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At least 127 records · Page 7

Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

Gupta, U. K.↗

Abnormal fault-recovery characteristics of the fault-tolerant multiprocessor uncovered using a new fault-injection methodology

An investigation was made in AIRLAB of the fault handling performance of the Fault Tolerant MultiProcessor (FTMP). Fault handling errors detected during fault injection experiments were characterized. In these fault injection experiments, the FTMP disabled a working unit instead of the faulted unit once in every 500 faults, on the average. System design weaknesses allow active faults to exercise a part of the fault management software that handles Byzantine or lying faults. Byzantine faults behave such that the faulted unit points to a working unit as the source of errors. The design's problems involve: (1) the design and interface between the simplex error detection hardware and the error processing software, (2) the functional capabilities of the FTMP system bus, and (3) the communication requirements of a multiprocessor architecture. These weak areas in the FTMP's design increase the probability that, for any hardware fault, a good line replacement unit (LRU) is mistakenly disabled by the fault management software.

Padilla, Peter A.↗

An evaluation of the use of new Doppler methods for detecting longitudinal function abnormalities in a pacing-induced heart failure model

BACKGROUND: Doppler tissue echocardiography and color M-mode Doppler flow propagation velocity have proven useful in evaluating cross-sections of patients with left ventricular (LV) dysfunction, but experience with serial changes is limited. Purpose and methods: We tested their use by evaluating the temporal changes of LV function in a pacing-induced congestive heart failure model. Rapid ventricular pacing was initiated and maintained in 20 dogs for 4 weeks. Echocardiography was performed at baseline and weekly during brief pacing cessation. RESULTS: With rapid pacing, LV volume significantly increased and ejection fraction (57%-28%), stroke volume (37-18 mL), and mitral annulus systolic velocity (16.1-6.6 cm/s) by Doppler tissue echocardiography significantly decreased, with ejection fraction and mitral annulus systolic velocity closely correlated (r = 0.706, P <.0001). In contrast to the mitral inflow velocities, mitral annulus early diastolic velocity decreased steadily (12.3-7.3 cm/s) resulting in a dramatic decrease in mitral annulus early/late (1.22-0.57) diastolic velocity with no tendency toward pseudonormalization. The color M-mode Doppler flow propagation velocity also showed significant steady decrease (57-24 cm/s) throughout the pacing period. Multiple regression analysis chose mitral annulus systolic velocity (r = 0.895, P <.0001) and propagation velocity (r = 0.782, P <.0001) for the most important factor predicting LV systolic and diastolic function, respectively. CONCLUSIONS: Doppler tissue echocardiography and color M-mode Doppler flow could evaluate the serial deterioration in LV dysfunction throughout the pacing period. These were more useful in quantifying progressive LV dysfunction than conventional ehocardiographic techniques, and were probably relatively independent of preload. These techniques could be suitable for longitudinal evaluation in addition to the cross-sectional study.

Evaluation Studies↗

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Bhaduri, Kanishka↗

Electron Bombardment on Dione: Surface Compositional Effects and Temperature Abnormalities

Saturn’s icy moons are composed primarily of water ice with minor other “contaminants,” such as CO2 ice, and a dark component thought to be organics, hematite and/or metallic Fe [1]. The space weathering process of electron bombardment is expected to be particularly important on the surfaces of Saturn’s inner moons (Mimas, Tethys, Dione, and Rhea), as they orbit within Saturn’s inner magnetosphere. Terrains exhibiting thermal anomalies (i.e., colder temperatures in the day and warmer temperatures at night than surrounding areas) correspond to regions of high energy electron bombardment [2; 3; 4; 5]. Energetic electrons impact the surfaces, sintering ice grains together, and this process becomes more effective for increasing particle energies [6]. Solar UV radiation, cosmic rays, dust in-fall, and cold plasma particles trapped in Saturn’s magnetic field also play an important role in altering the nature and the structure of the native surface ices by the implantation of contaminants, ionization, sputtering, and dissociation of water ice molecules [7]. Additionally, CO2 could be sourced from irradiation of dark organic material [8]. Many of these surface alterations are observable in Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) spectra [9]. We used similar methodologies as have been employed in previous works [e.g., 10] to derive surface temperatures from Cassini’s Composite Infrared Spectrometer (CIRS), which helped to isolate areas where space weathering due to electron bombardment is more predominant. The subtle changes in VIMS spectra were investigated using machine learning techniques. We present here our results for Dione. [1] Clark, R. N., et al. (2012) Icarus, 218, 831–860 [2] Howett, C. J. A., et al. (2020) Icarus, 113745 [3] Nordheim, T. A., et al. (2017) Icarus, 286, 56-68 [4] Paranicas, C., et al. (2012) Planetary and Space Science, 61, 60–65 [5] Paranicas, C., et al. (2014) Icarus, 234, 155–161 [6] Schaible, M. J., et al. (2016) Icarus, 0, 1–13 [7] Baragiola, R. A., et al., (2013) Astrophysics and Space Science Library, vol. 356. Springer [8] Mennella, V., et al. (2006) The Astrophysical Journal, 643(2), 923 [9] Scipioni, F., et al. (2017) Icarus, 290, 183-200 [10] Howett, C. J. A., et al. (2014) Icarus, 241, 239-247

C L Young↗

The Abnormally Hot June 2021 in the Western U.S.: A MERRA-2 perspective

An extreme heat wave occurred over much of the western US and Canada in late June 2021. We use daily and monthly near-surface temperature from MERRA-2 Reanalysis to compare the hot conditions of this year with the previous 40 years, particularly over the northwestern US that was severely affected.

T2m↗

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology

Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.

Plant Sciences↗

Background Information on the Protection Requirements in IEEE Std 1547-2018

As the DER penetration level has risen significantly in recent times, parameter settings and configuration of installed DERs are having direct impact on local electric distribution utilities as well as bulk power systems during normal and abnormal grid conditions. On the other hand, all grid-connected DERs in the United States of America must conform to the interconnection requirements prescribed in the IEEE Std 1547-2018. Because of these reasons, it is crucial to understand the expected DER performance during abnormal grid conditions considering the requirements of the IEEE Std 1547-2018. The aim of this document is twofold: (1) to summarize requirements in IEEE Std 1547-2018 having direct implications in distribution network protection, and (2) to provide the reader with knowledge and information that will be required for users to apply the requirements specified. Since the focus of this document is on distribution network protection in the presence of DERs, it is assumed that the readers have basic understanding of distribution network protection as well as working principles of DERs. This document starts with a brief introduction on protection systems for distribution network followed by a discussion on the impact of DER on the protection systems. Current practices to provide network protection in the presence of DER is briefly discussed. The document then explores the DER performance requirements in IEEE Std. 1547-2018; especially on those related to distribution network protection. Section 6.1 in IEEE Std. 1547-2018 provides an overview of capabilities and control requirements for DER under abnormal operating conditions. This section also introduces abnormal operating performance categories I, II, III. The DER response to various types of faults and grid conditions such as short-circuit faults and open phase conditions are discussed in section 6.2 in the standard while requirements for coordination with the Area EPS reclosing scheme are provided in section 6.3. Section 6.4 in the IEEE Std 1547-2018 specifies requirements for mandatory voltage tripping and ride-through requirements during low and high voltage disturbances and section 6.5 specifies similar requirements for low and high frequency disturbances. IEEE Std. 1547 requires that the conformance of the DERs to IEEE Std 1547-2018 requirements should be verified in accordance with IEEE Std. 1547. 1. There are several parameters and settings of DERs that need to be properly selected for reliable operation during abnormal grid condition while applying the requirements of IEEE Std 1547-2018. Key decisions for proper selections of parameter and settings are: 1. Determination of required DER abnormal operating performance category, 2. Determination of DER response (shall trip) to abnormal voltages and 3. Determination of DER response (shall trip) to abnormal frequency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Ocular screening tests of elementary school children

This report presents an analysis of 507 abnormal retinal reflex images taken of Huntsville kindergarten and first grade students. The retinal reflex images were obtained by using an MSFC-developed Generated Retinal Reflex Image System (GRRIS) photorefractor. The system uses a 35 mm camera with a telephoto lens with an electronic flash attachment. Slide images of the eyes were examined for abnormalities. Of a total of 1835 students screened for ocular abnormalities, 507 were found to have abnormal retinal reflexes. The types of ocular abnormalities detected were hyperopia, myopia, astigmatism, esotropia, exotropia, strabismus, and lens obstuctions. The report shows that the use of the photorefractor screening system is an effective low-cost means of screening school children for abnormalities.

Richardson, J.↗

2015 Sensorimotor Risk Standing Review Panel Evidence and Status Review For: the Risk of Impaired Control of Spacecraft/Associated Systems and Decreased Mobility Due to Vestibular/Sensorimotor Alterations Associated with Spaceflight

The 2015 Sensorimotor Risk Standing Review Panel (from here on referred to as the SRP) participated in a WebEx/teleconference with members of the Human Health Countermeasures (HHC) Element, representatives from the Human Research Program (HRP), NASA Headquarters, and NASA Research and Education Support Services (NRESS) on December 17, 2015 (list of participants is in Section VI of this report). The SRP reviewed the new Evidence Report for the Risk of Impaired Control of Spacecraft/Associated Systems and Decreased Mobility Due to Vestibular/Sensorimotor Alterations Associated with Spaceflight (from here on referred to as the 2015 Sensorimotor Evidence Report), and also received a status review of the Risk. The opening section of the 2015 Sensorimotor Evidence Report provides written descriptions of various incidents that have occurred during space missions. In most of these incidents, the main underlying contributing factors are not easy to identify unambiguously. For example, in section 1.9, a number of falls occurred while astronauts were walking on the moon. It is not clear to the SRP, however, why they fell. It is only possible to extrapolate from likely specific psychophysical or physiological abnormalities, but how these abnormalities were determined, and how they were directly responsible for the falls is unclear to the SRP. Section 2.1.2 on proprioception is very interesting, but the functional significance of the abnormalities detected is not clear. The SRP sees this as a problem throughout the report: a mapping between the component abnormalities identified and the holistic behaviors that are most relevant, for example, controlling the vehicle, and locomotion during egress, is generally lacking. The SRP thinks the cognitive section is too strongly focused on vestibular functioning. The SRP questions the notion that the main cognitive effects are mainly attributable to reversible vestibular changes induced by spaceflight. The SRP thinks that there can also be independent cognitive effects. The Functional Task Test (FTT) protocols and the Field Test are particularly valuable. The conclusion is that the unloading of major postural muscles experienced during spaceflight plays a central role in the alteration of functional task performance and balance control. This conclusion stands in contrast with the statements in other parts of the document that emphasize the role of vestibular changes on these functions. It would help to more fully integrate these two views on the predominant effects of spaceflight. Although the SRP thinks the countermeasures section is interesting, the proposed countermeasures are not well integrated with the abnormalities described in previous sections. The SRP thinks it would help enormously to have explicit links among each abnormality, its 2015 Sensorimotor Risk SRP Evidence Review Final Report 2 overall importance/impact on function, and the appropriate countermeasure that can be implemented to maintain adequate functioning.

Steinberg, Susan↗

Virtual sensor supervised learning for cyber-attack neutralization

An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.

97 MATHEMATICS AND COMPUTING↗