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Aircraft operations in the terminal area rely heavily on voice communications between pilots and air traffic controllers. This paper proposes a novel aircraft trajectory simulation framework by guiding the trajectory simulation following the voice command from controllers. Bayesian model selection is used for checking pilot compliances to controller commands with observed trajectories. This framework is named as Voice Communication-Augmented Simulation. The goal of the proposed study is to enable accurate trajectory predictions. The framework can act as a computer assistant for controllers to monitor pilot compliances and ensure safe operations. The proposed method is tested and validated with actual trajectory data from Sherlock Data Warehouse. The tests showed that the proposed framework can accurately simulate and monitor the flight level change of aircraft and update the approach procedure.

Collision-free path planning is a major challenge in managing unmanned aerial vehicles (UAVs) fleets, especially in uncertain environments. In this paper, we consider the design of UAV routing policies using multi-agent reinforcement learning, and propose a Multi-resolution, Multi-agent, Mean-field reinforcement learning algorithm, named 3M-RL, for flight planning, where multiple vehicles need to avoid collisions with each other while moving towards their destinations. In the system we consider, each UAV makes decisions based on local observations, and does not communicate with other UAVs. The algorithm trains a routing policy using an Actor-Critic neural network with multi-resolution observations, including detailed local information and aggregated global information based on mean-field. The algorithm tackles the curse-of-dimensionality problem in multi-agent reinforcement learning and provides a scalable solution. We test our algorithm in different complex scenarios in both 2D and 3D space and our simulation results show that 3M-RL result in good routing policies.

A new method for efficient trajectory planning to resolve potential conflicts among multiple aircraft is proposed. A brief review of aircraft trajectory planning and conflict resolution methods is ...

A novel efficient methodology for probabilistic material reliability analysis considering fine-scale microstructure stochasticity is proposed in this paper. Integrated computational material engineering requires efficient multiscale computational capabilities to enable computational design and validation. Two critical challenges are identified: handling uncertainties from microstructures and material properties; and handling the “curse of dimensionality” for probabilistic solvers. The proposed study addresses these two critical challenges. First, an analytical and hierarchical uncertainty quantification method is proposed for the explicit stochastic microstructure representation at the voxel-level. The hierarchy of uncertainties from both phase maps and uncertainties within each phase is modeled using an explicit Gaussian mixture random field. Analytical approximation for the arbitrary non-Gaussian random field is derived, which can facilitate the computation of gradient information in optimization. Following this, an efficient probabilistic solver using adjoint first-order reliability method combining the importance sampling is derived by formulating the material reliability analysis as a constrained optimization problem. The adjoint method is used to efficiently evaluate the responses and exact gradients with the help of the analytical Gaussian mixture random field. Several numerical examples for material reliability calculation with high-dimensional (voxel-level) random fields are subsequently employed to demonstrate and validate the proposed methodology. The results of the proposed method are quantitatively compared to those obtained via the classical first-order reliability method, direct Monte Carlo simulation, subset simulation, and the sequential importance sampling method. The comparisons indicate that the proposed method possesses high efficiency for high-dimensional material reliability problems.

This article develops a tight integrity risk bound for residual-based (RB) advanced receiver autonomous integrity monitoring (ARAIM). ARAIM measurement models include nominal biases accounting for unknown but bounded errors, and faults of unbounded magnitude. In RB methods, upper bounding the integrity risk requires that one finds the worst-case directions of both the multisatellite fault vector and of the all-in-view nominal bias vector. Previous methods only account for the worst-case fault direction assuming zero nominal bias. To address this issue, in this article, we derive a new bounding method in parity space. The method establishes a direct relationship between mean estimation error and RB test statistic noncentrality parameter, which accounts for both faults and nominal errors. ARAIM performance is evaluated to quantify the improvement provided by the proposed method over previous approaches.

A new method for online multiple-aircraft collision avoidance using an autonomous dependent surveillance-broadcast system is proposed. The key objective is to take advantage of satellite-based navi...

A novel method to determine probabilistic operational safety bound for rotary-wing unmanned aircraft systems (UAS) traffic management is proposed in this paper. The key idea is to combine a determi...

Sequential event prediction is a well-studied area and has been widely used in proactive management, recommender systems and healthcare. One major assumption of the existing sequential event prediction methods is that similar event sequence patterns in the historical record will repeat themselves, enabling us to predict future events. However, in reality, the assumption becomes less convincing when we are trying to predict rare or unique sequences. Furthermore, the representation of the event may be complex with hierarchical structures. In this paper, we aim to solve this issue by taking advantage of the multi-level or hierarchical representation of these rare events. We proposed to build a sequential Encoder-Decoder framework to predict the event sequences. More specifically, in the encoding layer, we built a hierarchical embedding representation for the events. In the decoding layer, we first predict the high-level events and the low-level events are generated according to a hierarchical graphical structure. We propose to link the encoding decoding layers with the temporal models for future event prediction. In this article, we further discussed applying the proposed model into the failure event prediction according to the aviation accident reports and have shown improved accuracy and model interpretability.

A probabilistic methodology for separation loss probability assessments is proposed in this paper. The key focus is on the effect of uncertainties from multiple Automatic Dependent Surveillance-Broadcast (ADS-B) systems on the separation loss probability assessment. First, a brief review of the ADS-B system and its associated uncertainty quantification metrics is discussed. It is found that most existing studies focus on the individual ADS-B uncertainty quantification for a single aircraft, which is not sufficient for separation loss probability assessment when two or more aircraft are involved. Next, a probabilistic positioning model with multiple aircraft is proposed and various types of uncertainties are included in the proposed model. Numerical simulations show that a navigation satellite fault can significantly affect separation error when individual aircraft see different satellite sets. Following this, several demonstration examples are illustrated to show the bounds for separation loss probability estimation. Finally, several conclusions and suggestions are discussed based on this study. One major finding is that the separation risk significantly increases when two nearby aircraft use different satellite sets to navigate. Real-time assessment of this risk should be performed.

Inferring an individual's ancestry or group membership using a small set of highly informative genetic markers is very useful in forensic and medical genetics. However, given the huge amount of SNP data available from a diverse of populations, it is challenging to develop informative panels by exhaustively searching for all possible SNP combinations. In this study, we formulate it as an algorithm problem of selecting an optimal set of SNPs that maximizes the inference accuracy while minimizes the set size. Built on this conception, we develop a computational approach that is capable of constructing ancestry informative panels from multi-population genome-wide SNP data efficiently. We evaluated the performance of the method by comparing the panel size and membership inference accuracy of the constructed SNP panels to panels selected through empirical procedures in previous studies. For the membership inference of population groups including Asian, European, African, East Asian and Southeast Asian, a 36-SNP panel developed by our approach has an overall accuracy of 99.07%, and a 21-SNP subset of the panel has an overall accuracy of 95.36%. In comparison, an existing panel requires 74 SNPs to achieve an accuracy of 94.14% on the same set of population groups. We further apply the method to four subpopulations within Europe (Finnish, British, Spanish and Italian); a 175-SNP panel can discriminate individuals of those European subpopulations with an accuracy of 99.36%, of which a 68-SNP subset can achieve an accuracy of 95.07%. We expect our method to be a useful tool for constructing ancestry informative markers in forensic genetics.

The workload of air traffic controllers (ATCs) is increasing due to the growing air traffic. Early alarms of loss of separation (LoS) events between aircraft are critical for ATCs to coordinate intensive traffic safely. The authors studied the time series of traffic densities and numbers of turning aircraft in a given sky section as early indicators of pending LoS. Simulator experiment produced data for comparing the prediction accuracies of the logistic regression models generated from the time series of traffic densities and numbers of turning aircraft, and combinations of these two. We studied different sections of the time series to examine the possibility of early detection and found that 1) the regression model based on the traffic density time series is more accurate than the model using the numbers of turning aircraft; 2) properly combining sections of the time series could produce models that achieve earlier predictions without losing accuracy.

Missing data occur when no data value is available for the variable in an observation. In this research, Bayesian data augmentation method is adopted and implemented for prediction with missing data. The data augmentation process is conducted through Bayesian inference with missing data assuming the multivariate normal distribution. Gibbs sampling is used to draw posterior simulations of the joint distribution of unknown parameters and unobserved quantities. The missing elements of the data are sampled conditional on the observed elements. The distribution of model parameters and variables with missing data can be obtained for reliability analysis. Two examples are given to illustrate the engineering application of Bayesian inference with missing data. The first example is to predict the yield strength of aging pipeline by fusing the incomplete surface information with missing data. The predictive performance is compared among direct surface indentation technique, linear regression with complete data and Bayesian inference with missing data. The second example is to predict the fatigue life of corroded steel reinforcing bar from the incomplete input dataset. The predicted fatigue lives are compared with experimental data. Both examples demonstrate that the Bayesian method can deal with missing data problem properly and show good predictive performance.

A probabilistic fatigue life prediction framework for concrete bridges is proposed in this study that considers the stress history from the construction stage to the operation stage. The proposed fatigue analysis framework combines the fatigue crack growth-based material life prediction model and a nonlinear structural analysis method. A reliability analysis is proposed using the developed probabilistic model to consider various uncertainties associated with the fatigue damage. A Bayesian network is established to predict the fatigue life of a concrete bridge according to the proposed framework. The proposed methodology is demonstrated using an experimental example for fatigue life prediction of a concrete box-girder. Comparison with experimental data of fatigue life shows a satisfactory accuracy using the proposed methodology, and the ratio of the posterior predicted mean (updating time n = 8) to the test value decreases to 33%–1% in the current investigation.

A novel damage imaging of engineering structures and materials methodology using the k-space method with adjoint method is proposed in this paper. The proposed methodology consists of two parts: the wave propagation solution algorithms for elastic solids in k-space and adjoint method-based optimization algorithms for damage imaging. One major advantage of the proposed method is the superior computational efficiency compared to the classical finite element method-based wave propagation and gradient-based optimization algorithms. The k-space formulation for wave propagation in 2D and 3D solids are discussed first. Adjoint method and a modified conjugate gradient method is presented for the inverse problem of damage imaging. Following this, parametric studies for measurement duration, number of sensors, noise effect, and computational efficiency are discussed in detail. Next, demonstration examples for concrete structures with and without reinforcement are presented. Several conclusions and future work are given based on the proposed method.

Comprehending how ground incidents and accidents arise and propagate during air traffic control is vital to both the efficiency and safety of air transportation systems. Historical data about airport operational events capture some processes about how improper collaboration among air traffic controllers and pilots result in incidents, accidents, and delays, but could hardly cover all possible air traffic control failures in various environmental conditions. The computational simulation could use historical data to identify repetitive aircraft movement behaviors and air traffic control processes shared in multiple air traffic control sessions and create stochastic models that represent the probabilities of the occurrence of certain aircrafts movements and control events under various contexts. This paper synthesizes four scenarios of aircraft operations around ramp areas during air traffic peak time for supporting the development of a quantitative spatiotemporal simulation that can help predict air traffic control risks based on historical data.

A novel approach for reliability assessment of aging gas pipeline systems based on a Bayesian network methodology is proposed in this paper with a focus on the improvement of the pipeline strength prediction. A multimodal diagnosis is performed by assessing the variation in the mechanical property (e.g., yield strength) within the pipe in terms of material property measurements, such as microstructure, composition, and hardness. The multimodality measurements are then integrated with the Bayesian network information fusion model. Prototype testing is carried out for model verification, validation and demonstration. The model updating scheme employs a Markov Chain Monte Carlo algorithm to infer the posterior distribution of the pipe strength using the multimodality measurements, whereas, the priors are derived from the literature knowledge of such systems. Moreover, through-thickness studies of pipe cross-sections are performed to demonstrate the mechanical property variation from the surface to bulk. Finally, data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. Discussions on the observations and future work are provided.

Trajectory prediction is an essential component of the next generation national air transportation system. Reliable trajectory prediction models need to consider uncertainties coming from multiple sources. Environmental factor is one of the most significant reasons affecting trajectory prediction models and is the focus of this study. This paper propose an advanced Bayesian Deep Learning method for aircraft trajectory prediction considering weather impacts. A brief review of both deterministic and probabilistic trajectory prediction methods is given, with a specific focus on learning-based methods. Next, a deterministic trajectory prediction model with classical deep learning methods is proposed to handle both spatial and temporal information using a nested convolution neural network, recurrent neural network, and fully-connected neural network. Following this, the deterministic neural network model is extended to be a Bayesian deep learning model to consider uncertainties where the posterior distributions of parameters are estimated with variational inference for enhanced efficiency. Both mean prediction and confidence intervals are obtained giving the last on-file flight plans and weather data in the region. The proposed methodology is validated using air traffic and weather data from the Sherlock data warehouse. Data pre-processing procedures for big data analytics are discussed in detail. Demonstration and metrics-based validation are performed during severe convective weather conditions for several air traffic control centers. The results show a significant reduction in prediction variance. A comparison with existing methods is also performed. Several conclusions and future works are given based on the proposed study.

Eliminating accidents while maintaining the integrity of the National Airspace System is one of the central objectives of the Next Generation Air Transportation System. This paper presents a Bayesian framework for accurate trajectory and accident prediction in National Airspace System using a high-fidelity trajectory simulation platform. Various uncertainties in aircraft trajectory prediction due to pilot behavior and weather effects are included as random variables in the simulations. Bayesian-Entropy method fuses available observation data (e.g., positioning system) with existing physical constraints (e.g. runway location) to update these simulation parameters. The posterior distributions of parameters are used to predict the probability of an adverse incident and time-remaining to incident. The proposed Bayesian updating scheme offers a flexible and rigorous way for adverse incident diagnostics and prognostics in current and future Air Traffic Management. Two realistic examples are given to show that it is possible to derive advance warning using the proposed methodology. The approach integrates data from a simulation model, with real-time traffic data streams and available physical constraints, using the Bayesian-Entropy information fusion methodology. This advance warning will allow the pilots/controllers to take actions to mitigate adverse incident in the National Airspace System.

The development of convective weather avoidance algorithm is crucial for aviation operations and it is also a key objective of the next generation air traffic management system. This paper proposes a novel network architecture that embeds convolutional layers into long short-time memory (LSTM) cells to predict the trajectory, based on the convective weather condition along the flight plan before the aircraft takeoff. The data used in the experiments are history flight track data, the last on-file flight plan, and the time-dependent convective weather map. The history flight data are taken from NASA Sherlock database and the weather data used in this paper is the Echo Top (ET) convective weather product from Corridor Integrated Weather System (CIWS). The experiment is conducted using three months history data over the period from Nov 1, 2018 through Feb 5, 2019 with the flights from John F. Kennedy International Airport (JFK) to Los Angeles International Airport (LAX) but the methodology can be applied to the flights between any arbitrary two airports. Interpolation is performed on flight plans and real history tracks to fix the fold number of LSTM cell and also reduce computation complexity. The training loss is defined as the standard Mean Squared Error (MSE) of the predicted tracks and the real history tracks. Adam optimizer is used for backpropagation. Learning from the real historical flight data, the out-of-sample test shows that 47.0\% of the predicted flight tracks are able to reduce the deviation compared to the last on-file flight plan. The overall variance is reduced by 12.3\%.

This paper proposed a novel method to determine probabilistic operational safety bound for unmanned aircraft traffic management. The key idea is to implement probabilistic uncertainty quantification and design the operational safety bound shape considering UAV’s heading direction. Operational safety bound is used to identify a virtual geographic boundary to protect aircraft and to ensure airspace safety. The proposed operational safety bound is calculated as a function of vehicle performance characteristics, state of vehicle, weather and other probabilistic parameters that affect the real position of vehicle such as position error from the Global Positioning System (GPS). It is calculated individually for each vehicle using real-time data and probability simulation. It considers the heading direction of vehicle and thus it is an anisotropic design. Monte Carlo simulations are conducted to estimate the operational safety bound size with a specified probability of failure. Results indicate that uncertainty is crucial for the operational safety bound’s size. Sensitivity study shows that UAV speed has the largest effect on the operational safety bound size. Analysis of impact of failure probability shows that operational safety bound size increases with the decrease in allowable failure probability, but the bound size based on different operational safety bound concept increases at different rate.

Accurate estimation of the mechanical property of aging pipes is critical to maintain the safety and to scheduling maintenance. Destructive testing for mechanical properties measurement is very expensive and sometime impossible. Inference methods are needed for estimating the bulk properties by multimodality surface material measurements from nondestructive testing, such as chemical composition, volume fraction and hardness. Bayesian network modeling is utilized to integrate the information from various types of surface measurements for a more accurate bulk mechanical property estimation. To improve the approximation of the actual underlying model and avoid the risk of overfitting, Bayesian model averaging (BMA) of Bayesian networks is implemented to account for Bayesian network model uncertainty. The models considered are weighted based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. The predictive performance of single best model and BMA are compared by logarithmic scoring rule. The predictive capability of the proposed method is evaluated. It is shown that the Bayesian network and model averaging approach can provide more reliable results in predicting the bulk mechanical properties of the pipelines.

With ahead-of-time aircraft management, we are able to reduce aircraft collision and improve air traffic capacity. However, there are various impact factors which will cause a large deviation between the actual flight and the original flight plan. Such uncertainty will result in an inappropriate decision for flight management. In order to solve this problem, most of the existing research attempt to build up a stochastic trajectory prediction model to capture the influence of the weather. However, the complexity of the weather information and various human factors make it hard to build up an accurate trajectory prediction framework. Our approach considers the problem of trajectory deviation as the "anomaly" and builds up an analytics pipeline for anomaly detection, anomaly diagnostics, and anomaly prediction. For anomaly detection, we propose to apply the CUSUM chart to detect the abnormal trajectory point which differs from the flight plan. For anomaly diagnostics, we would like to link the entire anomalous trajectory sequences with the convective weather data and identify the important weather impact factors base on XGBoost and time-series feature engineering. For anomaly prediction, we will build up a point-wise prediction framework based on the Hidden Markov Model and Convectional LSTM to predict the probability that the pilot would deviate from the flight plan. Finally, we demonstrate the significance of the proposed method using real flight data from JFK to LAX.

Chromophoric dissolved organic matter (CDOM) retrieval remains to be a challenging task in water color remote sensing research due to its highly spatial and temporal variability. In this paper, we present a novel CDOM retrieval algorithm that takes advantage of the sparse learning, which can simultaneously perform feature selection and parameter estimation. More specifically, by incorporating the band interaction terms into the original spectral matrix and let it be the basis matrix, then the inversion task can be converted to a classical sparse regression problem, namely LASSO, which can be efficiently solved by the coordinate descend algorithm. Experimental results conducted on both simulated and in-situ datasets have demonstrated the efficiency and superiority of the proposed method over some conventional empirical algorithms.

Imbalanced data widely exist in many high-impact applications. An example is in air traffic control, where among all three types of accident causes, historical accident reports with ‘personnel issues’ are much more than the other two types (‘aircraft issues’ and ‘environmental issues’) combined. Thus, the resulting data set of accident reports is highly imbalanced. On the other hand, this data set can be naturally modeled as a network, with each node representing an accident report, and each edge indicating the similarity of a pair of accident reports. Up until now, most existing work on imbalanced data analysis focused on the classification setting, and very little is devoted to learning the node representations for imbalanced networks. To bridge this gap, in this paper, we first propose Vertex-Diminished Random Walk (VDRW) for imbalanced network analysis. It is significantly different from the existing Vertex Reinforced Random Walk by discouraging the random particle to return to the nodes that have already been visited. This design is particularly suitable for imbalanced networks as the random particle is more likely to visit the nodes from the same class, which is a desired property for learning node representations. Furthermore, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, where context sampling uses VDRW and the limited label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs from different classes. Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representations from imbalanced data.

This paper examines how runway incursion, one of the major risks in aviation system, arise and propagate during communications and operations necessary for air traffic control. Runway incursions (RIs) refer to incorrect presences of aircraft in protected areas designated for landing and take-off of aircraft. RIs can significantly jeopardize the runway safety. Communication errors between air traffic controllers and pilots are major causes of RIs. How to quantify the probabilistic dependence between contextual factors (airport layout, time of the day, etc.) and communication errors that lead to RIs is thus important for real-time alarming and accident prevention. This study presents a Bayesian Network (BN) modeling approach with a focus on modeling the communication errors causing RIs during aircraft take-offs and how different factors contribute to the accidents according to the information from the aviation accident reports. Major findings indicate that the proposed approach can predict the accident occurrences based on the risk knowledge of anomalies captured in the BN produced by the proposed approach. In practice, the proposed approach has the potential for establishing automated and preventive safety management in aviation systems.

Physics-guided machine learning (ML) is an emerging paradigm that combines both data-driven ML models and physics-based models together to fully take advantage of the data discovery ability of ML without losing the valuable physics/domain knowledge. This paper proposes a novel physics-guided ML method based on recurrent neural network (RNN) and multilayer perceptron (MLP) for structural dynamics simulation. The key idea is to integrate the underlying physics of structural dynamics into data-enabled ML models to ‘guide’ the training and prediction of ML models. First, structural dynamics formulation and the use of data-driven RNN and MLP for modeling dynamical systems are briefly reviewed, which leads to the development of the proposed physics-guided ML model. Physics-guided ML model contains physics-based layers to encode the known physics and data-driven layers to approximate the unknown relationships. Thus, the data-driven RNN and MLP are augmented with existing physics knowledge for better performance in simulations. Following this, several numerical case studies for structural dynamics are presented to demonstrate the proposed methodology. It is observed that: (1) compared with purely data-driven ML method, the proposed physics-guided ML method has better generalization ability and reduced training costs; (2) compared with physics-based modeling, the proposed method has improved computational efficiency and can handle partially unknown physics. Finally, conclusions and future works are presented based on the proposed study.

Serving as energy lifelines, pipelines remain one of the most efficient and economical ways to move natural resources. The mechanical properties of pipelines installed decades ago decline with time. To maintain the safety of vintage pipelines requires accurate estimation of the strength. This paper focus on the reliability-based strength prediction using nondestructive multimodality information by the method of Bayesian model averaging (BMA). A class of models are formed from all cases of linear combinations of the surface property measurements. The models are averaged based on the posterior model probabilities. Occam’s window is introduced to reduce the number of models under consideration while keeping the predictive accuracy. By not conditioning on any single model, BMA provide more reliable strength prediction by accounting for model uncertainties. In addition, the usefulness of the variables used to predict the strength are evaluated according to the frequency of appearance in the models with high posterior probabilities. The variables with paramount predictive importance can be selected by this way. Thus, BMA method shows advantages in both vintage pipe strength prediction and model selection.