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Making intelligent systems team players. A guide to developing intelligent monitoring systems

This reference guide for developers of intelligent monitoring systems is based on lessons learned by developers of the DEcision Support SYstem (DESSY), an expert system that monitors Space Shuttle telemetry data in real time. DESSY makes inferences about commands, state transitions, and simple failures. It performs failure detection rather than in-depth failure diagnostics. A listing of rules from DESSY and cue cards from DESSY subsystems are included to give the development community a better understanding of the selected model system. The G-2 programming tool used in developing DESSY provides an object-oriented, rule-based environment, but many of the principles in use here can be applied to any type of monitoring intelligent system. The step-by-step instructions and examples given for each stage of development are in G-2, but can be used with other development tools. This guide first defines the authors' concept of real-time monitoring systems, then tells prospective developers how to determine system requirements, how to build the system through a combined design/development process, and how to solve problems involved in working with real-time data. It explains the relationships among operational prototyping, software evolution, and the user interface. It also explains methods of testing, verification, and validation. It includes suggestions for preparing reference documentation and training users.

Land, Sherry A.↗

Lightning forecasting studies using LDAR, LLP, field mill, surface mesonet, and Doppler radar data

The ultimate goal of this research is to develop rules, algorithms, display software, and training materials that can be used by the operational forecasters who issue weather advisories for daily ground operations and launches by NASA and the United States Air Force to improve real-time forecasts of lightning. Doppler radar, Lightning Detection and Ranging (LDAR), Lightning Location and Protection (LLP), field mill (Launch Pad Lightning Warning System -- LPLWS), wind tower (surface mesonet) and additional data sets have been utilized in 10 case studies of thunderstorms in the vicinity of KSC during the summers of 1994 and 1995. These case studies reveal many intriguing aspects of cloud-to-ground, cloud-to-cloud, in-cloud, and cloud-to-air lightning discharges in relation to radar thunderstorm structure and evolution. They also enable the formulation of some preliminary working rules of potential use in the forecasting of initial and final ground strike threat. In addition, LDAR and LLP data sets from 1993 have been used to quantify the lightning threat relative to the center and edges of LDAR discharge patterns. Software has been written to overlay and display the various data sets as color imagery. However, human intervention is required to configure the data sets for proper intercomparison. Future efforts will involve additional software development to automate the data set intercomparisons, to display multiple overlay combinations in a windows format, and to allow for animation of the imagery. The software package will then be used as a tool to examine more fully the current cases and to explore additional cases in a timely manner. This will enable the formulation of more general and reliable forecasting guidelines and rules.

Forbes, Gregory S.↗

Intelligent Pilot Aids for Flight Re-Planning in Emergencies

Experimental studies were conducted with pilots to investigate the attributes of automation that would be appropriate for aiding pilots in emergencies. The specific focus of this year was on methods of mitigating automation brittleness. Brittleness occurs when the automatic system is used in circumstances it was not designed for, causing it to choose an incorrect action or make an inaccurate decision for the situation. Brittleness is impossible to avoid since it is impossible to predict every potential situation the automatic system will be exposed to over its life. However, operators are always ultimately responsible for the actions and decisions of the automation they are monitoring or using, which means they must evaluate the automation's decisions and actions for accuracy. As has been pointed out, this is a difficult thing for human operators to do. There have been various suggestions as to how to aid operators with this evaluation. In the study described in this report we studied how presentation of contextual information about an automatic system's decision might impact the ability of the human operators to evaluate that decision. This study focused on the planning of emergency descents. Fortunately, emergencies (e.g., mechanical or electrical malfunction, on-board fire, and medical emergency) happen quite rarely. However, they can be catastrophic when they do. For all predictable or conceivable emergencies, pilots have emergency procedures that they are trained on, but those procedures often end with 'determine suitable airport and land as quickly as possible.' Planning an emergency descent to an unplanned airport is a difficult task, particularly under the time pressures of an emergency. Automatic decision aids could be very efficient at the task of determining an appropriate airport and calculating an optimal trajectory to that airport. This information could be conveyed to the pilot through an emergency descent procedure listing all of the actions necessary to safely land the plane. However, there is still the potential problem of brittleness. This study examined the impact of contextual information in presentations of emergency descent procedures to see if they might impact the pilot's evaluation of the feasibility of the presented procedure. The study and its results are described in detail.

Pritchett, Amy R.↗

cAMP levels in fast- and slow-twitch skeletal muscle after an acute bout of aerobic exercise

The present study examined whether exercise duration was associated with elevated and/or sustained elevations of postexercise adenosine 3',5'-cyclic monophosphate (cAMP) by measuring cAMP levels in skeletal muscle for up to 4 h after acute exercise bouts of durations that are known to either produce (60 min) or not produce (10 min) mitochondrial proliferation after chronic training. Treadmill-acclimatized, but untrained, rats were run at 22 m/min for 0 (control), 10, or 60 min and were killed at various postexercise (0, 0.5, 1, 2, and 4 h) time points. Fast-twitch white and red (quadriceps) and slow-twitch (soleus) muscles were quickly excised, frozen in liquid nitrogen, and assayed for cAMP with a commercial kit. Unexpectedly, cAMP contents in all three muscles were similar to control (nonexercise) at most (21 of 30) time points after a single 10- or 60-min run. Values at 9 of 30 time points were significantly different from control (P < 0.05); i.e., 3 time points were significantly higher than control and 6 were significantly less than control. These data suggest that the cAMP concentration of untrained skeletal muscle after a single bout of endurance-type exercise is not, by itself, associated with exercise duration.

NASA Discipline Musculoskeletal↗

Intelligent Vision Systems Independent Research and Development (IR&D) 2006

This report summarizes results in conduct of research sponsored by the 2006 Independent Research and Development (IR&D) program at Marshall Space Flight Center (MSFC) at Redstone Arsenal, Alabama. The focus of this IR&D is neural network (NN) technology provided by Imagination Engines, Incorporated (IEI) of St. Louis, Missouri. The technology already has many commercial, military, and governmental applications, and a rapidly growing list of other potential spin-offs. The goal for this IR&D is implementation and demonstration of the technology for autonomous robotic operations, first in software and ultimately in one or more hardware realizations. Testing is targeted specifically to the MSFC Flat Floor, but may also include other robotic platforms at MSFC, as time and funds permit. For the purpose of this report, the NN technology will be referred to by IEI's designation for a subset configuration of its patented technology suite: Self-Training Autonomous Neural Network Object (STANNO).

Patrick, Clinton↗

NASA Dryden Status: Aerospace Control and Guidance Sub-Committee Meeting 109

NASA Dryden has been engaging in some exciting work that will enable lighter weight and more fuel efficient vehicles through advanced control and dynamics technologies. The main areas of emphasis are Enabling Light-weight Flexible Structures, real time control surface optimization for fuel efficiency and autonomous formation flight. This presentation provides a description of the current and upcoming work in these areas. Additionally, status is for the Dreamchaser pilot training activity and KQ-X autonomous aerial refueling.

Jacobson, Steven R.↗

Development of a Methodology to Conduct Usability Evaluation for Hand Tools that May Reduce the Amount of Small Parts that are Dropped During Installation while Processing Space Flight Hardware

Foreign object debris (FOD) is an important concern while processing space flight hardware. FOD can be defined as "The debris that is left in or around flight hardware, where it could cause damage to that flight hardware," (United Space Alliance, 2000). Just one small screw left unintentionally in the wrong place could delay a launch schedule while it is retrieved, increase the cost of processing, or cause a potentially fatal accident. At this time, there is not a single solution to help reduce the number of dropped parts such as screws, bolts, nuts, and washers during installation. Most of the effort is currently focused on training employees and on capturing the parts once they are dropped. Advances in ergonomics and hand tool design suggest that a solution may be possible, in the form of specialty hand tools, which secure the small parts while they are being handled. To assist in the development of these new advances, a test methodology was developed to conduct a usability evaluation of hand tools, while performing tasks with risk of creating FOD. The methodology also includes hardware in the form of a testing board and the small parts that can be installed onto the board during a test. The usability of new hand tools was determined based on efficiency and the number of dropped parts. To validate the methodology, participants were tested while performing a task that is representative of the type of work that may be done when processing space flight hardware. Test participants installed small parts using their hands and two commercially available tools. The participants were from three groups: (1) students, (2) engineers / managers and (3) technicians. The test was conducted to evaluate the differences in performance when using the three installation methods, as well as the difference in performance of the three participant groups.

Miller, Darcy↗

Effects of the 8 psia / 32% O2 Atmosphere on the Human in the Spaceflight Environment

Extravehicular activity (EVA) is at the core of a manned space exploration program. There are elements of exploration that may be safely and effectively performed by robots, but there are critical elements of exploration that will require the trained, assertive, and reasoning mind of a human crewmember. To effectively use these skills, NASA needs a safe, effective, and efficient EVA component integrated into the human exploration program. The EVA preparation time should be minimized and the suit pressure should be low to accommodate EVA tasks without undue fatigue, physical discomfort, or suit-related trauma. Commissioned in 2005, the Exploration Atmospheres Working Group (EAWG) had the primary goal of recommending to NASA an internal environment that allowed efficient and repetitive EVAs for missions that were to be enabled by the former Constellation Program. At the conclusion of the EAWG meeting, the 8.0 psia and 32% oxygen (O2) environment were recommended for EVA intensive phases of missions. As a result of selecting this internal environment, NASA gains the capability for efficient EVA with low risk of decompression sickness (DCS), but not without incurring additional negative stimulus of hypobaric hypoxia to the already physiologically challenging spaceflight environment. This paper provides a literature review of the human health and performance risks associated with the 8 psia/32% O2 environment. Of most concern are the potential effects on the central nervous system including increased intracranial pressure, visual impairment, sensorimotor dysfunction, and oxidative damage. Other areas of focus include validation of the DCS mitigation strategy, incidence and treatment of acute mountain sickness (AMS), development of new exercise countermeasures protocols, effective food preparation at 8 psia, assurance of quality sleep, and prevention of suit-induced injury. As a first effort, the trade space originally considered in the EAWG was re-evaluated looking for ways to decrease the hypoxic dose by further enriching the O2% or increasing the pressure. After discussion with the NASA engineering and materials community, it was determined that the O2 could be enriched from 32% to 34% and the pressure increased from 8.0 to 8.2 psia without significant penalty. These two small changes increase alveolar O2 pressure by 11 mmHg, which is expected to significantly benefit crewmembers. The 8.2/34 environment (inspired O2 pressure = 128 mmHg) is also physiologically equivalent to the staged decompression atmosphere of 10.2 psia / 26.5% O2 (inspired O2 pressure = 127 mmHg) used on 34 different shuttle missions for approximately a week each flight. Once decided, the proposed internal environment, if different than current experience, should be evaluated through appropriately simulated research studies. In many cases, the human physiologic concerns can be investigated effectively through integrated multi-discipline ground-based studies. Although missions proposing to use an 8.2/34 environment are still years away, it is recommended that these studies begin early enough to ensure that the correct decisions pertaining to vehicle design, mission operational concepts, and human health countermeasures are appropriately informed.

Environmental control↗

Early Career Hire Rapid Training and Development Program: Status Report

The aging of the industrialized workforce, particularly in the aerospace industry, has resulted in a very large generation gap in the workforce. The disproportionate size of Baby Boomers, increasing longevity and declining birth rates has made this phenomenon a reality that no organization can ignore. It is now critical that aerospace organizations prepare themselves for this watershed transformation in the workforce and take the initiative to prepare the incoming workforce with the skills and knowledge necessary to stay at the forefront. Last year the Jet Propulsion Laboratory launched a pioneering training program, known as Phaeton, to provide the knowledge, practice, experience, mentoring opportunities, and project life cycle exposure to our incoming generation of engineers. After 14 months of operation, now is the time to discuss the preliminary results of this new program.

Riley, Betsy N.↗

Evidence Report: Risk of Hypobaric Hypoxia from the Exploration Atmosphere

Extravehicular activity (EVA) is at the core of a manned space exploration program. Some elements of exploration may be safely and effectively performed by robots, but certain critical elements will require the trained, assertive, and reasoning mind of a human crewmember. To effectively use these skills, NASA needs a safe, effective, and efficient EVA component integrated into the human exploration program. The EVA preparation time should be minimized and the suit pressure should be low to accommodate EVA tasks without causing undue fatigue, physical discomfort, or suit-related trauma. Commissioned in 2005, the Exploration Atmospheres Working Group (EAWG) had the primary goal of recommending to NASA an internal environment that allowed efficient and repetitive EVAs for missions that were to be enabled by the former Constellation Program. At the conclusion of the EAWG meeting, the 8.0 psia and 32% oxygen (O2) environment were recommended for EVA-intensive phases of missions. After re-evaluation in 2012, the 8/32 environment was altered to 8.2 psia and 34% O2 to reduce the hypoxic stress to a crewmember. These two small changes increase alveolar O2 pressure by 11 mmHg, which is expected to significantly benefit crewmembers. The 8.2/34 environment (inspired O2 pressure = 128 mmHg) is also physiologically equivalent to the staged decompression atmosphere of 10.2 psia / 26.5% O2 (inspired O2 pressure = 127 mmHg) used on 34 different shuttle missions for approximately a week each flight. As a result of selecting this internal environment, NASA gains the capability for efficient EVA with low risk of decompression sickness (DCS), but not without incurring the additional negative stimulus of hypobaric hypoxia to the already physiologically challenging spaceflight environment. This report provides a review of the human health and performance risks associated with the use of the 8.2 psia / 34% O2 environment during spaceflight. Of most concern are the potential effects on the central nervous system (CNS), including increased intracranial pressure, visual impairment, sensorimotor dysfunction, and oxidative damage. Other areas of focus include validation of the DCS mitigation strategy, incidence and treatment of transient acute mountain sickness (AMS), development of new exercise countermeasure protocols, effective food preparation at 8.2 psia, assurance of quality sleep, and prevention of suit-induced injury. Although missions proposing to use an 8.2/34 environment are still years away, it is recommended that these studies begin early enough to ensure that the correct decisions pertaining to vehicle design, mission operational concepts, and human health countermeasures are appropriately informed.

Norcross, Jason R.↗

Science Training History of the Apollo Astronauts

Following President Kennedy’s initiation of Project Apollo, NASA underwent substantial changes in personnel, organization, and programs and faced a major question: what to do on the Moon after landing. Once a decision that science activities, particularly geoscience, should be pursued, considerable debate ensued over how to accomplish this. Questions arose over instruments and tools required, samples and photos to be returned, landing site selection, and crew composition. Answers to these questions required major efforts for planning traverses on the Moon and training the astronauts in the extensive procedures necessary in low gravity to use tools, set up instruments, take adequate photos, collect and document samples, and provide proper descriptions. In addition to astronauts on the surface, an astronaut in lunar orbit managed additional instruments, photography and verbal descriptions. Training for these activities averaged nearly one hundred hours per month for over a year for each crew. There were many problems as the training progressed: adjusting groups and backgrounds of the training personnel for the best combination of personalities and skills, overcoming logistical troubles, revising awkward procedures, determining optimum means of communications between all involved groups, and devising contingency procedures for real-time problems. By the last mission these problems were overcome.

Phinney, William C.↗

The Dark Energy Survey Supernova Programme: Modelling Selection Efficiency and Observed Core-collapse Supernova Contamination

The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accuratephotometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes anessential step for training and testing photometric classification algorithms, and for correcting biases introduced by selectioneffects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-seriesspectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies toconstruct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark EnergySurvey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations.We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data.We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and beforephotometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation,we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 percent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveyswithout fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmologyanalysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associatedsystematic uncertainty.

M Vincenzi↗

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗

Improving Realism in Reduced Gravity Simulators

Since man was first determined to walk on the moon, simulating the lunar environment became a priority. Providing an accurate reduced gravity environment is crucial for astronaut training and hardware testing. This presentation will follow the development of reduced gravity simulators to a final comparison of environments between the currently used systems. During the Apollo program era, multiple systems were built and tested, with several NASA centers having their own unique device. These systems ranged from marionette-like suspension devices where the subject laid on his side, to pneumatically driven offloading harnesses, to parabolic flights. However, only token comparisons, if any, were made between systems. Parabolic flight allows the entire body to fall at the same rate, giving an excellent simulation of reduced gravity as far as the biomechanics and physical perceptions are concerned. While the effects are accurate, there is limited workspace, limited time, and high cost associated with these tests. With all mechanical offload systems only the parts of the body that are actively offloaded feel any reduced gravity effects. The rest of the body still feels the full effect of gravity. The Partial Gravity System (Pogo) is the current ground-based offload system used to training and testing at the NASA Johnson Space Center. The Pogo is a pneumatic type system that allows for offloaded motion in the z-axis and free movement in the x-axis, but has limited motion in the y-axis. The pneumatic system itself is limited by cylinder stroke length and response time. The Active Response Gravity Offload System (ARGOS) is a next generation groundbased offload system, currently in development, that is based on modern robotic manufacturing lines. This system is projected to provide more z-axis travel and full freedom in both the x and y-axes. Current characterization tests are underway to determine how the ground-based offloading systems perform, how they compare to parabolic flights, and which of the systems is preferable for specific uses. These tests were conducted with six degree of freedom robots and manual inputs. Initial results show a definitive difference in abilities of the two offload systems.

Cowley, Matthew↗

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

Origins and Early History of Underwater Neutral Buoyancy Simulation of Weightlessness for EVA Procedures Development and Training: Winnowing and Regrowth - Part 2

The technique of neutral buoyancy during water immersion was applied to a variety of questions pertaining to human performance factors in the early years of the space age. It was independently initiated by numerous aerospace contractors at nearly the same time, but specific applications depended on the problems that the developers were trying to solve. Those problems dealt primarily with human restraint and maneuverability and were often generic across extravehicular activity (EVA) and intravehicular activity (IVA) worksites. The same groups often also considered fractional gravity as well as weightless settings and experimented with ballasting to achieve lunar and Mars-equivalent loads as part of their on-going research and development. Dr. John Charles reviewed the association of those tasks with contemporary perceptions of the direction of NASA's future space exploration activities and with Air Force assessments of the military value of man in space.

Charles, John B.↗

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗