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Results for “activation function”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Space station functional relationships analysis

A systems engineering process is developed to assist Space Station designers to understand the underlying operational system of the facility so that it can be physically arranged and configured to support crew productivity. The study analyzes the operational system proposed for the Space Station in terms of mission functions, crew activities, and functional relationships in order to develop a quantitative model for evaluation of interior layouts, configuration, and traffic analysis for any Station configuration. Development of the model involved identification of crew functions, required support equipment, criteria of assessing functional relationships, and tools for analyzing functional relationship matrices, as well as analyses of crew transition frequency, sequential dependencies, support equipment requirements, potential for noise interference, need for privacy, and overall compatability of functions. The model can be used for analyzing crew functions for the Initial Operating Capability of the Station and for detecting relationships among these functions. Note: This process (FRA) was used during Phase B design studies to test optional layouts of the Space Station habitat module. The process is now being automated as a computer model for use in layout testing of the Space Station laboratory modules during Phase C.

Tullis, Thomas S.↗
NASA NTRS1989

A unique problem of muscle adaptation from weightlessness: The deceleration deficiency

Decelerator problems of the knee are emphasized since the lower leg musculature is known to atrophy in response to weightlessness. However, other important decelerator functions are served by the shoulder muscles, in particular the rotator cuff muscles. Problems in these muscles often result in tears and dislocations as seen in baseball pitchers. It is noteworthy that at least one device currently exists that can measure concentric and eccentric muscle loading including a submaximal simulated free weight exercise (i.e., force-controlled) and simultaneously record integrated EMG analysis appropriate for assessment of all muscle functional activities. Studies should be undertaken to provide information as to the performance of maximal and submaximal exercise in space travelers to define potential problems and provide rationale for prevention.

Stauber, William T.↗
NASA NTRS1991

Using HST: From proposal to science

The following subject areas are covered: a short history; uses of network STSCII (general communication, science collaboration, functional activities, internal data management, and external data access); proposal/observation handling; DMF access; and future uses and requirements.

Shames, P.↗
NASA NTRS1993

Specification and testing for power by wire aircraft

A power by wire aircraft is one in which all active functions other than propulsion are implemented electrically. Other nomenclature are 'all electric airplane,' or 'more electric airplane.' What is involved is the task of developing and certifying electrical equipment to replace existing hydraulics and pneumatics. When such functions, however, are primary flight controls which are implemented electrically, new requirements are imposed that were not anticipated by existing power system designs. Standards of particular impact are the requirements of ultra-high reliability, high peak transient bi-directional power flow, and immunity to electromagnetic interference and lightning. Not only must the electromagnetic immunity of the total system be verifiable, but box level tests and meaningful system models must be established to allow system evaluation. This paper discusses some of the problems, the system modifications involved, and early results in establishing wiring harness and interface susceptibility requirements.

Hansen, Irving G.↗
NASA NTRS1994

CINDOC, CSIC, and Spanish R and D

The organizational structure and functional activities of the Spanish Center for Scientific Information and Documentation (CINDOC) are discussed. The library holds 8,500 journals, including all the Spanish scientific journals; 16,000 books and 20 CD-ROM data bases. CINDOC creates and distributes its own data bases that include all the articles published in Spanish scientific journals.

Delaviesca, Rosa↗
NASA NTRS1999

Expression of Novel Gene Products Upregulated by Disuse is Normalized by an Osteogenic Mechanical Stimulus: Evidence for the Molecular Basis of a Low Level Biomechanical Countermeasure for Osteoporosis?

The National Research Council's report entitled: A Strategy for Space Biology and Medical Science, highlighted several areas of fundamental scientific investigation which must be addressed to make long-term space exploration not only feasible, but safe. This "Goldberg Strategy," as well as several subsequent reports published by the NRC's Space Studies Board (e.g., Assessment of Programs in Space Biology and Medicine, Smith et. al., 1991), suggests that the principal hurdle to man's extended presence in space is the osteopenia which parallels reduced gravity. Ironically, the most significant risk to the skeleton may only be realized on return to normal gravitational fields, and full recovery of bone mass may never occur. Effective counter-measures to this microgravity induced bone loss are thus essential. Considering the similarities of space and aging induced osteopenia, an indisputable benefit of such a prophylaxis would be its potential as a treatment for the bone loss which plagues over 25 million people in the U.S. The osteogenic potential of mechanical strain is strongly frequency dependent, with sensitivity increasing up through at least 60 Hz (cycles per second). One hundred seconds per day of a 1 Hz cyclic loading will inhibit disuse osteopenia only if sufficient in magnitude to engender 1000 microstrain (mu(epsilon)) in the tissue. When loading is applied at 30 Hz, however, mechanical strains on the order of 5O mu(epsilon) (approx. 1% of the peak strains which occur in bone during vigorous functional activity), can stimulate bone formation in a duration dependent manner. In longer term animal studies, strains of less than 10 mu(epsilon), induced non-invasively via a whole body vibration, will stimulate bone formation on the surfaces of trabeculae, increase bone density, and improve strength. Finally, preliminary results from a double blind prospective clinical trial shows promise in inhibiting the bone loss which parallels the menopause. Based on these observations, we propose that these high frequency, low magnitude, mechanical strains effectively serve as a "surrogate" for musculoskeletal ground reaction forces, and thus represent an ideal countermeasure to the osteopenia which parallels microgravity conditions. The specific goal of this NASA funded work is to identify genes in bone upregulated by disuse, and to determine the efficacy of an osteogenic mechanical stimulus to downregulate their expression.

Rubin, C.↗
NASA NTRS2000

Overexpression of Human Bone Alkaline Phosphatase in Pichia Pastoris

The Pichiapastoris expression system was utilized to produce functionally active human bone alkaline phosphatase in gram quantities. Bone alkaline phosphatase is a key enzyme in bone formation and biomineralization, yet important questions about its structural chemistry and interactions with other cellular enzymes in mineralizing tissues remain unanswered. A soluble form of human bone alkaline phosphatase was constructed by deletion of the 25 amino acid hydrophobic C-terminal region of the encoding cDNA and inserted into the X-33 Pichiapastoris strain. An overexpression system was developed in shake flasks and converted to large-scale fermentation. Alkaline phosphatase was secreted into the medium to a level of 32mgAL when cultured in shake flasks. Enzyme activity was 12U/mg measured by a spectrophotometric assay. Fermentation yielded 880mgAL with enzymatic activity of 968U/mg. Gel electrophoresis analysis indicates that greater than 50% of the total protein in the fermentation is alkaline phosphatase. A purification scheme has been developed using ammonium sulfate precipitation followed by hydrophobic interaction chromatography. We are currently screening crystallization conditions of the purified recombinant protein for subsequent X-ray diffraction analyses. Structural data should provide additional information on the role of alkaline phosphatase in normal bone mineralization and in certain bone mineralization anomalies.

Karr, Laurel↗
NASA NTRS1996

Cloning and characterization of the murine homolog of the sno proto-oncogene reveals a novel splice variant

The cellular function(s) of the SNO protein remain undefined. To gain a better understanding of possible developmental roles of this cellular proto-oncogene, we have cloned two murine sno cDNAs and have investigated their expression patterns in embryonic and postnatal tissues. A single major transcript of 7.5 kb is detected in multiple tissues by Northern blot. However, reverse transcriptase polymerase chain reaction (RT-PCR) and RNAse protection assays revealed a novel splice variant in every tissue examined. Two isoforms, termed sno N and sno-dE3 (dE3, deletion within exon 3), were identified. The sno-dE3 isoform employs a novel 5' splice site located within the coding region of the third exon and deletes potential kinase recognition motifs. Transcripts of both sno isoforms accumulate ubiquitously but are most abundant in the developing central nervous system. The in situ hybridization patterns of sno expression during murine development suggest potential roles in tissues with a high degree of cellular proliferation. Expression in terminally differentiated tissues such as muscle and neurons indicates that SNO may have multiple functional activities.

Non-NASA Center↗
NASA NTRS2012

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in realtime. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors

Harrivel, Angela↗
NASA NTRS2012

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in real-time. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors.

Harrivel, Angela↗
NASA NTRSDate not supplied

Towards Surrogate Modeling of Subgrid Turbulent Transport for 3D Radiative Hydrodynamic Simulations of the Quiet Sun

In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.

Heliophysics↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2021

Project Final Report - Advanced Oxygen Separation from Air Using a Novel Mixed Matrix Membrane

This report describes a summary of the findings of the project. The technical work was divided into three activities: 1) technoeconomic analysis; 2) membrane component development (support layer, selective layer development, and gutter layer formulation); and 3) hollow fiber formation. The technoeconomic analysis includes operating, capital, and materials costs of an oxygen enrichment system with 1-3 stages. The analysis concluded that the desired gas quality (90-95% O2) cannot be reached with a single stage. The triple stage system yielded the best overall product quality at lower membrane performance levels but with the highest cost. For the membrane development activities, functionalized nanodiamonds have been shown to slightly improve membrane formation behaviors and gas permeability performance. For example, support layers based on polysulfone-nanodiamond composites have been formed that increase the material’s O2 permeability by over a factor of 150, however, at the cost of selectivity that suggests a change in transport mechanism. Furthermore, nanodiamonds have been found to improve membrane durability. Improvements in selective and gutter layers also have been accomplished. An additional accomplishment is the demonstration of phase inversion of polysulfone-nanodiamond composites that ensures that hollow fiber formation can be performed. Finally, the interfaces between support, gutter, and selective layers were briefly examined and the resulting membrane material consisting of all three layers gave good performance in an O2 separation from N2. The three-layer prototype membrane gave permeability of 29.2-32.2 Barrers, which equates to permeance of approximately 290-320 GPU, which is lower than the TEA informed goal of 500 GPU. O2/N2 selectivity ranged from 5.2-6.2, which is consistent with the less costly two-stage implementation of the technology. In summary, new materials have been developed for O2 concentration from air and are well-suited to a hollow fiber format.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm

The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.

D estefani, André↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.

Drgona, Jan↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stability analysis method and provide sufficient conditions of DMM's stochastic stability. The proposed stability analysis is based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation function on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.

Drgona, Jan↗
DOE OSTI2025

Optimal Control Strategy With Efficiency and Reliability Improvement for Offshore DC Microgrids

Offshore microgrids, due to their remote location and lack of external energy support, face significant challenges in wide-range load operation and maintenance. Consequently, efficiency and reliability are critical concerns for converters in offshore dc microgrids. This article presents an optimal control strategy aimed at enhancing both efficiency and reliability. A normalized nonlinear relationship between power loss and thermal stress of a paralleled converter is first established. Based on this, a dual-objective optimization function with an active weight function as well as a system overall performance index is established. The active weight function dynamically adjusts the control priority based on converter efficiency and switching device thermal stress. Then, the optimal power-sharing strategy is derived by the Lagrange multiplier method with the proposed optimal function. Additionally, to accommodate a wide load range, an optimal selection strategy for operating converter combinations is proposed, requiring only low-bandwidth communication. Experiment verification is given to validate the effectiveness of the proposed control strategy. The experiment results demonstrate that the proposed control strategy can improve the overall performance of offshore microgrids by optimizing efficiency and reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗