Paper uses LSTM autoencoder for ADS-B data to detect surveillance aircraft.
problem Detecting surveillance aircraft from ADS-B flight data.
method LSTM-based sequence autoencoder for ADS-B data.
result LSTM autoencoder effectively learns features for detecting surveillance aircraft.
Classi-Fly uses machine learning to infer aircraft categories from open data.
problem Lack of metadata for aircraft in open data sources.
method Machine learning approach based on aircraft movement patterns.
result Correct aircraft category inference with over 88% accuracy.
The article develops a model for predicting aircraft motion in terminal airspace.
problem Predicting aircraft motion for collision avoidance and safety analysis.
method Fits a probabilistic generative model to historical position data of aircraft landings and takeoffs.
result The model generates realistic trajectories, provides accurate predictions, and captures statistical properties of aircraft trajectories.
UAVs use RL to outperform a greedy shooter in 2D engagements.
problem Optimizing UAV pursuit-evasion strategies in 2D environments.
method Reinforcement learning applied to a greedy shooter strategy in a 2D aerial engagement.
result Deep Q-learning UAV outperforms greedy shooter UAV in simulations.
Paper uses diffusion models to design electric aircraft quickly.
problem Designing electric aircraft efficiently and accurately.
method Simulation-based inference with hierarchical diffusion models.
result Rediscovers known aircraft design trends and laws.
Machine learning improves aircraft performance prediction by analyzing flight data.
problem Limited aircraft performance models based on industry-wide guidelines.
method Use machine learning on flight data to estimate drag and lift coefficients.
result Excellent accuracy in real-life data, supporting aerodynamics principles.
Bayesian optimization reduces computational effort in aircraft design optimization.
problem High computational cost in industrial aircraft design optimization.
method Constrained Bayesian optimization (Super Efficient Global Optimization with Mixture of Experts)
result Significant computational efficiency improvements over existing Isight optimizers.
A modern aircraft may require on the order of thousands of custom shims to fill gaps between structural components in the airframe that arise due to manufacturing tolerances adding up across large structures. These shims are necessary to eliminate gaps, maintain structural performance, and minimize pull-down forces req…
Bayesian optimization improves with transfer learning for aircraft design.
problem Cold start problem in Bayesian optimization for aircraft design.
method Ensemble of surrogate models using transfer learning in a constrained Bayesian optimization framework.
result Significant improvement in convergence and prediction accuracy.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
problem Limited fleet capacity and operational structure hinder electric aircraft transition.
method Multi-period mixed-integer linear programming (MILP) framework.
result Electric aircraft can reduce emissions by over 70% within five years.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
problem Limited run-to-failure data samples for accurate RUL prognosis.
method Federated learning framework, decentralized validation, and robust aggregation methods.
result The federated learning framework leads to more accurate RUL prognosis for five out of six airlines.
Deep neural network approximates collision avoidance table for aircraft systems.
problem High dimensionality of collision avoidance state space leads to large numeric tables.
method Used deep neural networks to approximate the numeric tables, using asymmetric loss functions and gradient descent.
result Reduced storage space by a factor of 1000, enabling current avionics systems.
Paper examines how dimension reduction affects aircraft load prediction.
problem Efficiently predicting aircraft loads for derivative models.
method Compared PCA, polynomial fitting, and combined techniques on regression trees.
result AdaBoost with Random Forest offers promising results for load estimation.
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
New method optimizes aircraft design with reduced computation using multi-fidelity models.
problem Efficiently solve complex aircraft design problems with limited computational resources.
method Proposes novel multi-fidelity selection strategies that consider both objective and constraint information.
result Shows 86% to 200% more constraint compliant solutions with a limited budget. Adapts Bayesian optimization for mixed constraints in aircraft design.
problem Optimizing expensive black box functions with mixed constraints.
method Super efficient global optimization with upper trust bound for constraints, Gaussian process uncertainty, refinement procedure.
result Superior performance on aircraft design problem compared to state-of-the-art solvers.
Deep reinforcement learning system improves air traffic control efficiency and safety.
problem High-density, dynamic, and stochastic air traffic control challenges.
method Deep multi-agent reinforcement learning framework using actor-critic model with PPO loss function.
result Framework resolves 99.97% of all conflicts in extreme high-density scenarios.
Paper reduces hyperparameters in mixed-categorical Gaussian processes for green aircraft optimization.
problem High-dimensional mixed-categorical Gaussian processes with many hyperparameters.
method Innovative dimension reduction algorithm using partial least squares regression.
result Significant reduction in fuel consumption (439 kg) for a green aircraft.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
VerifAI toolkit improves neural network-based aircraft taxiing system safety.
problem Improving safety of autonomous aircraft taxiing systems using neural networks.
method Unified approach to formal analysis and retraining of AI systems, including falsification, debugging, and retraining.
result Improved neural network performance and reduced failure cases in aircraft taxiing system.
Deep neural nets predict aircraft flight paths from weather data.
problem Accurate prediction of aircraft trajectories for aviation efficiency.
method Deep generative convolutional recurrent neural network with tree-based matching.
result Model accurately predicts aircraft flight paths from weather data.
Improved bio-surveillance through automated document classification.
problem Tracking infectious diseases across global news alerts.
method Recurrent neural networks, TF-IDF, Naive Bayes, logistic regression.
result 97% recall and 93.3% accuracy in bio-surveillance event classification.
Classifiers and beamforming algorithms improved audio surveillance detection accuracy.
problem Detecting surveillance sound events with high accuracy and efficiency.
method Evaluated seven classifiers and two beamforming algorithms; used data augmentation and tested with varying SNR levels.
result SVM and Delay-and-Sum (DaS) combination achieved the highest accuracy (86.0%), but had high computational cost.
The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice arodynamic stability in order to incorporate stealth technology intro their shape, operate more efficiently, and are highly maneuverable. Therefore, modern flight management systems are reliant on mult…
A Ph.D. thesis proposes an efficient approach for optimizing aircraft eco-design with high-dimensional mixed integer variables.
problem Optimizing aircraft eco-design with high-dimensional mixed integer variables.
method EGO optimization approach based on sequential enrichment of adaptive surrogate models, adapted for high-dimensional and mixed variable spaces.
result Effective parameterization tools significantly reduce the number of design variables, enabling optimization of complex aircraft concepts.
New algorithm rSVDdpd improves robustness and scalability for video surveillance background modeling.
problem Camera tampering and noisy videos make background separation challenging.
method Introduces rSVDdpd, a robust singular value decomposition technique for scalable video surveillance.
result Demonstrates superior performance on benchmark and real-life datasets.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Bayesian optimization reduces hyperparameters for mixed variable design problems.
problem Optimizing designs with a large number of mixed continuous, integer, and categorical variables.
method Adaptive dimension reduction using partial least squares for fewer hyperparameters.
result Significant improvement in performance compared to genetic algorithms.
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
New method optimizes searchers' allocation on perimeters over time.
problem Optimizing searchers' allocation on perimeters to detect intrusions.
method Combinatorial multi-armed bandit (CMAB) with upper confidence bound approach.
result Upper and lower bounds on expected performance of the method.
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
Supervised learning improves disease outbreak detection accuracy.
problem Early detection of infectious disease outbreaks to protect public health.
method Developed a supervised learning approach based on hidden Markov models for disease outbreak detection.
result Reduces false positive rate by up to 50% while maintaining sensitivity.
Paper describes a video surveillance system for highway traffic events.
problem Detecting specific sequences of situations in highway traffic videos.
method Compares RNN and CNN architectures for analyzing video frames and sequences.
result Best architecture performs well in real conditions.
A new classification method based on Minimum Spanning Trees
problem Improving classification in supervised learning
method Proposing a classification algorithm based on Minimum Spanning Trees
result The proposed method is effective and computationally efficient
A new framework uses deep reinforcement learning to improve aircraft separation in busy airspace.
problem Improving aircraft separation in high-density, dynamic airspace constrained by human controllers.
method Proximal Policy Optimization with an attention network for distributed vehicle autonomy.
result The framework significantly reduces offline training time and increases performance.
CBMs improve interpretability in RUL prediction for aircraft engines.
problem Lack of interpretability in deep learning models for asset prognostics.
method Concept Bottleneck Models (CBMs) for RUL prediction.
result CBMs achieve comparable or superior performance to black-box models while being more interpretable.
Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.…
Challenge to separate Earth's magnetic field from vehicle's magnetic field for accurate navigation.
problem Separate Earth's magnetic field from vehicle's magnetic field for accurate magnetic navigation.
method Use machine learning (ML) and integrate physics of magnetic navigation (SciML) to remove aircraft magnetic field from total magnetic field.
result A model can be constructed to effectively remove aircraft magnetic field from the dataset.
Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomal…
Airlines optimize fuel loading with better flight time predictions.
problem Flight time uncertainties and their impact on fuel consumption.
method Developed a spatial weighted recurrent neural network model.
result The model provides more accurate flight time predictions, reducing fuel consumption.
Develops game theory framework for UAS integration into NAS.
problem Predicting outcomes of UAS integration into NAS.
method Game theory, reinforcement learning, level-k reasoning.
result Proposes a modeling framework for human pilot behavior.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
problem Identifying atypical malaria transmission patterns in Ghana's spatiotemporal surveillance data.
method Consensus-based anomaly detection framework applied to monthly malaria surveillance data.
result High-burden areas are not necessarily those with the most frequent anomalous transmission.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
Flusion combines multiple data sources to improve flu forecasts.
problem Accurate flu predictions to improve public health actions.
method Ensemble model combining gradient boosting quantile regression and Bayesian autoregressive models.
result Flusion was the top-performing model in the CDC's influenza prediction challenge.
Paper tackles hard shape constraints in kernel machines.
problem Enforcing shape requirements in a hard fashion is challenging.
method Tightened second-order cone constrained reformulation for kernel machines.
result Performance guarantees and efficiency demonstrated in various applications.
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Viral zoonoses have emerged as the key drivers of recent pandemics. Human infection by zoonotic viruses are either spillover events -- isolated infections that fail to cause a widespread contagion -- or species jumps, where successful adaptation to the new host leads to a pandemic. Despite expensive bio-surveillance ef…