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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Balanced systems and structures - Reduction, assignment, and perturbations

New results on properties of balanced linear systems and structures are presented. Balanced representation is defined for systems with poles at an imaginary axis or at the origin. Grammians do not exist in this case, but antigrammians are introduced which make balanced reduction possible. System grammians of specified properties are obtained by assigning the sensor and actuator configuration. The latter is determined using input-output assignment procedures introduced for general systems and specified to structures. A system is said to be uniformly balanced if all its Hankel singular values are equal.

Gawronski, Wodek↗
NASA NTRS2009

Efficient Method for Optimizing Placement of Sensors

A computationally efficient method has been developed to enable optimization of the placement of sensors for the purpose of diagnosis of a complex engineering system (e.g., an aircraft or spacecraft). The method can be used both in (1) designing a sensor system in which the number and positions of sensors are initially not known and must be determined and (2) adding sensors to a pre-existing system to increase the diagnostic capability. The optimal-sensor-placement problem can be summarized as involving the following concepts, issues, and subproblems: a) Degree of Diagnosability - This is a concept for characterizing the set of faults that can be discriminated by use of a given set of sensors. b) Minimal Sensor Set - The idea is one of finding a minimal set of sensors that guarantees a specific degree of diagnosability. c) Minimal-Cost Sensors - In a case in which different sensors are assigned with different costs, it is desired to choose the least costly set of sensors that affords a specific degree of diagnosability.

Fijany, Amir↗
NASA NTRS1988

A figure control sensor for the Large Deployable Reflector (LDR)

A sensing and control system is required to maintain high optical figure quality in a segmented reflector. Upon detecting a deviation of the segmented surface from its ideal form, the system drives segment mounted actuators to realign the individual segments and thereby return the surface to its intended figure. When the reflector is in use, a set of figure sensors will determine positions of a number of points on the back surface of each of the reflector's segments, each sensor being assigned to a single point. By measuring the positional deviations of these points from previously established nominal values, the figure sensors provide the control system with the information required to maintain the reflector's optical figure. The optical lever, multiple wavelength interferometer, and electronic capacitive sensor, the most promising technologies for the development of the figure sensor, are illustrated. It is concluded that to select a particular implementation of the figure sensors, performance requirement will be refined and relevant technologies investigated further.

Bartman, R.↗
NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗
NASA NTRS2011

Demonstration of a Particle Impact Monitoring System for Crewed Space Exploration Modules

When micrometeorite or debris impacts occur on a space habitat, crew members need to be quickly informed of the likely extent of damage, and be directed to the impact location for possible repairs. The goal of the Habitat Particle Impact Monitoring System (HIMS) is to develop a fully automated, end-to-end particle impact detection system for crewed space exploration modules, both in space and on the surfaces of Solar System bodies. The HIMS uses multiple thin film piezo-polymer vibration sensors to detect impacts on a surface, and computer processing of the acoustical signals to characterize the impacts. Development and demonstration of the HIMS is proceeding in concert with NASA's Habitat Demonstration Unit (HDU) Project. The HDU Project is designed to develop and test various technologies, configurations, and operational concepts for exploration habitats. This paper describes the HIMS development, initial testing, and HDU integration efforts. Initial tests of the system on the HDU were conducted at NASA?s 2010 Desert Research and Technologies Studies (Desert-RATS). Four sensor locations were assigned near the corners of a rectangular pattern. To study the influence of wall thickness, three sets of four sensors were installed at different layer depths: on the interior of the PEM wall, on the exterior of the same wall, and on the exterior of a layer of foam insulation applied to the exterior wall. Once the system was activated, particle impacts were periodically applied by firing a pneumatic pellet gun at the exterior wall section. Impact signals from the sensors were recognized by a data acquisition system when they occurred, and recorded on a computer for later analysis. Preliminary analysis of the results found that the HIMS system located the point of impact to within 8 cm, provided a measure of the impact energy / damage produced, and was insensitive to other acoustic events. Based on this success, a fully automated version of this system will be completed and demonstrated as part of a crew "Caution/Warning" system at the 2011 Desert-RATS, along with a crew response procedure.

Opiela, J. N.↗
NASA NTRS2012

Debris Impact Detection Instrument for Crewed Modules

When micrometeoroid or debris impacts occur on a space habitat, crew members need to be quickly informed of the likely extent of damage, and be directed to the impact location for possible repairs. This is especially important because the outer walls of pressurized volumes are often not easily accessible, blocked by racks or cabinets. The goal of the Habitat Particle Impact Monitoring System (HIMS) is to develop a fully automated, end-to-end particle impact detection system for crewed space exploration modules. The HIMS uses multiple passive, thin film piezo-polymer vibration sensors to detect impacts on a surface, and computer processing of the acoustical signals to characterize the impacts. Development and demonstration of the HIMS is proceeding in concert with NASA's Habitat Demonstration Unit (HDU) Project. The HDU Project is designed to develop and test various technologies, configurations, and operational concepts for exploration habitats. This paper describes the HIMS development, initial testing, and HDU integration efforts. Initial tests of the system on the HDU were conducted at NASA s 2010 and 2011 Desert Research and Technologies Studies (Desert-RATS or D-RATS). The HDU lab module, as seen from above, has an open circular floorplan divided into eight wedge-shaped Segments. The side wall of the module -- the surface used for this technology demonstration -- is a hard fiberglass composite covered with a layer of sprayed-on foam insulation. Four sensor locations were assigned near the corners of a rectangular pattern on the wall of one segment of the HDU lab module. The flat, self-adhesive sensors were applied to the module during its initial outfitting. To study the influence of the wall s construction (thickness and materials), three sets of four sensors were installed at different layer depths: on the interior of the module s wall, on the exterior of the same wall, and on the exterior of the foam insulation. The signal produced when a vibration passes through a sensor is first sent through a pre-amplifier. The amplified signal then is sent to the data acquisition and data processing systems. The vibration data from the sensors are then processed and reduced to a form suitable for presentation to the crew.

Opiela, J.↗
NASA NTRS1994

Centrifugal Liquid/Gas Separator With Phase Detectors

Centrifugal liquid/gas separator that includes phase (liquid or gas) detectors helps ensure exclusiveness of each phase at its assigned outlet. Acoustic sensors in centrifugal liquid/gas separator measure speeds of sound in nominally pure liquid and nominally pure gas at liquid and gas outlets respectively. When speed of sound is that of pure liquid or gas, valve opens to let liquid or gas flow out.

Schneider, Steven J.↗
NASA NTRS2008

Intelligent Integrated Health Management for a System of Systems

An intelligent integrated health management system (IIHMS) incorporates major improvements over prior such systems. The particular IIHMS is implemented for any system defined as a hierarchical distributed network of intelligent elements (HDNIE), comprising primarily: (1) an architecture (Figure 1), (2) intelligent elements, (3) a conceptual framework and taxonomy (Figure 2), and (4) and ontology that defines standards and protocols. Some definitions of terms are prerequisite to a further brief description of this innovation: A system-of-systems (SoS) is an engineering system that comprises multiple subsystems (e.g., a system of multiple possibly interacting flow subsystems that include pumps, valves, tanks, ducts, sensors, and the like); 'Intelligent' is used here in the sense of artificial intelligence. An intelligent element may be physical or virtual, it is network enabled, and it is able to manage data, information, and knowledge (DIaK) focused on determining its condition in the context of the entire SoS; As used here, 'health' signifies the functionality and/or structural integrity of an engineering system, subsystem, or process (leading to determination of the health of components); 'Process' can signify either a physical process in the usual sense of the word or an element into which functionally related sensors are grouped; 'Element' can signify a component (e.g., an actuator, a valve), a process, a controller, an actuator, a subsystem, or a system; The term Integrated System Health Management (ISHM) is used to describe a capability that focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, prognosis of future anomalies), and provide data, information, and knowledge (DIaK) not just data to control systems for safe and effective operation. A major novel aspect of the present development is the concept of intelligent integration. The purpose of intelligent integration, as defined and implemented in the present IIHMS, is to enable automated analysis of physical phenomena in imitation of human reasoning, including the use of qualitative methods. Intelligent integration is said to occur in a system in which all elements are intelligent and can acquire, maintain, and share knowledge and information. In the HDNIE of the present IIHMS, an SoS is represented as being operationally organized in a hierarchical-distributed format. The elements of the SoS are considered to be intelligent in that they determine their own conditions within an integrated scheme that involves consideration of data, information, knowledge bases, and methods that reside in all elements of the system. The conceptual framework of the HDNIE and the methodologies of implementing it enable the flow of information and knowledge among the elements so as to make possible the determination of the condition of each element. The necessary information and knowledge is made available to each affected element at the desired time, satisfying a need to prevent information overload while providing context-sensitive information at the proper level of detail. Provision of high-quality data is a central goal in designing this or any IIHMS. In pursuit of this goal, functionally related sensors are logically assigned to groups denoted processes. An aggregate of processes is considered to form a system. Alternatively or in addition to what has been said thus far, the HDNIE of this IIHMS can be regarded as consisting of a framework containing object models that encapsulate all elements of the system, their individual and relational knowledge bases, generic methods and procedures based on models of the applicable physics, and communication processes (Figure 2). The framework enables implementation of a paradigm inspired by how expert operators monitor the health of systems with the help of (1) DIaK from various sources, (2) software tools that assist in rapid visualization of the condition of the system, (3) analical software tools that assist in reasoning about the condition, (4) sharing of information via network communication hardware and software, and (5) software tools that aid in making decisions to remedy unacceptable conditions or improve performance.

Smith, Harvey↗
NASA NTRS2012

Security Data Warehouse Application

The Security Data Warehouse (SDW) is used to aggregate and correlate all JSC IT security data. This includes IT asset inventory such as operating systems and patch levels, users, user logins, remote access dial-in and VPN, and vulnerability tracking and reporting. The correlation of this data allows for an integrated understanding of current security issues and systems by providing this data in a format that associates it to an individual host. The cornerstone of the SDW is its unique host-mapping algorithm that has undergone extensive field tests, and provides a high degree of accuracy. The algorithm comprises two parts. The first part employs fuzzy logic to derive a best-guess host assignment using incomplete sensor data. The second part is logic to identify and correct errors in the database, based on subsequent, more complete data. Host records are automatically split or merged, as appropriate. The process had to be refined and thoroughly tested before the SDW deployment was feasible. Complexity was increased by adding the dimension of time. The SDW correlates all data with its relationship to time. This lends support to forensic investigations, audits, and overall situational awareness. Another important feature of the SDW architecture is that all of the underlying complexities of the data model and host-mapping algorithm are encapsulated in an easy-to-use and understandable Perl language Application Programming Interface (API). This allows the SDW to be quickly augmented with additional sensors using minimal coding and testing. It also supports rapid generation of ad hoc reports and integration with other information systems.

Vernon, Lynn R.↗
NASA NTRS1982

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.

Litman, V.↗
NASA NTRS1986

Space remote sensors

Space remote sensors are analyzed as a component of a higher level 'robotic' system and the implications for sensor parameter selection and priority assignment are described. The EOS program and three of its particular sensor types are considered as examples of the principles discussed.

Keller, Sam↗
NASA NTRS1998

Surface-Wind Anomalies in North-Atlantic and North Pacific from SSM/I Observations: Influence on Temperature of Adjoining Land Regions

Surface winds over the oceans are derived from Special Sensor Microwave Imager (SSM/I) measurements, assigning direction by Variational Analysis Method (VAM). Validations by comparison with other measurements indicate highly-satisfactory data quality. Providing global coverage from 1988, the dataset is a convenient source for surface-wind climatology. In this study, the interannual variability of zonal winds is analyzed concentrating on the westerlies in North Atlantic and North Pacific, above 30 N. Interannual differences in the westerlies exceeding 10 m sec (exp -1) are observed over large regions, often accompanied by changes of the same magnitude in the easterlies below 30 N. We concentrate on February/March, since elevated temperatures, by advancing snow-melt, can produce early spring. The extremely strong westerlies in 1997 observed in these months over North Atlantic (and also North Pacific) apparently contributed to large surface-temperature anomalies in western Europe, on the order of +3 C above the climatic monthly average for England and France. At these latitudes strong positive anomalies extended in a ring around the globe. We formulated an Index of South westerlies for the North Atlantic, which can serve as an indicator for day-by-day advection effects into Europe. In comparing 1997 and 1998 with the previous years, we establish significant correlations with the temperature anomalies (one to five days later, depending on the region, and on the season). This variability of the ocean-surface winds and of the temperature anomalies on land may be related to the El Nino/La Nina oscillations. Such large temperature fluctuations over large areas, whatever the cause, can be regarded as noise in attempts to assess long-term trends in global temperature.

Otterman, Joseph↗
NASA NTRS2011

Development of Tools and Techniques for Processing STORRM Flight Data

While at JSC for the summer of 2011, I was assigned to work on the sensor test for Orion relative-navigation risk mitigation (STORRM) development test objective (DTO). The STORRM DTO was flown on-board Endeavor during STS-134. The objective of the STORRM DTO is to test the visual navigation system (VNS), which will be used as the primary relative navigation sensor for the Orion spacecraft. The VNS is a flash lidar system intended to provide both line of sight and range information during rendezvous and proximity operations. The STORRM DTO also serves as a testbed for the high-resolution docking camera. This docking camera will be used to provide piloting cues for the crew during proximity operations. These instruments were mounted next to the trajectory control sensor (TCS) in Endeavour s payload bay. My principle objective for the summer was to generate a best estimated trajectory (BET) for Endeavor using the flight data collected by the VNS during rendezvous and the unprecedented re-rendezvous with the ISS. I processed the raw images from the VNS to produce range and bearing measurements. I then aggregated these measurements and extracted the measurements corresponding to individual reflectors. I combined the information contained in these measurements with data from the Endeavour's inertial sensors using Kalman smoothing techniques to ultimately produce a BET. This work culminated with a final presentation of the result to division management. Development of this tool required that traditional linear smoothing techniques be modified in a novel fashion to permit for the inclusion of non-linear measurements. This internship has greatly helped me further my career by providing exposure to real engineering projects. I also have benefited immensely from the mentorship of the engineers working on these projects. Many of the lessons I learned and experiences I had are of particular value because then can only be found in a place like JSC.

Robinson, Shane↗
NASA NTRS1994

Advanced computer architecture specification for automated weld systems

This report describes the requirements for an advanced automated weld system and the associated computer architecture, and defines the overall system specification from a broad perspective. According to the requirements of welding procedures as they relate to an integrated multiaxis motion control and sensor architecture, the computer system requirements are developed based on a proven multiple-processor architecture with an expandable, distributed-memory, single global bus architecture, containing individual processors which are assigned to specific tasks that support sensor or control processes. The specified architecture is sufficiently flexible to integrate previously developed equipment, be upgradable and allow on-site modifications.

Katsinis, Constantine↗
NASA NTRS1976

Control elements for an unmanned Martian roving vehicle

The roving vehicle simulator was operated autonomously under control of the simulated on-board computer. With the microwave radar obstacle sensor mounted and operating, it was able to avoid a student placed in its path and to return to the originally assigned direction when that path was clear. The tactile obstacle sensor was able to detect impassable obstacles while allowing the vehicle to negotiate passable obstacles.

Wehe, R. L.↗
NASA NTRS2016

Unsupervised Framework to Monitor Lake Dynamics

A method of reducing processing time when assigning geographic areas to land cover labels using satellite sensor values includes a processor receiving a feature value for each pixel in a time series of frames of satellite sensor values, each frame containing multiple pixels and each frame covering a same geographic location. For each sub-area of the geographic location, the sub-area is assigned to one of at least three land cover labels. The processor determines a fraction function for a first sub-area assigned to a first land cover label. The sub-areas that were assigned to the first land cover label are reassigned to one of the second land cover label and the third land cover label based on the fraction functions of the sub-areas.

Boriah, Shyam↗
NASA NTRS1985

Eigenvalue Placement and Stabilization by Constrained Optimization

A pole placement algorithm is proposed which uses constrained nonlinear optimization techniques on a finite dimensional model of a linear n degree of freedom system. Low order feedback control is assumed where r poles may be assigned; r being the rank of the sensor coefficient matrix. It is shown that by combining feedback control theory methods with optimization techniques, one can ensure the stability characteristics of a system, and can alter its transient response.

Decaro, S. M.↗
NASA NTRS1993

On eigensystem assignment with dissipativity constraints

Stability of the closed-loop system is of vital importance when controller synthesis is being performed using a reduced order model of the open-loop plant. For passive systems such as flexible space structures, closed-loop stability is often ensured by low-authority dissipative controllers using collocated and compatible sensors and actuators. This paper considers the problem of eigensystem assignment for flexible structures using constant-gain output feedback, under the dissipativity constraints requiring that the gain matrices be nonnegative definite. A novel method is developed in which a sequential procedure is used to assign one complex-conjugate pair of closed-loop eigenvalues in each step. At each step, an iterative technique is employed for generating a nonnegative definite gain matrix that assigns a new pair while keeping the previously assigned pairs unchanged. This method can assign up to m closed-loop system eigenvalues, where m is the number of control inputs. A numerical example of damping enhancement for a large flexible structure is presented to demonstrate the approach.

Maghami, Peiman G.↗