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Automated biosensors for online use with NASA Water Monitoring System employs bioluminescence and chemiluminescence techniques to rapidly measure microbe contamination of water samples. System eliminates standard laboratory procedures requiring time duration of 24 hours or longer.
Efficient production of a wide range of commercial products based on submicron colloidal dispersions would benefit from instrumentation for online particle sizing, permitting real time monitoring and control of the particle size distribution. Recent advances in the technology of dynamic light scattering (DLS), especially improvements in algorithms for inversion of the intensity autocorrelation function, have made it ideally suited to the measurement of simple particle size distributions in the difficult submicron region. Crucial to the success of an online DSL based instrument is a simple mechanism for automatically sampling and diluting the starting concentrated sample suspension, yielding a final concentration which is optimal for the light scattering measurement. A proprietary method and apparatus was developed for performing this function, designed to be used with a DLS based particle sizing instrument. A PC/AT computer is used as a smart controller for the valves in the sampler diluter, as well as an input-output communicator, video display and data storage device. Quantitative results are presented for a latex suspension and an oil-in-water emulsion.
Data from a wind tunnel investigation of the flow fields around helicopter rotors were presented. A two component laser velocimeter was used to measure the velocity fields of two 2.1 m diameter rotors. A minicomputer-based online data system is described which monitored, reduced, and plotted the results. Tip vortices constitute the primary disturbances in the flow field, but present theories do not predict vortex positions and velocity distributions with sufficient accuracy.
The standard nitrogen dioxide (NO2) data product, produced from measurements by the Ozone Monitoring Instrument (OMI), are publicly available online from the NASA GESDISC facility. Important data fields include total and tropospheric column densities, as well as collocated data for cloud fraction and cloud top height, surface albedo and snow/ice coverage, at the resolution of the OMI instrument (12 km x 26 km, at nadir). The retrieved NO2 data have been validated, principally under clear-sky conditions. The first public-release version has been available since September 2006. An improved version of the data product, which includes a number of new data fields, and improved estimates of the retrieval uncertainties will be released by the end of 2007. This talk will describe the standard NO2 data product, including details that are essential for the use of the data for air quality studies. We will also describe the principal improvements with the new version of the data product.
Reliability is the ability to perform well and consistently. More formally, reliability is defined as the mathematical probability that a system does not fail during a specified time period under its specified operating conditions. The specified operating conditions often go beyond the nominal environment to include variations and challenges encountered in operational use. The difficulty is that systems are often operated outside of their specified operating conditions and, if they fail, the designers are in theory blameless. Unanticipated damaging events include internal failures, external disruptions in supporting systems, accidents, and repurposing. The most common explanation of a system failure is human error, which is usually the first assumption of the system designers. Robustness is the capability to perform without failure under a wide range of conditions that go beyond the specified operating conditions. The first step towards improving robustness would be to expand the system’s specified operating conditions to include a wider range of anticipated challenges, especially human error. Beyond this, there is a need for general approach to reduce the impact of unanticipated future events, the unknown unknowns, by improving the system’s general ability to cope. Robustness can be improved by providing additional processing capacity, larger flow control buffers, increased backup storage, more online redundancy, and more capable supervisory monitoring and control.
The limits of coding with joint constraints on detected and undetected error rates Programming errors occur frequently in large software systems, and even more so if these systems are concurrent. In the past, researchers have developed specialized programs to aid programmers detecting concurrent programming errors such as deadlocks, livelocks, starvation and data races. In this work we propose a language extension to the aspect-oriented programming language AspectJ, in the form of three new built-in pointcuts, lock(), unlock() and may be Shared(), which allow programmers to monitor program events where locks are granted or handed back, and where values are accessed that may be shared amongst multiple Java threads. We decide thread-locality using a static thread-local objects analysis developed by others. Using the three new primitive pointcuts, researchers can directly implement efficient monitoring algorithms to detect concurrent programming errors online. As an example, we expose a new algorithm which we call RACER, an adoption of the well-known ERASER algorithm to the memory model of Java. We implemented the new pointcuts as an extension to the Aspect Bench Compiler, implemented the RACER algorithm using this language extension and then applied the algorithm to the NASA K9 Rover Executive. Our experiments proved our implementation very effective. In the Rover Executive RACER finds 70 data races. Only one of these races was previously known.We further applied the algorithm to two other multi-threaded programs written by Computer Science researchers, in which we found races as well.
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A diagnostic tool was developed for detecting fatigue damage to spur gears, spiral bevel gears, and rolling element bearings. This diagnostic tool was developed and evaluated experimentally by collecting oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig, Spiral Bevel Gear Test Facility, and the 500hp Helicopter Transmission Test Stand. During each test, data from an online, in-line, inductance type oil debris sensor was monitored and recorded for the occurrence of pitting damage. Results indicate oil debris alone cannot discriminate between bearing and gear fatigue damage.
The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculations, and in turn, render the remaining useful life prediction uncertain. It is challenging to identify and characterize the various sources of uncertainty in prognosis, understand how each of these sources of uncertainty affect the uncertainty in the remaining useful life prediction, and thereby compute the overall uncertainty in the remaining useful life prediction. In order to achieve these goals, this paper proposes that the task of estimating the remaining useful life must be approached as an uncertainty propagation problem. In this context, uncertainty propagation methods which are available in the literature are reviewed, and their applicability to prognostics and health monitoring are discussed.
An innovative approach to fault management was recently demonstrated for the NASA LeRC Space Station Freedom (SSF) power system testbed. This project capitalized on research in model-based reasoning, which uses knowledge of a system's behavior to monitor its health. The fault management system (FMS) can isolate failures online, or in a post analysis mode, and requires no knowledge of failure symptoms to perform its diagnostics. An in-house tool called MARPLE was used to develop and run the FMS. MARPLE's capabilities are similar to those available from commercial expert system shells, although MARPLE is designed to build model-based as opposed to rule-based systems. These capabilities include functions for capturing behavioral knowledge, a reasoning engine that implements a model-based technique known as constraint suspension, and a tool for quickly generating new user interfaces. The prototype produced by applying MARPLE to SSF not only demonstrated that model-based reasoning is a valuable diagnostic approach, but it also suggested several new applications of MARPLE, including an integration and testing aid, and a complement to state estimation.
This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.
Self-adaptive systems (SAS) can reconfigure at run time in response to changing situations to express acceptable behaviors in the face of uncertainty. With respect to game design, such situations may include user input, emergent behaviors, performance concerns, and combinations thereof. Typically an SAS is modeled as a feedback loop that functions within an existing system, with operations including monitoring, analyzing, planning, and executing (i.e., MAPE-K) to enable online reconfiguration. This paper presents a conceptual approach for extending software engineering artifacts to be self-adaptive within the context of game design. We have modified a game developed for creative coding education to include a MAPE-K self-adaptive feedback loop, comprising run-time adaptation capabilities and the software artifacts required to support adaptation.
The main objective was to determine the feasibility of using a computer-based interactive information recall module for the Life Sciences Project Division (LSPD) at NASA, Johnson Space Center. LSPD personnel prepare payload experiments to test and monitor physiological functions in zero gravity. Training refreshers and other types of online help are needed to support personnel in their tasks during mission testing and in flight. Results of a survey of other hypermedia and multimedia developers and lessons learned by the developer of the LSPD prototype module are presented. Related issues and future applications are also discussed and further hypermedia development within the LSPD is recommended.
ANITA (Analysing Interferometer for Ambient Air) is a flight experiment precursor for a permanent continuous air quality monitoring system on the ISS (International Space Station). For the safety of the crew, ANITA can detect and quantify quasi-online and simultaneously 33 gas compounds in the air with ppm or sub-ppm detection limits. The autonomous measurement system is based on FTIR (Fourier Transform Infra-Red spectroscopy). The system represents a versatile air quality monitor, allowing for the first time the detection and monitoring of trace gas dynamics in a spacecraft atmosphere. ANITA operated on the ISS from September 2007 to August 2008. This paper summarizes the results of ANITA s air analyses with emphasis on comparisons to other measurements. The main basis of comparison is NASA s set of grab samples taken onboard the ISS and analysed on ground applying various GC-based (Gas Chromatography) systems.
Simple Technique samples an entire spool of welding wire to test for contamination or unauthorized filler wire. Since unit monitors entire length of wire used in welding, completed welds need not be inspected individually. Technique would save time, avoiding human interface and production delays.
Background: SERVIR -- the Regional Visualization and Monitoring System -- helps people use Earth observations and predictive models based on data from orbiting satellites to make timely decisions that benefit society. SERVIR operates through a network of regional hubs in Mesoamerica, East Africa, and the Hindu Kush-Himalayas. USAID and NASA support SERVIR, with the long-term goal of transferring SERVIR capabilities to the host countries. Objective/Purpose: The purpose of this presentation is to describe how the SERVIR system helps the SERVIR regions cope with eight areas of societal benefit identified by the Group on Earth Observations (GEO): health, disasters, ecosystems, biodiversity, weather, water, climate, and agriculture. This presentation will describe environmental health applications of data in the SERVIR system, as well as ongoing and future efforts to incorporate additional health applications into the SERVIR system. Methods: This presentation will discuss how the SERVIR Program makes environmental data available for use in environmental health applications. SERVIR accomplishes its mission by providing member nations with access to geospatial data and predictive models, information visualization, training and capacity building, and partnership development. SERVIR conducts needs assessments in partner regions, develops custom applications of Earth observation data, and makes NASA and partner data available through an online geospatial data portal at SERVIRglobal.net. Results: Decision makers use SERVIR to improve their ability to monitor air quality, extreme weather, biodiversity, and changes in land cover. In past several years, the system has been used over 50 times to respond to environmental threats such as wildfires, floods, landslides, and harmful algal blooms. Given that the SERVIR regions are experiencing increased stress under larger climate variability than historic observations, SERVIR provides information to support the development of adaptation strategies for nations affected by climate change. Conclusions: SERVIR is a platform for collaboration and cross-agency coordination, international partnerships, and delivery of web-based geospatial information services and applications. SERVIR makes a variety of geospatial data available for use in studies of environmental health outcomes.
Shuttle astronauts typically sleep only 6 to 6.5 hours per day while in orbit. This sleep loss is related to recurrent sleep cycle shifting--due to mission-dependent orbital mechanics and mission duration requirements-- and associated circadian displacement of sleep, the operational demands of space flight, noise and space motion sickness. Such sleep schedules are known to produce poor subjective sleep quality, daytime sleepiness, reduced attention, negative mood, slower reaction times, and impaired daytime alertness. Countermeasures to allow crew members to obtain an adequate amount of sleep and maintain adequate levels of neurobehavioral performance are being developed and investigated. However, it is necessary to develop methods that allow effective and attainable in-flight monitoring of vigilance to evaluate the effectiveness of these countermeasures and to detect and predict online critical decrements in alertness/performance. There is growing evidence to indicate that sleep loss and associated decrements in neurobehavioral function are reflected in the spectral composition of the electroencephalogram (EEG) during wakefulness as well as in the incidence of slow eye movements recorded by the electro-oculogram (EOG). Further-more, our preliminary data indicated that these changes in the EEG during wakefulness are more pronounced when subjects are in a supine posture, which mimics some of the physiologic effects of microgravity. Therefore, we evaluate the following hypotheses: (1) that during a 40-hour period of wakefulness (i.e., one night of total sleep deprivation) neurobehavioral function deteriorates, the incidence of slow eye-movements and EEG power density in the theta frequencies increases especially in frontal areas of the brain; (2) that the sleep deprivation induced deterioration of neurobehavioral function and changes in the incidence of slow eye movements and the spectral composition of the EEG are more pronounced when subjects are in a supine position; and (3) that based on assessment of slow-eye movements and quantitative on-line topographical analyses of EEG during wakefulness an EEG and or EOG parameter can be derived/constructed which accurately predicts changes in neurobehavioral function.
Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data, researchers can develop systems that automatically quantify the socioeconomic impacts resulting from flood disaster events.