Generation and representation of synthetic environments is a key factor in using a simulation system for the particular application it is designed for. Realtime networked simulation places additional constraints on th...
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ISBN:
(纸本)0819418390
Generation and representation of synthetic environments is a key factor in using a simulation system for the particular application it is designed for. Realtime networked simulation places additional constraints on the successful creation of these environments. When these networked simulators operate in close proximity to the ground, the constraints on the synthetic database become even more stringent. This paper describes some of the challenges associated with the creation and usage of these synthetic databases for ground and near-ground applications in a networked simulation system. The representation of the information for the same terrain region can be drastically different based on the needs of a simulation platform, or the specific application. The paper touches on these diverse computing needs and on how they affect the creation and exchange of a synthetic terrain database in a networked heterogeneous system. For example, creation of electronic or paper maps demands a data representation scheme that may not be suitable for a thermal sight simulator, even though they are both intended to depict and operate in the same geographical region. The key steps in creating synthetic databases are reviewed and the trade-offs in source data selection, geometric representations, and issues such as interchange and interoperability are highlighted. Some management considerations in improving the process, quality, and tool development are also discussed.
The development of process models with the aid of statistical methods has proved to be a considerably more reliable method for predicting the intensity of quality characteristics in series production. This method invo...
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ISBN:
(纸本)1566763193
The development of process models with the aid of statistical methods has proved to be a considerably more reliable method for predicting the intensity of quality characteristics in series production. This method involves the recorded process parameters being checked by (non)linear regression processes as to their information value within a statistical process model that is still to be compiled. The systematic modeling procedure makes it possible for influencing variables which are only of slight relevance, or are not relevant at all, to be excluded, following the specification of a confidence level. The selection of the parameters that determine the model is thus performed by the process itself. It is essentially conceivable for statistical modeling to be applied for two different approaches: modeling at a fixed point of operation and modeling through analysis of the results of a test plan.
Many of the difficulties encountered in the development of process models can be expressed as inverse problems. Important examples of these are the estimation of boundary conditions, the transfer of data between mater...
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ISBN:
(纸本)0873392973
Many of the difficulties encountered in the development of process models can be expressed as inverse problems. Important examples of these are the estimation of boundary conditions, the transfer of data between material models operating on different scales and the derivation of minimal material and process models for real time processcontrol. This paper comments on the benefits of viewing such problems as being solvable through the application of inverse analysis techniques. Two such techniques, using Sensitivity Coefficients and a Genetic Algorithm are commented upon. For the latter recent, initial, results are presented for the estimation of temperature dependent convection coefficients with and without temperature measurement noise.
Multivariate statistical modelling based on vibration signal analysis was performed at commercial scale grinding. The source digital signals consist of three channels of mechanical vibrations obtained at the axial, ho...
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Multivariate statistical modelling based on vibration signal analysis was performed at commercial scale grinding. The source digital signals consist of three channels of mechanical vibrations obtained at the axial, horizontal and vertical directions. The feed rate, power draw, pulp temperature were collected automatically by the control system while samples of the feed material and ground product of the ball mill were manually taken to determine the particle size distributions and pulp densities. Using projection to the latent structure (PLS) and/or principle component regression (PCR), empirical models between grinding parameters of interests and the vibration signals were built based on the training data set collected in two weeks, thus the new grinding parameters could be automatically predicted whenever the vibration signals were known. The modelling results show that both the PCR and PLS model can be used to predict grinding parameters online.
A recently developed undergraduate controls laboratory combines hands-on real time processdata acquisition and feedback control with interactive modeling, system identification, analysis and controller design. The ex...
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A recently developed undergraduate controls laboratory combines hands-on real time processdata acquisition and feedback control with interactive modeling, system identification, analysis and controller design. The experiments are structured such that the students will learn and test problem solving strategies. The laboratory promotes rapid learning by the use of interactive graphics in all stages of the design process, and during processdata acquisition. In the experiments, students are asked to validate the linear and nonlinear models, and to compare experimental results with their analysis. In other assignments, students examine measurement signal noise and design noise filters. Similarly, students design linear compensators such as lead and lag networks for the validated plant model, using continuous design methods. The finalized design is discretized by the software and validated experimentally.
A systematic approach is presented for modeling qualitative properties in semiconductor manufacturing processes. This approach is based on the fuzzy logic theory, and on the statistical analysis of categorical data. A...
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ISBN:
(纸本)0819418463
A systematic approach is presented for modeling qualitative properties in semiconductor manufacturing processes. This approach is based on the fuzzy logic theory, and on the statistical analysis of categorical data. A fuzzy inference system can be designed and created by training data obtained either from human expert knowledge, or automatically extracted from statistically designed experiments. Before being used to design the fuzzy system, the data extracted from the designed experiments can be processed and filtered with the help of linear and logistic regression analysis. After the establishment of the initial inference system, the fuzzy membership functions can be tuned adaptively to accommodate process changes.
Many chemical sensors based on fiber optics and absorption spectroscopy have been reported in applications ranging from biomedical and environmental monitoring to industrial processcontrol. In these diverse applicati...
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Many chemical sensors based on fiber optics and absorption spectroscopy have been reported in applications ranging from biomedical and environmental monitoring to industrial processcontrol. In these diverse applications, the analyte can be probed directly, by measuring its intrinsic absorption, or by incorporating some transduction mechanism such as a reagent chemistry to enhance sensitivity and selectivity. Physical and performance requirements are placed on a device depending on its intended use. In applications such as chemical process monitoring, survivability and the assurance of the long-term quality of the analytical data are paramount. The above needs have resulted in devices that now employ multivariate dataanalysis, complex sampling interfaces, and reagent renewal mechanisms. The response from such systems can provide information not only about target analyte(s), but can also signal the presence of interferences, and may potentially be used to follow ser,sor degradation. Examples are given of devices currently being investigated along with a discussion of some of the remaining material, chemical, and optical challenges.
The main purpose of this paper is to introduce a new method of control loop performance analysis of the closed-loop MIMO process. This new approach is based on Filtering and subsequent CORrelation (FCOR) analysis of t...
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The main purpose of this paper is to introduce a new method of control loop performance analysis of the closed-loop MIMO process. This new approach is based on Filtering and subsequent CORrelation (FCOR) analysis of the output and the filtered data. The steps required in the implementation of the FCOR algorithm are: i) estimation of the time delay or the interactor matrix of the MIMO system and ii) evaluation of control performance with MIMO minimum variance control as a benchmark standard. The proposed algorithm is evaluated by application to a simulated and an industrial process.
We present the DEVise (data exploration via visualization environment) toolkit designed for visual exploration of stream data. data of this type are collected continuously from sources such as remote sensors, program ...
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ISBN:
(纸本)0819417572
We present the DEVise (data exploration via visualization environment) toolkit designed for visual exploration of stream data. data of this type are collected continuously from sources such as remote sensors, program traces, and the stock market. A typical application involves looking for correlations, which may not be precisely defined, by experimenting with graphical representations. This includes selectively comparing data from multiple sources, selective viewing by zooming and scrolling at various resolutions, and querying the underlying data from the graphics. DEVise is designed to provide greater support than packages such as AVS or Khoros for this type of application. First, by abandoning the network flow model of AVS and Khoros in favor of a database query model, we are able to incorporate many performance improvements for visualizing large amounts of data. To our knowledge, this is the first attempt to eliminate data size limitations in a visualization package. Second, by structuring the stand-alone graphics module of most existing tools into user accessible components, users can quickly create, destroy, or interconnect the components to generate new visualizations. This flexibility greatly increases the ease with which users can browse their data. Finally, through limited programming, users can query the underlying data through the graphical representation for more information about the records used to generate the graphical representation.
This paper addresses the signal modeling problem in colored noise. As contrasted to the reported literatures in which the modeling of non-Gaussian ARMA signal corrupted by Gaussian noise is studied, this paper focus o...
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This paper addresses the signal modeling problem in colored noise. As contrasted to the reported literatures in which the modeling of non-Gaussian ARMA signal corrupted by Gaussian noise is studied, this paper focus on the AR modeling of Gaussian signal embedded in non-Gaussian ARMA noise. We show that after prefiltering the output data via the AR polynomial of the non-Gaussian noise model, a new special higher-order Yule-Walker equation which is based on the correlation of the filtered output process can be used to estimate the parameters of the AR Gaussian signal. Simulation examples are presented to demonstrate the effectiveness of the new approach.
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