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Supporting Local Government Public Health and Air Quality Decision-Making with A Sub-City Scale Air Quality Forecasting System from Data Fusion of Models, Satellite, In-Situ Measurements, and Low-Cost Sensors
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Supporting Local Government Public Health and Air Quality Decision-Making With A Sub-City Scale Air Quality Forecasting System From Data Fusion of Models, Satellite, in-Situ Measurements, and Low-Cost Sensors
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Robust Trajectory Optimization for NRHO Rendezvous Using SPICE Kernel Relative Motion
In this paper, robust optimization is performed on trajectory correction maneuvers during the lunar lander return phase of an Artemis mission, treating the trajectory from one hour after low lunar orbit departure to arrival in the vicinity of the lunar Gateway as a relative motion problem. To enable rapid stochastic optimization techniques requiring many candidate trajectories, SPICE kernel relative motion as implemented by the Quadratic Interpolated State Transition (QIST) system is used as the underlying dynamics propagation. The optimization is performed with a genetic optimizer using linear covariance (LinCov) software in a simplified operational context, taking into account the availability of navigation sensors with varying measurement models, ranges, and accuracies. No numerical integration is used, since the relative motion around Gateway is fully characterized with the a priori computation of the QIST coefficients. Maneuver placements are computed to optimize the minimum 3σ delta-v of the trajectory, the position dispersion at a target point, and a convex combination of these two metrics. An order of magnitude runtime improvement is provided over legacy methods with less than 10% error introduced. All QIST results are shown to be in-family with legacy methods. The tradespace for optimal delta-v design is found to range from 77.0 to 93.9 m/s, while the range of optimal dispersion is between 1.4 and 11.7 km.
The relationship of sensor parameters to applications data analysis
A stochastic model for the data acquisition system in a multispectral scanner system, like the one utilized by the LANDSAT satellites, is presented. A list of noise sources which are known or presumed to have a significant effect in the information extraction process was constructed. Since the shot noise introduced by the photodetectors in the sensor system is signal level dependent, an atmospheric model was adopted which could adequately describe the amount of radiation that gets into the sensors based on the atmospheric transmittance. An analysis was carried out to find the output spectral statistics in terms of the input signal statistics and the system parameters. This was integrated into a set of FORTRAN programs that when supplied with, the class statistics, the noise levels introduced by the sensor system, the atmospheric transmittance, and the atmospheric path radiance, can be used to estimate the classification performance. In order to show the beneficts of this model a series of runs were performed in which the Thematic Mapper multispectral scanner was the system under consideration.
Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks
Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.
A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems
Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.
Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications
Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.
Design Methods and Practices for Fault Prevention and Management in Spacecraft
Integrated Systems Health Management (ISHM) is intended to become a critical capability for all space, lunar and planetary exploration vehicles and systems at NASA. Monitoring and managing the health state of diverse components, subsystems, and systems is a difficult task that will become more challenging when implemented for long-term, evolving deployments. A key technical challenge will be to ensure that the ISHM technologies are reliable, effective, and low cost, resulting in turn in safe, reliable, and affordable missions. To ensure safety and reliability, ISHM functionality, decisions and knowledge have to be incorporated into the product lifecycle as early as possible, and ISHM must be considered as an essential element of models developed and used in various stages during system design. During early stage design, many decisions and tasks are still open, including sensor and measurement point selection, modeling and model-checking, diagnosis, signature and data fusion schemes, presenting the best opportunity to catch and prevent potential failures and anomalies in a cost-effective way. Using appropriate formal methods during early design, the design teams can systematically explore risks without committing to design decisions too early. However, the nature of ISHM knowledge and data is detailed, relying on high-fidelity, detailed models, whereas the earlier stages of the product lifecycle utilize low-fidelity, high-level models of systems and their functionality. We currently lack the tools and processes necessary for integrating ISHM into the vehicle system/subsystem design. As a result, most existing ISHM-like technologies are retrofits that were done after the system design was completed. It is very expensive, and sometimes futile, to retrofit a system health management capability into existing systems. Last-minute retrofits result in unreliable systems, ineffective solutions, and excessive costs (e.g., Space Shuttle TPS monitoring which was considered only after 110 flights and the Columbia disaster). High false alarm or false negative rates due to substandard implementations hurt the credibility of the ISHM discipline. This paper presents an overview of the current state of ISHM design,and a review of formal design methods to make recommendations about possible approaches to enable the ISHM capabilities to be designed-in at the system-level, from the very beginning of the vehicle design process.
New Aerosol Models for the Retrieval of Aerosol Optical Thickness and Normalized Water-Leaving Radiances from the SeaWiFS and MODIS Sensors Over Coastal Regions and Open Oceans
We describe the development of a new suite of aerosol models for the retrieval of atmospheric and oceanic optical properties from the SeaWiFs and MODIS sensors, including aerosol optical thickness (tau), angstrom coefficient (alpha), and water-leaving radiance (L(sub w)). The new aerosol models are derived from Aerosol Robotic Network (AERONET) observations and have bimodal lognormal distributions that are narrower than previous models used by the Ocean Biology Processing Group. We analyzed AERONET data over open ocean and coastal regions and found that the seasonal variability in the modal radii, particularly in the coastal region, was related to the relative humidity, These findings were incorporated into the models by making the modal radii, as well as the refractive indices, explicitly dependent on relative humidity, From those findings, we constructed a new suite of aerosol models. We considered eight relative humidity values (30%, 50%, 70%, 75%, 80%, 85%, 90%. and 95%) and, for each relative humidity value, we constructed ten distributions by varying the fine-mode fraction from zero to 1. In all. 80 distributions (8Rh x 10 fine-mode fractions) were created to process the satellite data. We. also assumed that the coarse-mode particles were nonabsorbing (sea salt) and that all observed absorptions were entirely due to fine-mode particles. The composition of fine mode was varied to ensure that the new models exhibited the same spectral dependence of single scattering albedo as observed in the AERONET data,
Application of model reference adaptive control to a flexible remote manipulator arm
An exact modal state-space representation is derived in detail for a single-link, flexible remote manipulator with a noncollocated sensor and actuator. A direct model following adaptive controller is designed to control the torque at the pinned end of the arm so as to command the free end to track a prescribed sinusoidal motion. Conditions that must be satisfied in order for the controller to work are stated. Simulation results to date are discussed along with the potential of the model following adaptive control scheme in robotics and space environments.
The NASA Applied Sciences Program: Volcanic Ash Observations and Applications
Since 2000, the NASA Applied Sciences Program has been actively transitioning observations and research to operations. Particular success has been achieved in developing applications for NASA Earth Observing Satellite (EOS) sensors, integrated observing systems, and operational models for volcanic ash detection, characterization, and transport. These include imager applications for sensors such as the MODerate resolution Imaging SpectroRadiometer (MODIS) on NASA Terra and Aqua satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NASA/NOAA Suomi NPP satellite; sounder applications for sensors such as the Atmospheric Infrared Sounder (AIRS) on Aqua, and the Cross-track Infrared Sounder (CrIS) on Suomi NPP; UV applications for the Ozone Mapping Instrument (OMI) on the NASA Aura Satellite and the Ozone Mapping Profiler Suite (OMPS) on Suomi NPP including Direct readout capabilities from OMI and OMPS in Alaska (GINA) and Finland (FMI):; and lidar applications from the Caliop instrument coupled with the imaging IR sensor on the NASA/CNES CALIPSO satellite. Many of these applications are in the process of being transferred to the Washington and Alaska Volcanic Ash Advisory Centers (VAAC) where they support operational monitoring and advisory services. Some have also been accepted, transitioned and adapted for direct, onboard, automated product production in future U.S. operational satellite systems including GOES-R, and in automated volcanic cloud detection, characterization and alerting tools at the VAACs. While other observations and applications remain to be developed for the current constellation of NASA EOS sensors and integrated with observing and forecast systems, future requirements and capabilities for volcanic ash observations and applications are also being developed. Many of these are based on technologies currently being tested on NASA aircraft, Unmanned Aerial Systems (UAS) and balloons. All of these efforts and the potential advances that will be realized by integrating them are shared in this presentation.
Abstract - Belbas, Nicholas (EC2)
Originally, I was brought into the Design and Analysis Branch in the Crew and Thermal Systems to work on administrative tasks like archiving and scheduling. However, I ended up splitting my time between secretarial tasks and a technical project. My technical project was originally meant to be a wireless sensor package for the 20ft Spacecraft Thermal Vacuum Chamber in the B7 High Bay. I would be using a miniature wifi development board and a temperature/humidity sensor along with custom 3D modeling to accomplish this. However, after some discussion with my technical mentor, the plan was changed to a mobile autonomous self-charging sensor platform. A mobile platform will allow the sensors to be moved around without depressurizing the chamber. Also, the self-charging aspect of the package allows for almost unlimited time in the chamber. If the on-board battery runs low, the robot can easily be driven to its charging dock and continue to transmit while charging. The driving base is based around a Raspberry Pi 3 board with a 12C PMW DC Motor controller and a PWM controller driving two small gear motors. The sensor transmitter itself is a RHT03 temperature and humidity sensor and Cozir CO2 sensor connected to an ESP8266 Huzzah board. The power distribution system utilizes a pair of 3.7v 3600mah lipo batteries wired to Powerboost 500 boards. Also, the self-charging mechanism utilizes two 12v-max inductive charging coils wired into the same Powerboost boards as the battery. The Raspberry pi is running Python 3.3 for the driving base and Javascript MJPEG library for transmitting live video from the onboard camera. The sensor package is running Arduino-based C++ and the program capturing the data is running PyqtGraph Python and HTML. The shell of the robot itself is a 3D printed case that will (work in progress) snap together. The photo to the left shows the two halves separated from each other. The black shell contains the power distribution boards and connectors while the white shell contains the driving base and data systems.
Carbon Nanotube Gas Sensor Using Neural Networks
The need to identify the presence and quantify the concentrations of gases and vapors is ubiquitous in NASA missions and societal applications. Sensors for air quality monitoring in crew cabins and ISS have been actively under development (Ref. 1). In particular, measuring the concentration of CO2 and NH3 is important because high concentrations of these gases pose a risk to ISS crew health. Detection of fuel and oxidant leaks in crew vehicles is critical for ensuring mission safety. Accurate gas and vapor concentrations can be measured, but this typically requires bulky and expensive instrumentation. Recently, inexpensive sensors with low power demands have been fabricated for use on the International Space Station (ISS). Carbon Nanotube (CNT) based chemical sensors are one type of these sensors. CNT sensors meet the requirements for low cost and ease of fabrication for deployment on the ISS. However, converting the measured signal from the sensors to human readable indicators of atmospheric air quality and safety is challenging. This is because it is difficult to develop an analytical model that maps the CNT sensor output signal to gas concentration. Training a neural network on CNT sensor data to predict gas concentration is more effective than developing an analytic approach to calculate the concentration from the same data set. With this in mind a neural network was created to tackle this challenge of converting the measured signal into CO2 and NH3 concentration values.
Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities
An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.
Guidelines for spaceborne microwave remote sensors
A handbook was developed to provide information and support to the spaceborne remote sensing and frequency management communities: to guide sensor developers in the choice of frequencies; to advise regulators on sensor technology needs and sharing potential; to present sharing analysis models and, through example, methods for determining sensor sharing feasibility; to introduce developers to the regulatory process; to create awareness of proper assignment procedures; to present sensor allocations; and to provide guidelines on the use and limitations of allocated bands. Controlling physical factors and user requirements and the regulatory environment are discussed. Sensor frequency allocation achievable performance and usefulness are reviewed. Procedures for national and international registration, the use of non-allocated bands and steps for obtaining new frequency allocations, and procedures for reporting interference are also discussed.
Sensor Selection and Data Validation for Reliable Integrated System Health Management
For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.