Object-oriented database systems aim at meeting the data modelling, performance, cooperative design and version management requirements of current advanced applications, such as CAD (computer-aided design), CAM (compu...
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Object-oriented database systems aim at meeting the data modelling, performance, cooperative design and version management requirements of current advanced applications, such as CAD (computer-aided design), CAM (computer-aided manufacturing), CASE (computer-aided software engineering), CIM (computer integrated manufacturing), hypermedia and expert systems. This paper presents the ALEX Object Manager, which is a part of the ALEX object-oriented database management system that is being developed based on the ODMG-93 standard. The system decomposition and process layout are presented, some implementation problems are discussed and the current status of the system is reported.
This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a gro...
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This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a group of certain nonlinear structures into a linear ARMAX structure. The model can be used for a best linear approximation of the system, as well as for the estimation of resulting unmodeled dynamics, by a hierarchical implementation of recursive identification. Then robust fault detection is performed based on thresholding approach using Kullback discrimination information as fault detection index, in which the estimated unmodeled dynamics is incorporated.
This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi...
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This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi-ARX-model based on interpolation. In the case where the faults occur on the ARMAX model part, a KDI-based "robust" fault detection is performed, in which multi-ARX-model part is treated as error due to nonlinear undermodeling. In other cases, the model is transformed into several local ARMAX models and fault detection is performed by using the KDI to discriminate each identified local model. In this paper, we mainly concentrate our discussion on the latter cases.
We extend the framework of continuous time deadbeat control, which is given by Nobuyama et al. to multi-constraints control in time and frequency domains for continuous time MIMO systems. Two conditions for the free p...
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We extend the framework of continuous time deadbeat control, which is given by Nobuyama et al. to multi-constraints control in time and frequency domains for continuous time MIMO systems. Two conditions for the free parameter of H/sub /spl infin// suboptimal controllers are shown, which correspond to a deadbeat control interpolation condition and H/sub /spl infin// norm constraint. By restricting modified free parameters of H/sub /spl infin// controllers to commensurate time delay functions, we give all controllers which satisfy the two conditions simultaneously. Moreover, we show that time domain constraints of L/sub 2/ or L/sub 1/ norms are also reduced to finite dimensional convex problems and these mixed problems can be solved numerically.
This paper is concerned with an application of a digital adaptive control system to a servo mechanism. Mechanical systems in general include nonlinearities and additional uncertainties and which result from inertia an...
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This paper is concerned with an application of a digital adaptive control system to a servo mechanism. Mechanical systems in general include nonlinearities and additional uncertainties and which result from inertia and friction. So it is difficult to ensure robust performance and high accuracy for motion control. The digital adaptive control system presented here is considered to overcome the above stated problem. Experimental results from a feed drive system demonstrate the effectiveness of the proposed control scheme.
In this paper we consider the fault isolation problem for the linear time varying systems. Our approach is based on characterization of the observability of LTV systems by Kalman's rank condition, which permits us...
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ISBN:
(纸本)9783952426906
In this paper we consider the fault isolation problem for the linear time varying systems. Our approach is based on characterization of the observability of LTV systems by Kalman's rank condition, which permits us to design fault detection filters, feeding also the derivatives of the inputs and the outputs. We prove, using a computable method, that the isolation problem can be solved by generalized Luenberger's observer if and only if the detectability and the weak separability of fault signatures holds.
作者:
Milanovic, JV[?]Jovica V. Milanovic (1967) received the Dipl.-Ing. (Elec.) and M.Sc. (Elec. Eng.) degrees from the University of Belgrade. Yugoslavia. in 1987 and I99 I
respectively. One year he worked with “Energoproject-MDD”- Engineering and Contracting Co. in Belgrade as an engineer in designing power plants and substations. In late 1988 hejoined the Faculty of Electrical Engineering of the University of Belgrade first as an associate teaching assistant and then (since late 199 I) as teaching assistant at the Dept. of Power Converters and Drives. Between March 1993 and January 1996 he completed his Ph.D. at the University of Newcastle. Australia. at the Dept. of Electrical and Computer Engineering. Since February 1996 he is lecturer at the Department of Electrical Engineering and Computer Science at the University of Tasmania Austnlia. His major interests include synchronous machines and power system transients control and stability. (The University of Tasmania. Dept. of Electrical Engineering and Computer Science GPO Box 252-65 Hobart Tas 7001 Australia.Te1+61 362/262-753 Fax+61 369/262 136. e-mail: Jovica.Milanovic@eecs.utas.edu.au)
The paper presents the overview of load modelling for power-system damping and stability studies with main conclusions summarized from previous research. The attempt has been made to present a cross-section of the mos...
The paper presents the overview of load modelling for power-system damping and stability studies with main conclusions summarized from previous research. The attempt has been made to present a cross-section of the most exploited existing load models together with the most important conclusions drawn from their implementation in power-system stability programs. The current trends and latest results in the domain of damping of electromechanical oscillations in power systems are also presented. The effects of load dynamics on damping of electromechanical oscillations were analyzed on the basis of one of generic load models proposed in the past. The recent results further encourage investigation in this area and highlights the importance of proper load modelling.
The design of hydraulic control systems is a complex and time-consuming task that, at the moment, cannot be automated completely. Nevertheless, important design subtasks like simulation or control concept selection ca...
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The design of hydraulic control systems is a complex and time-consuming task that, at the moment, cannot be automated completely. Nevertheless, important design subtasks like simulation or control concept selection can be efficiently supported by a computer. Prerequisite for a successful support is a well-founded analysis of a hydraulic system's structure. The paper in hand contributes right here. It provides a systematics for analyzing a hydraulic system at different structural levels and illustrates how structural information can be used within the design process. A further central matter of this paper is the automatic extraction of structural information from a circuit diagram by means of graph-theoretical investigations.
A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent engineering of Layered Structure...
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A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent engineering of Layered Structures, provides designers with an immediate assessment of the impacts of their decisions on several disciplines which are important to the performance of a modern naval topside structure, including electromagnetic interference effects (EMI), radar cross section (RCS), structural integrity, cost, and weight. Preliminary analysis modules in each of these disciplines are integrated to operate from a common set of design variables and a common materials database. Performance in each discipline and an overall fitness function for the concept are then evaluated. A graphical user interface (GUI) is used to define requirements and to display the results from the technical analysis modules. Optimization techniques, including feasible sequential quadratic programming (FSQP) and exhaustive search are used to modify the design variables to satisfy all requirements simultaneously. The development of this tool, the technical modules, and their integration are discussed noting the decisions and compromises required to develop and integrate the modules into a prototype conceptual design tool.
There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the deter...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the determination of model structure. Both mean prediction error and An Information Criterion (AIC) are useful in model selection;the model with the smallest mean prediction error or AIC is selected from among a set of models as the best one. In this way they give a solution to the problem of model selection. Due to huge search space, however, the mean prediction error or AIC alone is not powerful enough to find the best model structure from among all the candidates. In the present paper the authors propose to use both a structural learning with forgetting and the mean prediction error or AIC to find a model with better generalization ability. Jordan networks and buffer networks, popular in the modeling of time series, are examined in this paper. The structural learning with forgetting and backpropagation (BP) learning are applied to compare the learning and prediction performance of these two types of models. Simulation results demonstrate that the structural learning with forgetting has better generalization ability than BP learning both in Jordan networks and buffer networks.
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