Zhang ◽  
Yijing Li ◽  
Tian Li
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Social Robots ◽  
Service Robot ◽  
Road Traffic Accidents ◽  
Information Communication ◽  
Content Type ◽  
Time Autonomous

PurposeIn recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high. Therefore, the use of social service robots to manage, supervise and warn real-time traffic information has become an inevitable trend of traffic safety management.Design/methodology/approachIn order to explore the inherent objective development law of road traffic accidents, in this paper, the factor analysis (FA) is used to explore the main influencing factors of traffic accidents, then the random forest algorithm is applied to build an FA–RF-based road traffic accident severity prediction model to predict two- and three-category accidents.FindingsBy comprehensively comparing the classification results of the two- and the three-category accident prediction, it also finds that due to the intersection between injuries and fatalities and the lack of necessarily external environmental information, the FA–RF model has a large degree of misjudgment for injuries and fatalities. Therefore, it is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.Originality/value(1) A fusion model of FA–RF is considered to predict traffic accidents, which can be applied in traffic service robot. (2) It is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

scholarly journals Failure Prediction in Commercial Banks with a Hybrid Prediction Model

2014 ◽  
Vol 1 (2) ◽  
pp. 209-220
Author(s):  
Yinhua Li ◽  
Yong Shi ◽  
Anqiang Huang ◽  
Haizhen Yang
Keyword(s):  
Prediction Model ◽  
Commercial Banks ◽  
Failure Prediction ◽  
Hybrid Prediction
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