scholarly journals Digital Modelling of Underground Volumes, Including the Visualization of Confidence Levels for the Positioning of Subsurface Objects

2021 ◽  
Vol 11 (8) ◽  
pp. 3483
Author(s):  
Kamel Adouane ◽  
Fabian Boujon ◽  
Bernd Domer
Keyword(s):  
Data Quality ◽  
Tree Roots ◽  
Confidence Levels ◽  
Class Level ◽  
Geometrical Data ◽  
Digital Modelling ◽  
Empirical Means

This paper addresses the issue of offering a consistent 3D visual rendering of subsurface objects when databases face non-completion. Digital modelling of subsurface objects, like utility lines, underground buildings or tree roots, is a difficult task. Data available are incomplete and not precise. The in situ acquisition of existing objects to increase data quality is complex and, therefore, costly. In this paper, a methodology to obtain missing spatial and geometrical data through field or empirical means is proposed. In addition, confidence levels are assigned to existing and derived spatial and geometrical attributes. They are consolidated on a class level and visualized through a bounding shape, called secondary object.

scholarly journals Improving Radar Refractivity Retrieval by Considering the Change in the Refractivity Profile and the Varying Altitudes of Ground Targets

2016 ◽  
Vol 33 (5) ◽  
pp. 989-1004 ◽  
Author(s):  
Ya-Chien Feng ◽  
Frédéric Fabry ◽  
Tammy M. Weckwerth
Keyword(s):  
Boundary Layer ◽  
Data Quality ◽  
Temporal Change ◽  
Numerical Models ◽  
Vertical Gradient ◽  
Target Range ◽  
Constant Height ◽  
Measured Phase

AbstractAccurate radar refractivity retrievals are critical for quantitative applications, such as assimilating refractivity into numerical models or studying boundary layer and convection processes. However, the technique as originally developed makes some simplistic assumptions about the heights of ground targets () and the vertical gradient of refractivity (). In reality, the field of target phases used for refractivity retrieval is noisy because of varying terrain and introduces estimation biases. To obtain a refractivity map at a constant height above terrain, a 2D horizontal refractivity field at the radar height must be computed and corrected for altitude using an average . This is achieved by theoretically clarifying the interpretation of the measured phase considering the varying and the temporal change of . Evolving causes systematic refractivity biases, as it affects the beam trajectory, the associated target range, and the refractivity field sampled between selected targets of different heights. To determine and changes, a twofold approach is proposed: first, can be reasonably inferred based on terrain height; then, a new method of estimation is devised by using the property of the returned powers of a pointlike target at successive antenna elevations. The obtained shows skill based on in situ tower observation. As a result, the data quality of the retrieved refractivity may be improved with the newly added information of and .

scholarly journals Applications with a brighter X-ray source and new detectors from Agilent

2014 ◽  
Vol 70 (a1) ◽  
pp. C1734-C1734
Author(s):  
Zoltan Gal ◽  
Tadeusz Skarzynski ◽  
Fraser White ◽  
Oliver Presly ◽  
Adrian Jones ◽  
...  
Keyword(s):  
Data Quality ◽  
Single Crystal ◽  
Measurement System ◽  
Single Crystal Diffraction ◽  
X Ray ◽  
Vacuum Technology ◽  
Intelligent Measurement ◽  
Wide Range

Agilent Technologies develop and supply X-ray systems for single-crystal diffraction research, including the SuperNova; a compact, highly reliable and very low maintenance instrument providing X-ray data of the highest quality; and the PX Scanner for testing and characterization of protein crystals in their original crystallization drops (in-situ). The SuperNova and PX Scanner are constantly improving, with recent enhancements including a new range of detectors using an Intelligent Measurement System. The Eos S2, Atlas S2 and Titan S2 detector range employs a smart sensitivity control of the electronic gain and is capable of instantaneously switching its binning modes thus providing unprecedented flexibility in tuning every exposure to provide the highest data quality for a wide range of experiments. We have also developed a completely new micro-focus X-ray source based on Gradient Vacuum technology, with novel filament and target designs. This novel source is an integral part of the new Agilent GV1000 X-ray diffractometer, which has been designed for applications that require even higher brightness of the X-ray beam.

In Situ Neonatal Mock Codes: Assessing the Impact

2020 ◽  
Vol 39 (1) ◽  
pp. 29-34
Author(s):  
Emily K. Rivera ◽  
Leah M. Siple ◽  
Eunice J. Wicks ◽  
Heather S. Johnson ◽  
Caren M. Skov
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Outcome Measures ◽  
Nursing Staff ◽  
Neonatal Resuscitation ◽  
Confidence Levels ◽  
Mock Codes ◽  
The Impact

PurposeTo assess the impact of a quality improvement (QI) project to increase nursing staff confidence in responding to neonatal emergencies.DesignMandatory neonatal emergency in situ scenarios done quarterly.SampleBedside NICU nursing staff and the subset of NICU nurses that attend all high-risk deliveries and neonatal emergencies on the obstetrics unit.Outcome MeasuresConfidence levels in responding to neonatal emergencies, demonstrating neonatal resuscitation skills, and communicating effectively during an emergency.ResultsSixty-eight NICU nurses completed the pre- and postintervention surveys. Self-reported confidence levels increased in all areas measured. Overall, the percentage of nursing staff that reported confidence in being able to participate in a neonatal emergency increased from 48 percent to 77 percent.

scholarly journals Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors

2021 ◽  
Author(s):  
Julian Peters ◽  
Lorenz Ott ◽  
Matthias Dörr ◽  
Thomas Gwosch ◽  
Sven Matthiesen
Keyword(s):  
Regression Model ◽  
Data Quality ◽  
Gear Tooth ◽  
Measurement Data ◽  
Tooth Wear ◽  
Sensor Data ◽  
Acceleration Sensors ◽  
In Situ Sensors ◽  
In Situ Sensor

AbstractGear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability.

scholarly journals Data Quality of In-Situ Stresses by Hydraulic Fracturing Method

2012 ◽  
Vol 128 (7) ◽  
pp. 449-454
Author(s):  
Toshinori SATO ◽  
Takeo TANNO ◽  
Ryoichi HIKIMA ◽  
Hiroyuki SANADA ◽  
Harumi KATO
Keyword(s):  
Hydraulic Fracturing ◽  
Data Quality ◽  
In Situ Stresses

Strength of tree roots and landslides on Prince of Wales Island, Alaska

1979 ◽  
Vol 16 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Tien H. Wu ◽  
William P. McKinnell III ◽  
Douglas N. Swanston
Keyword(s):  
Shear Strength ◽  
Root System ◽  
Forest Cover ◽  
Safety Factors ◽  
In Situ Tests ◽  
Tree Roots ◽  
Stability Of Slopes ◽  
Before And After ◽  
The Stability

The stability of slopes before and after removal of forest cover was investigated. Porewater pressures and shear strengths were measured and the soil properties were determined by laboratory and in situ tests. A model of the soil–root system was developed to evaluate the contribution of tree roots to shear strength. The computed safety factors are in general agreement with observed behaviors of the slopes. Decay of tree roots subsequent to logging was found to cause a reduction in the shear strength of the soil–root system.

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