Finite State Automata Research Articles

A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection

Construction and deployment of finite state automata from the regular expressions might results in huge overhead and results in the state explosion problem which is in need of large memory space, high bandwidth and additional computational time. To overcome this problem, a new framework is proposed, and several intelligent optimization algorithms are reviewed and compared in this framework. The proposed approach is called intelligent optimization grouping algorithms (IOGA), which intends to group regular expression intelligently. IOGAs are used to allocate the regular expression sets into various groups and to build independent deterministic finite automata (DFA) for each group. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among memory utilization and computational time. This study reviews and compares the various alternatives of IOGA including genetic algorithm, ant colony optimization, particle swarm optimization, bacterial foraging optimization, artificial bee colony algorithm, biogeography based optimization, cuckoo search, firefly algorithm, bat algorithm and flower pollination algorithm for solving the problem of DFA state explosion and also for improving the overall efficiency of deep packet inspection (DPI). The discussions state that by effectively using these grouping algorithms along with DFA based DPI, the number of states can be reduced, providing a balance between the memory consumption, time complexity, throughput, inspection speed, convergence speed and grouping time.

Modeling for supervisor synthesis \u2013 a lock-bridge combination case study

Designing supervisory controllers for high-tech systems is becoming increasingly complex due to demands for verified safety, higher quality and availability, and extending functionality. Supervisor synthesis is a method to automatically derive a supervisor from a model of the plant and a model of the control requirements. While supervisor synthesis is an active research topic, only a few reports exist on industrial applications. One of the reasons for this is the lack of acquaintance of control engineers with modeling and specifying in the framework of automata. In addition to this, there are no clear guidelines for obtaining the necessary models for synthesis. In this paper, we describe a general way of modeling for the plant and the requirements in order to contribute towards the acceptance of supervisor synthesis in industry. This way of modeling is illustrated with an industrial case study in which a supervisory controller is synthesized for the Algera complex. The Algera complex consists of a waterway lock and a movable bascule bridge. The supervisor has to control 80 actuators based on the observations from 96 discrete sensors, in response to 63 control commands available from the operator. We show how to model the plant as a collection of extended finite-state automata, how to model the requirement as a collection of event conditions, how to synthesize the monolithic supervisor, and how to validate the resulting supervisor using continuous-time simulation.

Automata complete computation with Hodgkin–Huxley neural networks composed of synfire rings

Synfire rings are neural circuits capable of conveying synchronous, temporally precise and self-sustained activities in a robust manner. We propose a cell assembly based paradigm for abstract neural computation centered on the concept of synfire rings. More precisely, we empirically show that Hodgkin–Huxley neural networks modularly composed of synfire rings are automata complete. We provide an algorithmic construction which, starting from any given finite state automaton, builds a corresponding Hodgkin–Huxley neural network modularly composed of synfire rings and capable of simulating it. We illustrate the correctness of the construction on two specific examples. We further analyze the stability and robustness of the construction as a function of changes in the ring topologies as well as with respect to cell death and synaptic failure mechanisms, respectively. These results establish the possibility of achieving abstract computation with bio-inspired neural networks. They might constitute a theoretical ground for the realization of biological neural computers.

Implementation of Lexical Analysis on Assignment Statements in C++ Programming Language

A compiler is a computer program that translates computer code written in one programming language (the source language) into another language (the target language). The name compiler is primarily used for programs that translate source code from a high-level programming language to a lower level language [7]. The three main processes of compilation are lexical analysis, syntax analysis and semantic analysis. A compiler has two components, front-end and back-end. Front-end portion of a compiler has to do to main tasks lexical analysis and syntax analysis. On the lexical analysis, input source code are scanned and tokenized into various tokens [6]. In the system, front-end portion of the compiler, lexical analysis is used. There are many token elements in C++ programming language. In this system, line break token, white space tokens (space and tab) and operators (+,-,*,/,=,+= and so on) are used as token elements for the assignment statements of C++ source program. This system is taken all the assignment statements of C++ program as input. Of course, the extracted assignment statements may be literals or values assignment statement (e.g. x=3; or pi= 3.142;), variable assignment statement (e.g. x=y; or x=z;) and expression assignment statement (e.g. a=b+c; or x=y*z; or a=b*(c+d); and produced symbol table, step by step recognized table by using finite state automata and lexeme table.

A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances

Reinforcement Learning (RL) is limited in practice by its poor explainability, which is responsible for insufficient trustiness from users, unsatisfied interpretation for human intervention, inadequate analysis for future improvement, etc. This paper seeks to partially characterize the interplay between dynamical environments and a previously-proposed Disturbance OBserver net (DOB-net). The DOB-net is trained via RL and offers optimal control for a set of Partially Observable Markovian Decision Processes (POMDPs). The transition function of each POMDP is largely determined by the environments (excessive external disturbances). This paper proposes an Attention-based Abstraction (A2) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs. A2 first quantizes the controlled platform by learning continuous-discrete interfaces. Then it extracts the KMMN by finding the key hidden states and transitions that attract sufficient attention from the DOB-net. Within the resultant KMMN, three patterns of cyclic switchings (between key hidden states) are found, and saturated controls are shown synchronized with unknown disturbances. Interestingly, the found switchings have previously appeared in the control design for often-saturated systems. They are interpreted via an analogy to the discrete-event subsystem of hybrid control.

Classification of the Hysteresis Phenomena of CO Oxidation in Hydrogen Production

The study is motivated by a review of experimental and numerical evidence for the existence of bistability and hysteresis in CO oxidation on platinum group metals. In this paper, a detailed taxonomy of CO oxidation on Pd(111) is achieved by considering it as classification problem of hysteresis functions. This consideration offers a way of classifying the hysteresis behavior into several types using the mathematics of finite state automata, and in this way a through classification is achieved. The classification can provide a global framework for understanding the hysteresis behavior of CO oxidation reaction on Pd(111).

Bengali Stop Word and Phrase Detection Mechanism

Though plenty of research works have been done on stop word/phrase detection, there is no work done on Bengali stop words and stop phrases. This research innovates the definition and classification of Bengali stop words and phrases and implements two approaches to identify them. First one is a corpus-based approach, while the second one is based on the finite-state automaton. Performance of both approaches is measured and compared. Result analysis shows that corpus-based method outperforms the finite-state automaton-based method. The corpus-based and finite-state automaton-based method shows 90% and 80% of accuracy, respectively, for stop word detection and 80% and 70% accuracy, respectively, for stop phrase detection.

Linear temporal logic for hybrid dynamical systems: Characterizations and sufficient conditions

This paper introduces operators, semantics, characterizations, and solution-independent conditions to guarantee temporal logic specifications for hybrid dynamical systems. Hybrid dynamical systems are given in terms of differential inclusions – capturing the continuous dynamics – and difference inclusions – capturing the discrete dynamics or events – with constraints. State trajectories (or solutions) to such systems are parameterized by a hybrid notion of time. For such broad class of solutions, the operators and semantics needed to reason about temporal logic are introduced. Characterizations of temporal logic formulas in terms of dynamical properties of hybrid systems are presented – in particular, forward invariance and finite time attractivity. These characterizations are exploited to formulate sufficient conditions assuring the satisfaction of temporal logic formulas — when possible, these conditions do not involve solution information. Combining the results for formulas with a single operator, ways to certify more complex formulas are pointed out, in particular, via a decomposition using a finite state automaton. Academic examples illustrate the results throughout the paper.

Affordance-based modeling of a human-robot cooperative system for area exploration

The cooperation of humans and robots is ubiquitous in modern systems, while human ability to cooperate has been limitedly investigated in terms of systems theory. A formal model is proposed to describe the human capability for the cooperation based on the finite state automata (FSA) and the affordances theory. Unlike the previous studies focused on conceptual approaches, real and virtual experiments are conducted to investigate human actions in a cooperative system with a human and a robot. A modeling scheme is provided to implement agent-based simulations for the cooperative system using the proposed affordance-based FSA. The developed simulation for the cooperation problem can reproduce the patterns of the actual experiments in terms of affordances and supervisory capability. The modular architecture of the agent-based framework allows establishing open-ended algorithms for action selections with their isolated effects under physical constraints, which need to be revised to deal with human-involved cooperation problems.

Design of Supervisors for Active Diagnosis in Discrete Event Systems

In this article, we present an active diagnosis method to enhance diagnosability of a plant modeled by a finite-state automaton. Some properties of active diagnosis and silent blocking are studied. To avoid silent blocking, the notion of $stop$ -free event set is proposed, and a $stop$ -free control policy that does not introduce silent blocking is formulated. We develop a heuristic method based on the verifier of the plant to compute a feasible $stop$ -free event set that guarantees the existence of a valid control policy. With the $stop$ -free event set, the set of disabled edges is computed, and an online control policy that is based on the current diagnostic state is computed, which guarantees that the closed-loop system is diagnosable. The structural complexity of the proposed control structure is polynomial with respect to the number of states of the plant.

Aggregation-based minimization of finite state automata

We present a minimization algorithm for non-deterministic finite state automata that finds and merges bisimulation-equivalent states. The bisimulation relation is computed through partition aggregation, in contrast to existing algorithms that use partition refinement. The algorithm simultaneously generalises and simplifies an earlier one by Watson and Daciuk for deterministic devices. We show the algorithm to be correct and run in time O left( n^2 r^2 left| varSigma right| right) , where n is the number of states of the input automaton M, r is the maximal out-degree in the transition graph for any combination of state and input symbol, and left| varSigma right| is the size of the input alphabet. The algorithm has a higher time complexity than derivatives of Hopcroft’s partition-refinement algorithm, but represents a promising new solution approach that preserves language equivalence throughout the computation process. Furthermore, since the algorithm essentially computes the maximal model of a logical formula derived from M, optimisation techniques from the field of model checking become applicable.

Learning With Interpretable Structure From Gated RNN.

The interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models. In this article, we focus on recurrent neural networks (RNNs), especially gated RNNs whose inner mechanism is still not clearly understood. We find that finite-state automaton (FSA) that processes sequential data have a more interpretable inner mechanism according to the definition of interpretability and can be learned from RNNs as the interpretable structure. We propose two methods to learn FSA from RNN based on two different clustering methods. With the learned FSA and via experiments on artificial and real data sets, we find that FSA is more trustable than the RNN from which it learned, which gives FSA a chance to substitute RNNs in applications involving humans' lives or dangerous facilities. Besides, we analyze how the number of gates affects the performance of RNN. Our result suggests that gate in RNN is important but the less the better, which could be a guidance to design other RNNs. Finally, we observe that the FSA learned from RNN gives semantic aggregated states, and its transition graph shows us a very interesting vision of how RNNs intrinsically handle text classification tasks.

A Novel Sequence Generation Approach to Diagnose Faults in Reconfigurable Scan Networks

With the complexity of nanoelectronic devices rapidly increasing, an efficient way to handle large number of embedded instruments became a necessity. The IEEE 1687 standard was introduced to provide flexibility in accessing and controlling such instrumentation through a reconfigurable scan chain. Nowadays, together with testing the system for defects that may affect the scan chains themselves, the diagnosis of such faults is also important. This article proposes a method for generating stimuli to precisely identify permanent high-level faults in a IEEE 1687 reconfigurable scan chain: the system is modeled as a finite state automaton where faults correspond to multiple incorrect transitions; then, a dynamic greedy algorithm is used to select a sequence of inputs able to distinguish between all possible faults. Experimental results on the widely-adopted ITC'02 and ITC'16 benchmark suites, as well as on synthetically generated circuits, clearly demonstrate the applicability and effectiveness of the proposed approach: generated sequences are two orders of magnitude shorter compared to previous methodologies, while the computational resources required remain acceptable even for larger benchmarks.

New predictability verification tests for discrete-event systems modeled by finite state automata

Abstract This paper deals with the problem of copredictability verification of discrete event systems (DES) modeled by automata in less restrictive scenarios, i.e., avoiding the assumptions on language liveness and absence of cycles of states connected by unobservable events only. We present necessary and sufficient conditions for a regular language to be copredictable, and adapt previous results on diagnosability verification to develop two new strategies to verify copredictability: the first one is based on a diagnoser-like automaton, and the second one by using verifiers.

Asynchronous Silent Programmable Matter Achieves Leader Election and Compaction

We study models and algorithms for Programmable Matter (PM), that is matter with the ability to change its physical properties (e.g., shape or optical properties) in a programmable fashion. PM can be implemented by assembling a system of weak self-organizing computational elements, called particles, that can be programmed via distributed algorithms to collectively achieve some global task. Recent advances in the production of nanotechnologies have rendered such systems increasingly possible in practice, thus triggering research interests from many areas of computer science. The most established models for PM assume that particles: are modeled as finite state automata; are all identical, executing the same algorithm based on local observation of the surroundings; live and operate in the cells of a hexagonal grid; can move from one cell to another by repeatedly alternating between a contracted state (a particle occupies one cell) and an expanded state (a particle occupies two neighboring cells). Given these elementary features, it is rather hard to design distributed algorithms even for basic tasks and, in fact, all existing solutions to solve fundamental problems via PM have resorted to endowing PM systems with various capabilities to overcome such hardness, thus assuming quite unrealistic features. In this paper, we move toward more realistic computational models for PM. Specifically, we first introduce SILBOT, a new modeling approach that relaxes several assumptions used in previous ones. Second, we present a distributed algorithm to solve, in the SILBOT model, a foundational primitive for PM, namely Leader Election. This algorithm works in O(n) rounds for all initial configurations of n particles that are both connected (i.e. particles induce a connected graph) and compact (i.e. without holes, that is no empty cells surrounded by particles occur). As usual in asynchronous contexts, a round is intended as the time within which all particles have been activated at least once. Third, we show that, if the initial configuration admits holes, it is impossible to achieve leader election while preserving connectivity. Finally, by slightly empowering the robots, we design an algorithm to handle initial configurations admitting holes that in O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) rounds solves the leader election problem while obtaining also compaction.

Revisiting strong detectability of networked discrete-event systems

Abstract In this paper, we propose a new notion of strong networked detectability of networked discrete-event systems (DESs) modeled by finite-state automata (FSAs) with respect to a first-in, first-out channel with communication losses and bounded transmission delays. We use a new method to obtain a polynomial-time algorithm for verifying strong networked detectability. Moreover, we study the relationship between the new notion and its counterpart studied by [Sasi and Lin 2018]. We prove that strong networked detectability is strictly weaker than its counterpart in [Sasi and Lin 2018] and also obtain a polynomial-time verification algorithm for its counterpart without any assumption, which is more efficient than the exponential-time algorithm given by [Sasi and Lin 2018] under several assumptions. Particularly, for FSAs with respect to an ideal channel, under two fundamental assumptions of deadlock-freeness and having no unobservable reachable cycle, we show that strong networked detectability is equivalent to its counterpart studied by [Sasi and Lin 2018].

Critical Observability for Automata and Petri Nets

Critical observability is a property of cyber-physical systems to detect whether the current state belongs to a set of critical states. In safety-critical applications, critical states model operations that may be unsafe or of a particular interest. De Santis et al. introduced critical observability for linear switching systems, and Pola et al. adapted it for discrete-event systems, focusing on algorithmic complexity. We study the computational complexity of deciding critical observability for systems modeled as (networks of) finite-state automata and Petri nets. We show that deciding critical observability is: 1) NL-complete for finite automata, i.e., it is efficiently verifiable on parallel computers; 2) PSPACE-complete for networks of finite automata, i.e., it is very unlikely solvable in polynomial time; and 3) undecidable for labeled Petri nets, but becoming decidable if the set of critical states (markings) is finite or cofinite, in which case the problem is as hard as the nonreachability problem for Petri nets.

Instant detectability of discrete-event systems

Abstract Detectability is a basic property that describes whether an observer can use the current and past values of an observed output sequence produced by a system to reconstruct its current state. We consider particular properties called instant strong detectability and instant weak detectability, where the former implies that for each possible infinite observed output sequence each prefix of the output sequence allows reconstructing the current state, the latter implies that some infinite observed output sequence (if it exists) satisfies that each of its prefixes allows reconstructing the current state. For discrete-event systems modeled by finite-state automata, we give a linear-time verification algorithm for the former in the size of an automaton, and also give a polynomial-time verification algorithm for the latter.

Construction of an Energy Saving Model Based on FA and Its Applications for Detection of Battery Power Consumption

Although some algebraic theories such as partial order set, lattice and their properties on fuzzy set had been studied, these algebraic theories on fuzzy automata (FA) have not been discussed at present. For saving the energy of the battery so as to make it play the best performance, this paper proposes first an algebraic system on a finite-state deterministic fuzzy automaton (FDFA), including the partial order set, lattice and its properties. Second, this paper constructs a detection model system to monitor the consumption of battery power, and the detection model consists of four parts, as well as including supervision function network and decision-making process. Third, this paper gives the whole dynamic process of consumption and consumption rate of battery power, as well as discusses the comparison of the detection accuracy of between the detection model and other detection methods in the battery power consumption by simulation. Through the homomorphism and the isomorphic mapping of the lattice, the partial state of the FDFA is studied to realize the function of the whole state. These research results will provide a theoretical reference, system modeling and application background for many departments such as control, detection, tracking, identification and so on.

Order-Preserving Languages for the Supervisory Control of Automated Manufacturing Systems

Automated manufacturing systems (AMSs) consist of computer-controlled interconnected manufacturing components (MCs) that are used to transport and process different product types. Each product type requires a certain sequence of processing steps in different MCs. Hereby, multiple product types can share processing steps on the same MC and the paths of different products types can overlap. In this paper we consider the modeling of AMSs in the scope of supervisory control for discrete event systems (DES). On the one hand, a suitable AMS model must allow the representation of sequential and concurrent processing steps in MCs. On the other hand, such model must be able to track different product types traveling through the AMS so as to process the products correctly. While previous work is commonly concerned with the first requirement, this paper identifies that the existing literature lacks a general treatment of the second requirement. Accordingly, we first introduce order-preserving (OP) languages that preserve the order of different product types in MCs and we propose a suitable finite state automaton model for OP languages. Then, we show that the composition of OP languages again leads to an OP language. That is, modeling MCs by OP languages, an OP model of a complete AMS that is suitable for supervisory control is obtained. In addition, it is possible to use both OP models and non-OP models for general AMSs, where MCs have different properties. We demonstrate the applicability of the proposed modeling technique by a flexible manufacturing system example.