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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Evaluation of Learning Associated with Multiple Exposures to Computerized Dynamic Posturography

Computerized dynamic posturography has been used to quantitatively assess the time course of functional sensorimotor recovery after exposure to spaceflight or to groundbased analogs such as head-down bed rest. An assessment of balance recovery may be confounded as subjects develop new strategies through repeated exposures to test paradigms. The purpose of this control study was to characterize the learning effects of sensory organization and motor control tests across multiple sessions. METHODS: Twenty-eight healthy subjects were tested over four sessions. To examine the effects of between-session interval, subjects were assigned to one of four groups in which the interval between the 1 st and 2nd sessions was 7 (+/- 1) days, 14 (+/-1) days, 28 (+/-2) days, or 56 (+/-3) days. The interval between remaining sessions was 28 (+/-4) days. Peak-to-peak anterior-posterior sway was measured during standard Sensory Organization Tests (SOTs) using either fixed or unstable sway-referenced support with eyes open, eyes closed, or sway-referenced vision. Sway was also measured during modified SOTs using eyes-closed conditions with either static or dynamic head tilts. Postural recovery to unexpected support surface perturbations (translations or rotations) was measured during Motor Control Tests. The test order was block randomized across subjects. RESULTS: The learning effects varied with test condition. There were no measurable differences with a stable support surface. The more challenging conditions (unstable support surface with and without head tilts) led to greater differences and took more trials to stabilize. The effect of time interval between the first two sessions was negligible across conditions. Evidence suggested that learning carried across similar conditions (such as unstable support SOTs). DISCUSSION: Familiarization session and/or trials are recommended to minimize learning effects when characterizing functional recovery after exposure to altered sensory environments. The number of practice trials required depends on task difficulty and similarity across conditions. Learning statement: This presentation will review the learning effects of computerized d

Dean, S. Lance↗
NASA NTRS2009

Active Learning with Irrelevant Examples

An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item to present to the expert for labeling. Experiments on several data sets have demonstrated that the Relevance Bias approach significantly decreases the number of irrelevant items queried and also accelerates learning speed.

Wagstaff, Kiri↗
NASA NTRS2006

Logistics Lessons Learned in NASA Space Flight

The Vision for Space Exploration sets out a number of goals, involving both strategic and tactical objectives. These include returning the Space Shuttle to flight, completing the International Space Station, and conducting human expeditions to the Moon by 2020. Each of these goals has profound logistics implications. In the consideration of these objectives,a need for a study on NASA logistics lessons learned was recognized. The study endeavors to identify both needs for space exploration and challenges in the development of past logistics architectures, as well as in the design of space systems. This study may also be appropriately applied as guidance in the development of an integrated logistics architecture for future human missions to the Moon and Mars. This report first summarizes current logistics practices for the Space Shuttle Program (SSP) and the International Space Station (ISS) and examines the practices of manifesting, stowage, inventory tracking, waste disposal, and return logistics. The key findings of this examination are that while the current practices do have many positive aspects, there are also several shortcomings. These shortcomings include a high-level of excess complexity, redundancy of information/lack of a common database, and a large human-in-the-loop component. Later sections of this report describe the methodology and results of our work to systematically gather logistics lessons learned from past and current human spaceflight programs as well as validating these lessons through a survey of the opinions of current space logisticians. To consider the perspectives on logistics lessons, we searched several sources within NASA, including organizations with direct and indirect connections with the system flow in mission planning. We utilized crew debriefs, the John Commonsense lessons repository for the JSC Mission Operations Directorate, and the Skylab Lessons Learned. Additionally, we searched the public version of the Lessons Learned Information System (LLIS) and verified that we received the same result using the internal version of LLIS for our logistics lesson searches. In conducting the research, information from multiple databases was consolidated into a single spreadsheet of 300 lessons learned. Keywords were applied for the purpose of sorting and evaluation. Once the lessons had been compiled, an analysis of the resulting data was performed, first sorting it by keyword, then finding duplication and root cause, and finally sorting by root cause. The data was then distilled into the top 7 lessons learned across programs, centers, and activities.

Evans, William A.↗
NASA NTRS2017

Lessons Learned from Applying Design Thinking in a NASA Rapid Design Study in Aeronautics

In late 2015, NASA's Aeronautics Research Mission Directorate (ARMD) funded an experiment in rapid design and rapid teaming to explore new approaches to solving challenging design problems in aeronautics in an effort to cultivate and foster innovation. This report summarizes several lessons learned from the rapid design portion of the study. This effort entailed learning and applying design thinking, a human-centered design approach, to complete the conceptual design for an open-ended design challenge within six months. The design challenge focused on creating a capability to advance experimental testing of autonomous aeronautics systems, an area of great interest to NASA, the US government as a whole, and an entire ecosystem of users and developers around the globe. A team of nine civil servant researchers from three of NASA's aeronautics field centers with backgrounds in several disciplines was assembled and rapidly trained in design thinking under the guidance of the innovation and design firm IDEO. The design thinking process, while used extensively outside the aerospace industry, is less common and even counter to many practices within the aerospace industry. In this report, several contrasts between common aerospace research and development practices and design thinking are discussed, drawing upon the lessons learned from the NASA rapid design study. The lessons discussed included working towards a design solution without a set of detailed design requirements, which may not be practical or even feasible for management to ascertain for complex, challenging problems. This approach allowed for the possibility of redesigning the original problem statement to better meet the needs of the users. Another lesson learned was to approach problems holistically from the perspective of the needs of individuals that may be affected by advances in topic area instead of purely from a technological feasibility viewpoint. The interdisciplinary nature of the design team also provided valuable experience by allowing team members from different technological backgrounds to work side-by-side instead of dividing into smaller teams, as is frequently done in traditional multidisciplinary design. The team also learned how to work with qualitative data obtained primarily through the 70-plus interviews that were conducted over the course of this project, which was a sharp contrast to using quantitative data with regards to identifying, capturing, analyzing, storing, and recalling the data. When identifying potential interviewees who may have useful contributions to the design subject area, the team found great value in talking to non-traditional users and potential beneficiaries of autonomous aeronautics systems whose impact on the aeronautics autonomy ecosystem is growing swiftly. Finally, the team benefitted from using "sacrificial prototyping," which is a method of rapidly prototyping draft concepts and ideas with the intent of enabling potential users to provide significant feedback early in the design process. This contrasts the more common approach of using expensive prototypes that focus on demonstrating technical feasibility. The unique design approach and lessons learned by the team throughout this process culminated in a final design concept that was quite different than what the team originally assumed would be the design concept initially. A summary of the more usercentered final design concept is also provided.

McGowan, Anna-Maria↗
NASA NTRSDate not supplied

QuantifyML: How good is my machine learning model?

This paper presents an approach, QuantifyML, which employs model counting to assess the learnability and robustness of machine learning models. Typically the efficacy of machine learning models is determined by computing their accuracy statistically on test data sets. However, this may be misleading, if the test data is not representative of the problem that is being studied. Further, two different models may have the same accuracy on a given data set, measured statistically, but may be very different in their behavior on unseen data. Also, models with high accuracy could have poor adversarial robustness. In QuantifyML, our goal is to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into a C program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise counts w.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, expressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the robustness of trained models around given inputs. Our evaluation demonstrates these applications of QuantifyML on decision trees and neural networks trained to learn relational properties of graphs, for which we know the ground truth, and to perform image classification, for which we do not have the ground truth, but we can quantify local robustness.

Deep Neural Networks↗
NASA NTRSDate not supplied

QuantifyML: How Good is My Machine Learning Model?

We present QuantifyML, which applies model counting to assess the learn ability, safety, and robustness of machine learning models. Typically, the efficacy of machine learning models is determined by computing their accuracy statistically on test datasets. However, this may be misleading, if the test data is not representative of the problem that is being studied. With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into aC program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise countsw.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, ex-pressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the safety and robustness of trained models.

Deep Neural Networks↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRS2022

L1 Adaptive Control with Switched Reference Models: Application to Learn-to-Fly

Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control. To ensure safe learning of the vehicle dynamics on the fly, this paper presents an L1 adaptive control (L1AC) based scheme, which actively estimates and compensates for the discrepancy between the intermediately learned dynamics and the actual dynamics. First, to incorporate the periodic update of the learned model within the L2F framework, this paper extends the L1AC architecture to handle a switched reference system subject to unknown time-varying parameters and disturbances. The paper also includes analysis of both transient and steady-state performance of the L1AC architecture in the presence of non-zero initialization error for the state predictor. Second, the paper presents how the proposed L1AC scheme is integrated into the L2F framework, including its interaction with the baseline controller and the real-time modeling module. Finally, flight tests on an unmanned aerial vehicle (UAV) validate the efficacy of the proposed control and learning scheme.

Steven Snyder↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗
NASA NTRSDate not supplied

Environment Adversarial Reinforcement Learning

This paper presents a training method for increasing performance of reinforcement learning agents. The method is named Environment Adversarial Reinforcement Learning. The method requires the reinforcement learning environment to be parameterizeable. Over the course of training, environment parameters are updated in a direction of increasing difficulty for the agent. The direction for these updates is found using a performance prediction network trained on data from tests of the agent under varying environment parameters. The method was tested on a CartPole environment. A 28-58\% improvement in mean return was found when comparing performance to a baseline reinforcement learning algorithm on both easy and hard versions of the task.

machine learning↗
NASA NTRS1994

Improving Learning Performance Through Rational Resource Allocation

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

resource optimization↗
NASA NTRS2011

Constellation Program: Lessons Learned: Executive Summary - Volume 1

This document (Volume I) provides an executive summary of the lessons learned from the Constellation Program. A companion Volume II provides more detailed analyses for those seeking further insight and information. In this volume, Section 1.0 introduces the approach in preparing and organizing the content to enable rapid assimilation of the lessons. Section 2.0 describes the contextual framework in which the Constellation Program was formulated and functioned that is necessary to understand most of the lessons. Context of a former program may seem irrelevant in the heady days of new program formulation. However, readers should take some time to understand the context. Many of the lessons would be different in a different context, so the reader should reflect on the similarities and differences in his or her current circumstances. Section 3.0 summarizes key findings developed from the significant lessons learned at the program level that appear in Section 4.0. Readers can use the key findings in Section 3.0 to peruse for particular topics, and will find more supporting detail and analyses in Section 4.0 in a topical format. Appendix A contains a white paper describing the Constellation Program formulation that may be of use to readers wanting more context or background information. The reader will no doubt recognize some very similar themes from previous lessons learned, blue-ribbon committee reviews, National Academy reviews, and advisory panel reviews for this and other large-scale human spaceflight programs; including Apollo, Space Shuttle, Shuttle/Mir, and the ISS. This could represent an inability to learn lessons from previous generations; however, it is more likely that similar challenges persist in the Agency structure and approach to program formulation, budget advocacy, and management. Perhaps the greatest value of these Constellation lessons learned can be found in viewing them in context with these previous efforts to guide and advise the Agency and its stakeholders.

Constellation Program↗
NASA NTRS2014

CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

Reinforcement Learning↗
NASA NTRS2014

Learning Extended Finite State Machines

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

Register Automata↗
NASA NTRS2017

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

software↗
NASA NTRS2017

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

GUI↗