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At least 739 records · Page 41

All sources NASA NTRS DOE OSTI

NASA NTRS1991

Problem solving with genetic algorithms and Splicer

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Bayer, Steven E.↗

NASA NTRS2010

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.

Rash, James L.↗

NASA NTRS1990

On recursive least-squares filtering algorithms and implementations

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Hsieh, Shih-Fu↗

NASA NTRS1987

Adaptive proximate time optimal servomechanisms: Discrete time case

Following a review of the continuous time proximate time-optimal servomechanism (PTOS), a discrete-time PTOS is developed, along with conditions for its stability. An algorithm is proposed for adapting PTOS when the plant parameters are not known yielding APTOS (adaptive PTOS). Under ideal conditions APTOs is shown to be uniformly asymptotically stable. Simulation results demonstrate the predicted performance, and experimental results validate the practically of APTOS.

Workman, M. L.↗

DOE OSTI2023

Adaptive variational quantum minimally entangled typical thermal states for finite temperature simulations

Scalable quantum algorithms for the simulation of quantum many-body systems in thermal equilibrium are important for predicting properties of quantum matter at finite temperatures. Here we describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm for which we adopt an adaptive variational approach to perform the required quantum imaginary time evolution. The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits, which are suitable for noisy intermediate-scale quantum (NISQ) hardware. We benchmark AVQMETTS on statevector simulators and perform thermal energy calculations of integrable and nonintegrable quantum spin models in one and two dimensions and demonstrate an approximately linear system-size scaling of the circuit complexity. We further map out the finite-temperature phase transition line of the two-dimensional transverse field Ising model. Finally, we study the impact of noise on AVQMETTS calculations using a phenomenological noise model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1994

Synthetic aperture radar signal data compression using block adaptive quantization

This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.

Kuduvalli, Gopinath↗

DOE OSTI2024

Cloud-based Testbed for Adaptive Under-Frequency Load Shedding with High DER Penetration

Increasing penetration of distributed energy resources and behind-the-meter renewables may soon disrupt the efficacy of critical protection schemes, such as under-frequency load shedding (UFLS). Improved data exchange and coordination across the transmission-distribution boundary will be required to maintain reliability of bulk electric system. Standards-based data integration platforms using agreed-upon semantic vocabularies, such as the Common Information Model, will be key to enabling adaptive protection schemes requiring synthesized data from both the bulk power system and behind-the-meter resources. This paper introduces a cloud-based open-source data integration environment and UFLS clustering algorithm being developed to enable adaptive relay coordination between transmission and distribution utilities in the state of Vermont.

Anderson, Alexander A.↗

NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗

NASA NTRS2005

Reconfiguration of Analog Electronics for Extreme Environments

This paper argues in favor of adaptive reconfiguration as a technique to expand the operational envelope of analog electronics for extreme environments (EE). On a reconfigurable device, although component parameters change in EE, as long as devices still operate, albeit degraded, a new circuit design, suitable for new parameter values, may be mapped into the reconfigurable structure to recover the initial circuit function. Laboratory demonstrations of this technique were performed by JPL in several independent experiments in which bulk CMOS reconfgurable devices were exposed to, and degraded by, high temperatures (approx.300 C) or radiation (300kRad TID), and then recovered by adaptive reconfiguration using evolutionary search algorithms.

evolutionary search algorithms↗

DOE OSTI2025

Adaptive Linear State Estimation for Unbalanced Distribution System

The inclusion of PMU functionality in distribution relays enables the implementation of a linear state estimator (LSE) in Distribution Systems (DS). However, the unbalanced topology and phase coupling in distribution lines necessitate modifications to the LSE formulation. Additionally, the higher fault frequency in distribution systems requires a state estimation approach that is resilient to contingencies. This work proposes an adaptive linear state estimation algorithm tailored for unbalanced distribution systems with single-phase and two-phase laterals. Furthermore, a modified Optimal PMU Placement (OPP) strategy is introduced to ensure full observability in distribution systems with single-phase and two-phase buses. To maintain adaptability to topology changes, the state estimator incorporates circuit breaker status data provided by PMUs, ensuring robust performance during topology changes triggered by faults. The performance of the algorithm is verified on the IEEE 13-bus, 34-bus, and 123-bus systems.

PMUs↗

NASA NTRS2017

Meteorological Inventory of Rain-On-Snow Events in the Canadian Arctic Archipelago and Satellite Detection Assessment Using Passive Microwave Data

The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.

Canadian Arctic Archipelago (CAA)↗

NASA NTRS2017

Surface-Height Determination of Crevassed Glaciers-Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration.The Advanced Topographic Laser Altimeter System aboard NASAs future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information.We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in auto-adaptive threshold determination. The auto-adaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Braer in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting lidar data over complex and simple surfaces.

Herzfeld, Ute C.↗

DOE OSTI2020

"Sintering" Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

3D printing, Additive Manufacturing, polymer, sint↗

NASA NTRS1990

Temperature and melt solid interface control during crystal growth

Findings on the adaptive control of a transparent Bridgman crystal growth furnace are summarized. The task of the process controller is to establish a user specified axial temperature profile by controlling the temperatures in eight heating zones. The furnace controller is built around a computer. Adaptive PID (Proportional Integral Derivative) and Pole Placement control algorithms are applied. The need for adaptive controller stems from the fact that the zone dynamics changes with respect to time. The controller was tested extensively on the Lead Bromide crystal growth. Several different temperature profiles and ampoule's translational rates are tried. The feasibility of solid liquid interface quantification by image processing was determined. The interface is observed by a color video camera and the image data file is processed to determine if the interface is flat, convex or concave.

Batur, Celal↗

NASA NTRS1991

Self adaptive solution strategies: Locally bound constrained Newton Raphson solution algorithms

A summary is given of strategies which enable the automatic adjustment of the constraint surfaces recently used to extend the range and numerical stability/efficiency of nonlinear finite element equation solvers. In addition to handling kinematic and material induced nonlinearity, both pre-and postbuckling behavior can be treated. The scheme employs localized bounds on various hierarchical partitions of the field variables. These are used to resize, shape, and orient the global constraint surface, thereby enabling essentially automatic load/deflection incrementation. Due to the generality of the approach taken, it can be implemented in conjunction with the constraints of an arbitrary functional type. To benchmark the method, several numerical experiments are presented. These include problems involving kinematic and material nonlinearity, as well as pre- and postbuckling characteristics. Also included is a list of papers published in the course of the work.

Padovan, Joe↗

NASA NTRS1993

General-Purpose Time-Accurate Adaptive-Grid Method

Adaptive-grid method provides for generation of time-accurate grids used to solve wide variety of problems numerically. Intended use in computation of compressible flows. Part of solution method including algebraic grid-movement scheme; algorithm solving, by use of explicit numerical-integration scheme, Euler equations of unsteady flow; and temporal coupling routine utilizing grid-prediction/correction method to link dynamic grid with Euler-equation-solving algorithm. Improves ability to adapt grid to multiple features of solution and involves use of coarse grid in prediction stage of temporal coupling reducing central-processing-unit cost of obtaining new grid.

Bockelie, Michael J.↗

NASA NTRS1996

A Structured Grid Based Solution-Adaptive Technique for Complex Separated Flows

The objective of this work was to enhance the predictive capability of widely used computational fluid dynamic (CFD) codes through the use of solution adaptive gridding. Most problems of engineering interest involve multi-block grids and widely disparate length scales. Hence, it is desirable that the adaptive grid feature detection algorithm be developed to recognize flow structures of different type as well as differing intensity, and adequately address scaling and normalization across blocks. In order to study the accuracy and efficiency improvements due to the grid adaptation, it is necessary to quantify grid size and distribution requirements as well as computational times of non-adapted solutions. Flow fields about launch vehicles of practical interest often involve supersonic freestream conditions at angle of attack exhibiting large scale separate vortical flow, vortex-vortex and vortex-surface interactions, separated shear layers and multiple shocks of different intensity. In this work, a weight function and an associated mesh redistribution procedure is presented which detects and resolves these features without user intervention. Particular emphasis has been placed upon accurate resolution of expansion regions and boundary layers. Flow past a wedge at Mach=2.0 is used to illustrate the enhanced detection capabilities of this newly developed weight function.

Thornburg, Hugh↗

DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗