Interpolation of Spatial Surfaces and Inferring Subsurface Transitions Using Electrical Conductivity

Precision agriculture techniques are becoming more popular within the agriculture community as producers demand more return from an ever-decreasing amount of farmland. Increased environmental regulations are forcing farmers to reduce the input of fertilizers and agrochemicals on their crops. Innovative techniques in precision agriculture are enhancing traditional decision-making processes by offering multiple layers of data for a production field. It is difficult to determine the complex interactions that exist between factors affecting crop growth and the resultant management decisions. Strategies in precision agriculture attempt to modify customary practices in order to address the known variability of field conditions. This case study evaluated some of the tools used to create spatial data maps and the relationship of those maps to various soil properties. Electromagnetic induction (EMI) and ground-penetrating radar (GPR) were used to examine the similarities and differences among spatial and temporal variations of soil water content, soil texture, and bulk soil electrical conductivity (ECa) on a large research watershed in southwestern Tennessee. A protocol was developed that identifies spatial variations in ECa patterns using geographical information system (GIS) maps. Soil cores were collected in areas of contrasting conductivity, which were identified by temporal ECa maps. Repeated spatial measurements of ECa, starting near field capacity and then progressing through the draining and drying process, supplied visually shifting patterns that correspond to dynamic soil moisture variations and subsurface morphology transitions. iv After several seasons of acquiring data for other studies, it was noted that spatial ECa patterns remained somewhat similar across data gathering events, shifting only in relative amplitude in relation to seasonal moisture levels. The overall ECa patterns remained somewhat similar, regardless of field moisture conditions. Soil morphology was considered constant over the data acquisition period, with subsurface moisture variations being the major influence in differing ECa maps during the same period. Follow-up soil coring analysis supported this assumption in this case study. The interpolation of spatial ECa maps creates a continuous surface that contains values at unsampled locations. Inverse distance weighted (IDW), ordinary kriging (OK), and radial basis function (RBF) were examined as potential interpolation algorithms. Data were gathered to investigate the influences of short-term conductivity shifts over the data collection period, as well as from travel route patterns and instrument orientation. Using root-mean-squared error (RMSE) to quantify the transformation accuracy of ECa maps, a data collection method and an appropriate geostatistical model were determined for this particular case study. Analysis showed that a bidirectional travel path produced the highest quality map, as transformation inaccuracies were reduced when measurements were obtained in a manner by which all measurements were temporally contiguous. A skilled application of ordinary kriging (OK) also increased map quality in comparison to the inverse distance weighted (IDW) and radial basis function (RBF) interpolation methods. Due to variability in our data, we are not able to recommend the use of a single interpolation algorithm for all data gathering scenarios

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Electromagnetic Interference Estimation via Conditional Neural Processing

The goal of this thesis is to determine the efficacy of employing Machine Learning (ML) to solve Joint Urgent Operational Need (JUON) CC-0575, which aims to develop a Common Operating Picture (COP) of the Global Positioning System (GPS) Electromagnetic Interference (EMI) environment. With the growing popularity of Artificial Neural Networks (ANNs), ML solutions are quickly gaining traction in businesses, academia and government. This in turn allows for problem solutions that were previously inconceivable using the classical programming paradigm. This thesis proposes a method to develop a COP of the battlefield via ANN ingestion of multiple-source signals and sensors. We conduct three separate experiments with varying amounts of EMI interference sources (single, double, and triple jammer datasets). The type of ANN developed to address this problem is a Conditional Neural Process (CNP) with residual connections. The model is developed to provide the estimated EMI environment as well as a measure of confidence in its estimates, as the specific application of this model could lead to loss of life in the event the model estimates are taken as truth. The model resulted in an EMI estimator that was neutral on the single jammer test data set, yet aggressive on the multiple jammer test data sets

Enhancing space transportation: The NASA program to develop electric propulsion

The NASA Office of Aeronautics, Exploration, and Technology (OAET) supports a research and technology (R and T) program in electric propulsion to provide the basis for increased performance and life of electric thruster systems which can have a major impact on space system performance, including orbital transfer, stationkeeping, and planetary exploration. The program is oriented toward providing high-performance options that will be applicable to a broad range of near-term and far-term missions and vehicles. The program, which is being conducted through the Jet Propulsion Laboratory (JPL) and Lewis Research Center (LeRC) includes research on resistojet, arcjets, ion engines, magnetoplasmadynamic (MPD) thrusters, and electrodeless thrusters. Planning is also under way for nuclear electric propulsion (NEP) as part of the Space Exploration Initiative (SEI)

High-speed civil transport flight- and propulsion-control technological issues

Technology advances required in the flight and propulsion control system disciplines to develop a high speed civil transport (HSCT) are identified. The mission and requirements of the transport and major flight and propulsion control technology issues are discussed. Each issue is ranked and, for each issue, a plan for technology readiness is given. Certain features are unique and dominate control system design. These features include the high temperature environment, large flexible aircraft, control-configured empennage, minimizing control margins, and high availability and excellent maintainability. The failure to resolve most high-priority issues can prevent the transport from achieving its goals. The flow-time for hardware may require stimulus, since market forces may be insufficient to ensure timely production. Flight and propulsion control technology will contribute to takeoff gross weight reduction. Similar technology advances are necessary also to ensure flight safety for the transport. The certification basis of the HSCT must be negotiated between airplane manufacturers and government regulators. Efficient, quality design of the transport will require an integrated set of design tools that support the entire engineering design team

Electromagnetic Interference to Flight Navigation and Communication Systems: New Strategies in the Age of Wireless

Electromagnetic interference (EMI) promises to be an ever-evolving concern for flight electronic systems. This paper introduces EMI and identifies its impact upon civil aviation radio systems. New wireless services, like mobile phones, text messaging, email, web browsing, radio frequency identification (RFID), and mobile audio/video services are now being introduced into passenger airplanes. FCC and FAA rules governing the use of mobile phones and other portable electronic devices (PEDs) on board airplanes are presented along with a perspective of how these rules are now being rewritten to better facilitate in-flight wireless services. This paper provides a comprehensive overview of NASA cooperative research with the FAA, RTCA, airlines and universities to obtain laboratory radiated emission data for numerous PED types, aircraft radio frequency (RF) coupling measurements, estimated aircraft radio interference thresholds, and direct-effects EMI testing. These elements are combined together to provide high-confidence answers regarding the EMI potential of new wireless products being used on passenger airplanes. This paper presents a vision for harmonizing new wireless services with aeronautical radio services by detecting, assessing, controlling and mitigating the effects of EMI

Delineating Field Variation Using Apparent Electrical Conductivity in an Ozark Highlands Agroforestry System

Little to no work has been conducted assessing field variability using repeated electromagnetic induction (EMI) apparent electrical conductivity (ECa) surveys in agroforestry (AF) systems within regions similar to the Ozark Highlands. The objectives of this thesis were to identify i) spatiotemporal ECa variability; ii) ECa-derived soil management zones (SMZs); iii) correlations among EMI-ECa and in-situ, sentential-site soil properties; iv) whether fewer, EMI-ECa surveys could be conducted to capture similar ECa variance as mid-monthly EMI-ECa surveys; v) correlations between ECa and forage yield, tree growth, and terrain attributes based on plant (forage and tree) species, and fertility treatments, and ECa-derived SMZs, and vi); and terrain attributes that have the largest contribution to ECa variability at a 20-year-old, 4.25-ha, AF system in the Ozark Highlands of northwest Arkansas. Between August 2020 and July 2021, 12, mid-monthly ECa surveys were conducted and soil-sensor-based volumetric water content and ECa measurement were made and soil samples for gravimetric water content, EC, and pH were collected from various soil depths at fixed locations. Fourteen terrain attributes of the AF site were obtained. Tree diameter at breast height (DBH) and tree height (TH) measurements were made in December 2020 and March 2021, respectively, and total forage yield samples were collected seven times during Summer 2018 and 2019. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged between 1.8 to 18.0 and 3.1 to 25.8 mS m-1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. Largest measured ECa occurred within the local drainage way, which has mapped inclusions with aquic soil moisture regimes, or areas of potential groundwater movement, and smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. A positive (r2 = 0.4; P \u3c 0.05) linear relationship occurred over time between PRP ECa standard deviation, with a negative linear relationship (r2 = 0.93; P \u3c 0.05) between HCP ECa coefficient of variation across season (i.e., Summer to Spring). The K-means-clustering method was used to delineate three precision SMZs that were reflective of areas with similar ECa and ECa variability. Relationships between ECa and tree properties were generally stronger within the whole-site, averaged across tree property and ECa configuration (| r | = 0.38), than the SMZs, averaged across tree property, ECa configuration, and SMZ (| r | = 0.27). The strength of the SMZs’ terrain-attribute-PRP-ECa relationships were 9 to 205% greater than that for the whole-site. Whole-site, multi-linear regressions showed that Slope Length and Steepness (LS)-Factor (10.5%), Mid-slope (9.4%), and Valley Depth (7.2%) were terrain attributes that had the greatest influence (i.e., largest percent of total sum of squares) on PRP ECa variability, whereas Valley Depth (15.3%), Wetness Index (11.9%), and Mid-slope (11.2%) had the greatest influence on HCP ECa variability. Results of this study show how ECa varies and relates to soil, plant (i.e., DBH and TH and forage yield), and terrain attributes in AF systems with varying topography that could be used to influence AF management

Delineating Field Variation Using Apparent Electrical Conductivity in an Ozark Highlands Agroforestry System

Little to no work has been conducted assessing field variability using repeated electromagnetic induction (EMI) apparent electrical conductivity (ECa) surveys in agroforestry (AF) systems within regions similar to the Ozark Highlands. The objectives of this thesis were to identify i) spatiotemporal ECa variability; ii) ECa-derived soil management zones (SMZs); iii) correlations among EMI-ECa and in-situ, sentential-site soil properties; iv) whether fewer, EMI-ECa surveys could be conducted to capture similar ECa variance as mid-monthly EMI-ECa surveys; v) correlations between ECa and forage yield, tree growth, and terrain attributes based on plant (forage and tree) species, and fertility treatments, and ECa-derived SMZs, and vi); and terrain attributes that have the largest contribution to ECa variability at a 20-year-old, 4.25-ha, AF system in the Ozark Highlands of northwest Arkansas. Between August 2020 and July 2021, 12, mid-monthly ECa surveys were conducted and soil-sensor-based volumetric water content and ECa measurement were made and soil samples for gravimetric water content, EC, and pH were collected from various soil depths at fixed locations. Fourteen terrain attributes of the AF site were obtained. Tree diameter at breast height (DBH) and tree height (TH) measurements were made in December 2020 and March 2021, respectively, and total forage yield samples were collected seven times during Summer 2018 and 2019. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged between 1.8 to 18.0 and 3.1 to 25.8 mS m-1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. Largest measured ECa occurred within the local drainage way, which has mapped inclusions with aquic soil moisture regimes, or areas of potential groundwater movement, and smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. A positive (r2 = 0.4; P \u3c 0.05) linear relationship occurred over time between PRP ECa standard deviation, with a negative linear relationship (r2 = 0.93; P \u3c 0.05) between HCP ECa coefficient of variation across season (i.e., Summer to Spring). The K-means-clustering method was used to delineate three precision SMZs that were reflective of areas with similar ECa and ECa variability. Relationships between ECa and tree properties were generally stronger within the whole-site, averaged across tree property and ECa configuration (| r | = 0.38), than the SMZs, averaged across tree property, ECa configuration, and SMZ (| r | = 0.27). The strength of the SMZs’ terrain-attribute-PRP-ECa relationships were 9 to 205% greater than that for the whole-site. Whole-site, multi-linear regressions showed that Slope Length and Steepness (LS)-Factor (10.5%), Mid-slope (9.4%), and Valley Depth (7.2%) were terrain attributes that had the greatest influence (i.e., largest percent of total sum of squares) on PRP ECa variability, whereas Valley Depth (15.3%), Wetness Index (11.9%), and Mid-slope (11.2%) had the greatest influence on HCP ECa variability. Results of this study show how ECa varies and relates to soil, plant (i.e., DBH and TH and forage yield), and terrain attributes in AF systems with varying topography that could be used to influence AF management