11,373 research outputs found
Performance Monitoring of Control Systems using Likelihood Methods
Evaluating deterioration in performance of control systems using closed loop operating data is addressed. A framework is proposed in which acceptable performance is expressed as constraints on the closed loop transfer function impulse response coefficients. Using likelihood methods, a hypothesis test is outlined to determine if control deterioration has occurred. The method is applied to a simulation example as well as data from an operational distillation column, and the results are compared to those obtained using minimum variance estimation approaches
Detecting an Intermittent Change of Unknown Duration
Oftentimes in practice, the observed process changes statistical properties
at an unknown point in time and the duration of a change is substantially
finite, in which case one says that the change is intermittent or transient. We
provide an overview of existing approaches for intermittent change detection
and advocate in favor of a particular setting driven by the intermittent nature
of the change. We propose a novel optimization criterion that is more
appropriate for many applied areas such as the detection of threats in
physical-computer systems, near-Earth space informatics, epidemiology,
pharmacokinetics, etc. We argue that controlling the local conditional
probability of a false alarm, rather than the familiar average run length to a
false alarm, and maximizing the local conditional probability of detection is a
more reasonable approach versus a traditional quickest change detection
approach that requires minimizing the expected delay to detection. We adopt the
maximum likelihood (ML) approach with respect to the change duration and show
that several commonly used detection rules (CUSUM, window-limited CUSUM, and
FMA) are equivalent to the ML-based stopping times. We discuss how to choose
design parameters for these rules and provide a comprehensive simulation study
to corroborate intuitive expectations.Comment: 34 pages, 7 figures, 6 table
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
Spike detection using the continuous wavelet transform
This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution
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