In this article, we focus on relatively new maintenance and operational scheduling challenges that are faced by the United States Air Force concerning low-observable (LO) or stealth aircraft. The LO capabilities of an aircraft degrade stochastically as it flies, making it difficult to make maintenance scheduling decisions. Maintainers can address these damages, but must decide, which aircraft should be put into maintenance, and for how long. Using data obtained from an active duty Air Force F-22 wing and interviews with Air Force maintainers and program specialists, we model this problem as a generalization of the well-known restless multiarmed bandit superprocess. Specifically, we use an extension of the traditional model to allow for actions that require varying lengths of time, and generate two separate index policies from a single model; one for maintenance actions and one for the flying action. These index policies allow maintenance schedulers to intuitively, quickly, and effectively rank a fleet of aircraft based on each aircraft's LO status and decide, which aircraft should enter into LO maintenance and for how long, and which aircraft should be used to satisfy daily sortie requirements. Finally, we present extensive data-driven, detailed simulation results, where we compare the performance of the index policies against policies currently used by the Air Force, as well as some other possible more naive heuristics. The results indicate that the index policies significantly outperform existing policies in terms of fully mission capable (FMC) rates. In particular, the experiments highlight the importance of coordinated maintenance and flying decisions.
The grouping of sensory stimuli into categories is fundamental to cognition. Previous research in the visual and auditory systems supports a two‐stage processing hierarchy that underlies perceptual categorization: (a) a “bottom‐up” perceptual stage in sensory cortices where neurons show selectivity for stimulus features and (b) a “top‐down” second stage in higher level cortical areas that categorizes the stimulus‐selective input from the first stage. In order to test the hypothesis that the two‐stage model applies to the somatosensory system, 14 human participants were trained to categorize vibrotactile stimuli presented to their right forearm. Then, during an fMRI scan, participants actively categorized the stimuli. Representational similarity analysis revealed stimulus selectivity in areas including the left precentral and postcentral gyri, the supramarginal gyrus, and the posterior middle temporal gyrus. Crucially, we identified a single category‐selective region in the left ventral precentral gyrus. Furthermore, an estimation of directed functional connectivity delivered evidence for robust top‐down connectivity from the second to first stage. These results support the validity of the two‐stage model of perceptual categorization for the somatosensory system, suggesting common computational principles and a unified theory of perceptual categorization across the visual, auditory, and somatosensory systems.
Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via simulation. Motivated by a desire for machine learning methods that can be trained on experimental data, we propose two self-supervised machine learning algorithms that are trained solely on images that are defect-free. Our proposed methods use principal components analysis (PCA) and convolutional neural networks (CNN) to analyze a TEM image and predict the location of a defect. Using simulated TEM images, we show that PCA can be used to accurately locate point defects in the case where there is no imaging noise. In the case where there is imaging noise, we show that incorporating a CNN dramatically improves model performance. Our models rely on a novel approach that uses the residual between a TEM image and its PCA reconstruction.
The 2010 earthquake in Haiti caused nearly 112,000 fatalities making it one of the deadliest natural disasters ever recorded in the western hemisphere. In the weeks following the disaster, the United States Air Force Air Mobility Command moved over 29,000 passengers and 18,000 tons of relief aid in support of the relief effort, Operation Unified Response. During the first 96 hours of the operation 59% of aircraft transporting relief aid to Haiti arrived late. In order to assist planners in responding to future disaster relief efforts, we introduce a mixed integer programming (MIP) model that reduces the time required to deliver available relief aid into Haiti. The aircraft routing schedule outputted by the model demonstrates that our optimized airlift network increases the amount of relief aid delivered in the first 96 hours of the operation. Due to issues with the tractability of the MIP, we introduce an aircraft routing heuristic for use in real-world humanitarian relief operations. We show that our heuristic is able to produce similar results to the optimization, provides greater flexibility to account for realistic planning considerations, and solves within seconds.
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