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

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NASA NTRS1997

Utilizing the EUVE Innovative Technology Testbed to Reduce Operations Cost for Present and Future Orbiting Mission

This report summarizes work done under Cooperative Agreement (CA) on the following testbed projects: TERRIERS - The development of the ground systems to support the TERRIERS satellite mission at Boston University (BU). HSTS - The application of ARC's Heuristic Scheduling Testbed System (HSTS) to the EUVE satellite mission. SELMON - The application of NASA's Jet Propulsion Laboratory's (JPL) Selective Monitoring (SELMON) system to the EUVE satellite mission. EVE - The development of the EUVE Virtual Environment (EVE), a prototype three-dimensional (3-D) visualization environment for the EUVE satellite and its sensors, instruments, and communications antennae. FIDO - The development of the Fault-Induced Document Officer (FIDO) system, a prototype application to respond to anomalous conditions by automatically searching for, retrieving, and displaying relevant documentation for an operators use.

Source record↗

NASA NTRS1988

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Gettig, Gary A.↗

NASA NTRS2022

Foraging with MUSHROOMS: A Mixed-integer Linear Programming Scheduler for Multimessenger Target of Opportunity Searches with the Zwicky Transient Facility

Electromagnetic follow-up of gravitational-wave detections is very resource intensive, taking up hours of limited observation time on dozens of telescopes. Creating more efficient schedules for follow-up will lead to a commensurate increase in counterpart location efficiency without using more telescope time. Widely used in operations research and telescope scheduling, mixed-integer linear programming is a strong candidate to produce these higher-efficiency schedules, as it can make use of powerful commercial solvers that find globally optimal solutions to provided problems. We detail a new target-of-opportunity scheduling algorithm designed with Zwicky Transient Facility in mind that uses mixed-integer linear programming. We compare its performance to gwemopt, the tuned heuristic scheduler used by the Zwicky Transient Facility and other facilities during the third LIGO–Virgo gravitational-wave observing run. This new algorithm uses variable-length observing blocks to enforce cadence requirements and to ensure field observability, along with having a secondary optimization step to minimize slew time. We show that by employing a hybrid method utilizing both this scheduler and gwemopt, the previous scheduler used, in concert, we can achieve an average improvement in detection efficiency of 3%–11% over gwemopt alone for a simulated binary neutron star merger data set consistent with LIGO–Virgo's third observing run, highlighting the potential of mixed-integer target of opportunity schedulers for future multimessenger follow-up surveys.

B Parazin↗

NASA NTRS2022

Systematic Kmtnet Planetary Anomaly Search V. Complete Sample of 2018 Prime-Field

We complete the analysis of all 2018 prime-field microlensing planets identified by the Korea Microlensing Telescope Network (KMTNet) Anomaly Finder. Among the ten previously unpublished events with clear planetary solutions, eight are either unambiguously planetary or are very likely to be planetary in nature: OGLE-2018-BLG-1126, KMT-2018-BLG-2004, OGLE-2018-BLG-1647, OGLE-2018-BLG-1367, OGLE-2018-BLG-1544, OGLE-2018-BLG-0932, OGLE-2018-BLG-1212, and KMT-2018-BLG-2718. Combined with the four previously published new Anomaly Finder events and 12 previously published (or in preparation) planets that were discovered by eye, this makes a total of 24 2018 prime-field planets discovered or recovered by Anomaly Finder. Together with a paper in preparation on 2018 subprime planets, this work lays the basis for the first statistical analysis of the planet mass-ratio function based on planets identified in KMTNet data. By systematically applying the heuristic analysis to each event, we identified the small modification in their formalism that is needed to unify the so-called close-wide and inner-outer degeneracies.

gravitational lensing: micro↗

NASA NTRS2013

Mixed Integer Programming and Heuristic Scheduling for Space Communication

Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.

Lee, Charles H.↗

NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗

NASA NTRS2003

Using a Portfolio of Algorithms for Planning and Scheduling

The Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles, includes a subsystem that utilizes a portfolio of heuristic algorithms that work synergistically to solve problems. The nature of the synergy of the specific algorithms is that their likelihoods of success are negatively correlated: that is, when a combination of them is used to solve a problem, the probability that at least one of them will succeed is greater than the sum of probabilities of success of the individual algorithms operating independently of each other. In ASPEN, the portfolio of algorithms is used in a planning process of the iterative repair type, in which conflicts are detected and addressed one at a time until either no conflicts exist or a user-defined time limit has been exceeded. At each choice point (e.g., selection of conflict; selection of method of resolution of conflict; or choice of move, addition, or deletion) ASPEN makes a stochastic choice of a combination of algorithms from the portfolio. This approach makes it possible for the search to escape from looping and from solutions that are locally but not globally optimum.

Sherwood, Robert↗

NASA NTRS1989

A Boltzmann machine for the organization of intelligent machines

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.

Moed, Michael C.↗

NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Memarsadeghi, Nargess↗