On the expressiveness of the approach to constructing PLC-programs by LTL-specification

Kuzmin, E. V.; Ryabukhin, D. A.; Sokolov, Valery A.

doi:10.3103/s0146411616070130

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Разумеется, интересно также проверить практическую применимость предложенного в статьях [4][5][6]8] формализма для спецификации поведения последовательных реагирующих систем. По-видимому, для этой цели можно обратиться к задаче верификации программируемых логических контроллеров (ПЛК) [23,24] и сравнить выразительные возможности предложенного нами расширения темпоральных логик с другими формальными средствами спецификации и верификации ПЛК [25][26][27][28].…”

Section: заключениеunclassified

On the Model Checking Problem for Some Extension of CTL*

Gnatenko¹,

Zakharov

2021

Aut. Control Comp. Sci.

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Sequential reactive systems include programs and devices that work with two streams of data and convert input streams of data into output streams. Such information processing systems include controllers, device drivers, computer interpreters.e result of the operation of such computing systems are in nite sequences of pairs of events of the request-response type, and, therefore, nite transducers are most o en used as formal models for them.e behavior of transducers is represented by binary relations on in nite sequences, and so, traditional applied temporal logics (like HML, LTL, CTL, mu-calculus) are poorly suited as speci cation languages, since omega-languages, not binary relations on omega-words are used for interpretation of their formulae. To provide temporal logics with the ability to de ne properties of transformations that characterize the behavior of reactive systems, we introduced new extensions of these logics, which have two distinctive features: 1) temporal operators are parameterized, and languages in the input alphabet of transducers are used as parameters; 2) languages in the output alphabet of transducers are used as basic predicates. Previously, we studied the expressive power of new extensions Reg-LTL and Reg-CTL of the well-known temporal logics of linear and branching time LTL and CTL, in which it was allowed to use only regular languages for parameterization of temporal operators and basic predicates. We discovered that such a parameterization increases the expressive capabilities of temporal logic, but preserves the decidability of the model checking problem. For the logics mentioned above, we have developed algorithms for the veri cation of nite transducers. At the next stage of our research on the new extensions of temporal logic designed for the speci cation and veri cation of sequential reactive systems, we studied the veri cation problem for these systems using the temporal logic Reg-CTL*, which is an extension of the Generalized Computational Tree Logics CTL*. In this paper we present an algorithm for checking the satis ability of Reg-CTL* formulae on models of nite state transducers and show that this problem belongs to the complexity class ExpSpace.

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Section: заключениеunclassified

On the Model Checking Problem for Some Extension of CTL*

Gnatenko¹,

Zakharov

2021

Aut. Control Comp. Sci.

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Section: заключениеunclassified

On the Model Checking Problem for Some Extension of CTL*

Романович

Анатольевич

2020

Model. anal. inf. sist.

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Sequential reactive systems include programs and devices that work with two streams of data and convert input streams of data into output streams. Such information processing systems include controllers, device drivers, computer interpreters. The result of the operation of such computing systems are infinite sequences of pairs of events of the request-response type, and, therefore, finite transducers are most often used as formal models for them. The behavior of transducers is represented by binary relations on infinite sequences, and so, traditional applied temporal logics (like HML, LTL, CTL, mu-calculus) are poorly suited as specification languages, since omega-languages, not binary relations on omega-words are used for interpretation of their formulae. To provide temporal logics with the ability to define properties of transformations that characterize the behavior ofreactive systems, we introduced new extensions ofthese logics, which have two distinctive features: 1) temporal operators are parameterized, and languages in the input alphabet oftransducers are used as parameters; 2) languages in the output alphabet oftransducers are used as basic predicates. Previously, we studied the expressive power ofnew extensions Reg-LTL and Reg-CTL ofthe well-known temporal logics oflinear and branching time LTL and CTL, in which it was allowed to use only regular languages for parameterization of temporal operators and basic predicates. We discovered that such a parameterization increases the expressive capabilities oftemporal logic, but preserves the decidability of the model checking problem. For the logics mentioned above, we have developed algorithms for the verification of finite transducers. At the next stage of our research on the new extensions of temporal logic designed for the specification and verification of sequential reactive systems, we studied the verification problem for these systems using the temporal logic Reg-CTL*, which is an extension ofthe Generalized Computational Tree Logics CTL*. In this paper we present an algorithm for checking the satisfiability of Reg-CTL* formulae on models of finite state transducers and show that this problem belongs to the complexity class ExpSpace.

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“…More specifically, biLSTM splits the state neurons in a forward state sequence , and backward state sequence ; which indicates that the output of the forward and backward states are not connected. This can be viewed in Figure (2). Alternatively, the process of the biLSTM can be expressed here as:…”

Section: Machine Learning Based Neural Networkmentioning

confidence: 99%

“…Originally, the rules leading the control system are defined by the wiring. When these rules are changed, the wiring has to be changed that is a time-consuming [2].…”

Section: Introductionmentioning

confidence: 99%

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Al-Radhi¹,

Al-Kamil

Szakács

2020

Journal of Petroleum Research and Studies

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Degassing station breakdowns can be dangerous to the operator health and the environment. Programmable logic controllers (PLCs) are key modules of manufacturing control systems that are applied in the complex oil and gas units to reduce manpower and unnecessary faults. However, feeding a PLC with data is a difficult part due to the need of system log files which records all events that occur in the oil fields and provide visibility to a given environment. Moreover, most critical chemical processing plants and oil distributions are visualized and inspected by Supervisory Control and Data Acquisition Systems (SCADA). These systems have been focused on safety, and there are issues that they could be the target of worldwide terrorists. Along with the frequently rising internet-related attacks, there is indication that our degassing stations may similarly be susceptible; for that reason, it is essential to secure PLC and SCADA from undesired incidents. Recently, machine learning (ML) has been increasing interest in industrial systems to detect, identify, and store information. Therefore, we propose to apply an advance ML based on deep neural networks to the PLC system with the purpose of: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systems and its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4) Making predictive maintenance possible. The SIMATIC S7-1214 CPU universal TIA platform was used as the main decision-making module. Experimental results demonstrate the effectiveness and utility of the proposed approach to process large amounts of data analytics and sensor measurements, allows it to spot potential problems and provide possible solutions.

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On the expressiveness of the approach to constructing PLC-programs by LTL-specification

Cited by 12 publications

References 8 publications

On the Model Checking Problem for Some Extension of CTL*

On the Model Checking Problem for Some Extension of CTL*

On the Model Checking Problem for Some Extension of CTL*

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

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