The "Oracle Problem" and Its Impacts in Mixed Reality
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Machine Learning↗
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Optimal Regulator Algorithms For The Control Of Linear Systems (ORACLS)
Control theory design package offers engineer full range of subroutines to manipulate and solve Linear-Quadratic-Gaussian types of problems. ORACLS is rigorous tool, intended for multi-input and multi-output dynamic systems in both continuous and discrete form. Written in FORTRAN.
Frisch, Harold P.↗
A Circuit-Based Quantum Algorithm Driven by Transverse Fields for Grover's Problem
We designed a quantum search algorithm, giving the same quadratic speedup achieved by Grover's original algorithm; we replace Grover's diffusion operator (hard to implement) with a product diffusion operator generated by transverse fields (easy to implement). In our algorithm, the problem Hamiltonian (oracle) and the transverse fields are applied to the system alternatively. We construct such a sequence that the corresponding unitary generates a closed transition between the initial state (even superposition of all states) and a modified target state, which has a high degree of overlap with the original target state.
quantum computing↗
Reuse at the Software Productivity Consortium
The Software Productivity Consortium is sponsored by 14 aerospace companies as a developer of software engineering methods and tools. Software reuse and prototyping are currently the major emphasis areas. The Methodology and Measurement Project in the Software Technology Exploration Division has developed some concepts for reuse which they intend to develop into a synthesis process. They have identified two approaches to software reuse: opportunistic and systematic. The assumptions underlying the systematic approach, phrased as hypotheses, are the following: the redevelopment hypothesis, i.e., software developers solve the same problems repeatedly; the oracle hypothesis, i.e., developers are able to predict variations from one redevelopment to others; and the organizational hypothesis, i.e., software must be organized according to behavior and structure to take advantage of the predictions that the developers make. The conceptual basis for reuse includes: program families, information hiding, abstract interfaces, uses and information hiding hierarchies, and process structure. The primary reusable software characteristics are black-box descriptions, structural descriptions, and composition and decomposition based on program families. Automated support can be provided for systematic reuse, and the Consortium is developing a prototype reuse library and guidebook. The software synthesis process that the Consortium is aiming toward includes modeling, refinement, prototyping, reuse, assessment, and new construction.
Weiss, David M.↗
Translating user requirements into enrollment
We review some of Oracle's functionality in this document, particularly items that caused us problems when we went live with Oracle Advanced Benefits (OAB).
Oracle Advanced Benefits OAB enrollment requiremen↗
ORACLS - A linear-quadratic-Gaussian computer-aided design package
ORACLS, an acronym denoting Optimal Regular Algorithms for the Control of Linear Systems, is a collection of FORTRAN coded subroutines dedicated to the formulation and solution of the Linear-Quadratic-Gaussian (LQG) design problem modeled in both continuous and discrete form. The ORACLS system is under continuous development at the NASA Langley Research Center, Hampton, Virginia, and is widely used by universities and industry within the U.S.A. The current (operational) ORACLS version as well as new software under development is described.
Armstrong, E. S.↗
Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm
In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (r(e): R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of r(e) (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.
SPECTRAL-RESOLUTION LIDAR↗
Automatically generated acceptance test: A software reliability experiment
This study presents results of a software reliability experiment investigating the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multi-version experiment previously conducted at the NASA Langley Research Center, in which the launch interceptor problem is used as a model. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations, and for the employment of this test method on other applications.
Protzel, Peter W.↗
Integrating Micro-computers with a Centralized DBMS: ORACLE, SEED AND INGRES
Users of ADABAS, a relational-like data base management system (ADABAS) with its data base programming language (NATURAL) are acquiring microcomputers with hopes of solving their individual word processing, office automation, decision support, and simple data processing problems. As processor speeds, memory sizes, and disk storage capacities increase, individual departments begin to maintain "their own" data base on "their own" micro-computer. This situation can adversely affect several of the primary goals set for implementing a centralized DBMS. In order to avoid this potential problem, these micro-computers must be integrated with the centralized DBMS. An easy to use and flexible means for transferring logic data base files between the central data base machine and micro-computers must be provided. Some of the problems encounted in an effort to accomplish this integration and possible solutions are discussed.
Hoerger, J.↗
Control-System Design Program
Control-theory design package, Optimal Regulator Algorithms for Control of Linear Systems (ORACLS), developed to aid in design of controllers and optimal filters for systems modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, Linear-Quadratic-Gaussian (LQG) problem, most widely accepted method of determining optimal control policy. Provides for solution to time-in-variant continuous or discrete LQG problems. Attractive to control-system designer providing rigorous tool for dealing with multi-input and multi-output dynamic systems in continuous and discrete form. CDO version written in FORTRAN IV. VAX version written in FORTRAN 77.
Frisch, Harold P.↗
ORACLS - A modern control theory design package
A digital computer program (ORACLS) for implementing the optimal regulator theory approach to the design of controllers for linear time-invariant systems is described. The user-oriented program employs the latest numerical techniques and is applicable to both the digital and continuous control problems.
Armstrong, E. S.↗
Expert system decision support for low-cost launch vehicle operations
Progress in assessing the feasibility, benefits, and risks associated with AI expert systems applied to low cost expendable launch vehicle systems is described. Part one identified potential application areas in vehicle operations and on-board functions, assessed measures of cost benefit, and identified key technologies to aid in the implementation of decision support systems in this environment. Part two of the program began the development of prototypes to demonstrate real-time vehicle checkout with controller and diagnostic/analysis intelligent systems and to gather true measures of cost savings vs. conventional software, verification and validation requirements, and maintainability improvement. The main objective of the expert advanced development projects was to provide a robust intelligent system for control/analysis that must be performed within a specified real-time window in order to meet the demands of the given application. The efforts to develop the two prototypes are described. Prime emphasis was on a controller expert system to show real-time performance in a cryogenic propellant loading application and safety validation implementation of this system experimentally, using commercial-off-the-shelf software tools and object oriented programming techniques. This smart ground support equipment prototype is based in C with imbedded expert system rules written in the CLIPS protocol. The relational database, ORACLE, provides non-real-time data support. The second demonstration develops the vehicle/ground intelligent automation concept, from phase one, to show cooperation between multiple expert systems. This automated test conductor (ATC) prototype utilizes a knowledge-bus approach for intelligent information processing by use of virtual sensors and blackboards to solve complex problems. It incorporates distributed processing of real-time data and object-oriented techniques for command, configuration control, and auto-code generation.
Szatkowski, G. P.↗
Bios-3: Siberian experiments in bioregenerative life support
The Russian experience with the bioregenerative life support system Bios-3 at Krasnoyarsk, Siberia, is reviewed. A brief review of other bioregenerative systems examines Biosphere 2 in Oracle, Arizona, and the Bios-1 and Bios-2 systems that preceded Bios-3. Physical details of the Bios-3 facility are provided. The use of Chlorella and higher plants for gas exchange is examined. Long-term studies of human habitation are discussed. Other topics include microflora in Bios-3, the theory of closed systems, and problems for the future.
Non-NASA Center↗
Mission Operations Planning and Scheduling System (MOPSS)
MOPSS is a generic framework that can be configured on the fly to support a wide range of planning and scheduling applications. It is currently used to support seven missions at Goddard Space Flight Center (GSFC) in roles that include science planning, mission planning, and real-time control. Prior to MOPSS, each spacecraft project built its own planning and scheduling capability to plan satellite activities and communications and to create the commands to be uplinked to the spacecraft. This approach required creating a data repository for storing planning and scheduling information, building user interfaces to display data, generating needed scheduling algorithms, and implementing customized external interfaces. Complex scheduling problems that involved reacting to multiple variable situations were analyzed manually. Operators then used the results to add commands to the schedule. Each architecture was unique to specific satellite requirements. MOPSS is an expert system that automates mission operations and frees the flight operations team to concentrate on critical activities. It is easily reconfigured by the flight operations team as the mission evolves. The heart of the system is a custom object-oriented data layer mapped onto an Oracle relational database. The combination of these two technologies allows a user or system engineer to capture any type of scheduling or planning data in the system's generic data storage via a GUI.
Wood, Terri↗
Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters: The maximum entropy approach, part 2
A validated technology data base is being developed in the areas of control/structures interaction, deployment dynamics, and system performance for Large Space Structures (LSS). A Ground Facility (GF), in which the dynamics and control systems being considered for LSS applications can be verified, was designed and built. One of the important aspects of the GF is to verify the analytical model for the control system design. The procedure is to describe the control system mathematically as well as possible, then to perform tests on the control system, and finally to factor those results into the mathematical model. The reduction of the order of a higher order control plant was addressed. The computer program was improved for the maximum entropy principle adopted in Hyland's MEOP method. The program was tested against the testing problem. It resulted in a very close match. Two methods of model reduction were examined: Wilson's model reduction method and Hyland's optimal projection (OP) method. Design of a computer program for Hyland's OP method was attempted. Due to the difficulty encountered at the stage where a special matrix factorization technique is needed in order to obtain the required projection matrix, the program was successful up to the finding of the Linear Quadratic Gaussian solution but not beyond. Numerical results along with computer programs which employed ORACLS are presented.
Hsia, Wei Shen↗
ORACLS: A system for linear-quadratic-Gaussian control law design
A modern control theory design package (ORACLS) for constructing controllers and optimal filters for systems modeled by linear time-invariant differential or difference equations is described. Numerical linear-algebra procedures are used to implement the linear-quadratic-Gaussian (LQG) methodology of modern control theory. Algorithms are included for computing eigensystems of real matrices, the relative stability of a matrix, factored forms for nonnegative definite matrices, the solutions and least squares approximations to the solutions of certain linear matrix algebraic equations, the controllability properties of a linear time-invariant system, and the steady state covariance matrix of an open-loop stable system forced by white noise. Subroutines are provided for solving both the continuous and discrete optimal linear regulator problems with noise free measurements and the sampled-data optimal linear regulator problem. For measurement noise, duality theory and the optimal regulator algorithms are used to solve the continuous and discrete Kalman-Bucy filter problems. Subroutines are also included which give control laws causing the output of a system to track the output of a prescribed model.
Armstrong, E. S.↗
Application of the LQG/LTR technique to robust controller synthesis for a large flexible space antenna
The problem of synthesizing a robust controller is considered for a large, flexible space-based antenna by using the linear-quadratic-Gaussian (LQG)/loop transfer recovery (LTR) method. The study is based on a finite-element model of the 122-m hoop/column antenna, which consists of three rigid-body rotational modes and the first 10 elastic modes. A robust compensator design for achieving the required performance bandwidth in the presence of modeling uncertainties is obtained using the LQG/LTR method for loop-shaping in the frequency domain. Different sensor actuator locations are analyzed in terms of the pole/zero locations of the multivariable systems and possible best locations are indicated. The computations are performed by using the LQG design package ORACLS augmented with frequency domain singular value analysis software.
Joshi, S. M.↗
Automated Knowledge Discovery From Simulators
A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.
Burl, Michael↗