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NASA NTRS2014

Impacts of Light Use Efficiency and fPAR Parameterization on Gross Primary Production Modeling

This study examines the impact of parameterization of two variables, light use efficiency (LUE) and the fraction of absorbed photosynthetically active radiation (fPAR or fAPAR), on gross primary production(GPP) modeling. Carbon sequestration by terrestrial plants is a key factor to a comprehensive under-standing of the carbon budget at global scale. In this context, accurate measurements and estimates of GPP will allow us to achieve improved carbon monitoring and to quantitatively assess impacts from cli-mate changes and human activities. Spaceborne remote sensing observations can provide a variety of land surface parameterizations for modeling photosynthetic activities at various spatial and temporal scales. This study utilizes a simple GPP model based on LUE concept and different land surface parameterizations to evaluate the model and monitor GPP. Two maize-soybean rotation fields in Nebraska, USA and the Bartlett Experimental Forest in New Hampshire, USA were selected for study. Tower-based eddy-covariance carbon exchange and PAR measurements were collected from the FLUXNET Synthesis Dataset. For the model parameterization, we utilized different values of LUE and the fPAR derived from various algorithms. We adapted the approach and parameters from the MODIS MOD17 Biome Properties Look-Up Table (BPLUT) to derive LUE. We also used a site-specific analytic approach with tower-based Net Ecosystem Exchange (NEE) and PAR to estimate maximum potential LUE (LUEmax) to derive LUE. For the fPAR parameter, the MODIS MOD15A2 fPAR product was used. We also utilized fAPAR chl, a parameter accounting for the fAPAR linked to the chlorophyll-containing canopy fraction. fAPAR chl was obtained by inversion of a radiative transfer model, which used the MODIS-based reflectances in bands 1-7 produced by Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. fAPAR chl exhibited seasonal dynamics more similar with the flux tower based GPP than MOD15A2 fPAR, especially in the spring and fall at the agricultural sites. When using the MODIS MOD17-based parameters to estimate LUE, fAPAR chl generated better agreements with GPP (r2= 0.79-0.91) than MOD15A2 fPAR (r2= 0.57-0.84).However, underestimations of GPP were also observed, especially for the crop fields. When applying the site-specific LUE max value to estimate in situ LUE, the magnitude of estimated GPP was closer to in situ GPP; this method produced a slight overestimation for the MOD15A2 fPAR at the Bartlett forest. This study highlights the importance of accurate land surface parameterizations to achieve reliable carbon monitoring capabilities from remote sensing information.

grossprimary↗

NASA NTRS2016

MapX: An In Situ, Full-Frame X-Ray Spectroscopic Imager for the Biogenic Elements

Microbial life exploits microscale disequilibria at boundaries where valence, chemical potential, pH, Eh, etc. vary on a length scale commensurate with the organisms themselves - tens to hundreds of micrometers. These disequilibria can exist within cracks or veins in rocks and ice, at inter- or intra-crystalline boundaries, at sediment/water or sediment/atmosphere interfaces, or even within fluid inclusions trapped inside minerals. The detection of accumulations of the biogenic elements C,N,O,P,S at appropriate concentrations on or in a mineral/ice substrate would constitute permissive evidence of extant life, but context is also required. Does the putative biosignature exist in a habitable environment? Under what conditions of P, T, and chemical potential was the host mineralogy formed? MapX is an arm-deployed contact instrument that directly images the biogenic elements C, N, O, P, S, as well as the cations of the rock-forming minerals (Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe) and important anions such as Cl, Fl. The instrument provides element images having ≤100 micron lateral spatial resolution over a 2.5 cm X 2.5 cm area, as well as quantitative XRF spectra from ground-selected or instrument-selected Regions of Interest (ROI) on the sample. Quantitative XRF spectra from ROI can be translated into mineralogies using ground- or instrument-based algorithms. Either an X-ray tube source (X-ray fluorescence) or a radioisotope source such as 244-Cm (alpha-particle and gamma-ray fluorescence) can be used, and characteristic X-rays emitted from the sample are imaged onto an X-ray sensitive CCD through an X-ray MicroPore Optic (MPO). As a fluorescent source, 244-Cm is highly desirable in a MapX instrument intended for life detection since high-energy alpha-particles are unrivaled in fluorescence yield for the low-Z elements. The MapX design as well as baseline performance requirements for a MapX instrument intended for life detection/identification of habitable environments will be presented.

micro-XRF spectrometer↗

NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗

NASA NTRS2012

An Evaluation of Controller and Pilot Performance, Workload and Acceptability under a NextGen Concept for Dynamic Weather Adapted Arrival Routing

In todays terminal operations, controller workload increases and throughput decreases when fixed standard terminal arrival routes (STARs) are impacted by storms. To circumvent this operational constraint, Prete, Krozel, Mitchell, Kim and Zou (2008) proposed to use automation to dynamically adapt arrival and departure routing based on weather predictions. The present study examined this proposal in the context of a NextGen trajectory-based operation concept, focusing on the acceptability and its effect on the controllers ability to manage traffic flows. Six controllers and twelve transport pilots participated in a human-in-the-loop simulation of arrival operations into Louisville International Airport with interval management requirements. Three types of routing structures were used: Static STARs (similar to current routing, which require the trajectories of individual aircraft to be modified to avoid the weather), Dynamic routing (automated adaptive routing around weather), and Dynamic Adjusted routing (automated adaptive routing around weather with aircraft entry time adjusted to account for differences in route length). Spacing Responsibility, whether responsibility for interval management resided with the controllers (as today), or resided with the pilot (who used a flight deck based automated spacing algorithm), was also manipulated. Dynamic routing as a whole was rated superior to static routing, especially by pilots, both in terms of workload reduction and flight path safety. A downside of using dynamic routing was that the paths flown in the dynamic conditions tended to be somewhat longer than the paths flown in the static condition.

Johnson, Walter W.↗

NASA NTRS2011

Exploring the Model Design Space for Battery Health Management

Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.

Saha, Bhaskar↗

NASA NTRS2019

A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

With the advancement in high performance computing and numerical optimization techniques,engineering design optimization problems are becoming more complex, larger scale,higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed anew optimization framework, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previouslyperceived. However, the original version of this framework, in the process of adaptive samplingto explore and exploit the design space, finds only a single sample point per iteration. The efforthere builds upon this previously developed optimization framework to include multiple infillsampling capability that combines the concept of generalized expected improvement function,unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGOwith the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficultengineering design optimization problems. The results reveal the performance of the newapproach is problem dependent. When applied to a ten-bar truss problem, the newly proposedmultiple infill strategy consistently leads to a better design solutions when compared to theexisting CPTV method (implemented with the context of the AMIEGO framework). On theother hand, when applied to a mixed-integer high fidelity wing topology optimization problem- MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferiorsolution as compared to CPTV approach. These results also reveal that a small number ofstarting points, in general, are sufficient to lead to a good overall solution.

Mixed-integer optimization↗

NASA NTRS2020

Design of Autonomous Medical Response Agent (AMRA) Aggregate Information Dashboard (AID)

Future astronauts in deep space missions will rely on tools and technologies empowering them to self-diagnose and self-treat medical conditions. Given communications delays and limited bandwidth in future long-duration exploration missions (LDEMs), medical decision support technologies must empower the crew to manage routine medical activities, acute medical incidents, as well as emergency medical scenarios independently from ground support.The Autonomous Medical Response Agent (AMRA) is envisioned as a digital tool enabling crew to issue medical complaints and interact with a medical decision support algorithm which develops a differential diagnosis and recommends a treatment protocol for the condition. AMRA will draw from individual crew medical history in addition to crew symptoms to more efficiently identify high-risk medical conditions. A new symptom could be indicative of a chronic condition or a normal adaptation to long-duration spaceflight, but could just as easily be indicative of an adverse vehicle condition affecting the entire crew.While real-time communication with a flight surgeon may not possible, the crew will nonetheless require a means to communicate and document both routine and emergency medical incidents to ground support. Conversely, flight surgeons and medical specialists on the ground will need to understand information such as crew vitals or responses to medical check-ups and examinations within the larger context of crew schedule, mission activities, and vehicle performance. A user interface which establishes communication protocols between an individual crew member and AMRA, as well as ground support to the crew is a significant area of research demanding input and consideration.The design of AMRA AID is intended to: a) represent routine medical activities as well as new (unplanned) medical incidents within the larger context of crew schedule and mission activities, and b) increase confidence between ground support and crew members over the course of LDEMs. Maintaining situation awareness of unplanned medical incidents between ground and crew will be a critical element within LDEMs. Two medical incidents headache and difficulty breathing are being explored within a user interface prototype which captures communications protocols between crew members and mission control, human health monitoring, vehicle or environmental monitoring, as well as crew schedule and mission activities holistically.

Yashar, M.↗

NASA NTRS1982

A unified method for evaluating real-time computer controllers: A case study

A real time control system consists of a synergistic pair, that is, a controlled process and a controller computer. Performance measures for real time controller computers are defined on the basis of the nature of this synergistic pair. A case study of a typical critical controlled process is presented in the context of new performance measures that express the performance of both controlled processes and real time controllers (taken as a unit) on the basis of a single variable: controller response time. Controller response time is a function of current system state, system failure rate, electrical and/or magnetic interference, etc., and is therefore a random variable. Control overhead is expressed as a monotonically nondecreasing function of the response time and the system suffers catastrophic failure, or dynamic failure, if the response time for a control task exceeds the corresponding system hard deadline, if any. A rigorous probabilistic approach is used to estimate the performance measures. The controlled process chosen for study is an aircraft in the final stages of descent, just prior to landing. First, the performance measures for the controller are presented. Secondly, control algorithms for solving the landing problem are discussed and finally the impact of the performance measures on the problem is analyzed.

Shin, K. G.↗

NASA NTRS1985

A unified method for evaluating real-time computer controllers and its application

Shin, K. G.↗

NASA NTRS2011

Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions

The push-pull framework was developed in hopes that an infrastructure would be created that could literally connect to any given remote site, and (given a set of restrictions) download files from that remote site based on those restrictions. The Cataloging and Archiving Service (CAS) has recently been re-architected and re-factored in its canonical services, including file management, workflow management, and resource management. Additionally, a generic CAS Crawling Framework was built based on motivation from Apache s open-source search engine project called Nutch. Nutch is an Apache effort to provide search engine services (akin to Google), including crawling, parsing, content analysis, and indexing. It has produced several stable software releases, and is currently used in production services at companies such as Yahoo, and at NASA's Planetary Data System. The CAS Crawling Framework supports many of the Nutch Crawler's generic services, including metadata extraction, crawling, and ingestion. However, one service that was not ported over from Nutch is a generic protocol layer service that allows the Nutch crawler to obtain content using protocol plug-ins that download content using implementations of remote protocols, such as HTTP, FTP, WinNT file system, HTTPS, etc. Such a generic protocol layer would greatly aid in the CAS Crawling Framework, as the layer would allow the framework to generically obtain content (i.e., data products) from remote sites using protocols such as FTP and others. Augmented with this capability, the Orbiting Carbon Observatory (OCO) and NPP (NPOESS Preparatory Project) Sounder PEATE (Product Evaluation and Analysis Tools Elements) would be provided with an infrastructure to support generic FTP-based pull access to remote data products, obviating the need for any specialized software outside of the context of their existing process control systems. This extensible configurable framework was created in Java, and allows the use of different underlying communication middleware (at present, both XMLRPC, and RMI). In addition, the framework is entirely suitable in a multi-mission environment and is supporting both NPP Sounder PEATE and the OCO Mission. Both systems involve tasks such as high-throughput job processing, terabyte-scale data management, and science computing facilities. NPP Sounder PEATE is already using the push-pull framework to accept hundreds of gigabytes of IASI (infrared atmospheric sounding interferometer) data, and is in preparation to accept CRIMS (Cross-track Infrared Microwave Sounding Suite) data. OCO will leverage the framework to download MODIS, CloudSat, and other ancillary data products for use in the high-performance Level 2 Science Algorithm. The National Cancer Institute is also evaluating the framework for use in sharing and disseminating cancer research data through its Early Detection Research Network (EDRN).

Foster, Brian M.↗

NASA NTRS2012

Free-Flight Terrestrial Rocket Lander Demonstration for NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) System

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project is chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. The ALHAT System must be capable of identifying and avoiding surface hazards to enable a safe and accurate landing to within tens of meters of designated and certified landing sites anywhere on a planetary surface under any lighting conditions. Since its inception in 2006, the ALHAT Project has executed four field test campaigns to characterize and mature sensors and algorithms that support real-time hazard detection and global/local precision navigation for planetary landings. The driving objective for Government Fiscal Year 2012 (GFY2012) is to successfully demonstrate autonomous, real-time, closed loop operation of the ALHAT system in a realistic free flight scenario on Earth using the Morpheus lander developed at the Johnson Space Center (JSC). This goal represents an aggressive target consistent with a lean engineering culture of rapid prototyping and development. This culture is characterized by prioritizing early implementation to gain practical lessons learned and then building on this knowledge with subsequent prototyping design cycles of increasing complexity culminating in the implementation of the baseline design. This paper provides an overview of the ALHAT/Morpheus flight demonstration activities in GFY2012, including accomplishments, current status, results, and lessons learned. The ALHAT/Morpheus effort is also described in the context of a technology path in support of future crewed and robotic planetary exploration missions based upon the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN).

Rutishauser, David K.↗

NASA NTRS2005

Adaptable Constrained Genetic Programming: Extensions and Applications

An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.

Janikow, Cezary Z.↗

NASA NTRS2005

Modeling Leadership Styles in Human-Robot Team Dynamics

The recent proliferation of robotic systems in our society has placed questions regarding interaction between humans and intelligent machines at the forefront of robotics research. In response, our research attempts to understand the context in which particular types of interaction optimize efficiency in tasks undertaken by human-robot teams. It is our conjecture that applying previous research results regarding leadership paradigms in human organizations will lead us to a greater understanding of the human-robot interaction space. In doing so, we adapt four leadership styles prevalent in human organizations to human-robot teams. By noting which leadership style is more appropriately suited to what situation, as given by previous research, a mapping is created between the adapted leadership styles and human-robot interaction scenarios-a mapping which will presumably maximize efficiency in task completion for a human-robot team. In this research we test this mapping with two adapted leadership styles: directive and transactional. For testing, we have taken a virtual 3D interface and integrated it with a genetic algorithm for use in &le-operation of a physical robot. By developing team efficiency metrics, we can determine whether this mapping indeed prescribes interaction styles that will maximize efficiency in the teleoperation of a robot.

Cruz, Gerardo E.↗

NASA NTRSDate not supplied

Implementing Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) Cloud-Clearing Algorithm into the NASA GEOS: Focus on the 2017 Atlantic Tropical Cyclone Season

Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.

Niama Boukachaba↗

NASA NTRS1994

Visually guided grasping to study teleprogrammation within the BAROCO testbed

This paper describes vision functionalities required in future orbital laboratories; in such systems, robots will be needed in order to execute the on-board scientific experiments or servicing and maintenance tasks under the remote control of ground operators. For this sake, ESA has proposed a robotic configuration called EMATS; a testbed has been developed by ESTEC in order to evaluate the potentialities of EMATS-like robot to execute scientific tasks in automatic mode. For the same context, CNES develops the BAROCO testbed to investigate remote control and teleprogrammation, in which high level primitives like 'Pick Object A' are provided as basic primitives. In nominal situations, the system has an a priori knowledge about the position of all objects. These positions are not very accurate, but this knowledge is sufficient in order to predict the position of the object which must be grasped, with respect to the manipulator frame. Vision is required in order to insure a correct grasping and to guarantee a good accuracy for the following operations. We describe our results about a visually guided grasping of static objects. It seems to be a very classical problem, and a lot of results are available. But, in many cases, it lacks a realistic evaluation of the accuracy, because such an evaluation requires tedious experiments. We propose several results about calibration of the experimental testbed, recognition algorithms required to locate a 3D polyhedral object, and the grasping itself.

Devy, M.↗

NASA NTRS2015

Flight Deck Interval Management Avionics: Eye-Tracking Analysis

Interval Management (IM) is one NexGen method for achieving airspace efficiencies. In order to initiate IM procedures, Air Traffic Control provides an IM clearance to the IM aircraft's pilots that indicates an intended spacing from another aircraft (the target to follow - or TTF) and the point at which this should be achieved. Pilots enter the clearance in the flight deck IM (FIM) system; and once the TTF's Automatic Dependent Surveillance-Broadcast signal is available, the FIM algorithm generates target speeds to meet that IM goal. This study examined four Avionics Conditions (defined by the instrumentation and location presenting FIM information) and three Notification Methods (defined by the visual and aural alerts that notified pilots to IM-related events). Current commercial pilots flew descents into Dallas/Fort-Worth in a high-fidelity commercial flight deck simulation environment with realistic traffic and communications. All 12 crews experienced each Avionics Condition, where order was counterbalanced over crews. Each crew used only one of the three Notification Methods. This paper presents results from eye tracking data collected from both pilots, including: normalized number of samples falling within FIM displays, normalized heads-up time, noticing time, dwell time on first FIM display look after a new speed, a workload-related metric, and a measure comparing the scan paths of pilot flying and pilot monitoring; and discusses these in the context of other objective (vertical and speed profile deviations, response time to dial in commanded speeds, out-of-speed-conformance and reminder indications) and subjective measures (workload, situation awareness, usability, and operational acceptability).

Latorella, Kara↗

NASA NTRS2000

Tropospheric Ozone Pollution Transport Traced from the TOMS (Total Ozone Mapping Spectrometer) Instrument During the Nashville-1999 Campaign

Over the past several years, we have developed two new tropospheric ozone retrievals from the TOMS (Total Ozone Mapping Spectrometer) satellite instrument that are of sufficient resolution to follow pollution episodes. The modified-residual technique [Hudson and Thompson, 1998; Thompson and Hudson, 1999] uses v. 7 TOMS total ozone and is applicable to tropical regimes in which the wave-one pattern in total ozone is observed. The TOMS-direct method [("TDOT" = TOMS Direct Ozone in the Troposphere; Frolov et al., 2000] represents a new algorithm that uses TOMS radiances directly (i.e., not previously processed for TOMS ozone) to extract tropospheric ozone in regions of constant stratospheric ozone and tropospheric ozone displaying high mixing ratios and variability characteristic of pollution. These events tend to occur in certain meteorological regimes. For example, mid-latitude pollution usually occurs on the backside of subtropical fronts, as low pv, usually moist air intrudes to the extra-tropics. July 1999 was a month characterized by robust pollution in the eastern US, with high ozone, as detected by TOMS, originating over south central states and moving up the Atlantic seaboard. This corresponds to 50-80 DU in tropospheric ozone column depth. In most cases, further transport occurred to the North Atlantic, with ozone plumes traveling to western Europe in 4-5 days. Examples of high ozone and transit across boundaries within the US, as well as US->Europe, give a regional context for model results and field measurements taken in the SE US during the Nashville-1999 campaign period. Validation of the TDOT maps is made with ozonesondes taken during that time. TDOT maps also show ozone pollution from Asia traveling to the western US in July 1999.

Thompson, Anne M.↗

NASA NTRS1993

Genetic learning in rule-based and neural systems

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Smith, Robert E.↗