Review on deep learning applications in frequency analysis and control of modern power system

Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation

Fast pressure distribution prediction of airfoils using deep learning

Turbulence closure for high Reynolds number airfoil flows by deep neural networks

Traditional methods for plan path prediction have low accuracy and stability In this paper, we propose a novel approach for plan path prediction based on relative motion between positions (RMBP) by mining historical flight trajectories A probability statistical model is introduced to model the stochastic factors during the whole flight process The model object is the sequence of velocity vectors in the three-dimensional Earth space First, we model the moving trend of aircraft including the speed (constant, acceleration, or deceleration), yaw (left, right, or straight), and pitch (climb, descent, or cruise) using a hidden Markov model (HMM) under the restrictions of aircraft performance parameters Then, several Gaussian mixture models (GMMs) are used to describe the conditional distribution of each moving trend Once the models are built, machine learning algorithms are applied to obtain the optimal parameters of the model from the historical training data After completing the learning process, the velocity vector sequence of the flight is predicted by the proposed model under the Bayesian framework, so that we can use kinematic equations, depending on the moving patterns, to calculate the flight position at every radar acquisition cycle To obtain higher prediction accuracy, a uniform interpolation method is used to correct the predicted position each second Finally, a plan trajectory is concatenated by the predicted discrete points Results of simulations with collected data demonstrate that this approach not only fulfils the goals of traditional methods, such as the prediction of fly-over time and altitude of waypoints along the planned route, but also can be used to plan a complete path for an aircraft with high accuracy Experiments are conducted to demonstrate the superiority of this approach to some existing methods

An algorithm for trajectory prediction of flight plan based on relative motion between positions

This paper proposes a routing strategy by minimizing an integrated transmission cost including route cost, distance cost, and traffic pressure cost, which are quantified based on the available transportation routes and travel distance of edges, and the degree of nodes, respectively. In the routing strategy, an arriving rate function is proposed based on the characteristics of heterogeneous traffic demand of nodes, and two parameters, congestion parameter and cost importance parameter are proposed to evaluate the importance of different transmission costs. By tuning the parameters, an optimal routing strategy with high capacity is obtained for different applications. The numerical results on theoretical networks (Watts–Strogatz small-world network and Barabasi–Albert network) and real networks (the bus and metro network in Beijing, China) show that the generated new routing strategy can significantly improve the system capacity and operation efficiency. Moreover, the significance of the proposed parameters on evaluating the importance of different transmission costs is precisely proved based on the experimental results, which indicates the effectiveness of the proposed routing strategy.

An optimal routing strategy for transport networks with minimal transmission cost and high network capacity

In order to improve the accuracy and robustness of the air traffic prediction, in this paper, a recurrent 3D convolutional neural network (R-3DCNN) based model is proposed to consider the spatial and temporal air traffic transitions comprehensively. A new data representation, i.e., traffic situation graphics (TSG), is firstly proposed to illustrate the traffic flow situations in a single instant. A TSG is generated by splitting the 3D earth space with fixed grid map and flight levels. Motivated by the applications of deep neural network, the 3D CNN and long short-term memory (LSTM) blocks are introduced to extract high-level features (spatial and temporal) from an TSG sequence. The proposed TSG also allows us to consider some real-time factors to enrich the input information. Thus, the model input is determined by combining the traffic situations on different flight levels with areas affected by other real-time factors, such as the adverse weathers, important activities and general aviation flights. The model output is the traffic flow on different flight levels for studied airspaces at next prediction instant. The busiest routes in China are used to conduct evaluation experiments. To determine the influence of temporal dependencies, the length of input sequence is set to 30, 60 and 90 minutes before the prediction instant to select optimal architecture of the proposed model. By evaluating the prediction results with three statistical factors, we can draw the conclusion that the proposed model can obtain accurate and stable prediction results of air traffic flow with distribution on different flight levels.

Research on the Air Traffic Flow Prediction Using a Deep Learning Approach

To improve the accuracy and stability of flight trajectory prediction, a novel four-dimensional (4-D) trajectory management approach is proposed in this paper, which consists of the estimation and updating procedure. Historical flight trajectories are proved to be safe and feasible based on the real-time traffic situation, and serve as the data foundation of 4-D trajectory management in this paper. To achieve the goal of 4-D trajectory management, we firstly apply probabilistic statistical models and machine learning approach to predict the fly-over time and altitude of waypoints along the planning route before the flight takes off. Hidden Markov Models (HMMs) are regarded as the probabilistic model to represent the position and altitude transition patterns of the aircraft during the flight operation. The EM algorithm is applied to optimize model parameters of HMMs to fit the training data (historical trajectory set). Then the models with optimized parameters are used to predict the pre-takeoff 4-D trajectory by inferring an optimal hidden state sequence. Finally, after the flight takes off, we propose an algorithm to correct the pre-takeoff prediction results by considering the trajectory similarity between collected path of current execution and its historical trajectories. Simulations with real data show that the prediction results (fly-over time and altitude) of our proposed algorithm are more accurate than that of other existing methods, and would tend to be more credible after correcting with the proposed algorithm. Moreover, the prediction errors of our approach are stable during the whole flight, which is the bottleneck of existing d\eterministic models.

/pdf/approach-for-4-d-trajectory-management-based-on-hmm-and-3v8m8efmur.pdf

Hong Liu

Papers

Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation

An algorithm for trajectory prediction of flight plan based on relative motion between positions

An optimal routing strategy for transport networks with minimal transmission cost and high network capacity

Research on the Air Traffic Flow Prediction Using a Deep Learning Approach

Approach for 4-d trajectory management based on hmm and trajectory similarity