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At least 1,729 records · Page 96

Self-learning fuzzy controllers based on temporal back propagation

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

Jang, Jyh-Shing R.↗

Definitions in use by the visible and near-infrared, and thermal working groups

The Calibration Advisory Panel (CAP) is composed of calibration experts from each of the Earth Observing System (EOS) instruments, science investigation, and cross-calibration teams. These members come from a variety of institutions and backgrounds. In order to facilitate an exchange of ideas, and assure a common basis for communication, it was desirable to assemble this list of definitions. These definitions were developed for use by the visible and near-infrared working group, and the thermal infrared working group. Where necessary or appropriate, deviations from these for specific instruments or other sensor types are given in the individual calibration plans. The definitions contained in this document are derived, wherever possible, from definitions accepted by international and national metrological commissions including the United States National Institute of Standards and Technology (NIST), the International Bureau of Weights and Measures (BIPM), the International Electrotechnical Commission (IEC), the International Organization for Standardization (ISO), and the International Organization of Legal Metrology (OIML).

Bruegge, Carol J.↗

Lesson Learned

ASA will begin naming "a few hundred" engineers soon who will constitute the Independent. Technical Authority (ITA) recommended by the Columbia Accident Investigation Board to pro- vide an effective check and balance on space programs for safety. Within the next month, the directors of Johnson and Kennedy space centers will name "a couple of dozen at each place" to backstop human spaceflight engineers, according to Theron M. Bradley, Jr., NASA chief engineer. By the time the new fiscal year begins Oct. 1, the ITA should be fully staffed across the agency, with a system of "warrants" delineating who is an expert in what. Overseeing the whole organization

Morring, Frank, Jr.↗

Knowledge Base Editor (SharpKBE)

The SharpKBE software provides a graphical user interface environment for domain experts to build and manage knowledge base systems. Knowledge bases can be exported/translated to various target languages automatically, including customizable target languages.

Tikidjian, Raffi↗

Improving Customer Waiting Time at a DMV Center Using Discrete-Event Simulation

Virginia's Department of Motor Vehicles (DMV) serves a customer base of approximately 5.6 million licensed drivers and ID card holders and 7 million registered vehicle owners. DMV has more daily face-to-face contact with Virginia's citizens than any other state agency [1]. The DMV faces a major difficulty in keeping up with the excessively large customers' arrival rate. The consequences are queues building up, stretching out to the entrance doors (and sometimes even outside) and customers complaining. While the DMV state employees are trying to serve at their fastest pace, the remarkably large queues indicate that there is a serious problem that the DMV faces in its services, which must be dealt with rapidly. Simulation is considered as one of the best tools for evaluating and improving complex systems. In this paper, we use it to model one of the DMV centers located in Norfolk, VA. The simulation model is modeled in Arena 10.0 from Rockwell systems. The data used is collected from experts of the DMV Virginia headquarter located in Richmond. The model created was verified and validated. The intent of this study is to identify key problems causing the delays at the DMV centers and suggest possible solutions to minimize the customers' waiting time. In addition, two tentative hypotheses aiming to improve the model's design are tested and validated.

Arnaout, Georges M.↗

Managing Toxicological Risks: The Legacy of Shuttle Operations

Space toxicology greatly matured as a result of research and operations associated with the Shuttle. Materials offgassing had been a manageable concern since the Apollo days, but we learned to pay careful attention to compounds that could escape containment, to combustion events, to toxic propellants, to overuse of utility compounds, and to microbial and human metabolites. We also learned that flying real-time hardware to monitor air pollutants was a pathway with unanticipated speed bumps. Each new orbiter was tested for any excess offgassing products that could pollute the air during flight. In the late 1990s toxicologists and safety experts developed a 5-level toxicity rating system to guide containment of toxic compounds. This system is now in use aboard the International Space Station (ISS). Several combustion events during Shuttle Mir and also during Shuttle free-flight impelled toxicologists to identify hardware capable of monitoring toxic products; however, rapid adaptation of the hardware for the unique conditions of spaceflight caused unexpected missteps. Current and planned combustion analyzers would be useful to commercial partners that wish to manage the risk of health effects from thermal events. Propellants received special attention during the Shuttle program because of the possibility of bringing them into the habitable volume on extravehicular activity suits. Monitors for the airlocks were developed to mitigate this risk. Utility materials, such as lubricants, posed limited toxicological problems because water was not recovered. One clearly documented case of microbial metabolites polluting the Shuttle atmosphere was noted, and this has implications for commercial flights and control of microbes. Finally, carbon dioxide, the major human metabolite, episodically presented air quality problems aboard Shuttle, especially when nominal air flows were obstructed. Commercial vehicles must maintain robust air circulation given the anticipated high density of human occupants.

James, John T.↗

Development and Execution of Autonomous Procedures Onboard the International Space Station to Support the Next Phase of Human Space Exploration

Now that major assembly of the International Space Station (ISS) is complete, NASA's focus has turned to using this high fidelity in-space research testbed to not only advance fundamental science research, but also demonstrate and mature technologies and develop operational concepts that will enable future human exploration missions beyond low Earth orbit. The ISS as a Testbed for Analog Research (ISTAR) project was established to reduce risks for manned missions to exploration destinations by utilizing ISS as a high fidelity micro-g laboratory to demonstrate technologies, operations concepts, and techniques associated with crew autonomous operations. One of these focus areas is the development and execution of ISS Testbed for Analog Research (ISTAR) autonomous flight crew procedures intended to increase crew autonomy that will be required for long duration human exploration missions. Due to increasing communications delays and reduced logistics resupply, autonomous procedures are expected to help reduce crew reliance on the ground flight control team, increase crew performance, and enable the crew to become more subject-matter experts on both the exploration space vehicle systems and the scientific investigation operations that will be conducted on a long duration human space exploration mission. These tests make use of previous or ongoing projects tested in ground analogs such as Research and Technology Studies (RATS) and NASA Extreme Environment Mission Operations (NEEMO). Since the latter half of 2012, selected non-critical ISS systems crew procedures have been used to develop techniques for building ISTAR autonomous procedures, and ISS flight crews have successfully executed them without flight controller involvement. Although the main focus has been preparing for exploration, the ISS has been a beneficiary of this synergistic effort and is considering modifying additional standard ISS procedures that may increase crew efficiency, reduce operational costs, and raise the amount of crew time available for scientific research. The next phase of autonomous procedure development is expected to include payload science and human research investigations. Additionally, ISS International Partners have expressed interest in participating in this effort. The recently approved one-year crew expedition starting in 2015, consisting of one Russian and one U.S. Operating Segment (USOS) crewmember, will be used not only for long duration human research investigations but also for the testing of exploration operations concepts, including crew autonomy.

beyond low earth orbit (BLEO)↗

Improving Human/Autonomous System Teaming Through Linguistic Analysis

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such integration requires humans to work closely with autonomous systems, forming human and autonomous agent teams. The intention behind such teaming is that a team composed of both humans and autonomous agents will operate better than homogenous teams. Procedures exist for licensing pilots to operate in the national airspace system and current work is being done to define methods for validating the function of autonomous systems, however there is no method in place for assessing the interaction of these two disparate systems. Moreover, currently these systems are operated primarily by subject matter experts, limiting their use and the benefits of such teams. Providing additional information about the ongoing mission to the operator can lead to increased usability and allow for operation by non-experts. Linguistic analysis of the context of verbal communication provides insight into the intended meaning of commonly heard phrases such as "What's it doing now?" Analyzing the semantic sphere surrounding these common phrases enables the prediction of the operator's intent and allows the interface to supply the operator's desired information.

Meszaros, Erica L.↗

Thinking Inside the Box: A Hands-On Student Activity for Building a Contamination Containment Glovebox to Encourage Problem Solving in a Collaborative Environment

Contamination containment gloveboxes are sealed containers where operators outside the glovebox can safely manipulate hazardous or contamination-sensitive materials inside the glovebox through glove ports on the container. Containment gloveboxes are used in the space industry to study sample materials returned from space such as lunar, asteroid, and comet materials, to manufacture sensitive instruments and space ight hardware, and to perform contamination-sensitive experiments. A hands-on activity has been developed by contamination control engineer and STEM education experts as part of the Virginia Earth System Science Scholars (VESSS) summer academy program to teach high school students about gloveboxes and about problem solving in a collaborative environment. In this activity, student teams are given a list of common household materials (e.g. plastic bags, plastic straws, aluminum foil, tape, disposable gloves, etc.) for building their own glovebox. Students work together to sketch and design their glovebox before being provided a kit of materials, which they must purchase along with any extra materials with a mock team budget. They are given 30 minutes to build their glovebox, however, they do not know what will be placed inside the glovebox or what operations they will need to perform within the glovebox. At the end of the build time, teams are given a small, sealed paper envelope which they place in the glovebox. Each team selects a team member to be designated as the operator, and the operator places their hands in the gloves of the glovebox. The operator uses their gloves to open the envelope in the glovebox, and they discover they have 20 minutes to assemble a jigsaw puzzle. However, the puzzle is \contaminated" with talcum powder. After the 20 minute time period, teams are judged on how well they completed and cleaned the contaminated puzzle. In a post-activity debrief, teams discuss lessons learned such as how the actual built box di ered from the sketched design, the challenge of managing a budget for materials, how the team dealt with surprises, and if their glovebox allowed enough room for the operator to perform the task. This activity has been part of the VESSS summer academy since 2016, and teaches students teamwork, creativity, hands-on experimentation, communication, and reasoning skills. In a demonstration at IAC, a volunteer from the audience will be chosen to assemble a glovebox during the presentation using the same kit of materials and will demonstrate handling puzzle pieces in the completed glovebox.

Elaine Seasly↗

Developing Natural Language Processing and Supervised Learning Techniques to Classify Mars Tasks

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), employed Natural Language Processing (NLP) and machine learning techniques to analyze and classify Mars tasks. A list of 1,058 Mars tasks was provided by HRP experts including binary labeling of 18 Human System Task Categories (HSTCs). We developed an NLP model using Google's BERT language model to capture the semantic and syntactic nuances of these tasks. Supervised training was initially applied to a subset of the NLP-analyzed tasks to assess the model's effectiveness in classifying the remaining tasks. Incorporating HSTC descriptions significantly enhanced the classification accuracy for 9 out of the 18 HSTCs and reduced training time. To address the issue of severe class imbalance in the HSTC data, we introduced innovative weighting and sampling techniques for data augmentation. We then fine-tune BERT to implement a pairwise relatedness scoring method, allowing us to cluster tasks based on their relatedness and similarity, getting a step closer to labeling the tasks without supervision. In this presentation we guide you through data preprocessing, deciphering key syntax components using BERT, and performing supervised classification of the Mars tasks. This work showcases the potential use of advanced NLP techniques to analyze Mars missions to be incorporated into various crew health and performance analyses.

GenAI↗

Honeywell Cascade Distiller System Performance Testing Interim Results

The ability to recover and purify water through physiochemical processes is crucial for realizing long-term human space missions, including both planetary habitation and space travel. Because of their robust nature, distillation systems have been actively pursued as one of the technologies for water recovery. The Cascade Distillation System (CDS) is a vacuum rotary distillation system with potential for greater reliability and lower energy costs than existing distillation systems. The CDS was previously under development through Honeywell and NASA. In 2009, an assessment was performed to collect data to support down-selection and development of a primary distillation technology for application in a lunar outpost water recovery system. Based on the results of this testing, an expert panel concluded that the CDS showed adequate development maturity, TRL-4, together with the best product water quality and competitive weight and power estimates to warrant further development. The Advanced Exploration Systems (AES) Water Recovery Project (WRP) worked to address weaknesses identified by The Panel; namely bearing design and heat pump power efficiency. Testing at the NASA-JSC Advanced Exploration System Water Laboratory (AES Water Lab) using a prototype Cascade Distillation Subsystem (CDS) wastewater processor (Honeywell International, Torrance, Calif.) with test support equipment and control system developed by Johnson Space Center was performed to evaluate performance of the system with the upgrades. The CDS will also have been challenged with ISS analog waste streams and a subset of those being considered for Exploration architectures. This paper details interim results of the AES WRP CDS performance testing.

Callahan, Michael R.↗

Learning to Control Advanced Life Support Systems

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Subramanian, Devika↗

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed.

Loftin, R. Bowen↗

Medical Telemetry

Telemetry is the process whereby physiological or other data is acquired by instruments, translated into radio signals and j sent to a receiving station where the signals are decoded and recorded. Extensively used in I space operations, it is finding new Earth applications, among them transmission of medical data between emergency vehicles and hospitals. For example, transmission of an electrocardiogram from an ambulance to a hospital enables a physician to read the telemetered EKG and advise ambulance attendants on emergency procedures. Central Medical Emergency Dispatch (CMED) operates as a regional emergency medical communications center for Cleveland, Ohio and Cuyahoga County. The CMED system includes radio and telephone communications from hospital-to-hospital and from ambulance-to-hospital, but for improved emergency life support CMED sought to add a county-wide telemetry capability. The problem was that there were only eight radio frequencies available for telemetry and there were more than 30 potential users in Cleveland alone. NASA's Lewis Research Center volunteered its expert assistance. The Center's engineers studied the systems of other telemetry using cities, surveyed area hospitals to assure compatibility of telemetry equipment, and advised what types of equipment would be needed in emergency vehicles and at the various hospitals. The Lewis plan suggested that CMED be designated the central coordinating agency for the Cuyahoga County system, monitoring all telemetry frequencies and, when requested, assigning one not in use or one to be used at a sufficient distance that it would create no interference problem.

Source record↗

EnDEVR: An Environment for Data Engineering in VR

Organizations within the public and private sectors are looking to improve their data science operations for research, development, and operational purposes. As this interest in data science grows, so too does interest in tools and programs that facilitating such operations. This paper presents EnDEVR, the Environment for Data Engineering in Virtual Reality, a user-extensible system that allows for the execution of custom data science functions. We describe the user-centered design process for the EnDEVR system, based on the needs of subject matter experts within the aerospace and aviation industries. Fundamental to this design is a method for users to integrate their existing analysis code (that may leverage third-party vendors) as usable objects in the virtual environment; this allows organizations to operate without the price tag and lifetime obligations associated with commercial data science toolkits, a concept known as “vendor lock-in”. We then present our system design with iterative implementations, and conclude with lessons learned. The presented work may provide insight into future user-centered design processes for VR applications, particularly for digital transformation stakeholders to circumvent vendor lock-in.

Human-centered computing↗

Harmonizing Food Systems Emissions Accounting for More Effective Climate Action

Food systems—encompassing activities in food production, land-use change, supply chains and waste management—contribute significantly to climate change. Recent estimates indicate that food systems produce over 30% of annual anthropogenic greenhouse gas (GHG) emissions (about 20% of CO 2 , 50% of CH 4 , and 75% of N 2 O), with the Intergovernmental Panel on Climate Change (IPCC) estimating a notably broad range of 23%–42% of global GHG emissions. This paper synthesizes current research on the contributions of food systems to climate change, highlights challenges in quantifying their impact and proposes a harmonized accounting framework for more effective climate action. We recommend that an expert committee aligned with the IPCC develop guidance for food systems emissions accounting in four key areas, including: (1) defining system boundaries and nomenclature; (2) developing protocols to allocate broader sectoral emissions to food systems; (3) prioritizing critical areas for research into activity data and emissions factors; and (4) developing a balanced framework for evaluating the impact of mitigation interventions in light of other food systems imperatives. The committee should be integrated into two key international policy processes—the United Nations Framework Convention on Climate Change and the United Nations Food Systems Summit—to support coordinated action towards global net-zero goals. Guidance from the committee could significantly improve the ability of governments, companies, and researchers to estimate, report, monitor and ultimately reduce the climate impacts of food systems.

GHG accounting↗

Acquisition, representation and rule generation for procedural knowledge

Current research into the design and continuing development of a system for the acquisition of procedural knowledge, its representation in useful forms, and proposed methods for automated C Language Integrated Production System (CLIPS) rule generation is discussed. The Task Analysis and Rule Generation Tool (TARGET) is intended to permit experts, individually or collectively, to visually describe and refine procedural tasks. The system is designed to represent the acquired knowledge in the form of graphical objects with the capacity for generating production rules in CLIPS. The generated rules can then be integrated into applications such as NASA's Intelligent Computer Aided Training (ICAT) architecture. Also described are proposed methods for use in translating the graphical and intermediate knowledge representations into CLIPS rules.

Ortiz, Chris↗

Real-Time Sensor Validation System Developed

Real-time sensor validation improves process monitoring and control system dependability by ensuring data integrity through automated detection of sensor data failures. The NASA Lewis Research Center, Expert Microsystems, and Intelligent Software Associates have developed an innovative sensor validation system that can automatically detect automated sensor failures in real-time for all types of mission-critical systems. This system consists of a sensor validation network development system and a real-time kernel. The network development system provides tools that enable systems engineers to automatically generate software that can be embedded within an application. The sensor validation methodology captured by these tools can be scaled to validate any number of sensors, and permits users to specify system sensitivity. The resulting software reliably detects all types of sensor data failures.

Zakrajsek, June F.↗