Search NASA⌕ Search

SEARCH · Search NASA

Results for “Serial Learning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Alternating prism exposure causes dual adaptation and generalization to a novel displacement

In two experiments, we examined the hypothesis that repeatedly adapting and readapting to two mutually conflicting sensory environments fosters the development of a separate adaptation to each situation (dual adaptation) as well as an increased ability to adapt to a novel displacement (adaptive generalization). In the preliminary study, subjects alternated between adapting their visuomotor coordination to 30-diopter prismatic displacement and readapting to normal vision. Dual adaptation was observed by the end of 10 alternation cycles. However, an unconfounded test of adaptive generalization was prevented by an unexpected prism-adaptive shift in preexposure baselines for the dual-adapted subjects. In the primary experiment, the subjects adapted and readapted to opposite 15-diopter displacements for a total of 12 cycles. Both dual adaptation and adaptive generalization to a 30-diopter displacement were obtained. These findings may be understood in terms of serial reversal learning and 'learning to learn'.

Welch, Robert B.↗
NASA NTRS2001

Processing bottlenecks in dual-task performance: structural limitation or strategic postponement?

Recent evidence indicates that a central bottleneck causes much of the slowing that occurs when two tasks are performed at the same time. This bottleneck might reflect a structural limitation inherent in the cognitive architecture. Alternatively, the bottleneck might reflect strategic (i.e., voluntary) postponement, induced by instructions to emphasize one task over the other. To distinguish structural limitations from strategic postponement, we examine a new paradigm in which subjects are told to place equal emphasis on both tasks and to emit both responses at about the same time. An experiment using this paradigm demonstrated patterns of interference that cannot easily be attributed to strategic postponement, preparation effects, or conflicts in response production. The data conform closely to the predictions of structural central bottleneck models.

Attention↗
NASA NTRS1998

Memory monitoring by animals and humans

The authors asked whether animals and humans would use similarly an uncertain response to escape indeterminate memories. Monkeys and humans performed serial probe recognition tasks that produced differential memory difficulty across serial positions (e.g., primacy and recency effects). Participants were given an escape option that let them avoid any trials they wished and receive a hint to the trial's answer. Across species, across tasks, and even across conspecifics with sharper or duller memories, monkeys and humans used the escape option selectively when more indeterminate memory traces were probed. Their pattern of escaping always mirrored the pattern of their primary memory performance across serial positions. Signal-detection analyses confirm the similarity of the animals' and humans' performances. Optimality analyses assess their efficiency. Several aspects of monkeys' performance suggest the cognitive sophistication of their decisions to escape.

NASA Discipline Space Human Factors↗
NASA NTRS1998

Effects of sleep loss, time of day, and extended mental work on implicit and explicit learning of sequences

Tacit knowledge is part of many professional skills and can be studied experimentally with implicit-learning paradigms. The authors explored the effects of 2 different stressors, loss of sleep and mental fatigue, on implicit learning in a serial-response time (RT) task. In the 1st experiment, 1 night of sleep deprivation was shown to impair implicit but not explicit sequence learning. In the 2nd experiment, no impairment of both types of sequence learning was found after 1.5 hr of mental work. Serial-RT performance, in contrast, suffered from both stressors. These findings suggest that sleep deprivation induces specific risks for automatic, skill-based behavior that are not present in consciously controlled performance.

Learning↗
NASA NTRS2023

Designing Learning Experiences With A Low-Cost Robotic Arm

Robots have gained immense popularity in Hollywood and growth globally in both industrial manufacturing and non-manufacturing environments and applications such as healthcare, service sector, and space exploration. To date, there are several examples of simple and low-cost educational robotic platforms and commercially available platforms (e.g., Lego Mindstorms, VEX Robotics) for incorporation into existing curriculum. However, most low-cost examples require access to rapid-prototyping tools, such as 3D printers to manufacture the structure of the robot, while commercially available platforms are relatively expensive (> $1,000). Although the low-cost, open-source examples provide increased access, these examples require design and manufacturing tasks that would be considered outside the learning objectives of an upper-level robotics course and are better suited for other introductory courses. Thus, we asked, how can a robotic platform be incorporated into existing robotics curriculum to enhance students' learning experiences? To explore this research question, we introduce a low-cost (<$200), untethered, and transportable robotic platform that is easy to assemble using off-the-shelf components. This kit can be powered from a laptop computer and does not rely on access to rapid-prototyping tools such as 3D printers or laser cutters, making this a more accessible option in undergraduate engineering courses. Specifically, we aimed to investigate the design of experiential learning experiences for the mathematical modeling of the forward and inverse kinematics of a serial robotic arm that complements existing robotics curriculum. The experiential learning experience focuses on traditional written answer, simulation in MATLAB, and finally implementation on a robotic platform. Few studies examined this accessible option when evaluating experiential learning experiences that complement existing robotics curricula. To assess the impact of this robotic arm kit in an undergraduate course, we implemented an educational intervention that allowed us insight into student perceptions, takeaways on the course and activities involving the robotic arm, and the impact of the course on their career outlook when comparing activities that involved use of the robotic arm and those that did not. Details on the design, development and implementation of the learning activities is provided. Both quantitative and qualitative data were collected and analyzed. The results presented in this paper discuss students finding the learning activities on the robotic arms more helpful than those without, and that students found high value in the hands-on experiences and real-world scenarios offered by the activities using the robotic arm. Challenges to implementation of the robotic arms are discussed, including students’ prior knowledge of using robotic arms.

Eric J. Markvicka↗
NASA NTRS2016

Usable Translational Hand Controllers for NASA's Habitability Design Center

This summer I was given the opportunity to work at the Habitability Design Center (HDC). NASA Johnson Space Center's HDC is currently developing Cislunar and Mars spacecraft mockups. I contributed to this effort by designing from scratch low cost, functional translational hand controllers (THCs) that will be used in spacecraft mission simulation in low to medium fidelity exploration spacecraft mockups. This project fell under the category of mechatronics, a combination of mechanical, electrical, and computer engineering. Being an aerospace engineering student, I was out of my comfort zone. And that was a wonderful thing. The autonomy that my mentor, Dr. Robert Howard, allowed me gave me the opportunity to learn by trying, failing, and trying again. This project was not only a professional success for me, but a significant learning experience. I appreciated the freedom that I had to take the time to learn new things for myself rather than blindly follow instructions. I was the sole person working on this project, and was required to work independently to solve the many hardware and software challenges that the project entailed. I researched THCs that have been used on the ISS, the Space Shuttle, and the Orion MPVC and based my design off of these. I worked through many redesigns before finding an optimal configuration of the necessary mechanisms and electrical components for the THC. Once I had a functional hardware design, I dove into the challenge of getting an Arduino Uno, an extremely low cost and easily programmable microcontroller, to behave as a human interface device. The THCs I built needed to be able to integrate to a mission simulation designed by NASA's Graphics and Visualization Lab. This proved to be the most challenging aspect of the project. To accomplish this I learned how to change the firmware of the USB serial converter microcontroller. The process was very complicated as it involved multiple software programs and manual flashing of pins on the Arduino itself. When I successfully achieved the goal I wrote an instructions manual for the process so that the HDC will be able to do this easily in the future for any human interface device they may want to create. I also created a detailed CAD model of my THC design with construction instructions. My THC utilizes three ultrasonic sensors, one for each axis of motion. I wrote a code that stimulates these sensors continuously and feeds back values from each axis ranging from -%100 to %100 of the min/max position in relation to the neutral position of each axis. This was the data that the Graphics and Visualization Lab required to interface with their simulation. I truly enjoyed working in the HDC surrounded by passionate, proactive, and brilliantly creative people. I felt valued and respected as a part of their team. I was given the time and support of my mentor whenever I asked for it. Beyond my positive project experience, I was fortunate enough to be able to take advantage of many of the extra activities that JSC has to offer. I took Russian Phase One during my lunch break every day and can now read Russian and accomplish basic verbal communication. I was heavily involved with the co-tern music video, which led to numerous incredible experiences and friendships. I saw every facility, attended every lecture, and met everyone that I could. I had coffee with Lauri Hansen. I played on an intern volleyball team at the Gilruth. I traveled to Michoud, Stennis, NOLA, and Big Bend National Park. I had the time of my life and I fell in love with JSC. In the next month I will return to Maryland and start my senior year of my undergraduate degree. As I work through it I will remember that all of the studying I do, all of the concentration I give, and all of the sacrifices that I make for school are so that I can work at a place like JSC where I am proud and excited to go into work every day. This internship was an invaluable experience for me, both professionally and personally.

Westbrook, Kimberly↗
NASA NTRS2007

Integration and Testing Challenges of Small, Multiple Satellite Missions: Experiences From The Space Technology 5 Project

This brief presentation describes the mechanical and electrical integration activities and environmental testing challenges of the Space Technology 5 (ST5) Project. Lessons learned during this process are highlighted, including performing mechanical activities serially to gain efficiency through repetition and performing electrical activities based on the level of subsystem expertise available.

Sauerwein, Timothy A.↗
DOE OSTI2025

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Filling data analysis gaps in time-resolved crystallography by machine learning

There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Trujillo, Justin (ORCID:0000000285505360)↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
NASA NTRS1987

Parallel image compression

A parallel compression algorithm for the 16,384 processor MPP machine was developed. The serial version of the algorithm can be viewed as a combination of on-line dynamic lossless test compression techniques (which employ simple learning strategies) and vector quantization. These concepts are described. How these concepts are combined to form a new strategy for performing dynamic on-line lossy compression is discussed. Finally, the implementation of this algorithm in a massively parallel fashion on the MPP is discussed.

Reif, John H.↗
DOE OSTI2025

TorchBraid: High-Performance Layer-Parallel Training of Deep Neural Networks with MPI and GPU Acceleration

TorchBraid is a high-performance implementation of layer-parallel training for deep neural networks (DNNs) supporting MPI-based parallelism and GPU acceleration. Layer-parallel training has been developed to overcome the serialization inherent in forward and backward propagation of DNNs that limits utilization of computational resources in the strong scaling limit. To achieve this, TorchBraid integrates the PyTorch neural network framework with the state-of-the-art XBraid time-parallel library. Furthermore, this article presents the use and performance of TorchBraid, in addition to solutions for overcoming the algorithmic challenges inherent in combining automatic differentiation with layer-parallel. Results are presented with and without GPU acceleration for the Tiny ImageNet and MNIST image classification data sets, as well as recurrent neural networks. Overall, TorchBraid enables fast training of DNNs, both in a strong and weak scaling context. In addition to the TorchBraid software, several new advances in applying layer-parallel algorithms are detailed. Integration of layer-parallel with data-parallel algorithms is presented for the first time, showing the computational advantages of the combination. Standard deep learning techniques, like batch-normalization, are developed for layer-parallel training. Finally, a new approach combining layer-parallel with spatial coarsening in order to accelerate training for 3D image classification shows roughly a 10× speedup over serial execution.

Layer-parallel↗
NASA NTRS1995

3-D Reconstruction of Macular Type II Cell Innervation Patterns in Space-Flight and Control Rats

A semiautomated method for reconstructing objects from serial thin sections has been developed in the Biocomputation Center. The method is being used to completely, for the first time, type II hair cells and their innervations. The purposes are to learn more about the fundamental circuitry of the macula on Earth and to determine whether changes in connectivities occur under space flight conditions. Data captured directly from a transmission electron microscope via a video camera are sent to a graphics workstation. There, the digitized micrographs are mosaicked into sections and contours are traced, registered and displayed by semiautomated methods. Current reconstructions are of type II cells from the medial part of rat maculas collected in-flight on the Space Life Sciences-2 mission, 4.5 hrs post-flight, and from a ground control. Results show that typical type II cells receive processes from tip to six nearby calyces or afferents. Nearly all processes are elongated and have bouton-like enlargements; some have numerous vesicles. Multiple (2 to 4) processes from a single calyx to a type II cell are common, and approximately 1/3 of the processes innervale 2 or 3 type II cells or a neighboring cluster. From 2% to 6% of the cells resemble type I cells morphologically but have demi-calyces. Thus far, increments in synaptic number in type II cells of flight rats are prominent along processes that supply two hair cells. It is clear that reconstruction methods provide insights into details of macular circuitry not obtainable by other techniques. The results demonstrate a morphological basis for interactions between adjacent receptive fields through feed back-feed forward connections, and for dynamic alterations in receptive field range and activity during preprocessing of linear acceleratory information by the maculas. The reconstruction method we have developed will find further applications in the study of the details of neuronal architecture of more complex systems, to seek out shared organizational properties or neuronal networks and to understand better localization of synaptic changes in altered environments.

Ross, Muriel Dorothy↗
NASA NTRS1989

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Johnston, Mark D.↗
DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Performance-Aligned LLMs for Generating Fast HPC Code

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. Here, we demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP parallel code.

Computer science↗
NASA NTRS2000

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Non-NASA Center↗