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Generally speaking, many photonic engineers, while working in a systems development mode, still focus on presenting the unique physical details of the optical elements, instead of using functional representation to describe the system. The purpose of this presentation is to introduce symbols that can be used to represent the functional intent of most of the phased array architecture.
Customizable Digital Receivers for Radar Two-Camera Acquisition and Tracking of a Flying Target Visual Data Analysis for Satellites A Data Type for Efficient Representation of Other Data Types Hand-Held Ultrasonic Instrument for Reading Matrix Symbols Broadband Microstrip-to-Coplanar Strip Double-Y Balun A Topographical Lidar System for Terrain-Relative Navigation Programmable Low-Voltage Circuit Breaker and Tester Electronic Switch Arrays for Managing Microbattery Arrays Topics covered include: Lower-Dark-Current, Higher-Blue-Response CMOS Imagers; Fabricating Large-Area Sheets of Single-Layer Graphene by CVD; Support for Diagnosis of Custom Computer Hardware; Providing Goal-Based Autonomy for Commanding a Spacecraft; Dynamic Method for Identifying Collected Sample Mass; Optimal Planning and Problem-Solving; Attitude-Control Algorithm for Minimizing Maneuver Execution Errors; Grants Document-Generation System; Heat-Storage Modules Containing LiNO3 3H2O and Graphite Foam; Precipitation-Strengthened, High-Temperature, High-Force Shape Memory Alloys; Improved Relief Valve Would Be Less Susceptible to Failure; Safety Modification of Cam-and-Groove Hose Coupling; Using Composite Materials in a Cryogenic Pump; Using Electronic Noses to Detect Tumors During Neurosurgery; Producing Newborn Synchronous Mammalian Cells; Smaller, Lower-Power Fast-Neutron Scintillation Detectors; Rotationally Vibrating Electric-Field Mill; Estimating Hardness from the USDC Tool-Bit Temperature Rise; Particle-Charge Spectrometer; Automated Production of Movies on a Cluster of Computers; FIDO-Class Development Rover; and Tone-Based Command of Deep Space Probes Using Ground Antennas.
A mathematical model of an advanced helicopter is described. The model is suitable for use in control/display research involving piloted simulation. The general design approach for the six degree of freedom equations of motion is to use the full set of nonlinear gravitational and inertial terms of the equations and to express the aerodynamic forces and moments as the reference values and first order terms of a Taylor series expansion about a reference trajectory defined as a function of longitudinal airspeed. Provisions for several different specific and generic flight control systems are included in the model. The logic required to drive various flight control and weapon delivery symbols on a pilot's electronic display is also provided. Finally, the model includes a simplified representation of low altitude wind and turbulence effects. This model was used in a piloted simulator investigation of the effects of control system and display variations for an attack helicopter mission.
Research in cognitive science suggests that much of cognition involves the rapid manipulation of complex data structures. However, it is very unclear how this could be realized in neural networks or connectionist systems. A core question is: how could the interconnectivity of items in an abstract-level data structure be neurally encoded? The answer appeals mainly to positional relationships between activity patterns within neural arrays, rather than directly to neural connections in the traditional way. The new method was initially devised to account for abstract symbolic data structures, but it also supports cognitively useful spatial analogue, image-like representations. As the neural model is based on massive, uniform, parallel computations over 2D arrays, the massively parallel processor is a convenient tool for simulation work, although there are complications in using the machine to the fullest advantage. An MPP Pascal simulation program for a small pilot version of the model is running.
The development of a Natural Language Interface which is semantic-based and uses Conceptual Dependency representation is presented. The system was developed using Lisp and currently runs on a Symbolics Lisp machine. A key point is that the parser handles morphological analysis, which expands its capabilities of understanding more words.
A BDD (Boolean Decision Diagram) is a data structure for the compact representation of a Boolean function. It is equipped with efficient algorithms for minimization and for applying Boolean operators. The use of BDDs for representing Boolean functions, combined with symbolic algorithms, facilitated a leap in the capability of model checking for the verification of systems with a huge number of states. Recently BDDs were considered as an efficient representation of data for Runtime Verification (RV). We review here the basic theory of BDDs and summarize their use in model checking and specifically in runtime verification.
The TacSat3 project is applying Integrated Systems Health Management (ISHM) technologies to an Air Force spacecraft for operational evaluation in space. The experiment will demonstrate the effectiveness and cost of ISHM and vehicle systems management (VSM) technologies through onboard operation for extended periods. We present two approaches to automatic testcase generation for ISHM: 1) A blackbox approach that views the system as a blackbox, and uses a grammar-based specification of the system's inputs to automatically generate *all* inputs that satisfy the specifications (up to prespecified limits); these inputs are then used to exercise the system. 2) A whitebox approach that performs analysis and testcase generation directly on a representation of the internal behaviour of the system under test. The enabling technologies for both these approaches are model checking and symbolic execution, as implemented in the Ames' Java PathFinder (JPF) tool suite. Model checking is an automated technique for software verification. Unlike simulation and testing which check only some of the system executions and therefore may miss errors, model checking exhaustively explores all possible executions. Symbolic execution evaluates programs with symbolic rather than concrete values and represents variable values as symbolic expressions. We are applying the blackbox approach to generating input scripts for the Spacecraft Command Language (SCL) from Interface and Control Systems. SCL is an embedded interpreter for controlling spacecraft systems. TacSat3 will be using SCL as the controller for its ISHM systems. We translated the SCL grammar into a program that outputs scripts conforming to the grammars. Running JPF on this program generates all legal input scripts up to a prespecified size. Script generation can also be targeted to specific parts of the grammar of interest to the developers. These scripts are then fed to the SCL Executive. ICS's in-house coverage tools will be run to measure code coverage. Because the scripts exercise all parts of the grammar, we expect them to provide high code coverage. This blackbox approach is suitable for systems for which we do not have access to the source code. We are applying whitebox test generation to the Spacecraft Health INference Engine (SHINE) that is part of the ISHM system. In TacSat3, SHINE will execute an on-board knowledge base for fault detection and diagnosis. SHINE converts its knowledge base into optimized C code which runs onboard TacSat3. SHINE can translate its rules into an intermediate representation (Java) suitable for analysis with JPF. JPF will analyze SHINE's Java output using symbolic execution, producing testcases that can provide either complete or directed coverage of the code. Automatically generated test suites can provide full code coverage and be quickly regenerated when code changes. Because our tools analyze executable code, they fully cover the delivered code, not just models of the code. This approach also provides a way to generate tests that exercise specific sections of code under specific preconditions. This capability gives us more focused testing of specific sections of code.
The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.
Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots.
The intention of the scheduling system developed at the Fraunhofer-Institute for Material Flow and Logistics is the support of a scheduler working in a job-shop. Due to the existing requirements for a job-shop scheduling system the usage of flexible knowledge representation and processing techniques is necessary. Within this system the attempt was made to combine the advantages of symbolic AI-techniques with those of neural networks.
A method and system is disclosed which provides precise multi-function digitally implementable modulation for a communication system. The invention provides a modulation signal for a communication system in response to an input signal from a data source. A digitized time response is generated from samples of a time domain representation of a spectrum profile of a selected modulation scheme. The invention generates and stores coefficients for each input symbol in accordance with the selected modulation scheme. The output signal is provided by a plurality of samples, each sample being generated by summing the products of a predetermined number of the coefficients and a predetermined number of the samples of the digitized time response. In a specific illustrative implementation, the samples of the output signals are converted to analog signals, filtered and used to modulate a carrier in a conventional manner. The invention is versatile in that it allows for the storage of the digitized time responses and corresponding coefficient lookup table of a number of modulation schemes, any of which may then be selected for use in accordance with the teachings of the invention.
This paper addresses the critical need for the development of intelligent or assisting software tools for the scientist who is working in the initial problem formulation and mathematical model representation stage of research. In particular, examples of that representation in fluid dynamics and instability theory are discussed. The creation of a mathematical model that is ready for application of certain solution strategies requires extensive symbolic manipulation of the original mathematical model. These manipulations can be as simple as term reordering or as complicated as discovery of various symmetry groups embodied in the equations, whereby Backlund-type transformations create new determining equations and integrability conditions or create differential Grobner bases that are then solved in place of the original nonlinear PDEs. Several examples are presented of the kinds of problem formulations and transforms that can be frequently encountered in model representation for fluids problems. The capability of intelligently automating these types of transforms, available prior to actual mathematical solution, is advocated. Physical meaning and assumption-understanding can then be propagated through the mathematical transformations, allowing for explicit strategy development.
Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.
Several short summaries of the work performed during this reporting period are presented. Topics discussed in this document include: (1) resilient seeded errors via simple techniques; (2) knowledge representation for engineering design; (3) analysis of faults in a multiversion software experiment; (4) implementation of parallel programming environment; (5) symbolic execution of concurrent programs; (6) two computer graphics systems for visualization of pressure distribution and convective density particles; (7) design of a source code management system; (8) vectorizing incomplete conjugate gradient on the Cyber 203/205; (9) extensions of domain testing theory and; (10) performance analyzer for the pisces system.
Assembly robots combine the benefits of speed and accuracy with the capability of adaptation to changes in the work environment. However, an impediment to the use of robots is the complexity of the man-machine interface. This interface can be improved by providing a means of using a priori-knowledge and reasoning capabilities for controlling and monitoring the tasks performed by robots. Robots ought to be able to perform complex assembly tasks with the help of only supervisory guidance from human operators. For such supervisory quidance, it is important to express the commands in terms of the effects desired, rather than in terms of the motion the robot must undertake in order to achieve these effects. A suitable knowledge representation can facilitate the conversion of task level descriptions into explicit instructions to the robot. Such a system would use symbolic relationships describing the a priori information about the robot, its environment, and the tasks specified by the operator to generate the commands for the robot.
In this work, we employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the nonperturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach, which opens opportunities to discover new domain knowledge in other physical systems.
A MACSYMA implementation of the Laplace transform for special functions is described. The generalized hypergeometric functions are used as a basis for the representation of approximately fifty special functions. Only a relatively small number of formulas that generally involve generalized hypergeometric functions are utilized for the integration stage. A sample of actual examples and their timing is provided.
Abstract. The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.