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An intercomparison of ozone profiling instruments, two differential absorption lidars, a microwave radiometer, electrochemical concentration sondes, and the SAGE II satellite instrument is presented.
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The detection of changes in random process variance and bandwidth was studied. Psychophysical thresholds for these two parameters were determined using an adaptive staircase technique for second order random processes at two nominal periods (1 and 3 seconds) and damping ratios (0.2 and 0.707). Thresholds for bandwidth changes were approximately 9% of nominal except for the (3sec,0.2) process which yielded thresholds of 12%. Variance thresholds averaged 17% of nominal except for the (3sec,0.2) process in which they were 32%. Detection times for suprathreshold changes in the parameters may be roughly described by the changes in RMS velocity of the process. A more complex model is presented which consists of a Kalman filter designed for the nominal process using velocity as the input, and a modified Wald sequential test for changes in the variance of the residual. The model predictions agree moderately well with the experimental data. Models using heuristics, e.g. level crossing counters, were also examined and are found to be descriptive but do not afford the unification of the Kalman filter/sequential test model used for changes in mean.
The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.
Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.
We acquired and analyzed mid-infrared images of Jupiter's disk at selected wavelengths from NASA's Infrared Telescope Facility (IRTF) from 1996 to 2011, including a period of large-scale changes of cloud color and albedo. We derived the 100-300 mbar temperature structure, together with tracers of vertical motion: the thickness of a 600- mbar cloud layer, the 300-mbar abundance of the condensable gas NH3, and the 400- mbar para- vs. ortho-H2 ratio. The biggest visual change was detected in the normally dark South Equatorial Belt (SEB) that 'faded' to a light color in 2010, during which both cloud thickness and NH3 abundance rose; both returned to their pre-fade levels in 2011, as the SEB regained its normal dark color. The cloud thickness in Jupiter's North Temperate Belt (NTB) increased in 2002, coincident with its visible brightening, and its NH3 abundance spiked in 2002-2003. Jupiter's Equatorial Zone (EZ), a region marked by more subtle but widespread color and albedo change, showed high cloud thickness variability between 2007 and 2009. In Jupiter's North Equatorial Belt (NEB), the cloud thickened in 2005, then slowly decreased to a minimum value in 2010-2011. No temperature variations were associated with any of these changes, but we discovered temperature oscillations of approx.2-4 K in all regions, with 4- or 8-year periods and phasing that was dissimilar in the different regions. There was also no detectable change in the para- vs. ortho-H2 ratio over time, leading to the possibility that it is driven from much deeper atmospheric levels and may be time-invariant. Our future work will continue to survey the variability of these properties through the Juno mission, which arrives at Jupiter in 2016, and to connect these observations with those made using raster-scanned images from 1980 to 1993 (Orton et al. 1996 Science 265, 625).
The shapes and elevations of barrier islands may change dramatically over a short period of time such as during a storm. Even between storms, sediment is constantly shifting to and from these islands and between different areas of the islands at varying rates and in varying amounts. Coastal scientists and engineers, however, are currently unable to measure these changes occurring over an entire barrier island at once. This three-year project, which is funded by NASA and jointly conducted by the Bureau of Economic Geology and the Center for Space Research at The University of Texas at Austin, is designed to overcome this problem by developing the use of interferometry from airborne synthetic aperture radar (AIRSAR) to detect changes in coastal topography. Surrogate measures of topography observed in fully polarimetric AIRSAR are also being investigated.
The current generation of state-of-the-art global climate models are being run at increasingly higher horizontal resolutions, with the goal of resolving organised deep convection explicitly. How a kilometre-scale resolution impacts the representation of the atmospheric boundary layer is, however, not well known. Using statistical analysis on global fields as well as high-frequency data at selected locations, produced with the Integrated Forecasting System (IFS) model for the Next Generation Earth-system Models (nextGEMS) project, we investigate the horizontal resolution dependence of some boundary-layer processes. We find that a change in resolution from 9 to 2.8 km causes no substantial changes to boundary-layer properties and processes at most of the locations studied, although some global changes are detected that indicate circulation changes. Small changes to the boundary-layer depth and structure are found in the Tropics. The short simulation length and lack of data for optimal boundary-layer analysis limits the conclusions, especially in relation to the connection between the boundary layer and the atmosphere general circulation.
The EOS Moderate Resolution Imaging Spectrometer (MODIS) is being developed by NASA for flight on the Earth Observing System (EOS) series of satellites, the first of which (EOS-AM-1) is scheduled for launch in 1998. This document describes the algorithms and their theoretical basis for the MODIS Level 1B characterization, calibration, and geolocation algorithms which must produce radiometrically, spectrally, and spatially calibrated data with sufficient accuracy so that Global change research programs can detect minute changes in biogeophysical parameters. The document first describes the geolocation algorithm which determines geodetic latitude, longitude, and elevation of each MODIS pixel and the determination of geometric parameters for each observation (satellite zenith angle, satellite azimuth, range to the satellite, solar zenith angle, and solar azimuth). Next, the utilization of the MODIS onboard calibration sources, which consist of the Spectroradiometric Calibration Assembly (SRCA), Solar Diffuser (SD), Solar Diffuser Stability Monitor (SDSM), and the Blackbody (BB), is treated. Characterization of these sources and integration of measurements into the calibration process is described. Finally, the use of external sources, including the Moon, instrumented sites on the Earth (called vicarious calibration), and unsupervised normalization sites having invariant reflectance and emissive properties is treated. Finally, algorithms for generating utility masks needed for scene-based calibration are discussed. Eight appendices are provided, covering instrument design and additional algorithm details.
Kennedy Space Center scientists developed a hydrogen leak sensor utilizing a combination of chemochromic pigment and polymer that can be molded or fiber spun into rigid or flexible shapes such as tape. The sensor turns a dark color when exposed to hydrogen gas. This sensor has proven to be very effective for pinpointing the exact location of leaks in hydrogen gas lines and fittings at launch pads. Kennedy Space Center exclusively licensed this technology to the University of Central Florida (UCF), who also holds patents that are complimentary to KSC's. UCF has bundled the patents and exclusively licensed the portfolio to HySense Technology LLC, a startup company founded by a UCF professor who supports the UCF Florida Solar Energy Center (FSEC). HySense has fully developed its product (known as Intellipigment"TM"), and currently has five commercial customers. The company recently won the $100,000 first-place award at the CAT5 innovation competition at the Innovation Concourse of the Southeast: Safety & Manufacturing event in Orlando, FL. Commercial production and sales of this technology by HySense Technology will make this leak sensor widely available for use by NASA, DoD, and industries that utilize hydrogen gas.
The demand for automated and effective monitoring techniques has soared with the increased digitization of industrial monitoring systems. State-of-the-art machine learning methods are effectively detecting abrupt changes in system states. However, these methods lack comparable maturity in detecting subtle changes that may be signs of incipient faults. This manuscript argues that the current anomaly detection methods can be enhanced by exploring weak patterns to enable subtle variation detection. Specifically, the concept of semi-supervised learning is employed, with labels representing knowledge about some anomalous conditions of a system. The basic idea is to extract a candidate set of weak patterns discarded by state-of-the-art baselining algorithms. With few labeled anomalous data, the algorithm selects the weak patterns and allows for their possible fusion using the highest sensitivity to the labeled anomalies. Here, the method’s applicability is demonstrated using a representative pressurized water reactor (PWR) model simulated by Dymola.
This study presents the first direct comparison of variations in seasonal GWS derived from GRACE TWS and simulated SM with GW-level measurements in a semiarid region. Results showed that variations in GWS and SM are the main sources controlling TWS changes over the High Plains, with negligible storage changes from surface water, snow, and biomass. Seasonal variations in GRACE TWS compare favorably with combined GWS from GW-level measurements (total 2,700 wells, average 1,050 GW-level measurements per season) and simulated SM from the Noah land surface model (R = 0.82, RMSD = 33 mm). Estimated uncertainty in seasonal GRACE-derived TWS is 8 mm, and estimated uncertainty in TWS changes is 11 mm. Estimated uncertainty in SM changes is 11 mm and combined uncertainty for TWS-SM changes is 15 mm. Seasonal TWS changes are detectable in 7 out of 9 monitored periods and maximum changes within a year (e.g. between winter and summer) are detectable in all 5 monitored periods. Grace-derived GWS calculated from TWS-SM generally agrees with estimates based on GW-level measurements (R = 0.58, RMSD = 33 mm). Seasonal TWS-SM changes are detectable in 5 out of the 9 monitored periods and maximum changes are detectable in all 5 monitored periods. Good correspondence between GRACE data and GW-level measurements from the intensively monitored High Plains aquifer validates the potential for using GRACE TWS and simulated SM to monitor GWS changes and aquifer depletion in semiarid regions subjected to intensive irrigation pumpage. This method can be used to monitor regions where large-scale aquifer depletion is ongoing, and in situ measurements are limited, such as the North China Plain or western India. This potential should be enhanced by future advances in GRACE processing, which will improve the spatial and temporal resolution of TWS changes, and will further increase applicability of GRACE data for monitoring GWS.
Bone loss due to long‐duration spaceflight has been characterized by both DXA and QCT serial scans. It is unclear if these spaceflight‐induced changes in bone mineral density (BMD) and structure result in increased fracture incidence. NASA astronauts currently fly 5 to 6‐month missions on the International Space Station (ISS) and at least one 12‐month mission is planned. While NASA has measured areal BMD (by DXA) and volumetric BMD (by QCT) and has estimated hip strength (by finite element models of QCT data, no method has yet been used to examine bone micro‐architecture from lumbar spine (LS). DXA scans are routinely performed pre‐ and postflight on all ISS astronauts to follow BMD changes associated with spaceflight. Trabecular Bone Score (TBS) is a relatively new method that measures grey‐scale‐level texture information extracted from LS DXA images and correlates with 3D parameters of bone micro‐architecture. We evaluated the ability of LS TBS to discriminate changes in astronauts who have flown on ISS missions and to determine if TBS can provide additional information compared to DXA. Methods: Lumbar Spine (L1‐4) DXA scans from 51 astronauts (mean age, 47 +/- 4 yrs) were divided into 3 groups based on the exercise regimens performed onboard the ISS. "Pre‐ARED" (exercise using a load‐limited resistive exercise device, <300 lb), "ARED" (exercise with a high‐load resistive exercise device, up to 600 lb) and "Bisphos+ARED" group (ARED exercise and a 70‐mg alendronate tablet once a week before and during flight, starting 17 days before launch). DXA scans were performed and analyzed on a Hologic Discovery W using the same technician for the pre‐ and post‐flight scans. LSC for the LS in our laboratory is 0.025 g/sq. cm. TBS was performed at the Mercy Hospital, Cincinnati, Ohio on a similar Hologic computer. Data were analyzed using a paired, 2‐tailed Student's t‐test for the difference between pre‐ and postflight means. Percent change and % change per month are noted. Interpretation: Our data suggest that: TBS and DXA both detected significant decrements in the LS in these pre‐ ARED astronauts, not unexpected given the insufficient loads provided by this early exercise device. TBS did not detect significant changes in the ARED or Bisphos+ARED groups while DXA did detect significant changes in the ARED astronauts. These findings suggest that DXA and TBS are detecting independent effects of bone loss interventions tested in ISS astronauts in space, which may be due to distinct effects of interventions on mineral content of separate cortical vs. trabecular bone. Conclusion: TBS, in conjunction with DXA BMD, may provide additional insight into the nature of changes (or lack thereof) in the microstructure of trabecular bone and the areal BMD of vertebral bodies.
Conversion of agricultural land to a variety of urban uses is a major problem along the Wasatch Front, Utah. Although LANDSAT MSS data is a relatively coarse tool for discriminating categories of change in urban-size plots, its availability prompts a thorough test of its power to detect change. The procedures being applied to a test area in Salt Lake County, Utah, where the land conversion problem is acute are presented. The identity of land uses before and after conversion was determined and digital procedures for doing so were compared. Several algorithms were compared, utilizing both raw data and preprocessed data. Verification of results involved high quality color infrared photography and field observation. Two data sets were digitally registered, specific change categories internally identified in the software, results tabulated by computer, and change maps printed at 1:24,000 scale.
This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.
BACKGROUND . Calculated loss rates in trabecular (Trb) volumetric bone mineral density (vBMD), in astronauts after return from 6-month long-duration (LD) spaceflight, are rapid relative to menopause-induced loss rates and could contribute to the observed lack of recovery. Perforations in the Trb bony struts, skeletal fragility and fractures [5] have been associated with the rapid rate of bone loss in women due to menopause. The more rapid loss rate observed in astronauts could be inducing similar, irreversible losses of trabecular connectivity as seen in cadaver studies of female skeletons. These changes are not detectable by DXA technology and would not be discovered in astronauts during scheduled, triennial DXA testing for primary osteoporosis. High-resolution-peripheral quantitative computed tomography (HR-pQCT) of the lower leg have detected losses in Trb vBMD (vBMD) and changes in Trb microarchitecture (µArch) in astronauts immediately following routine 6-month spaceflights and continuing in some individuals for 1 or more years postflight with no signs of recovery. Resolution of these microarchitectural changes by whole body QCT, especially in the deeply embedded hip bone, is prohibitive due to excessive and unsafe radiation exposures. While HR-pQCT has sufficient resolution to study Trb µArch at safe and lower radiation exposure testing is restricted only to the lower leg and wrist. Hence, a technology to detect detrimental changes to Trb µArch in astronauts, especially of the hip, is needed to inform type and timing of countermeasures to irreversible deficits (preflight, inflight and postflight). METHODS . This HRP-funded study (2023-2027) will verify the sensitivity of a protocol for MRI scanning (on a 3T Siemens Vida) to detect changes to hip Trb µArch in persons following spinal cord injury (SCI); skeletal immobilization below the spinal lesion will serve as an analog for non-weight-bearing during spaceflight. The study period has been extended by 1 yr due to delayed Richmond IRB approval in Y1. A 3T MRI protocol will characterize serial changes in hip Trb µArch in SCI patients from time of admission at the Richmond VA Medical Center to their scheduled annual exam 12-months later. SCI patients with age, sex, and physical characteristics like ISS astronauts will be consented. The ability of 3T MRI hip scan to discriminate loss of trabecular connectivity subjects with SCI will be compared to age- and sex-matched ambulatory controls. A comparison between HR-pQCT and MRI measurements at the ankle (distal tibia) of study subjects will also be performed. A modified DXA scan analysis (3D-Shaper) will also be verified as a potential method for monitoring concurrent changes in hip Trb bone over 12-month. SIGNIFICANCE . Deliverables: a surveillance method for assessing for irreversible losses of connectivity and how skeletal changes after LD spaceflight(s) would modify the terrestrial risk of primary osteoporosis.
BACKGROUND . Calculated loss rates in trabecular (Trb) volumetric bone mineral density (vBMD), in astronauts after return from 6-month long-duration (LD) spaceflight, are rapid relative to menopause-induced loss rates and could contribute to the observed lack of recovery. Perforations in the Trb bony struts, skeletal fragility and fractures [5] have been associated with the rapid rate of bone loss in women due to menopause. The more rapid loss rate observed in astronauts could be inducing similar, irreversible losses of trabecular connectivity as seen in cadaver studies of female skeletons. These changes are not detectable by DXA technology and would not be discovered in astronauts during scheduled, triennial DXA testing for primary osteoporosis. High-resolution-peripheral quantitative computed tomography (HR-pQCT) of the lower leg have detected losses in Trb vBMD (vBMD) and changes in Trb microarchitecture (µArch) in astronauts immediately following routine 6-month spaceflights and continuing in some individuals for 1 or more years postflight with no signs of recovery. Resolution of these microarchitectural changes by whole body QCT, especially in the deeply embedded hip bone, is prohibitive due to excessive and unsafe radiation exposures. While HR-pQCT has sufficient resolution to study Trb µArch at safe and lower radiation exposure testing is restricted only to the lower leg and wrist. Hence, a technology to detect detrimental changes to Trb µArch in astronauts, especially of the hip, is needed to inform type and timing of countermeasures to irreversible deficits (preflight, inflight and postflight). METHODS . This HRP-funded study (2023-2027) will verify the sensitivity of a protocol for MRI scanning (on a 3T Siemens Vida) to detect changes to hip Trb µArch in persons following spinal cord injury (SCI); skeletal immobilization below the spinal lesion will serve as an analog for non-weight-bearing during spaceflight. The study period has been extended by 1 yr due to delayed Richmond IRB approval in Y1. A 3T MRI protocol will characterize serial changes in hip Trb µArch in SCI patients from time of admission at the Richmond VA Medical Center to their scheduled annual exam 12-months later. SCI patients with age, sex, and physical characteristics like ISS astronauts will be consented. The ability of 3T MRI hip scan to discriminate loss of trabecular connectivity subjects with SCI will be compared to age- and sex-matched ambulatory controls. A comparison between HR-pQCT and MRI measurements at the ankle (distal tibia) of study subjects will also be performed. A modified DXA scan analysis (3D-Shaper) will also be verified as a potential method for monitoring concurrent changes in hip Trb bone over 12-month. SIGNIFICANCE . Deliverables: a surveillance method for assessing for irreversible losses of connectivity and how skeletal changes after LD spaceflight(s) would modify the terrestrial risk of primary osteoporosis.