failure detection framework for stochastic discrete event systems with guaranteed error bounds this paper studies the online fault detection for stochastic discrete event systems dess under partial observability of events prior works have only studied the verification of the stochastic diagnosability s diagnosability property to the best of our knowledge this is a first paper that investigates the online detection schemes and also introduces the notions of their missed detections mds and false alarms fas due to the probabilistic nature of the problem mds and fas are possible even for s diagnosable systems and we establish that s diagnosability is a necessary and sufficient condition for achieving any desired levels of md and fa rates we also provide a detection scheme that can achieve the specified md and fa rates based on comparing a suitable detection statistic that we define with a suitable detection threshold that we algorithmically compute we also algorithmically compute the corresponding detection delay bound the detection scheme also works for non s diagnosable systems with the difference that in this case there exists a lower bound for achievable md rate that increases as the fa rate requirement is made more stringent by decreasing it

diagnosability of discrete event systems subject to permanent sensor failures this paper considers a failure diagnosis problem for discrete event systems subject to permanent sensor failures a notion of diagnosability subject to permanent sensor failures is introduced with respect to a certain nondeterministic observation mask for its verification an aggregated mealy automaton with a deterministic and state dependent observation mask is defined it is shown that the diagnosability of the aggregated mealy automaton is equivalent to the diagnosability of the original system subject to permanent sensor failures then a method for verifying the diagnosability of the aggregated mealy automaton is presented moreover the delay bound within which the occurrence of any failure string can be detected subject to permanent sensor failures is computed

a polynomial algorithm for testing diagnosability of discrete event systems failure diagnosis in large and complex systems is a critical task in the realm of discrete event systems sampath et al 1995 proposed a language based failure diagnosis approach they introduced the diagnosability for discrete event systems and gave a method for testing the diagnosability by first constructing a diagnoser for the system the complexity of this method of testing diagnosability is exponential in the number of states of the system and doubly exponential in the number of failure types we give an algorithm for testing diagnosability that does not construct a diagnoser for the system and its complexity is of fourth order in the number of states of the system and linear in the number of the failure types

polynomial time verification of diagnosability of partially observed discrete event systems the problem of verifying the properties of diagnosability and i diagnosability is considered we present new polynomial time algorithms for deciding diagnosability and i diagnosability these algorithms are based on the construction of a nondeterministic automaton called a verifier

on the decidability and complexity of diagnosability for labeled petri nets in this paper we investigate the decidability and complexity of the fault diagnosis problem in unbounded labeled petri nets first we show that checking diagnosability for unbounded petri nets is decidable we present a new necessary and sufficient condition for diagnosability which can be reduced to a model checking problem for unbounded petri nets then we show that checking diagnosability for unbounded petri nets is expspace complete this complexity result is further extended to various subclasses of petri nets to the best of our knowledge this is the first paper that establishes decidability and complexity results for diagnosability of unbounded petri nets

contour detection and hierarchical image segmentation this paper investigates two fundamental problems in computer vision contour detection and image segmentation we present state of the art algorithms for both of these tasks our contour detector combines multiple local cues into a globalization framework based on spectral clustering our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree in this manner we reduce the problem of image segmentation to that of contour detection extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms the automatically generated hierarchical segmentations can be interactively refined by user specified annotations computation at multiple image resolutions provides a means of coupling our system to recognition applications

edge detecting for range data using laplacian operators feature extraction in image data has been investigated for many years and more recently the problem of processing images containing irregularly distributed data has become prominent range data are now commonly used in the areas of image processing and computer vision however due to the data irregularity found in range images that occurs with a variety of image sensors direct image processing in particular edge detection is a nontrivial problem typically irregular range data would require to be interpolated to a regular grid prior to processing one example of an edge detection technique that can be directly applied to range images is the scan line approximation but this does not employ exact data locations therefore we present novel laplacian operators that can be applied directly to irregularly distributed data and in particular we focus on application to irregularly distributed 3 d range data for the purpose of edge detection within the data distribution framework commonly occurring in range data acquisition devices our results illustrate that the approach works well over a range of levels of irregularity of data distribution the use of laplacian operators on range data is also found to be much less susceptible to noise than the traditional use of laplacian operators on intensity images

verification of hybrid automata diagnosability by abstraction a notion of diagnosability for hybrid systems is defined which generalizes the common notion of observability we propose an abstraction procedure to translate a hybrid automaton into a timed automaton in order to verify observability and diagnosability properties we introduce a procedure to check diagnosability and show that for the system class of our abstraction namely for a subclass of timed automata the durational graphs the verification problem belongs to the complexity class p we apply our procedure to an electromagnetic valve system for camless engines

generalized detectability for discrete event systems in our previous work we investigated detectability of discrete event systems which is defined as the ability to determine the current and subsequent states of a system based on observation for different applications we defined four types of detectabilities weak detectability strong detectability weak periodic detectability and strong periodic detectability in this paper we extend our results in three aspects 1 we extend detectability from deterministic systems to nondeterministic systems such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems 2 we develop polynomial algorithms to check strong detectability the previous algorithms are based on observer whose construction is of exponential complexity while the new algorithms are based on a new automaton called detector 3 we extend detectability to d detectability while detectability requires determining the exact state of a system d detectability relaxes this requirement by asking only to distinguish certain pairs of states with these extensions the theory on detectability of discrete event systems becomes more applicable in solving many practical problems

an algorithm for calculating indistinguishable states and clusters in finite state automata with partially observable transitions this paper presents a new algorithm for efficiently calculating pairs of indistinguishable states in finite state automata with partially observable transitions the need to obtain pairs of indistinguishable states occurs in several classes of problems related to control under partial observation diagnosis or distributed control with communication for discrete event systems the algorithm obtains all indistinguishable state pairs in polynomial time in the number of states and events in the system another feature of the algorithm is the grouping of states into clusters and the identification of indistinguishable cluster pairs clusters can be employed to solve control problems for partially observed systems

occlusion models for natural images a statistical study of a scale invariant dead leaves model we develop a scale invariant version of matheron s dead leaves model for the statistics of natural images the model takes occlusions into account and resembles the image formation process by randomly adding independent elementary shapes such as disks in layers we compare the empirical statistics of two large databases of natural images with the statistics of the occlusion model and find an excellent qualitative and good quantitative agreement at this point this is the only image model which comes close to duplicating the simplest elementary statistics of natural images such as the scale invariance property of marginal distributions of filter responses the full co occurrence statistics of two pixels and the joint statistics of pairs of haar wavelet responses

construction of multiblock space time codes from division algebras with roots of unity as nonnorm elements the authors give a construction of multiblock space time block codes from cyclic division algebras with a given nth root of unity as the nonnorm element the construction uses local class field theory multiblock codes with m blocks can achieve an m fold increase in diversity

abstraction based failure diagnosis for discrete event systems in this paper we introduce the idea of abstraction based diagnosability for large scale composed discrete event systems that consist of multiple subsystems to this end we determine sufficient conditions such that diagnosability of the original system follows from diagnosability of an abstracted system model on a smaller state space in addition we prove that also the reverse implication is true if an additional requirement for the abstraction is fulfilled then we show how our method can be applied to compute abstracted models for the diagnosability verification of composed systems without enumerating the whole system state space in this way considerable computational savings can be achieved as illustrated by a small manufacturing system example

probabilistic framework for the characterization of surfaces and edges in range images with application to edge detection we develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images we use the geometrical nature of the data to derive an analytic expression for the joint probability density function pdf for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image we decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not in particular we show that this joint pdf is linked to the voigt pdf and not to the gaussian pdf as it is assumed in some applications we apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule we show in an objective evaluation that this new edge detector performs better than prior art edge detectors this proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images

statistical edge detection learning and evaluating edge cues we formulate edge detection as statistical inference this statistical edge detection is data driven unlike standard methods for edge detection which are model based for any set of edge detection filters implementing local edge cues we use presegmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses this approach emphasizes the necessity of modeling the image background the off edges we represent the conditional probability distributions nonparametrically and illustrate them on two different data sets of 100 sowerby and 50 south florida images multiple edges cues including chrominance and multiple scale are combined by using their joint distributions hence this cue combination is optimal in the statistical sense we evaluate the effectiveness of different visual cues using the chernoff information and receiver operator characteristic roc curves this shows that our approach gives quantitatively better results than the canny edge detector when the image background contains significant clutter in addition it enables us to determine the effectiveness of different edge cues and gives quantitative measures for the advantages of multilevel processing for the use of chrominance and for the relative effectiveness of different detectors furthermore we show that we can learn these conditional distributions on one data set and adapt them to the other with only slight degradation of performance without knowing the ground truth on the second data set this shows that our results are not purely domain specific we apply the same approach to the spatial grouping of edge cues and obtain analogies to nonmaximal suppression and hysteresis

detectability in stochastic discrete event systems abstract a discrete event system possesses the property of detectability if it allows an observer to perfectly estimate the current state of the system after a finite number of observed symbols i e detectability captures the ability of an observer to eventually perfectly estimate the system state in this paper we analyze detectability in stochastic discrete event systems sdes that can be modeled as probabilistic finite automata more specifically we define the notion of a detectability which characterizes our ability to estimate the current state of a given sdes with increasing certainty as we observe more output symbols the notion of a detectability is differentiated from previous notions for detectability in sdes because it takes into account the probability of problematic observation sequences that do not allow us to perfectly deduce the system state whereas previous notions for detectability in sdes considered each observation sequence that can be generated by the underlying system we discuss observer based techniques that can be used to verify a detectability and provide associated necessary and sufficient conditions we also prove that a detectability is a pspace hard problem

state based decentralized diagnosis of bi fuzzy discrete event systems recently we have introduced bi fuzzy discrete event systems bfdess and established their supervisory control theory under full observations and partial observations respectively compared with the models of discrete event systems and fuzzy discrete event systems bfdess are more satisfactory models to characterize some high uncertainty systems in this paper we study the fault diagnosis problems of bfdess first the framework of state based decentralized diagnosis is introduced then the notion of state based codiagnosability is defined and a necessary and sufficient condition for state based codiagnosability is provided in particular a polynomial time algorithm for state based codiagnosability verification and an online decentralized diagnosis method are presented in addition a compact state based decentralized diagnosis framework is introduced and the notion of state based jointly diagnosability and its verification algorithm are proposed finally the implication relationships between the two diagnosabilities are investigated

a new approach for diagnosability analysis of petri nets using verifier nets in this paper we analyze the diagnosability properties of labeled petri nets we consider the standard notion of diagnosability of languages requiring that every occurrence of an unobservable fault event be eventually detected as well as the stronger notion of diagnosability in k steps where the detection must occur within a fixed bound of k event occurrences after the fault we give necessary and sufficient conditions for these two notions of diagnosability for both bounded and unbounded petri nets and then present an algorithmic technique for testing the conditions based on linear programming our approach is novel and based on the analysis of the reachability coverability graph of a special petri net called verifier net that is built from the petri net model of the given system in the case of systems that are diagnosable in k steps we give a procedure to compute the bound k to the best of our knowledge this is the first time that necessary and sufficient conditions for diagnosability and diagnosability in k steps of labeled unbounded petri nets are presented

the diagnosability of petri net models using minimal explanations for a bounded petri net model the diagnosability property is usually checked via its regular language represented by the reachability graph rg however this is problematic because the computational complexity of the diagnosability test is polynomial in the cardinality of the state space of the model which is typically very large this limitation can be overcome by using for the diagnosability test an rof automaton with a state space significantly smaller than rg that generates the same language as rg after projecting out all non faulty unobservable transitions rof is efficiently constructed based on the calculation of the minimal explanations of the fault and of the observable transitions

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

safe diagnosability of stochastic discrete event systems recently safe diagnosability of discrete event systems dess was investigated by paoli and lafortune which was viewed as the first necessary step of fault tolerant supervision in this paper we consider the problem of safe diagnosability in the framework of stochastic discrete event systems sdess we define the notion of safe diagnosability for stochastic automata in which fault detection occurs before any given forbidden string in the failed mode of system is executed the relationship between diagnosability and safe diagnosability for sdess is analyzed in particular a necessary and sufficient condition for safe diagnosability of sdess is presented by constructing the recognizer of illegal language and the safe diagnoser some examples are described to illustrate the results

computation of minimal diagnosis bases of discrete event systems using verifiers abstract in order to diagnose the occurrence of a fault event in a discrete event system des it is first necessary to verify if the language of the system is diagnosable with respect to an observable event set and a fault event set in some cases the language of the system is also diagnosable even when a subset of the set of observable events under consideration is used as the actual observable event set among the benefits that such a reduction may bring we list the reduction in the number of sensors used in the diagnosis therefore reducing the cost of the system and the possibility to deploy the sensor redundancy to obtain a more reliable diagnosis decision in this work we propose two algorithms to find in a systematic way all minimal subsets of the observable event set that ensure the diagnosability of the des minimal diagnosis bases the methods are based on the construction of verifiers and have lower computational complexity than another method recently proposed in the literature

diagnosis of behaviors of interest in partially observed discrete event systems abstract in this paper we address the problem of diagnosing the behaviors of interest in discrete event systems to this end we introduce the notion of language diagnosability based on language specifications that generalizes diagnosability based on event specifications a polynomial time algorithm for verifying language diagnosability is developed building upon the verification algorithm we develop a polynomial time algorithm for computing the worst case detection delay of a given system the computation of the worst case detection delay involves the shortest path computation of a weighted directed graph we exploit a special weighting structure of the graph resulting from the verification algorithm which enables an algorithm with a lower complexity than the commonly used bellman ford shortest path algorithm

the computational complexity of decentralized discrete event control problems computational complexity results are obtained for decentralized discrete event control problems these results generalize the earlier work of tsitsiklis 1989 who showed that for a special class of centralized supervisory control problems under partial observation there is an algorithm for determining in polynomial time whether or not a solution exists the negative complexity results associated with tsitsiklis work also carry over to the decentralized case so that solution existence for the more general class is not decidable in polynomial time nor does there exist a polynomial time algorithm for producing supervisor solutions when such solutions exist

robust diagnosis of discrete event systems against intermittent loss of observations in the usual approaches to fault diagnosis of discrete event systems it is assumed that not only all sensors work properly but also all information reported by sensors always reaches the diagnoser any bad sensor operation or communication failure between sensors and the diagnoser can be regarded as loss of observations of events initially assumed as observable in such situations it may be possible that either the diagnoser stands still or report some wrong information regarding the fault occurrence in this paper we assume that intermittent loss of observations may occur and we propose an automaton model based on a new language operation language dilation that takes it into account we refer to this problem as robust diagnosability against intermittent loss of observations or simply robust diagnosability where the context allows we present a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton and propose two tests for robust language diagnosability one that deploys diagnosers and another one that uses verifiers we also extend the results to robust codiagnosability against intermittent loss of observations