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An adaptive neural network for auto-associative memories operating in continuous time is considered. A new learning algorithm for the weight matrix defined by explicit locations of desirable equilibrium points is introduced. The approach is based upon the minimum 'strength energy' of the weight matrix for each prescribed performance of the neural network.
Inflatable structures provide a significant volume savings for future NASA deep space missions. The complexity of these structures, however, provides difficulty for engineers in designing, analyzing, and testing. Common strain measurement systems for metallic parts cannot be used directly on fabrics. New technologies must be developed and tested to accuractly measure the strain of inflatable structures. This paper documents the testing of six candidate strain measurement devices for use on fabrics. The resistance devices tested showed significant hysteresis during creep and cyclic testing. The capacitive device, however, showed excellent results and little-to-no hysteresis. Because of this issue, only two out of the six proposed devices will continue in development. The resulting data and lessons learned from this effort provides direction for continued work to produce a structural health monitoring system for inflatable habitats.
Inflatable structures provide a significant volume savings for future NASA deep space missions. The complexity of these structures, however, provides difficulty for engineers in designing, analyzing, and testing. Common strain measurement systems for metallic parts cannot be used directly on fabrics. New technologies must be developed and tested to accurately measure the strain of inflatable structures. This paper documents the testing of six candidate strain measurement devices for use on fabrics. The resistance devices tested showed significant hysteresis during creep and cyclic testing. The capacitive device, however, showed excellent results and little-to-no hysteresis. Because of this issue, only two out of the six proposed devices will continue in development. The resulting data and lessons learned from this effort provides direction for continued work to produce a structural health monitoring system for inflatable habitats.
Loss of Signal, a NASA publication to be available in May 2014 presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goal of this book is to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews. This poster presents an outline of Loss of Signal contents and highlights from each of five sections - the mission and mishap, the response, the investigation, the analysis and the future.
Loss of Signal, a NASA publication to be available in May 2014, presents the aeromedical lessons learned from the Columbia accident that will enhance crew safety and survival on human space flight missions. These lessons were presented to limited audiences at three separate Aerospace Medical Association (AsMA) conferences: in 2004 in Anchorage, Alaska, on the causes of the accident; in 2005 in Kansas City, Missouri, on the response, recovery, and identification aspects of the investigation; and in 2011, again in Anchorage, Alaska, on future implications for human space flight. As we embark on the development of new spacefaring vehicles through both government and commercial efforts, the NASA Johnson Space Center Human Health and Performance Directorate is continuing to make this information available to a wider audience engaged in the design and development of future space vehicles. Loss of Signal summarizes and consolidates the aeromedical impacts of the Columbia mishap process-the response, recovery, identification, investigative studies, medical and legal forensic analysis, and future preparation that are needed to respond to spacecraft mishaps. The goals of this book are to provide an account of the aeromedical aspects of the Columbia accident and the investigation that followed, and to encourage aerospace medical specialists to continue to capture information, learn from it, and improve procedures and spacecraft designs for the safety of future crews.
Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.
As large language models (LLMs) continue to evolve, their capacity to replace humans as their surrogates is also improving. As increasing numbers of intelligent tutoring systems (ITSs) are embracing the integration of LLMs for digital tutoring, questions are arising as to how effective they are and if their hallucinatory behaviors diminish their perceived advantages. One critical question that is seldom asked if the availability, plurality, and relative weaknesses in the reasoning process of LLMs are contributing to the much discussed digital divide and equity and fairness in online learning. In this paper, we present an experiment with database design theory assignments and demonstrate that while their capacity to reason logically is improving, LLMs are still prone to serious errors. We demonstrate that in online learning and in the absence of a human instructor, LLMs could introduce inequity in the form of “wrongful” tutoring that could be devastatingly harmful for learners, which we call ignorant bias, in increasingly popular digital learning. We also show that significant challenges remain for STEM subjects, especially for subjects for which sound and free online tutoring systems exist. Based on the set of use cases, we formulate a possible direction for an effective ITS for online database learning classes of the future.
Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.
The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.
In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.
Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.
A method of using synthetic-discriminant-functions to facilitate learning in a pattern recognition system is discussed. Learning is accomplished by continually adding images to the training set used for synthetic discriminant functions (SDF) construction. Object identification is performed by efficiently searching a library of SDF filters for the maximum optical correlation. Two library structures are discussed - binary tree and multilinked graph - along with maximum ascent, back-tracking, perturbation, and simulated annealing searching techniques. By incorporating the distortion invariant properties of SDFs within a library structure, a robust pattern recognition system can be produced.
A flight simulator study of pilots' use of the Traffic-alert and Collision Avoidance System is described. Three levels of information on the location of other air traffic were presented to different groups of airline pilots. The amount of informtion on the location of other traffic had little effect on pilots' performance of the maneuvers commanded by the collision avoidance system. Measured crew responses were similar with no presentation of traffic location, with limited information, and with continuous traffic information. No learning effects were observed, and differences in flight experience did not contribute to the performance difference found.
In extensive form noncooperative game theory, at each instant t, each agent i sets its state x, independently of the other agents, by sampling an associated distribution, q(sub i)(x(sub i)). The coupling between the agents arises in the joint evolution of those distributions. Distributed control problems can be cast the same way. In those problems the system designer sets aspects of the joint evolution of the distributions to try to optimize the goal for the overall system. Now information theory tells us what the separate q(sub i) of the agents are most likely to be if the system were to have a particular expected value of the objective function G(x(sub 1),x(sub 2), ...). So one can view the job of the system designer as speeding an iterative process. Each step of that process starts with a specified value of E(G), and the convergence of the q(sub i) to the most likely set of distributions consistent with that value. After this the target value for E(sub q)(G) is lowered, and then the process repeats. Previous work has elaborated many schemes for implementing this process when the underlying variables x(sub i) all have a finite number of possible values and G does not extend to multiple instants in time. That work also is based on a fixed mapping from agents to control devices, so that the the statistical independence of the agents' moves means independence of the device states. This paper also extends that work to relax all of these restrictions. This extends the applicability of that work to include continuous spaces and Reinforcement Learning. This paper also elaborates how some of that earlier work can be viewed as a first-principles justification of evolution-based search algorithms.
The history of the space industry stretches far and above lunar landings to the construction of the International Space Station. For years, humans have sought to understand the nature of the universe. As society grows in knowledge and curiosity of space, the focus of maintaining the safety of the crew and vehicle habitability is of utmost importance to the National Aeronautics and Space Administration (NASA) community. Through the years, Payload Safety has developed not only as a Panel, but also as part of the NASA community, striving to enhance the efficiency and understanding of how business should be conducted as more International Partners become involved. This is the first in a series of papers and presentations in what is hoped to be an annual update that provides continuous challenges and lessons learned in the areas of communication, safety requirements and processes and other areas which have been vital to the Payload Safety Review Panel (PSRP).
This paper focuses on two key improvements to the photogrammetric analysis capabilities of the Capsule Parachute Assembly System (CPAS) for the Orion vehicle. The Engineering Development Unit (EDU) system deploys Drogue and Pilot parachutes via mortar, where an important metric is the muzzle velocity. This can be estimated using a high speed camera pointed along the mortar trajectory. The distance to the camera is computed from the apparent size of features of known dimension. This method was validated with a ground test and compares favorably with simulations. The second major photogrammetric product is measuring the geometry of the Main parachute cluster during steady-state descent using onboard cameras. This is challenging as the current test vehicles are suspended by a single-point attachment unlike earlier stable platforms suspended under a confluence fitting. The mathematical modeling of fly-out angles and projected areas has undergone significant revision. As the test program continues, several lessons were learned about optimizing the camera usage, installation, and settings to obtain the highest quality imagery possible.
Space flight missions are inherently risky. The mission operations assurance discipline grew out of strategies to control error and evolved into what today is an independent technical authority dedicated to achieving mission success. After the Mars Climate Orbiter loss, NASA made a set of recommendations for missions. Two of those recommendations led to the creation of today’s Mission Operations Assurance Manager role: (1) require an independent Mission Assurance representative during the operational phase of every flight project, and (2) require all flight projects to report and track post-launch anomalies. Since then, the MOAM role has been continually refined by lessons learned from its practice on more than 20 concurrent missions. The MOAM role requires significant operations experience as well as an additional set of skills. In the interests of developing a cadre of potential MOAMs, the MOAM training program was developed. It is a 14-unit set of presentations/discussions covering the 11 categories of MOAM tasks. It is offered to new MOAMs and interested systems engineers with an eye to preparing both types of personnel for present and future assignments, as well as building relationships among personnel of different organizations. MOAM training sessions include presentations on the various tools and processes employed by MOAMs, interspersed with mindset lessons illustrated via “war story” discussions of previous missions.
There are clear parallels between the ground processing of spacecraft and the maintenance of airline aircraft. In both cases, reliable human performance is critical to safe outcomes. Examples of human factors in airline maintenance and spacecraft ground processing illustrate the similarities between these two domains. The worldwide aviation industry began to pay close attention to human factors in maintenance after several maintenance-related disasters in the 1970s and 80s. Rather than simply applying solutions that had been developed for flight crew and air traffic controllers, the aviation industry developed human factors interventions specifically tailored for maintenance personnel. These interventions have led to safety improvements, greater reliability, and significant cost savings. Six human factors approaches from airline maintenance that can be usefully applied to enhance the quality and safety of ground processing are outlined. These are 1) Design for assembly, test, and maintenance, 2) Non technical skills training, 3) Improved design of documentation, 4) Reduction of iatrogenic quality lapses, 5) Barrier and control analysis, and 6) Continuous improvement based on learning from quality lapses.