scholarly journals UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction

2021 ◽  
Vol 13 (22) ◽  
pp. 12528
Author(s):  
Honghai Zhang ◽  
Yongjie Yan ◽  
Shan Li ◽  
Yuxin Hu ◽  
Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Estimation Method ◽  
Flight Trajectory ◽  
Trajectory Prediction ◽  
Simulation Experiments ◽  
Protection Zone ◽  
Behavior Intention ◽  
Dangerous Behaviors ◽  
Intention Estimation

Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

Information Technology in an Improved Quantum Genetic Algorithm Based on Dynamic Adjustment of Quantum Rotation Angle

2013 ◽  
Vol 859 ◽  
pp. 577-581
Author(s):  
Hui Xia Li ◽  
Yun Can Xue ◽  
Jian Qiang Zhang ◽  
Qi Wen Yang
Keyword(s):  
Genetic Algorithm ◽  
Information Technology ◽  
Rotation Angle ◽  
Local Optimum ◽  
Dynamic Adjustment ◽  
Excellent Performance ◽  
Simulation Experiments ◽  
Standard Quantum ◽  
Quantum Genetic Algorithm ◽  
Chaos Search

To overcome the shortcomings of precocity and being easily trapped into local optimum of the standard quantum genetic algorithm (QGA) , Information Technology in An Improved Quantum Genetic Algorithm based on dynamic adjustment of the quantum rotation angle of quantum gate (DAAQGA) was proposed. Mutation operation using the quantum not-gate is also introduced to enhance the diversity of population. Chaos search are also introduced into the modified algorithm to improve the search accuracy. Simulation experiments have been carried and the results show that the improved algorithm has excellent performance both in the preventing premature ability and in the search accuracy.

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

Estimation of Track Irregularity Based on Genetic Algorithm and Unscented Kalman Filtering

2012 ◽  
Author(s):  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filtering ◽  
High Speed ◽  
Measurement Data ◽  
Estimation Method ◽  
Coupling Model ◽  
Dynamic Responses ◽  
The Real ◽  
True Value ◽  
Track Irregularity

Track irregularity is the main excitation source of wheel-track interaction. Due to the difference of speed, axle load and suspension parameters between track inspection train and the operating trains, the data acquired from the inspection car cannot completely reflect the real status of track irregularity when the operating trains go through the rail. In this paper, an estimation method of track irregularity is proposed using genetic algorithm and Unscented Kalman Filtering. Firstly, a vehicle-track vertical coupling model is established, in which the high-speed vehicle is assumed as a rigid body with two layers of spring and damping system and the track is viewed as an elastic system with three layers. Then, the static track irregularity is estimated by genetic algorithm using the vibration data of vehicle and dynamic track irregularity which are acquired from the inspection car. And the dynamic responses of vehicle and track can be solved if the static track irregularity is known. So combining with vehicle track coupling model of different operating train, the potential dynamic track irregularity is solved by simulation, which the operating train could goes through. To get a better estimation result, Unscented Kalman Filtering (UKF) algorithm is employed to optimize the dynamic responses of rail using measurement data of vehicle vibration. The simulation results show that the estimated static track irregularity and the vibration responses of vehicle track system can go well with the true value. It can be realized to estimate the real rail status when different trains go through the rail by this method.

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