Showing papers on "Systems modeling published in 1990"

Systems: Concepts, Methodologies, and Applications

Abstract: Models and Modeling. A Systems Language. Systems Methodologies. Problem-Solving and Methodology. Management Control. Analysis of Business Information. Role Analysis. Appendices. References. Index.

Prediction and prescription in systems modeling

Abstract: Modeling is a principal tool for studying complex systems. Since models may be used for predictions, for analysis, or for prescription, we must ask what our goals are before we build our models. Historically, predictive numerical models have dominated our practice. Since the world we are modeling is orders of magnitude more complex than even the largest models our computers can handle, we must conserve computational power, first, by asking how much temporal detail we need and how much can be supported by available data and theories, second, by asking whether knowledge of steady states may not be more important than knowledge of temporal paths, third, by using the hierarchical properties of systems to aggregate and thereby simplify them, and, fourth, by substituting symbolic modeling, where appropriate, for numerical modeling.

Neural Networks In Dynamical Systems

Abstract: Multilayer networks and recurrent neural networks have proved extremely successful in pattern recognition problems as well as in associative learning. In this paper an attempt is made to demonstrate that both types of networks, combined in arbitrary configurations, will find application in complex dynamical systems. Well known results in linear systems theory and their extensions to conventional adaptive control theory are used to suggest models for the identification and control of nonlinear dynamic systems. The use of neural networks in dynamical systems raises many theoretical questions, some of which are discussed in the paper.

Fast-steering mirrors in optical control systems

Abstract: This paper contains a discussion of the functions of fast steering mirrors (FSMs) in optical systems which include target tracking, attenuation of disturbances including jitter, alignment beam and image stabilization, and extending optical sensor linear range. Dynamic and control modeling of the FSM mechanism, actuators, position sensors, and controllers are described in terms of mass and inertia properties, dynamic range, and bandwidth. Illustrative examples of the use of FSMs in optical systems and a simulation of a representative optical system using an FSM and corresponding results are shown. The use of software tools in performing control systems simulations is discussed.

Representing processes in the extended entity-relationship model

Abstract: Although closely related, functional and structural aspects of information systems are usually modeled independently of each other. The author proposes to combine functional and structural modeling into an integrated design methodology. He chooses for this purpose two popular and widely used models, the data-flow-oriented functional model and the extended entity-relationship (EER) data model. Since functional modeling generally precedes structural modeling in the design of information systems, the author examines how functional specifications can be represented using EER constructs. The methodology is based on using EER existence dependencies for representing the functional constraints implied by the process interactions. >

Qualitative physics: towards the automation of systems problem solving

Abstract: Key concepts and goals in the areas connected with qualitative and common-sense reasoning from the systems and simulation perspective are discussed. An examination is made of the general research agenda associated with common-sense reasoning about physical systems, and much overlap with existing systems concepts is found. It is suggested that cooperation with research in systems modeling could contribute to the progress of both fields because both are concerned with enhancing systems problem solving, with the computer in a subsidiary role (systems research) or taking the lead (qualitative reasoning). Some of the apparent misconceptions among artificial intelligence researchers about systems research are discussed and some suggestions for fruitful collaborations and research directions are offered. >

Modeling Time Varying Systems Using Hidden Control Neural Architecture

Abstract: Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful for modeling time invariant nonlinear systems, the inability of neural networks to characterize temporal variability has so far been an obstacle in applying them to complicated non stationary signals, such as speech. In this paper we present a network architecture, called "Hidden Control Neural Network" (HCNN), for modeling signals generated by nonlinear dynamical systems with restricted time variability. The approach taken here is to allow the mapping that is implemented by a multi layered neural network to change with time as a function of an additional control input signal. This network is trained using an algorithm that is based on "back-propagation" and segmentation algorithms for estimating the unknown control together with the network's parameters. The HCNN approach was applied to several tasks including modeling of time-varying nonlinear systems and speaker-independent recognition of connected digits, yielding a word accuracy of 99.1%.

Optimization of Simulation-Based Training Systems: Model Description, Implementation, and Evaluation

Abstract: : A model for the optimization of simulation-based training systems was developed using a systematic, top-down design procedure. The model consists of five tools that address the following problems: (a) determining which tasks should be trained by part-mission devices, full-mission simulators, or actual equipment; (b) specifying instructional features needed to train a set of tasks efficiently; (c) specifying the levels of fidelity that should be provided along several fidelity dimensions in order to meet task training requirements and satisfy cost limits; (d) determining the family of training devices that can train all required tasks at minimum cost; and (e) determining the optimal allocation of training time to training devices, given constraints on device use. The tools share common data on task requirements, training device features, and costs. A prototype decision support system was developed, and a formative evaluation was conducted. The model was demonstrated on Army rotary-wing aviation tasks, and specifications for application to armor maintenance were deployed. The report describes the model using the IDEFO (Integrated Computer- Aided Manufacturing Definition) system modeling language.

Model-based decision support and user modifiability

Abstract: Problems and relevant technology pertaining to the prospects for developing an expert-system-supported environment for modeling and problem solving are reviewed. Four aspects of research with model-based support systems are examined: (1) computer-assisted modeling; (2) knowledge-based modeling; (3) automated modeling environments; and (4) model-based query systems. An approach to equipping systems with the ability to support rapid domain-specific refinement by appropriate user-experts is explored. >

Modeling second-generation thermal imaging systems

Abstract: imaging systems) have become more complex and have improved in vertical performance. Inparticular, the effects of sampling and aliasing have not been included directly, but have had to beaccounted for by side calculations before entering the data. In this paper, an approach to modeling sec-ond generation TIS is described in which the effects of sampling on both signal and noise areaccounted for without requiring the user to make subsidiary calculations. The model istwo-dimensional, using both vertical and horizontal resolution in the prediction of recognition anddetection performance. A model for human perception is presented which differs slightly from thematched filter concept models and gives a closer match to measured data. The differences betweenmodeling scanning and staring systems is discussed, as well as between systems with on-focal- ratherthan off-focal-plane sampling. Proper treatment of the several sources of noise in sampled systems isanalyzed, including aliased noise.1 BACKGROUNDAs second generation Thermal Imaging Systems (TIS) have developed, it has become evident that newperformance models are needed for these systems. The standard model in the industry, the so-calledNVL Model', is inconvenient at best and inadequate at worst, for the prediction of performance ofthese advanced systems. I proposed an improved, two-dimensional performance model- in 1983. Thispaper will describe enhancements to the latter, emphasizing the differences between first and secondgeneration TIS and giving analytical form to modeling these differences.

Advanced sensor-simulation capability

Abstract: This paper provides an overview of an advanced simulation capability currently in use for analyzing visible and infrared sensor systems The software system, called VISTAS (VISIBLE/INFRARED SENSOR TRADES, ANALYSES, AND SIMULATIONS) combines classical image processing techniques with detailed sensor models to produce static and time dependent simulations of a variety of sensor systems including imaging, tracking, and point target detection systems Systems modelled to date include space-based scanning line-array sensors as well as staring 2-dimensional array sensors which can be used for either imaging or point source detection

A discrete-time neural network model for systems identification

Abstract: Neural networks can be powerful tools for nonlinear signal processing and systems modeling. The authors present a class of discrete-time, neural-network-based, nonlinear models suitable for such applications in a systems identification framework. The parameters for these models include a local interconnection neighborhood size, a time constant characterizing the neurons, a weight matrix, and input output connection matrices. The prediction error identification method trains the net to solve two problems: the prediction of a chaotic time series generated by the logistic function, and the demodulation of FSK (frequency-shift keying) signals. The proposed approach allows a speedup of 1 to 2 orders of magnitude while preserving important properties of its continuous-time analog, and its speed permits global minima to be determined by simulated annealing. The model allows multichannel applications for control or prediction

Hybrid expert system: neural-network methodology for nuclear plant monitoring and diagnostics

Abstract: A methodology is presented which couples expert systems to neural networks for the purpose of monitoring and diagnostics of large complex systems such as nuclear power plants. In order to provide timely, concise and task-specific information about the many aspects of the system's processes and to determine the state of the system based on the interpretation of potentially noisy data a model-referenced approach is utilized. In it a rule-based system performs the basic interpretation and processing of the data using heuristic reasoning. Having access to a set of pretrained neural networks that typify general classes of the system's state the expert system is able to perform diagnostic functions. It compares on-line data from the plant's sensors to the results of neural computations. This allows the diagnostic function to be performed with a speed comparable to that of the temporal evolution of the system. Hence corrective action can be taken by an operator. The set of pretrained neural networks provide implicitly a generalized model of the system which includes normal and off-normal states. Neural Networks are trained either by actual data or by data produced by a simulator. Coupling them to a rule-based system is a way of taking advantage of the best features of both. The speed and pattern recognition capabilities of the pretrained neural networks and the reasoning and interpretative power of symbolic computations. Information granules are used as the basic propositions that constitute the power plant's description in the knowledge-base of the expert system. They have the canonical form g = X is G is X, where X is a random variable described by a probability density function, G is a qualification or elastic constraint on the values of X; and X is a qualification of the proposition "X is G". This formulation encompasses two types of data: Data referring to observables or measured quantities from the system's sensors and calculated data coming from models of the plant such as from pretrained neural nets. Both the expert system and the neural networks are implemented in C and run in a VAX cluster.

TRW laser communication simulations

Abstract: The Block-Oriented System Simulator (BOSS), an operating system that provides an interactive environment for simulation-based analysis and design of radiofrequency and optical communication systems, has been developed BOSS facilitates a hierarchical block diagram approach to the system by first defining a detailed component module which is used in turn to define subsystem and larger system models The simulation can be executed in Monte Carlo fashion or in some cases using a processing-efficient semianalytic model Simulation results can be displayed as time waveforms, frequency domain spectra, eye diagrams, or BER plots A consistent user interface utilizing interactive graphics allows the modules to be easily reconfigured for analyzing design alternatives or defining a new system© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Image Decomposition by Data Dependent Systems

Abstract: This paper presents a new digital image processing approach called image decomposition. This approach is based on a recently developed system modeling, prediction, and analysis methodology called Data Dependent Systems (DDS). DDS is an innovative approach to the application and interpretation of the well known stochastic Autoregressive Moving Average (ARMA) models. The Green’s function form of these models provides a modal decomposition of the image data with boundary features captured in the model residuals and regional feature dynamics captured by the components of the Green’s function. This approach is unique in that it provides a method for image representation and scene identification which is not dependent on geometric descriptions.

Mathematical modeling and computer simulation of processes in energy systems

Abstract: Fluid flow, heat and mass transfer, combustion processes in energy equipments and plant components new thermal energy conversion technologies dynamics of energy systems and components integrated energy systems modeling and energy expert systems.

Smart electromagnetic structures: a neural network antenna

Abstract: Artificial neural networks(ANN5) and their ability to model and control dynamical systems for smart structures including sensors actuators and plants are being considered in our lab. Both linear and non-linear systems have been successfully modeled. We are presently working on two diverse regimes smart mechanical systems and smart electromagnetic systems. In order to better understand neural controllers as used in the smart electromagnetic structures we have directed our study of ANNs toward understanding the ability of the network to approximate system responses. We are training networks to mimic the desired output of the system. The damped sinusoid was chosen as the model and was approximated using a Jordan-like 8 iterative network. The results to date indicate that the ANNs can easily mimic these systemsthe question is whether the mechanism that the network applies can be related to the mechanisms that we understand for classical analysis. Sensor preprocessing represents a significant element in the smart material and structure concept. We are looking at certain network architectures as sensor preprocessors. Results from both these areas will be presented in this paper.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Optimization of manufacturing systems design : proceedings of the IFIP WG 5.3 International Conference on Modeling and Simulation for Optimization of Manufacturing Systems Design and Application, Tempe, Arizona, U.S.A., 8-10 November, 1989

Abstract: Product Definition Modeling and Simulation. Product Design, Planning and Design of Manufacturing Systems Based on Product Model Knowledge Bases (T. Kjellberg and L. Wingard). Bridging the Semantic Gap in Form Features: Applications of Objects, Types, and Schemata (R.E. Billo and R. Rucker). A Full Parameterized Geometric Modeling System Based on Technological Relationship Dependencies (J.C. Bocquet and E. Dupinet). Structured Design Methodology Combined with Expert Knowledge (Research Brief) (L. Nemes). Process Definition Modeling and Simulation. Experiences with High Developed Optimization Program for Cutting Values (D. Kochan, A. Nestler, Ch. Schone, J. Samisch). Spectrum Analysis for Statistical Control of Parts in CIM (J. Richard, M. Veron, G. Ris). The Application of Manufacturing System Theory (MST) to Dynamics of a Rigid-body System (K. Wang, and O. Bjorke). Management Systems Modeling and Simulation. A Knowledge Based Decision Aid System for Manufacturing Shop Control (C. Tahon, Xi Zhao, R. Soenen). Systems Modeling and Design Advancements. A Study of Different Modeling Methods for CIMS and FMS (Z. Deng, K. Wang, M. Huang, B. Huang, E. Bu). GRAI Method and Economic Performance Measurement System (B. Vallespir, G. Doumeingts, M. Bitton, M. Zanettin). Extending Structured Analysis Modeling with AI: An Application to MPPII Profiles and SFC Data Communications Requirements Specifications (A. Feller and R. Rucker). An Object Oriented Tool Based on Discreet Event Simulation for Analysis and Design of Manufacturing Systems (E. Borgen and J. Strandhagan). Creating an Integrated, Useful Systems Definition Technique (Research Brief) (D.L. Shunk). Simulation Advancements. The Use of the ADA Language for the Simulation and the Control of Robot-Based Cells (Y. Sallez and P. Lepot). Generic/Specific Modeling: An Improvement to CIM Simulation Techniques (G.T. Mackulak and J.K. Cochran). The Role of Heuristics and Simulation in Optimizing Semiconductor Manufacturing Processes and Flows (B. Chapman and B. Martensen). A High-Level Factory Simulation System (M.A. Melkanoff, B. Soetarman, Chen-Chung Kao, Chin-Wen, Cheng-Lo, M. Mansur).

Prototyping a discrete systems control for faster debugging and implementation

Abstract: The authors describe a set of concepts which cooperate to allow a discrete-system control fast description, and the associated method for real-time prototyping. One of these concepts is the IEC Standard 848 Preparation of Function Charts for Control Charts for Control Systems. Other concepts allow resource sharing, physical controlled system modeling, and interface between control and physical process definition. These concepts are implemented in a software tool that produces a closed-loop real-time simulator for a deep debugging (so that final debugging and start-up of the machines or the factory takes a very short time) and executive real-time software implementation (with fewer human coding errors). >

Multichamber And In-Situ Processing System Design And Control

Abstract: For multichamber and in-situ processing of microelectronic materials, there are few hundred steps in a manufacturing line. The design of such processing systems require careful simulation, modeling, and planning. Our basic approach is the idea of an interactive software design environment for equipment, process, and manufacturing line modeling and simulation, which provides a way to integrate the manufacturing line and its simulator tightly.

Real time data acquisition for expert systems in Unix workstations at Space Shuttle Mission Control

Abstract: A distributed system of proprietary engineering-class workstations is incorporated into NASA's Space Shuttle Mission-Control Center to increase the automation of mission control. The Real-Time Data System (RTDS) allows the operator to utilize expert knowledge in the display program for system modeling and evaluation. RTDS applications are reviewed including: (1) telemetry-animated communications schematics; (2) workstation displays of systems such as the Space Shuttle remote manipulator; and (3) a workstation emulation of shuttle flight instrumentation. The hard and soft real-time constraints are described including computer data acquisition, and the support techniques for the real-time expert systems include major frame buffers for logging and distribution as well as noise filtering. The incorporation of the workstations allows smaller programming teams to implement real-time telemetry systems that can improve operations and flight testing.

Second-generation thermal imaging system design-trades modeling

Abstract: The Night Vision Laboratory static performance model is considered for thermal viewing systems. Since the model is not initially intended to be a design tool and is not usable for conducting system or component design trades, it has to be restructured. The approach to updating the first-generation static performance model and to configuring it as a design tool is presented. Second-generation imaging systems exploit infrared focal-plane arrays, high-reliability cryogenic coolers, precision scanning devices, and high-speed digital electronics. They also use optical materials and coatings and optomechanical and electronics packaging techniques.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Methodological Principles of Uncertainty in Information Systems Modeling

Abstract: System modeling permeates all disciplines of science, both natural and artificial. The general concepts of system modeling are presented in summary fashion. The key role of uncertainty in system modeling is discussed including the principles of maximum and minimum uncertainty. Recent results regarding conceptualization of uncertainty, which demonstrate that uncertainty is a multidimensional concept, are overviewed, and the implications for modeling in information and software engineering are discussed.

GUFDIPP: the GUERAP user-friendly data-input program package for generating and modifying GUERAP III data files

Abstract: The straylight analysis program GUERAP III requires a complicated. input dataset Which discourages its use. The GUFDIPP program package provides an environment for swiftly generating, cliecking and maintaining GUERAP datasets, so making GUERAP more accessible.

Parallelism in backward-chained expert systems: experimental results

Abstract: There are many applications which may be done by an expert system in real time, if the system is capable of real-time response. The LISP and PROLOG-based expert systems have typically been too slow for real-time response. This has led to an effort to use other languages, the development of fast pattern matching techniques and other methods of improving the speed of expert systems. Another approach to developing faster expert systems is to make use of the emerging parallel processing computer technology. A further use for parallelism is to allow reasonable response time for large knowledge bases. The size of knowledge bases may become as large as 20,000 chunks of knowledge (and more) in the near future in medical and space applications. This paper describes the use of parallel processing in the EMYCIN backward chained rule-based model.

Architectural Modeling of Complex Information Systems

Abstract: This paper describes ongoing research, development, and application of architectural models to the design and life cycle support of complex information systems. The purpose of this development work is to define an overarching conceptual framework to rationally partition a complex problem and to integrate the solution components into a cohesive system. Experience at Boeing showed that no single architectural model is adequate to capture the complex interdependencies inherent in large information systems. This led to an approach that creates a distinct architecture for defining the problem (Requirements Architecture) and another for the projected solution (Solution Architecture). The complex mapping between the two distinct architectural models is analogous to the systems engineering allocation process.

Indentification of dynamic systems: a systematic approach

Abstract: A systematic procedure of parameter estimation for dynamic systems is presented as a basic approach for system modeling and control. With this approach, parameters of linear discrete- and/or continuous-time, multivariable dynamic systems can be estimated. The proposed technique has advantages over most of the existing techniques, such as flexibility, computational simplicity, and straightforwardness. On the basis of this concept as well as the derived algorithms, some new algorithms of system identification can be derived. Identification practice has demonstrated that the proposed approach gives good results. >

New approach to data representation in mobile robot models

Abstract: An approach to data representation in mobile robot models is described. In this method, the application is modeled using a stepwise modeling principle. System modeling determines the two basic models of the application: a functional model and a data model. These models are used to represent the functions and data structures of the mobile robot