11,718 research outputs found
Distributed State Machine Supervision for Long-baseline Gravitational-wave Detectors
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of
two identical yet independent, widely-separated, long-baseline
gravitational-wave detectors. Each Advanced LIGO detector consists of complex
optical-mechanical systems isolated from the ground by multiple layers of
active seismic isolation, all controlled by hundreds of fast, digital, feedback
control systems. This article describes a novel state machine-based automation
platform developed to handle the automation and supervisory control challenges
of these detectors. The platform, called \textit{Guardian}, consists of
distributed, independent, state machine automaton nodes organized
hierarchically for full detector control. User code is written in standard
Python and the platform is designed to facilitate the fast-paced development
process associated with commissioning the complicated Advanced LIGO
instruments. While developed specifically for the Advanced LIGO detectors,
Guardian is a generic state machine automation platform that is useful for
experimental control at all levels, from simple table-top setups to large-scale
multi-million dollar facilities.Comment: Version 2: 11 pages, 9 figures. Submitted to Review of Scientific
Instrument
Supervisory Control of Fuzzy Discrete Event Systems: A Formal Approach
Fuzzy {\it discrete event systems} (DESs) were proposed recently by Lin and
Ying [19], which may better cope with the real-world problems with fuzziness,
impreciseness, and subjectivity such as those in biomedicine. As a continuation
of [19], in this paper we further develop fuzzy DESs by dealing with
supervisory control of fuzzy DESs. More specifically, (i) we reformulate the
parallel composition of crisp DESs, and then define the parallel composition of
fuzzy DESs that is equivalent to that in [19]; {\it max-product} and {\it
max-min} automata for modeling fuzzy DESs are considered; (ii) we deal with a
number of fundamental problems regarding supervisory control of fuzzy DESs,
particularly demonstrate controllability theorem and nonblocking
controllability theorem of fuzzy DESs, and thus present the conditions for the
existence of supervisors in fuzzy DESs; (iii) we analyze the complexity for
presenting a uniform criterion to test the fuzzy controllability condition of
fuzzy DESs modeled by max-product automata; in particular, we present in detail
a general computing method for checking whether or not the fuzzy
controllability condition holds, if max-min automata are used to model fuzzy
DESs, and by means of this method we can search for all possible fuzzy states
reachable from initial fuzzy state in max-min automata; also, we introduce the
fuzzy -controllability condition for some practical problems; (iv) a number
of examples serving to illustrate the applications of the derived results and
methods are described; some basic properties related to supervisory control of
fuzzy DESs are investigated. To conclude, some related issues are raised for
further consideration
RULES BASED MODELING OF DISCRETE EVENT SYSTEMS WITH FAULTS AND THEIR DIAGNOSIS
Failure diagnosis in large and complex systems is a critical task. In the realm of discrete event systems, Sampath et al. 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. In this thesis, we give an algorithm for testing diagnosability that does not construct a diagnoser for the system, and its complexity is of 4th order in the number of states of the system and linear in the number of the failure types. In this dissertation we also study diagnosis of discrete event systems (DESs) modeled in the rule-based modeling formalism introduced in [12] to model failure-prone systems. The results have been represented in [43]. An attractive feature of rule-based model is it\u27s compactness (size is polynomial in number of signals). A motivation for the work presented is to develop failure diagnosis techniques that are able to exploit this compactness. In this regard, we develop symbolic techniques for testing diagnosability and computing a diagnoser. Diagnosability test is shown to be an instance of 1st order temporal logic model-checking. An on-line algorithm for diagnosersynthesis is obtained by using predicates and predicate transformers. We demonstrate our approach by applying it to modeling and diagnosis of a part of the assembly-line. When the system is found to be not diagnosable, we use sensor refinement and sensor augmentation to make the system diagnosable. In this dissertation, a controller is also extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, only one controllable event from among the ones allowed by the supervisor for the assembly line in automaton models
Order and disorder in everyday action: the roles of contention scheduling and supervisory attention
This paper describes the contention scheduling/supervisory attentional system approach to action selection and uses this account to structure a survey of current theories of the control of action. The focus is on how such theories account for the types of error produced by some patients with frontal and/or left temporoparietal damage when attempting everyday tasks. Four issues, concerning both the theories and their accounts of everyday action breakdown, emerge: first, whether multiple control systems, each capable of controlling action in different situations, exist; second, whether different forms of damage at the neural level result in conceptually distinct disorders; third, whether semantic/conceptual knowledge of objects and actions can be dissociated from control mechanisms, and if so what computational principles govern sequential control; and fourth, whether disorders of everyday action should be attributed to a loss of semantic/conceptual knowledge, a malfunction of control, or some combination of the two
Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)
This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988
Improving SIEM for critical SCADA water infrastructures using machine learning
Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset
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