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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Language Research Center's Computerized Test System (LRC-CTS) - Video-formatted tasks for comparative primate research

Automation of a computerized test system for comparative primate research is shown to improve the results of learning in standard paradigms. A mediational paradigm is used to determine the degree to which criterion in the learning-set testing reflects stimulus-response associative or mediational learning. Rhesus monkeys are shown to exhibit positive transfer as the criterion levels are shifted upwards, and the effectiveness of the computerized testing system is confirmed.

Rumbaugh, Duane M.↗
NASA NTRS2011

The Application of Lean Thinking Principles and Kaizen Practices for the Successful Development and Implementation of the Ares I-X Flight Test Rocket and Mission

On October 28, 2009 the Ares I-X flight test rocket launched from Kennedy Space Center and flew its suborbital trajectory as designed. The mission was successfully completed as data from the test, and associated development activities were analyzed, transferred to stakeholders, and well documented. Positive lessons learned from Ares I-X were that the application of lean thinking principles and kaizen practices are effective in streamlining development activities. Ares I-X, like other historical rocket development projects, was hampered by technical, cost, and schedule challenges and if not addressed boldly could have resulted in cancellation of the test. The mission management team conducted nine major meetings, referred to as lean events, across its elements to assess plans, procedures, processes, requirements, controls, culture, organization, use of resources, and anything that could be changed to optimize schedule or reduce risk. The preeminent aspect of the lean events was the focus on value added activities and the removal or at least reduction in non-value activities. Trained Lean Six Sigma facilitators assisted the Ares I-X developers in conducting the lean events. They indirectly helped formulate the mission s own unique methodology for assessing schedule. A core team was selected to lead the events and report to the mission manager. Each activity leveraged specialized participants to analyze the subject matter and its related processes and then recommended alternatives and solutions. Stakeholders were the event champions. They empowered and encouraged the team to succeed. The keys to success were thorough preparation, honest dialog, small groups, adherence to the Ares I-X ground rules, and accountability through disciplined reporting and tracking of actions. This lean event formula was game-changing as demonstrated by the success of Ares I-X. It is highly recommended as a management tool to help develop other complex systems efficiently. The key benefits for Ares I-X were obtaining unambiguous schedule margin, defining enabling options for risk reduction, and most importantly a stronger more unified team.

Askins, B. R.↗
NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗
NASA NTRSDate not supplied

Big Data Challenges at CCMC

Like other research centers, the Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) at NASA Goddard Space Flight Center (GSFC) is also experiencing the big data challenges. CCMC hosts over 80 space weather models for Runs On Request (ROR), Continuous Runs and Instant Runs simulation services for the research community. In addition, CCMC has started to support simulation output onboarding in response to the Open Science initiative. Overall, we have accumulated over petabytes of simulation output data and are rapidly growing. In this presentation, we will discuss our data and storage challenges. We will present our attempts to address our challenges and any associated lessons learned. CCMC uses Apache Airflow to ensure data transfer is consistent. We will give a brief overview on how we leverage Apache Airflow to enhance our environment.

space weather↗
NASA NTRSDate not supplied

Big Data Challenges at the Community Coordinated Modeling Center (CCMC)

Like other research centers, the Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) at NASA Goddard Space Flight Center (GSFC) is also experiencing the big data challenges. CCMC hosts over 80 space weather models for Runs On Request (ROR), Continuous Runs and Instant Runs simulation services for the research community. In addition, CCMC has started to support simulation output onboarding in response to the Open Science initiative. Overall, we have accumulated over petabytes of simulation output data and are rapidly growing. In this presentation, we will discuss our data and storage challenges. We will present our attempts to address our challenges and any associated lessons learned. CCMC uses Apache Airflow to ensure data transfer is consistent. We will give a brief overview on how we leverage Apache Airflow to enhance our environment.

space weather↗
NASA NTRS1985

Adaptive Inverse Control for Rotorcraft Vibration Reduction

This thesis extends the Least Mean Square (LMS) algorithm to solve the mult!ple-input, multiple-output problem of alleviating N/Rev (revolutions per minute by number of blades) helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the higher harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. For higher order transfer matrices, a rough estimate of the inverse is needed to start the algorithm efficiently. The simulation results indicate that the factor which most influences LMS adaptive inverse control is the product of the control relaxation and the the stability gain matrix. A small stability gain matrix makes the controller less sensitive to relaxation selection, and permits faster and more stable vibration reduction, than by choosing the stability gain matrix large and the control relaxation term small. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification. In the simulation studies, the LMS adaptive inverse control algorithm is shown to be capable of adapting the inverse (controller) matrix to track changes in the flight conditions. The algorithm converges quickly for moderate disturbances, while taking longer for larger disturbances. Perfect knowledge of the inverse matrix is not required for good control of the N/Rev vibration. However it is shown that measurement noise will prevent the LMS adaptive inverse control technique from controlling the vibration, unless the signal averaging method presented is incorporated into the algorithm.

Jacklin, Stephen A.↗
NASA NTRS1996

Lewis Research Center: Commercialization Success Stories

The NASA Lewis Research Center, located in Cleveland, Ohio, has a portfolio of research and technology capabilities and facilities that afford opportunities for productive partnerships with industry in a broad range of industry sectors. In response to the President's agenda in the area of technology for economic growth (Clinton/Gore 1993), the National Performance Review (1993), NASA's Agenda for Change (1994), and the needs of its customers, NASA Lewis Research Center has sought and achieved significant successes in technology transfer and commercialization. This paper discusses a sampling of Lewis Research Center's successes in this area, and lessons learned that Lewis Research Center is applying in pursuit of continuous improvement and excellence in technology transfer and commercialization.

Heyward, Ann O.↗
NASA NTRS2017

Effects of Motion Cues on the Training of Multi-Axis Manual Control Skills

The study described in this paper investigated the effects of two different hexapod motion configurations on the training and transfer of training of a simultaneous roll and pitch control task. Pilots were divided between two groups which trained either under a baseline hexapod motion condition, with motion typically provided by current training simulators, or an optimized hexapod motion condition, with increased fidelity of the motion cues most relevant for the task. All pilots transferred to the same full-motion condition, representing motion experienced in flight. A cybernetic approach was used that gave insights into the development of pilots use of visual and motion cues over the course of training and after transfer. Based on the current results, neither of the hexapod motion conditions can unambiguously be chosen as providing the best motion for training and transfer of training of the used multi-axis control task. However, the optimized hexapod motion condition did allow pilots to generate less visual lead, control with higher gains, and have better disturbance-rejection performance at the end of the training session compared to the baseline hexapod motion condition. Significant adaptations in control behavior still occurred in the transfer phase under the full-motion condition for both groups. Pilots behaved less linearly compared to previous single-axis control-task experiments; however, this did not result in smaller motion or learning effects. Motion and learning effects were more pronounced in pitch compared to roll. Finally, valuable lessons were learned that allow us to improve the adopted approach for future transfer-of-training studies.

flight simulators↗
NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗
NASA NTRS2011

The Effect of Different Operations Modes on Science Capabilities During the 2010 Desert-RATS Test: Insights from the Geologist Crewmembers

The 2010 Desert RATS field test utilized two Space Exploration Vehicles (prototype planetary rovers) and four crewmembers (2 per rover) to conduct a geologic traverse across northern Arizona while testing continuous and twice-per-day communications paired with operation modes of separating and exploring individually (Divide & Conquer) and exploring together (Lead & Follow), respectively. This report provides qualitative conclusions from the geologist crewmembers involved in this test as to how these modes of communications and operations affected our ability to conduct field geology. Each mode of communication and operation provided beneficial capabilities that might be further explored for future Human Spaceflight Missions to other solar system objects. We find that more frequent interactions between crews and an Apollo-style Science Team on the Earth best enables scientific progress during human exploration. However, during multiple vehicle missions, this communication with an Earth-based team of scientists, who represent "more minds on the problem", should not come at the exclusion of (or significantly decrease) communication between the crewmembers in different vehicles who have the "eyes on the ground". Inter-crew communications improved when discussions with a backroom were infrequent. Both aspects are critical and cannot be mutually exclusive. Increased vehicle separation distances best enable encounters with multiple geologic units. However, seemingly redundant visits by multiple vehicles to the same feature can be utilized to provide improved process-related observations about the development and modification of the local terrain. We consider the value of data management, transfer, and accessibility to be the most important lesson learned. Crews and backrooms should have access to all data and related interpretations within the mission in as close to real-time conditions as possible. This ensures that while on another planetary surface, crewmembers are as educated as possible with respect to the observations and data they will need to collect at any moment.

Bleacher, Jacob E.↗
NASA NTRS2011

The Application of Lean Thinking Principles and Kaizen Practices for the Successful Development and Implementation of the Ares I-X Flight Test Rocket and Mission

On October 28, 2009 the Ares I-X flight test rocket launched from Kennedy Space Center and flew its suborbital trajectory as designed. The mission was successfully completed as data from the test, and associated development activities were analyzed, transferred to stakeholders, and well documented. A positive lesson learned from Ares I-X was that the application of lean thinking principles and kaizen practices was very effective in streamlining development activities. Ares I-X, like other historical rocket development projects, was hampered by technical, cost, and schedule challenges and if not addressed boldly could have resulted in cancellation of the test. The mission management team conducted nine major meetings, referred to as lean events, across its elements to assess plans, procedures, processes, requirements, controls, culture, organization, use of resources, and anything that could be changed to optimize schedule or reduce risk. The preeminent aspect of the lean events was the focus on value added activities and the removal or at least reduction in non-value added activities. Trained Lean Six Sigma facilitators assisted the Ares I-X developers in conducting the lean events. They indirectly helped formulate the mission s own unique methodology for assessing schedule. A core team was selected to lead the events and report to the mission manager. Each activity leveraged specialized participants to analyze the subject matter and its related processes and then recommended alternatives and solutions. Stakeholders were the event champions. They empowered and encouraged the team to succeed. The keys to success were thorough preparation, honest dialog, small groups, adherence to the Ares I-X ground rules, and accountability through disciplined reporting and tracking of actions. This lean event formula was game-changing as demonstrated by Ares I-X. It is highly recommended as a management tool to help develop other complex systems efficiently. The key benefits for Ares I-X were obtaining unambiguous schedule margin, defining enabling options for risk reduction, and most importantly a stronger more unified team.

Askins, B. R.↗
NASA NTRS2010

Operational Lessons Learned from the Ares I-X Flight Test

The Ares I-X flight test, launched in 2009, is the first test of the Ares I crew launch vehicle. This development flight test evaluated the flight dynamics, roll control, and separation events, but also provided early insights into logistical, stacking, launch, and recovery operations for Ares I. Operational lessons will be especially important for NASA as the agency makes the transition from the Space Shuttle to the Constellation Program, which is designed to be less labor-intensive. The mission team itself comprised only 700 individuals over the life of the project compared to the thousands involved in Shuttle and Apollo missions; while missions to and beyond low-Earth orbit obviously will require additional personnel, this lean approach will serve as a model for future Constellation missions. To prepare for Ares I-X, vehicle stacking and launch infrastructure had to be modified at Kennedy Space Center's Vehicle Assembly Building (VAB) as well as Launch Complex (LC) 39B. In the VAB, several platforms and other structures designed for the Shuttle s configuration had to be removed to accommodate the in-line, much taller Ares I-X. Vehicle preparation activities resulted in delays, but also in lessons learned for ground operations personnel, including hardware deliveries, cable routing, transferred work and custodial paperwork. Ares I-X also proved to be a resource challenge, as individuals and ground service equipment (GSE) supporting the mission also were required for Shuttle or Atlas V operations at LC 40/41 at Cape Canaveral Air Force Station. At LC 39B, several Shuttle-specific access arms were removed and others were added to accommodate the in-line Ares vehicle. Ground command, control, and communication (GC3) hardware was incorporated into the Mobile Launcher Platform (MLP). The lightning protection system at LC 39B was replaced by a trio of 600-foot-tall towers connected by a catenary wire to account for the much greater height of the vehicle. Like Shuttle, Ares I-X will be stacked on a MLP and rolled out to the pad on a Saturn-era crawler-transporter. While Ares I-X was only held in place by the four hold-down posts on its aft skirt during rollout, a new vehicle stabilization system (VSS) attached to the vertical service structure kept the vehicle from undue swaying prior to launch at the pad, LC 39B. Following the launch, the flight test vehicle first stage was recovered with the aid of new parachutes resized to accommodate the five-segment-long first stage, which had a much greater length and mass than the Shuttle s reusable solid rocket boosters. After splashdown, recovery divers exercised extra care when handling the first stage to ensure that the flight data recorders in the fifth segment simulator were not damaged by exposure to sea water. The data recovered from the Ares I-X flight test will be very valuable in verifying the predicted environments and models used to design the vehicle. Lessons learned from Ares I-X will be shared with the Ares Projects through written and verbal reports and through integration of mission team members into the Project workforce.

Davis, Stephan R.↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS1994

Teacher Enhancement Institute

In a team building, team teaching strategy with four faculty, can learning strategies such as educational technology and problem based learning be provided to forty local teachers of primary, elementary, and secondary students? The impetus for the effort is to provide information about science and engineering at NASA and motivate students to pursue careers in science and technology. Teachers, identified and selected through a rigorous application procedure, participated in a two week workshop for a graduate credit. Teachers were exposed to computer applications such as INTERNET, MOSAIC, Power Machintosh word processing, NASA scientists, and laboratory experiments. Teachers were evaluated on level and quality of their participation, design of teacher application materials and relevant lesson plans and presentations. The results show that teachers, regardless of preparation and background, can learn science and engineering applications and develop relevant materials to transfer information to their classroom. Follow-up during the academic year will show that teachers are successfully using materials.

Simmons, Ron W.↗
NASA NTRS1984

What you thought you knew about motion sickness isn't necessarily so

Motion sickness symptoms, stimuli, and drug therapy are discussed. Autogenic feedback training (AFT) methods of preventing motion sickness are explained. Research with AFT indicates that participants who had AFT could withstand longer periods of Coriolis acceleration, participants with high or low susceptibility to motion sickness could control their symptoms with AFT, AFT for Coriolis acceleration is transferable to other motion sickness stimuli, and most people can learn AFT, though with varying rates of learning.

Autogenic Training↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
NASA NTRS1998

From the Chemistry Lab to Licensing

This is a story of technology maturation and transfer, and licensing. It traces the history of the recently patented ion- exchange material (IEM) from the accidental discovery that this polymer, a battery separator of marginal performance, picked up copper from distilled water passing through corroded copper tubing in the laboratory, to a point where five organizations and one individual have applied for licenses to manufacture and market it or to use it in a wide variety of applications. This story discusses in detail the problems of converting an immature technology into a mature and eventually commercialized technology, without dedicated resources. Readers will develop an appreciation for how the obstacles to maturation and licensing of the technology were faced and overcome. The lessons learned will be discussed, with the hope of enhancing the technology transfer process.

Savino, Joseph M.↗