Results for “Bad data” — Page 5

NASA NTRS2004

Formal Verification of Safety Properties for Aerospace Systems Through Algorithms Based on Exhaustive State-Space Exploration

The Runway Safety Monitor (RSM) designed by Lockheed Martin is part of NASA's effort to reduce aviation accidents. We developed a Petri net model of the RSM protocol and used the model checking functions of our tool SMART to investigate a number of safety properties in RSM. To mitigate the impact of state-space explosion, we built a highly discretized model of the system, obtained by partitioning the monitored runway zone into a grid of smaller volumes and by considering scenarios involving only two aircraft. The model also assumes that there are no communication failures, such as bad input from radar or lack of incoming data, thus it relies on a consistent view of reality by all participants. In spite of these simplifications, we were able to expose potential problems in the RSM conceptual design. Our findings were forwarded to the design engineers, who undertook corrective action. Additionally, the results stress the efficiency attained by the new model checking algorithms implemented in SMART, and demonstrate their applicability to real-world systems. Attempts to verify RSM with NuSMV and SPIN have failed due to excessive memory consumption.

Ciardo, Gianfranco↗

NASA NTRS2006

Formal Verification of the Runway Safety Monitor

The Runway Safety Monitor (RSM) designed by Lockheed Martin is part of NASA's effort to reduce runway accidents. We developed a Petri net model of the RSM protocol and used the model checking functions of our tool SMART to investigate a number of safety properties in RSM. To mitigate the impact of state-space explosion, we built a highly discretized model of the system, obtained by partitioning the monitored runway zone into a grid of smaller volumes and by considering scenarios involving only two aircraft. The model also assumes that there are no communication failures, such as bad input from radar or lack of incoming data, thus it relies on a consistent view of reality by all participants. In spite of these simplifications, we were able to expose potential problems in the RSM conceptual design. Our findings were forwarded to the design engineers, who undertook corrective action. Additionally, the results stress the efficiency attained by the new model checking algorithms implemented in SMART, and demonstrate their applicability to real-world systems.

Siminiceanu, Radu↗

NASA NTRS2003

RGS Spectroscopy of the Cygnus Loop XA Knot

The observations were performed at the end of April 2002, and the data were received in July 2002. Unfortunately, the observations were badly compromised by high levels of background radiation; one the three observations lost entirely. Two replacement observations were scheduled for November 2002, and were only made available in January of 2003. Consequently, we have had little time to grapple with the unusual data analysis challenges. The search for a postdoctoral fellow has been successfully concluded, and Manami Sasaki began working for us in January 2003. She will be supported in part by these funds, and will be working to help understand these data. Examination of the RGS 'Orders' images indicate the presence of broad emission lines (as expected for the diffuse XA knot). However, examination of the 'Spatial' dispersion/cross-dispersion images indicate that the emission is also broad in the cross-dispersion direction. (As a crosscheck, some of the 'Lockman Hole' datasets were also examined as representative 'sky background' datasets; in these, both types of images are relatively flat (outside the calibration source regions). The quicklook plots of the spectra show the expected O VII and O VIII lines, in addition to a complex around 35 Angstroms; the approx. 35 Angstrom line is likely the C V He-beta line at 34.97 Angstrom, but identifying the additional line(s) will require a more careful reduction of the data. Consequently, there is valuable information to be extracted from these data, but it is complicated by diffuse nature of the emission. Because the angular scale is large, we will have to make use of sky background datasets in order to do the background fitting. A color composite image of OM data in the three UV bands was presented at the 'How does the Galaxy Work?' meeting, and compared to optical and X-ray imaging data. Quantitative analysis will require obtaining the effective bandpasses of the UV filters so that the predominant line and continuum contributions can be identified using plasma shock emission models. In view of the facts that the data were only obtained relatively recently, and the complexity of the data analysis, we request a one year no-cost extension on the grant.

Gaetz, Terrance J.↗

NASA NTRS1993

Climatic variation of storms

Long-term variation of U.S. tornadoes were studied by obtaining the best possible data during the 75 years, 1916-90. The most difficult task was to estimate the number of early tornadoes in 1916-50 when the reporting efficiency was very bad, resulting in undercounting of the incidents. In generating the best possible data, Fujita's Tornado Tape produced at the University of Chicago, the book Significant Tornadoes, 1880-1989, by Thomas P. Grazulis, and the NSSFC Tornado Tape were combined. First, the annual number of tornadoes were smoothed to obtain the smoothed number of annual tornadoes which was normalized to the standard number of tornadoes defined as the mean number in the recent 30 years 1960-89. The normalization factor was obtained by computing the ration of smoothed and standard number. In the early years, the factor was in excess of 7. Thereafter, the normalized number of annual tornadoes was computed and plotted, finding that the results are very satisfactory. In order to visualize the variation of tornado activities, the tornado activity number (TAN) was initiated and computed. The TAN including all annual tornadoes show certain periodicity. For climatological evaluation, the peak-activity day of each year, such as Superoutbreak day, 3 April 1974, Palm Sunday, 11 April 1965 were eliminated. The TAN, excluding peak-day tornadoes, is of extreme interest.

Fujita, Ted↗

DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗

NASA NTRS1988

Infrared observations of outer planet satellites

This task supports IR observations of the outer planet satellites. These data provide vital information about the thermophysical properties of satellite surfaces, including internal heat sources for Io. Observations include both broad and narrow band measurementsin the 2 to 20 micrometer spectral range. The program in the last year has aimed at obtaining lonitude coverage on Io to establish stability of hot spot patterns previously reported. Several runs produced the most complete data set for an apparition since the start of the program. Unfortunately, bad weather limited coverage of key longitude ranges containing the largest known hot spot Loki. Among the preliminary results is the observation of an outburst in Io's thermal flux that was measured at 4.8, 8.7 and 20 micrometer. Analysis of the data has given the best evidence to date of silicate volcanism on Io; this is one of the most significant pieces of the puzzle as to the relative roles of silicate and sulfur volcanism on Io. Researchers are collaborating with J. Goguen (NRC RRA to finish reduction of mutual event data, which have already improved ephermeris information for the satellites. The data appear to place significant limits on the characteristics of any leading side hot spots.

Johnson, T. V.↗

NASA NTRS2015

Underestimation of Project Costs

Large projects almost always exceed their budgets. Estimating cost is difficult and estimated costs are usually too low. Three different reasons are suggested: bad luck, overoptimism, and deliberate underestimation. Project management can usually point to project difficulty and complexity, technical uncertainty, stakeholder conflicts, scope changes, unforeseen events, and other not really unpredictable bad luck. Project planning is usually over-optimistic, so the likelihood and impact of bad luck is systematically underestimated. Project plans reflect optimism and hope for success in a supposedly unique new effort rather than rational expectations based on historical data. Past project problems are claimed to be irrelevant because "This time it's different." Some bad luck is inevitable and reasonable optimism is understandable, but deliberate deception must be condemned. In a competitive environment, project planners and advocates often deliberately underestimate costs to help gain project approval and funding. Project benefits, cost savings, and probability of success are exaggerated and key risks ignored. Project advocates have incentives to distort information and conceal difficulties from project approvers. One naively suggested cure is more openness, honesty, and group adherence to shared overall goals. A more realistic alternative is threatening overrun projects with cancellation. Neither approach seems to solve the problem. A better method to avoid the delusions of over-optimism and the deceptions of biased advocacy is to base the project cost estimate on the actual costs of a large group of similar projects. Over optimism and deception can continue beyond the planning phase and into project execution. Hard milestones based on verified tests and demonstrations can provide a reality check.

Cost estimation↗

NASA NTRS1985

A Preliminary Investigation of Systematic Noise in Data Acquired with the Airborne Imaging Spectrometer

Systematic noise is present in Airborne Imaging Spectrometer (AIS) data collected on October 26, 1983 and May 5, 1984 in grating position 0 (1.2 to 1.5 microns). In the October data set the noise occurs as 135 scan lines of low DN's every 270 scan lines. The noise is particularly bad in bands nine through thirty, restricting effective analysis to at best ten of the 32 bands. In the May data the regions of severe noise have been eliminated, but systematic noise is present with three frequencies (3, 106 and 200 scan lines) in all thirty two bands. The periodic nature of the noise in both data sets suggests that it could be removed as part of routine processing. This is necessary before classification routines or statistical analyses are used with these data.

Masuoka, E.↗

NASA NTRS2011

Health Monitor for Multitasking, Safety-Critical, Real-Time Software

Health Manager can detect Bad Health prior to a failure occurring by periodically monitoring the application software by looking for code corruption errors, and sanity-checking each critical data value prior to use. A processor s memory can fail and corrupt the software, or the software can accidentally write to the wrong address and overwrite the executing software. This innovation will continuously calculate a checksum of the software load to detect corrupted code. This will allow a system to detect a failure before it happens. This innovation monitors each software task (thread) so that if any task reports "bad health," or does not report to the Health Manager, the system is declared bad. The Health Manager reports overall system health to the outside world by outputting a square wave signal. If the square wave stops, this indicates that system health is bad or hung and cannot report. Either way, "bad health" can be detected, whether caused by an error, corrupted data, or a hung processor. A separate Health Monitor Task is started and run periodically in a loop that starts and stops pending on a semaphore. Each monitored task registers with the Health Manager, which maintains a count for the task. The registering task must indicate if it will run more or less often than the Health Manager. If the task runs more often than the Health Manager, the monitored task calls a health function that increments the count and verifies it did not go over max-count. When the periodic Health Manager runs, it verifies that the count did not go over the max-count and zeroes it. If the task runs less often than the Health Manager, the periodic Health Manager will increment the count. The monitored task zeroes the count, and both the Health Manager and monitored task verify that the count did not go over the max-count.

Zoerner, Roger↗

NASA NTRS1985

Relationships between chondritic meteorites and planets

Chondrites formed in the solar nebula prior to the formation of planets; they probably constituted the bulk of preplanetary solids in the inner solar system. In the highly reduced enstatite chondrites 10% of Si is metallic; in the highly oxidized CM and CI chondrites 20-30% of Fe is in the +3 state. The high density of Mercury implies that nebular Fe was reduced, whereas the low density of Mars indicates that a large fraction was oxidized. Most rare gases in the terrestrial planets seem to have accreted trapped in grains; Venusian interelement ratios resemble those in enstatite chondrites; Earth and Mars ratios are more like those in ordinary or carbonaceous chondrites. These observations imply that enstatite chondrites formed near the Sun (near Venus?), the carbonaceous chondrites formed far from the Sun, the ordinary chondrites at an intermediate location. Compositional data on Mercury, comets, an asteroidal fragment, and the moons of Mars are badly needed.

Wasson, J. T.↗

NASA NTRS1988

Planning actions in robot automated operations

Action planning in robot automated operations requires intelligent task level programming. Invoking intelligence necessiates a typical blackboard based architecture, where, a plan is a vector between the start frame and the goal frame. This vector is composed of partially ordered bases. A partial ordering of bases presents good and bad sides in action planning. Partial ordering demands the use of a temporal data base management system.

Das, A.↗

NASA NTRS2011

Evaluating Suit Fit Using Performance Degradation

The Mark III suit has multiple sizes of suit components (arm, leg, and gloves) as well as sizing inserts to tailor the fit of the suit to an individual. This study sought to determine a way to identify the point an ideal suit fit transforms into a bad fit and how to quantify this breakdown using mobility-based physical performance data. This study examined the changes in human physical performance via degradation of the elbow and wrist range of motion of the planetary suit prototype (Mark III) with respect to changes in sizing and as well as how to apply that knowledge to suit sizing options and improvements in suit fit. The methods implemented in this study focused on changes in elbow and wrist mobility due to incremental suit sizing modifications. This incremental sizing was within a range that included both optimum and poor fit. Suited range of motion data was collected using a motion analysis system for nine isolated and functional tasks encompassing the elbow and wrist joints. A total of four subjects were tested with motions involving both arms simultaneously as well as the right arm only. The results were then compared across sizing configurations. The results of this study indicate that range of motion may be used as a viable parameter to quantify at what stage suit sizing causes a detriment in performance; however the human performance decrement appeared to be based on the interaction of multiple joints along a limb, not a single joint angle. The study was able to identify a preliminary method to quantify the impact of size on performance and to develop a means to gauge tolerances around optimal size. More work is needed to improve the assessment of optimal fit and to compensate for multiple joint interactions.

Margerum, Sarah E.↗

NASA NTRS2006

CFDP Performance over Weather-Dependent Ka-Band Channel

This study presents an analysis of the delay performance of the CCSDS File Delivery Protocol (CFDP) over weather-dependent Ka-band channel. The Ka-band channel condition is determined by the strength of the atmospheric noise temperature, which is weather dependent. Noise temperature data collected from the Deep Space Network (DSN) Madrid site is used to characterize the correlations between good and bad channel states in a two-state Markov model. Specifically, the probability distribution of file delivery latency using the CFDP deferred Negative Acknowledgement (NAK) mode is derived and quantified. Deep space communication scenarios with different file sizes and bit error rates (BERs) are studied and compared. Furthermore, we also examine the sensitivity of our analysis with respect to different data sampling methods. Our analysis shows that while the weather-dependent channel only results in fairly small increases in the average number of CFDP retransmissions required, the maximum number of transmissions required to complete 99 percentile, on the other hand, is significantly larger for the weather-dependent channel due to the significant correlation of poor weather states.

weather↗

NASA NTRS2006

CFDP Performance over Weather-dependent Ka-band Channel

This study presents an analysis of the delay performance of the CCSDS File Delivery Protocol (CFDP) over weather-dependent Ka-band channel. The Ka-band channel condition is determined by the strength of the atmospheric noise temperature, which is weather dependent. Noise temperature data collected from the Deep Space Network (DSN) Madrid site is used to characterize the correlations between good and bad channel states in a two-state Markov model. Specifically, the probability distribution of file delivery latency using the CFDP deferred Negative Acknowledgement (NAK) mode is derived and quantified. Deep space communication scenarios with different file sizes and bit error rates (BERs) are studied and compared. Furthermore, we also examine the sensitivity of our analysis with respect to different data sampling methods. Our analysis shows that while the weather-dependent channel only results in fairly small increases in the average number of CFDP retransmissions required, the maximum number of transmissions required to complete 99 percentile, on the other hand, is significantly larger for the weather-dependent channel due to the significant correlation of poor weather states.

deep space communications↗

NASA NTRS1986

Comparison of the 1984 and 1985 AIS data over the Singatse Range (Yerington), Nevada

The Singatse Range is composed of a series of 53 types of volcanic, plutonic, metamorphic, and sedimentary rocks. In addition the Jurassic plutonic rocks are also of economic interest for their copper mineralization which is contained in a porphyry dike swarm. The 1984 and 1985 flight results from the Airborne Imaging Spectrometer (AIS) instrument flown in the NASA/JPL C-130 aircraft are contrasted and compared. The 1984 data are less noisy than the 1985, in which many sets of vertical stripings from bad detectors can be seen. Significantly however, enough of the hydrothermal alteration patterns can be seen in each line at the mutual crossing points that one can say that the specific targets were detected in both year's flights. The spectra of both years are corrupted by the second-order effect from the grating, but 0-H bond absorption at essentially correct wavelengths for sericite and/or kaolinite can be seen.

Lyon, R. J. P.↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗

NASA NTRS2011

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

pattern identification↗