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NASA NTRS1992

A statistical-based scheduling algorithm in automated data path synthesis

In this paper, we propose a new heuristic scheduling algorithm based on the statistical analysis of the cumulative frequency distribution of operations among control steps. It has a tendency of escaping from local minima and therefore reaching a globally optimal solution. The presented algorithm considers the real world constraints such as chained operations, multicycle operations, and pipelined data paths. The result of the experiment shows that it gives optimal solutions, even though it is greedy in nature.

Jeon, Byung Wook↗

NASA NTRS2001

Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function - i.e. a ratio of two polynomials - of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGSs performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa.

Mengshoel, Ole J.↗

NASA NTRS1992

The use of expert systems in combination with active and passive microwave data to classify sea ice

The authors report on a system for sea ice classification which combines passive and active microwave data with algorithmic and heuristic classification techniques in order to classify sea ice unambiguously. They describe initial algorithmic classification, extraction of features and symbolic information, the application of data-dependent expert systems, and the establishment of feature correspondence between data sets.

Haverkamp, Donna↗

NASA NTRS1990

Evaluating scheduling algorithms for traffic with heterogeneous performance objectives

Two types of network traffic are considered: traffic with deadlines, for which the most important performance objective is based on loss rate, and packets without deadlines, for which the most important performance objective is based on mean delay. An optimal scheduling algorithm is presented to minimize weighted loss rate and weighted mean delay in the queues that form at the switches and at the network access points of a packet-switched network, where weights reflect the relative importance of packets. Although not practical for implementation, the algorithm is intended as a standard for the comparison of the performance of other scheduling algorithms. The algorithm is more general and lower computational complexity than previously published algorithms, enabling performance evaluation of some important scenarios that could not previously have been considered. Using the optimal performance results of this algorithm, the performance of the first-come-first-served, static priority, and earliest deadline first scheduling algorithms is evaluated. The results suggest that network efficiency could be improved by using a more sophisticated heuristic scheduling algorithm rather than one of the aforementioned algorithms.

Peha, Jon M.↗

NASA NTRS2003

Scheduling in the Face of Uncertain Resource Consumption and Utility

We discuss the problem of scheduling tasks that consume a resource with known capacity and where the tasks have varying utility. We consider problems in which the resource consumption and utility of each activity is described by probability distributions. In these circumstances, we would like to find schedules that exceed a lower bound on the expected utility when executed. We first show that while some of these problems are NP-complete, others are only NP-Hard. We then describe various heuristic search algorithms to solve these problems and their drawbacks. Finally, we present empirical results that characterize the behavior of these heuristics over a variety of problem classes.

Koga, Dennis↗

NASA NTRS1981

Pulse control of single degree-of-freedom system

The possibility of using active control for structural and mechanical systems is examined. Open loop adaptive control of structural systems subjected to deterministic and stochastic excitations is considered by means of an algorithm for a single-degree-of-freedom oscillator. The algorithm is heuristic and requires continuous monitoring of the state variables, with a pulse control applied when some prespecified level of control is exceeded. The control algorithm is based on the principle that the gradual rhythmic increase of the structural response can be eliminated by applying a pulse of suitable magnitude in the proper direction, thereby avoiding the application of large forces over sustained periods of time. The optimum pulse magnitude is determined as the minimum rms response of the system. Application to linear and nonlinear systems, as well as in chatter suppression, are outlined.

Udwadia, F. E.↗

NASA NTRS1994

Efficient bulk-loading of gridfiles

This paper considers the problem of bulk-loading large data sets for the gridfile multiattribute indexing technique. We propose a rectilinear partitioning algorithm that heuristically seeks to minimize the size of the gridfile needed to ensure no bucket overflows. Empirical studies on both synthetic data sets and on data sets drawn from computational fluid dynamics applications demonstrate that our algorithm is very efficient, and is able to handle large data sets. In addition, we present an algorithm for bulk-loading data sets too large to fit in main memory. Utilizing a sort of the entire data set it creates a gridfile without incurring any overflows.

Leutenegger, Scott T.↗

NASA NTRS1993

Retrieval of ice thickness from polarimetric SAR data

We describe a potential procedure for retrieving ice thickness from multi-frequency polarimetric SAR data for thin ice. This procedure includes first masking out the thicker ice types with a simple classifier and then deriving the thickness of the remaining pixels using a model-inversion technique. The technique used to derive ice thickness from polarimetric observations is provided by a numerical estimator or neural network. A three-layer perceptron implemented with the backpropagation algorithm is used in this investigation with several improved aspects for a faster convergence rate and a better accuracy of the neural network. These improvements include weight initialization, normalization of the output range, the selection of offset constant, and a heuristic learning algorithm. The performance of the neural network is demonstrated by using training data generated by a theoretical scattering model for sea ice matched to the database of interest. The training data are comprised of the polarimetric backscattering coefficients of thin ice and the corresponding input ice parameters to the scattering model. The retrieved ice thickness from the theoretical backscattering coefficients is compare with the input ice thickness to the scattering model to illustrate the accuracy of the inversion method. Results indicate that the network convergence rate and accuracy are higher when multi-frequency training sets are presented. In addition, the dominant backscattering coefficients in retrieving ice thickness are found by comparing the behavior of the network trained backscattering data at various incidence angels. After the neural network is trained with the theoretical backscattering data at various incidence anges, the interconnection weights between nodes are saved and applied to the experimental data to be investigated. In this paper, we illustrate the effectiveness of this technique using polarimetric SAR data collected by the JPL DC-8 radar over a sea ice scene.

Kwok, R.↗

NASA NTRS2010

Timeline-Based Space Operations Scheduling with External Constraints

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.

Chien, Steve↗

NASA NTRS1997

Neural Network Based Sensory Fusion for Landmark Detection

NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

Kumbla, Kishan -K.↗

NASA NTRS1972

Design of automata theory of cubical complexes with applications to diagnosis and algorithmic description

The following problems are considered: (1) methods for development of logic design together with algorithms, so that it is possible to compute a test for any failure in the logic design, if such a test exists, and developing algorithms and heuristics for the purpose of minimizing the computation for tests; and (2) a method of design of logic for ultra LSI (large scale integration). It was discovered that the so-called quantum calculus can be extended to render it possible: (1) to describe the functional behavior of a mechanism component by component, and (2) to compute tests for failures, in the mechanism, using the diagnosis algorithm. The development of an algorithm for the multioutput two-level minimization problem is presented and the program MIN 360 was written for this algorithm. The program has options of mode (exact minimum or various approximations), cost function, cost bound, etc., providing flexibility.

Roth, J. P.↗

NASA NTRS1996

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

Dynamic mesh adaption on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load imbalance among processors on a parallel machine. This paper describes the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A heuristic remapping algorithm is presented that assigns partitions to processors such that the redistribution cost is minimized. Results indicate that the parallel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors of an SP2 when 35% of the mesh is randomly adapted. For large-scale scientific computations, our load balancing strategy gives almost a sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remapper yields processor assignments that are less than 3% off the optimal solutions but requires only 1% of the computational time.

Biswas, Rupak↗

NASA NTRS1996

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

Dynamic mesh adaptation on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load inbalances among processors on a parallel machine. This paper described the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A heuristic remapping algorithm is presented that assigns partitions to processors such that the redistribution coast is minimized. Results indicate that the parallel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors of an SP2 when 35 percent of the mesh is randomly adapted. For large scale scientific computations, our load balancing strategy gives an almost sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remappier yields processor assignments that are less than 3 percent of the optimal solutions, but requires only 1 percent of the computational time.

Biswas, Rupak↗

NASA NTRS1972

A survey of an introduction to fault diagnosis algorithms

This report surveys the field of diagnosis and introduces some of the key algorithms and heuristics currently in use. Fault diagnosis is an important and a rapidly growing discipline. This is important in the design of self-repairable computers because the present diagnosis resolution of its fault-tolerant computer is limited to a functional unit or processor. Better resolution is necessary before failed units can become partially reuseable. The approach that holds the greatest promise is that of resident microdiagnostics; however, that presupposes a microprogrammable architecture for the computer being self-diagnosed. The presentation is tutorial and contains examples. An extensive bibliography of some 220 entries is included.

Mathur, F. P.↗

NASA NTRS1978

On the optimization of discrete structures with aeroelastic constraints

The paper deals with the problem of dynamic structural optimization where constraints relating to flutter of a wing (or other dynamic aeroelastic performance) are imposed along with conditions of a more conventional nature such as those relating to stress under load, deflection, minimum dimensions of structural elements, etc. The discussion is limited to a flutter problem for a linear system with a finite number of degrees of freedom and a single constraint involving aeroelastic stability, and the structure motion is assumed to be a simple harmonic time function. Three search schemes are applied to the minimum-weight redesign of a particular wing: the first scheme relies on the method of feasible directions, while the other two are derived from necessary conditions for a local optimum so that they can be referred to as optimality-criteria schemes. The results suggest that a heuristic redesign algorithm involving an optimality criterion may be best suited for treating multiple constraints with large numbers of design variables.

Mcintosh, S. C., Jr.↗

NASA NTRS1982

A DSN optimal spacecraft scheduling model

A computer model is described which uses mixed-integer linear programming to provide optimal DSN spacecraft schedules given a mission set and specified scheduling requirements. A solution technique is proposed which uses Bender's Method and a heuristic starting algorithm.

Webb, W. A.↗

NASA NTRS1985

An optimal spacecraft scheduling model for the NASA deep space network

Webb, W. A.↗

NASA NTRS1987

Using hybrid expert system approaches for engineering applications

In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a number of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.

Allen, R. H.↗