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NASA NTRS1991

NASA information sciences and human factors program

The Data Systems Program consists of research and technology devoted to controlling, processing, storing, manipulating, and analyzing space-derived data. The objectives of the program are to provide the technology advancements needed to enable affordable utilization of space-derived data, to increase substantially the capability for future missions of on-board processing and recording and to provide high-speed, high-volume computational systems that are anticipated for missions such as the evolutionary Space Station and Earth Observing System.

Source record↗

NASA NTRS2004

Computational Modeling of NEXT 2000-Hour Wear Test Results

Ion optics computational models are invaluable tools for the design of ion optics systems. In this study, a new computational model developed by an outside vendor for NASA Glenn Research Center (GRC) is presented. This model is a gun code which has been modified to model the plasma sheaths both upstream and downstream of the ion optics. The model handles multiple species (e.g. singly and doubly-charged ions) and includes a charge-exchange model for erosion estimates. The model uses commercially available solid design and meshing software, allowing high flexibility in ion optics geometric configurations. This computational model is compared to experimental results from the NASA Evolutionary Xenon Thruster (NEXT) 2000-hour wear test, including over-focusing along the edge apertures, pit-and-groove erosion due to charge exchange, and beamlet distortion at the edge of the hole pattern.

Malone, Shane P.↗

NASA NTRS2006

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Rai, Man Mohan↗

NASA NTRS2007

Generative Representations for Automated Design of Robots

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term generative representations as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity. Automated design of robots through synthetic evolutionary processes has already been demonstrated, but it is not clear whether genetically inspired search algorithms can yield designs that are sufficiently complex for practical engineering. The ultimate success of such algorithms as tools for automation of design depends on the scaling properties of representations of designs. A nongenerative representation (one in which each element of the encoded design is used at most once in translating to the design) scales linearly with the number of elements. Search algorithms that use nongenerative representations quickly become intractable (search times vary approximately exponentially with numbers of design elements), and thus are not amenable to scaling to complex designs. Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps: 1. Generative representations are generated in an evolutionary subprocess. 2. Each generative representation is a program that, when compiled, produces an assembly procedure. 3. In a computational simulation, a constructor executes an assembly procedure to generate a robot. 4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration. In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation. This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations.

Homby, Gregory S.↗

NASA NTRS1981

Theoretical studies of chromospheres and winds in cool stars

Calculated radiative losses from H, H-, Ca II, and Mg II show that cooling for the chromosphere of the supergiant epsilon Gem do not differ greatly from the solar law, although there are differences at approximately 6000K due to ionization effects. With a rough standard law for computation of stellar winds using the Hartmann-MacGregor theory and standard stellar evolutionary calculations, the wind velocities and temperatures in the HR diagram were systematically explored. Results show that cool winds with tempratures 1,000,00K are not possible for log g or = 2. Predicted wind velocities are approximately 1.5 to 2 x larger than observed, particularly for the most luminous cool stars. The ionization balance for the wind of alpha ORI and the hydrogen profile lines for T Tauri stars were computed using the PANDORA computer program.

Dupree, A. K.↗

NASA NTRS1991

Metal opacities and convective core overshooting in Population I stars

New stellar opacities computed by Rogers and Iglesias are found to lead to revised evolutionary models for stars of intermediate-to-high mass that show remarkably close agreement with observed stars, under otherwise standard physical assumptions. Most or all of the earlier discrepancies, except for the problem of predicted apsidal motion constants that are perhaps still too large, appear to have been resolved by using these new opacities with their increased metal line contribution. Thus, the implied interior metallicity of massive stars in the solar neighborhood now becomes 'normal'. Furthermore, the effective distance of convective overshooting beyond the classical convective core boundary is confirmed to be small and probably less than 0.2 times the local pressure scale height.

Stothers, Richard B.↗

NASA NTRS1996

Adaptive Fuzzy Systems in Computational Intelligence

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Berenji, Hamid R.↗

NASA NTRS2001

Gene Fusion: A Genome Wide Survey

As a well known fact, organisms form larger and complex multimodular (composite or chimeric) and mostly multi-functional proteins through gene fusion of two or more individual genes which have independent evolution histories and functions. We call each of these components a module. The existence of multimodular proteins may improves the efficiency in gene regulation and in cellular functions, and thus may give the host organism advantages in adaptation to environments. Analysis of all gene fusions in present-day organisms should allow us to examine the patterns of gene fusion in context with cellular functions, to trace back the evolution processes from the ancient smaller and uni-functional proteins to the present-day larger and complex multi-functional proteins, and to estimate the minimal number of ancestor proteins that existed in the last common ancestor for all life on earth. Although many multimodular proteins have been experimentally known, identification of gene fusion events systematically at genome scale had not been possible until recently when large number of completed genome sequences have been becoming available. In addition, technical difficulties for such analysis also exist due to the complexity of this biological and evolutionary process. We report from this study a new strategy to computationally identify multimodular proteins using completed genome sequences and the results surveyed from 22 organisms with the data from over 40 organisms to be presented during the meeting. Additional information is contained in the original extended abstract.

Liang, Ping↗

NASA NTRS1994

Oxgen-burning hydrodynamics. 1: Steady shell burning

With new hydrodynamic techniques, the relatively fast evolutionary stages of a star prior to core collapse may be explicitly computed in two spatial dimensions, with a treatment of the microphysics (e.g., nuclear reactions, equation of state, neutrino cooling) which is comparable to typical one-dimensional simulations. The nature of shell oxygen burning in a massive star, prior to core collapse, is used as a first example; it is of particular interest because it is (1) the region in which Ni-56 will be produced by the supernova shock, (2) the region of the 'mass cut', which will separate the collapsed core from the ejected mantle, (3) the site of much of the explosive nucleosynthesis, and (4) a suggested source of symmetry breaking to drive mixing instabilities which were observed in SN 1987A. The nature of the shell burning affects the size of the core which will collapse. The method is illustrated on this test case, and the character of the convection is examined.

Arnett, David↗

NASA NTRS1995

Efficient eigenvalue assignment by state and output feedback with applications for large space structures

The erection and deployment of large flexible structures having thousands of degrees of freedom requires controllers based on new techniques of eigenvalue assignment that are computationally stable and more efficient. Scientists at NASA Langley Research Center have developed a novel and efficient algorithm for the eigenvalue assignment of large, time-invariant systems using full-state and output feedback. The objectives of this research were to improve upon the output feedback version of this algorithm, to produce a toolbox of MATLAB functions based on the efficient eigenvalue assignment algorithm, and to experimentally verify the algorithm and software by implementing controllers designed using the MATLAB toolbox on the phase 2 configuration of NASA Langley's controls-structures interaction evolutionary model, a laboratory model used to study space structures. Results from laboratory tests and computer simulations show that effective controllers can be designed using software based on the efficient eigenvalue assignment algorithm.

Vannell, Eric C.↗

NASA NTRS1972

Stellar evolution of high mass based on the Ledoux criterion for convection

Theoretical evolutionary sequences of models for stars of 15 and 30 solar masses were computed from the zero-age main sequence to the end of core helium burning. During the earliest stages of core helium depletion, the envelope rapidly expands into the red-supergiant configuration. At 15 solar mass, a blue loop on the H-R diagram ensues if the initial metals abundance, initial helium abundance, or C-12 + alpha particle reaction rate is sufficiently large, or if the 3-alpha reaction rate is sufficiently small. These quantities affect the opacity of the base of the outer convection zone, the mass of the core, and the thermal properties of the core. The blue loop occurs abruptly and fully developed when the critical value of any of these quantities is exceeded, and the effective temperature range and fraction of the lifetime of core helium burning during the slow phase of the blue loop vary surprisingly little. At 30 solar mass no blue loop occurs for any reasonable set of input parameters.

Stothers, R.↗

NASA NTRS1995

Ultraviolet and optical observations of OB associations in M31

Images of the central and southern parts of the local group Sb spiral galaxy M31 were obtained by the Ultraviolet Imaging Telescope during the Astro-1 Spacelab mission. Stellar photometry is obtained for 611 stars in 59 van den Bergh associations in the near-UV A1 band (central wavelngth 2490 A), and for 130 of these stars in the far UV B1 band (central wavlength 1520 A). In addition, B-band magnitudes for 456 of the stars in approximately 30 associations, together with U- and R-band photometry for approximately 290 stars in approximately 15 associations are determined from CCD images. B-magnitudes from the catalog of Magnier et al. (1992) are given for 137 stars and R-magnitudes for 233 stars. Stellar positions are obtained from the ground based CCD images and from UIT images. Total fluxes in the UV bands are measured for 63 associations . Finding charts are presented in the NUV and FUV bands, as well as, one optical band. Tables give the images utilized in this investigation, the associations observed, with positions, total UV fluxes, extinctions estimated from color-magnitude diagrams, and stellar positions with photometry in up to five bands. UV color magnitude diagrams are presented with extinctions estimated by comparing the observed association color magnitude diagrams with colors and magnitudes of early-type stars computed from the IUE spectral atlas of Fanelli et al. (1992). Comparison with evolutionary models suggests a maximum initial mass approx. 85 solar mass. Ultraviolet extinctions within most M31 OB associations is found to follow an extinction curve similar to that of Hutchings et al. (1992). The variation in the fraction of near-UV stars also measured in the far-UV among the associations is probably caused by variations in age, extinction, and the degree of foreground contamination.

Hill, J. K.↗

NASA NTRS2006

Three-Function Logic Gate Controlled by Analog Voltage

The figure is a schematic diagram of a complementary metal oxide/semiconductor (CMOS) electronic circuit that performs one of three different logic functions, depending on the level of an externally applied control voltage, V(sub sel). Specifically, the circuit acts as A NAND gate at V(sub sel) = 0.0 V, A wire (the output equals one of the inputs) at V(sub sel) = 1.0 V, or An AND gate at V(sub sel) = -1.8 V. [The nominal power-supply potential (VDD) and logic "1" potential of this circuit is 1.8 V.] Like other multifunctional circuits described in several prior NASA Tech Briefs articles, this circuit was synthesized following an automated evolutionary approach that is so named because it is modeled partly after the repetitive trial-and-error process of biological evolution. An evolved circuit can be tested by computational simulation and/or tested in real hardware, and the results of the test can provide guidance for refining the design through further iteration. The evolutionary synthesis of electronic circuits can now be implemented by means of a software package Genetic Algorithms for Circuit Synthesis (GACS) that was developed specifically for this purpose. GACS was used to synthesize the present trifunctional circuit. As in the cases of other multifunctional circuits described in several prior NASA Tech Briefs articles, the multiple functionality of this circuit, the use of a single control voltage to select the function, and the automated evolutionary approach to synthesis all contribute synergistically to a combination of features that are potentially advantageous for the further development of robust, multiple-function logic circuits, including, especially, field-programmable gate arrays (FPGAs). These advantages include the following: This circuit contains only 9 transistors about half the number of transistors that would be needed to obtain equivalent NAND/wire/AND functionality by use of components from a standard digital design library. If multifunctional gates like this circuit were used in the place of the configurable logic blocks of present commercial FPGAs, it would be possible to change the functions of the resulting digital systems within shorter times. For example, by changing a single control voltage, one could change the function of thousands of FPGA cells within nanoseconds. In contrast, typically, the reconfiguration in a conventional FPGA by use of bits downloaded from look-up tables via a digital bus takes microseconds.

Zebulum, Ricardo↗

DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗

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 NTRS1980

Light curves for bump Cepheids computed with a dynamically zoned pulsation code

The dynamically zoned pulsation code developed by Castor, Davis, and Davison was used to recalculate the Goddard model and to calculate three other Cepheid models with the same period (9.8 days). This family of models shows how the bumps and other features of the light and velocity curves change as the mass is varied at constant period. The use of a code that is capable of producing reliable light curves demonstrates that the light and velocity curves for 9.8 day Cepheid models with standard homogeneous compositions do not show bumps like those that are observed unless the mass is significantly lower than the 'evolutionary mass.' The light and velocity curves for the Goddard model presented here are similar to those computed independently by Fischel, Sparks, and Karp. They should be useful as standards for future investigators.

Adams, T. F.↗

NASA NTRS2004

Autonomous Evolution of Dynamic Gaits with Two Quadruped Robots

A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10/min/min., which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO.

Hornby, Gregory S.↗

NASA NTRS2005

Faster Evolution of More Multifunctional Logic Circuits

A modification in a method of automated evolutionary synthesis of voltage-controlled multifunctional logic circuits makes it possible to synthesize more circuits in less time. Prior to the modification, the computations for synthesizing a four-function logic circuit by this method took about 10 hours. Using the method as modified, it is possible to synthesize a six-function circuit in less than half an hour. The concepts of automated evolutionary synthesis and voltage-controlled multifunctional logic circuits were described in a number of prior NASA Tech Briefs articles. To recapitulate: A circuit is designed to perform one of several different logic functions, depending on the value of an applied control voltage. The circuit design is synthesized following an automated evolutionary approach that is so named because it is modeled partly after the repetitive trial-and-error process of biological evolution. In this process, random populations of integer strings that encode electronic circuits play a role analogous to that of chromosomes. An evolved circuit is tested by computational simulation (prior to testing in real hardware to verify a final design). Then, in a fitness-evaluation step, responses of the circuit are compared with specifications of target responses and circuits are ranked according to how close they come to satisfying specifications. The results of the evaluation provide guidance for refining designs through further iteration.

Stoica, Adrian↗