The evolution graph monitor is defined and illustrated for on-line monitoring of computer-controlled processes with both discrete and continuous dynamics. Using a behavioral model specification of the process dynamics...
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The evolution graph monitor is defined and illustrated for on-line monitoring of computer-controlled processes with both discrete and continuous dynamics. Using a behavioral model specification of the process dynamics, the evolution graph monitor builds and maintain an efficient representation of the set of admissible system trajectories which are consistent with the on-line observations as they are received. In contrast to simulation-based methods, the evolution graph approach handles delayed, uncertain, and infrequent observations for systems with widely varying operating characteristics. A behavioral model specification and evolution graph monitor have been developed to perform on-line fault detection for an electric arc furnace in a continuous-caster steel mill. This application is used throughout the paper to illustrate the details of the approach.
This paper presents a method for detecting faults in systems with discrete observations. We examine systems representable by a behavioral model. The behavioral model framework characterizes the time evolution of syste...
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This paper presents a method for detecting faults in systems with discrete observations. We examine systems representable by a behavioral model. The behavioral model framework characterizes the time evolution of systems with discrete and continuous states and uncertainty in model parameters. Our method for detecting faults depends on an on-line encoding of the set of trajectories corresponding to a given observation and the dynamics specifications of the behavioral model. This encoding, called an evolution graph, is modified on-line as new observations are received. Faults are detected when the set of encoded trajectories is determined to be empty. This paper presents the modelling framework and discusses the construction and maintenance of the evolution graph.
To investigate the transition from the low density mode to the high density mode in an electron cyclotron resonance (ECR) discharge, a Langmuir probe and an E field probe were used to measure ion density and E field i...
To investigate the transition from the low density mode to the high density mode in an electron cyclotron resonance (ECR) discharge, a Langmuir probe and an E field probe were used to measure ion density and E field intensity as functions of axial position and power. The experiments were performed in argon at 0.13 Pa in a 7.9 cm diam cyclindrical source chamber propagating TE11 mode 2.45 GHz microwave power. Low mode was characterized by a standing wave throughout the plasma chamber and minimal power absorption. High mode exhibited nearly complete power absorption and no standing wave past the ECR zone. A sliding short (ss) was used to determine if the position of an E field null in the source chamber affected the transition between these two modes for various magnetic field configurations. The ss position had little effect on mode transition, relative power absorption or ion density when positioned downstream from a broad, large volume resonance zone (resonance near the mirror midplane). However, the plasma could not be ignited if the short was placed at or upstream from the large volume resonance *** the magnetic field was adjusted to yield a sharp, small volume resonance zone (resonance midway between the midplane and the throat), then positioning the ss to force an E field null at the resonance zone would prevent plasma ignition, even at 800 W forward power. The ion density exhibited a hysteresis (i.e., a direct jump from no plasma to a plasma density observed at other sliding short positions for that same forward power, thereafter following the ion density versus forward power dependence observed at nearby ss positions) on power cycling when the sliding short was within ∼1 cm of the broad resonance zone or within ∼1 cm of forcing a null in the sharp resonance zone caes. The ion density versus power curve for the broad volume case exhibited a change in slope over the transition region (Pforward ≊40 W) when the ion density at the resonance zone was ≊1×1011 cm
As multiprocessor software solutions find their way into critical applications such as avionics as a means of increasing performance and reliability, the need for both debugging and testing tools increases dramaticall...
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As multiprocessor software solutions find their way into critical applications such as avionics as a means of increasing performance and reliability, the need for both debugging and testing tools increases dramatically. The authors focus on the gathering of accurate run-time information providing support for both debugging and testing of embedded multiprocessor systems by using run-time identification of simple events and postexecution identification of compound events. Additionally, they propose a way of combining specification-based (black-box) system testing and automatic monitoring to substantially reduce the complexity, tedium, and cost of complete structural (white-box) testing.< >
Recently, a very simple nonlinear algorithm, the so-called Teager's algorithm, has been introduced to calculate the energy of a one-dimensional sequence [1]. In this paper, this algorithm is extended to two-dimens...
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Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis ha...
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Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis has shown that this type of nonlinear filter works like a local-mean-weighted highpass filter. Based on this analysis, a second type of nonlinear filter has been developed which works like local-mean-weighted bandpass filter. The proposed image contrast enhancement technique is based on combining the original image with its filtered version obtained using one of the two nonlinear filters. Very high quality enhancement has been achieved for natural images.< >
In this paper we define a class of continuous-time discrete event dynamic systems (DEDS) with two types of discrete-valued input and output signals: condition signals and event signals called Condition/Event systems (...
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In this paper we define a class of continuous-time discrete event dynamic systems (DEDS) with two types of discrete-valued input and output signals: condition signals and event signals called Condition/Event systems (C/E systems). We formally define C/E systems and models of C/E systems that are based on an extension of Petri nets (C/E PNs). We then introduce equivalent C/E PN models for C/E systems resulting from cascade and feedback interconnections via examples. The algorithms that compute these equivalent models can be found in reference [1] The paper concludes with directions for future research.
Finding depth-first-search (DFS) trees of graphs is one of the fundamental problems in graph theory which has many practical applications. However, there exists no NC parallel algorithm for this problem in general gra...
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Finding depth-first-search (DFS) trees of graphs is one of the fundamental problems in graph theory which has many practical applications. However, there exists no NC parallel algorithm for this problem in general graphs. NC parallel algorithms for finding DFS trees for the interval graphs and circular-arcs graphs are presented. These algorithms take O(log n) time using kappa n processors on the EREW model, where kappa is the number of cliques in a graph of n nodes.< >
An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algo...
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An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algorithm addresses the problems specific to neural element detection and localization, viz., images with minimal resolution, operators with small spatial supports, highly curved, filamentous features, large variation in feature intensity profile, poor signal-to-noise ratio, and determination of depth without stereo. One first finds the magnitude and orientation of the maximum intensity second directional derivative. A family of curves is locally fitted to these data, and the projections of the data on the curve family are found. If a pixel lies on a curve with sufficient total projection, it is labeled with the magnitude, orientation, curvature, spatial extent, and element displacement. Depth is interpolated from the spatial extent data for corresponding neighborhoods in three adjacent (in depth) images by using an approximation to the depth-dependent optical point spread function. Experimental results using photomicrographs of cat visual cortex are presented.< >
The problem of using the idle cycles of a number of high-performance workstations, interconnected by a high-speed network, for solving computationally intensive tasks is discussed. The classes of distributed applicati...
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The problem of using the idle cycles of a number of high-performance workstations, interconnected by a high-speed network, for solving computationally intensive tasks is discussed. The classes of distributed applications examined require some form of synchronization among the sub-tasks, hence the need for coscheduling to guarantee that sub-tasks start at the same time and execute at the same pace on a group of workstations. A model of the system that allows the definition of an objective function to be maximized is presented. Then a quadratic time and linear space algorithm is derived for computing the optimal coscheduling.< >
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