Network Of Finite State Machines Research Articles

Simple analysis of complex system safety based on Finite State Machine Network and phase space theory

Safety analysis of complex systems, such as aero-engines, has always been a challenging problem. In particular, the risk propagation caused by system functional interactions has become increasingly prominent, leading to the difficulty of traditional safety analysis methods. Existing methods are mostly based on manual experience and deduction with difficulty to completely analyze the whole system safety regions. In this paper, a model-based framework is proposed by combining Finite State Machine Network (FSMN) and phase space theory to perform safety analysis of complex systems from function-logic perspective. We first construct the system model and system state-transition network based on FSMN. Then combining with phase space theory, we identify system safety regions of the entire operating space from macro perspective, and investigate the system risk propagation from micro perspective. An aero-engine system is taken as an example to illustrate the proposed method. Our results show that this method has the ability to effectively identify the potential risk factors and explore emergent unknown risks, which can provide guidance for safety testing.

Design of decentralized critical observers for networks of finite state machines: A formal method approach

Motivated by safety-critical applications in cyber–physical systems, in this paper we study the notion of critical observability and design of observers for networks of Finite State Machines (FSMs). Critical observability corresponds to the possibility of detecting if the current state of an FSM is in a given region of interest, called set of critical states. A critical observer detects on-line the occurrence of critical states. When a large-scale network of FSMs is considered, the construction of such an observer is prohibitive because of the large computational effort needed. We propose a decentralized architecture for critical observers of networks of FSMs, where on-line detection of critical states is performed by local critical observers, each associated with an FSM of the network, which do not need to interact. For the efficient design of decentralized critical observers we first extend on-the-fly algorithms traditionally used in the community of formal methods for the verification and control design of FSMs. We then extend to networks of FSMs, bisimulation theory traditionally given in the community of formal methods for single FSMs. The proposed techniques provide a remarkable computational complexity reduction, as discussed throughout the paper and also demonstrated by means of illustrative examples.

Dynamic networks of finite state machines

Like distributed systems, biological multicellular processes are subject to dynamic changes and a biological system will not pass the survival-of-the-fittest test unless it exhibits certain features that enable fast recovery from these changes. In most cases, the types of dynamic changes a biological process may experience and its desired recovery features differ from those traditionally studied in the distributed computing literature. In particular, a question seldomly asked in the context of distributed digital systems, and that is crucial in the context of biological cellular networks, is whether the system can keep the changing components confined so that only nodes in their vicinity may be affected by the changes, but nodes sufficiently far away from any changing component remain unaffected.Based on this notion of confinement, we propose a new metric for measuring the dynamic changes recovery performance in distributed network algorithms operating under the Stone Age model (Emek and Wattenhofer, 2013) [1], where the class of dynamic topology changes we consider includes inserting/deleting an edge, deleting a node together with its incident edges, and inserting a new isolated node. Our main technical contribution is a distributed algorithm for maximal independent set (MIS) in synchronous networks subject to these topology changes that performs well in terms of the aforementioned new metric. Specifically, our algorithm guarantees that nodes which do not experience a topology change in their immediate vicinity are not affected and that all surviving nodes (including the affected ones) perform O((C+1)log2⁡n) computationally-meaningful steps, where C is the number of topology changes; in other words, each surviving node performs O(log2⁡n) steps when amortized over the number of topology changes. This is accompanied by a simple example demonstrating that the linear dependency on C cannot be avoided.

On Binary-State Phyllosilicate Automata

Phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A phyllosilicate automaton is a regular network of finite state machines, which mimics the structure of phyllosilicate. A node of a binary state phyllosilicate automaton takes states 0 and 1. A node updates its state in discrete time depending on a sum of states of its three (silicon nodes) or six (oxygen nodes) closest neighbors. We phenomenologically select the main types of patterns generated by phyllosilicate automata based on their shape: convex and concave hulls, almost circularly growing patterns, octagonal patterns, and those with dendritic growth; and, the patterns' interior: disordered, solid, labyrinthine. We also present the rules exhibiting traveling localizations.

ON OSCILLATORS IN PHYLLOSILICATE EXCITABLE AUTOMATA

Phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A phyllosilicate excitable automaton is a regular network of finite state machines, which mimics structure of a silicate sheet. A node of the silicate sheet is an automaton, which takes resting, excited and refractory states, and updates its state in discrete time depending on a sum of excited states of its three (silicon automata) or six (oxygen automata) closest neighbors. Oscillator is a localized compact configuration of nonquiescent states which undergoes finite growth and modification but returns to its original state in a finite number of steps. We show that phyllosilicate excitable automata exhibit waves and oscillating localizations (oscillators) dynamics. Basic types of oscillators are classified and characterized.

Game of life on phyllosilicates: Gliders, oscillators and still life

A phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A phyllosilicate automaton is a regular network of finite state machines --- silicon nodes and oxygen nodes --- which mimics structure of the phyllosilicate. A node takes states 0 and 1. Each node updates its state in discrete time depending on a sum of states of its three (silicon) or six (oxygen) neighbours. Phyllosilicate automata exhibit localizations attributed to Conway's Game of Life: gliders, oscillators, still lifes, and a glider gun. Configurations and behaviour of typical localizations, and interactions between the localizations are illustrated.

Symbolic optimization of interacting controllers based on redundancy identification and removal

This paper presents a binary decision diagram (BDD)-based algorithm for the optimization of the driven machine, M/sub 2/, of a finite-state machine (FSM) network with cascade connection, M/sub 1//spl rarr/M/sub 2/. The technique we propose relies on redundant faults identification and removal. A fault, f, located into machine M/sub 2/, is redundant with respect to the overall network if the driving machine M/sub 1/ is not able to generate any test sequence for such a fault. When the state transition graph (STG) specifications of the network components are available, the standard way for checking the redundancy condition for the considered fault requires one to first construct the product machine M/sub 2//spl times/M/sub 2//sup F/, where M/sub 2//sup F/ is the faulty FSM, then to connect it to the driving machine, and finally to perform reachability analysis on the composed machine M/sub 1//spl rarr/M/sub 2//spl times/M/sub 2//sup F/. Clearly, the size of such machine limits the applicability of the approach above to systems whose components have a few tens of states at most, even when symbolic traversal algorithms are used. Since we are interested in dealing with networks of larger FSM's (i.e., machines whose STGs can not be represented explicitly), we propose to use the product automaton P'=A/sub 1//spl times/A/sub f/, where A/sub 1/' is the finite automaton (FA) accepting all the output sequences of M/sub 1/, and A/sub f/ is the FA accepting all the test sequences for fault f, instead of machine M/sub 1//spl rarr/M/sub 2//spl times/M/sub 2//sup F/. This simplifies sensibly the task of the reachability analysis program, since A/sub f/ has considerably less states and less edges than the product machine M/sub 2//spl times/M/sub 2//sup F/ and, thus, the size of the BDD representation of its transition relation is much more easily manageable. In addition, differently from other approaches, automaton A/sub 1/' is not required to be deterministic and state minimal. This allows us to avoid the application of determinization and state minimization procedures whose complexity is exponential. We present experimental results For examples (i.e., network of interacting controllers) on which existing optimization methods are not applicable, due to the size of the component FSM's. We also provide a comparison to the data produced by state-of-the-art FSM network optimizers on small benchmarks in order to show the effectiveness of our approach.

Adaptive soft-input soft-output algorithms for iterative detection with parametric uncertainty

The soft-input soft-output (SISO) module is the basic building block for established iterative detection (ID) algorithms for a system consisting of a network of finite state machines. The problem of performing ID for systems having parametric uncertainty has received relatively little attention in the open literature. Previously proposed adaptive SISO (A-SISO) algorithms are either based on an oversimplified channel model, or have a complexity that grows exponentially with the observation length N (or the smoothing lag D). In this paper, the exact expressions for the soft metrics in the presence of parametric uncertainty modeled as a Gauss-Markov process are derived in a novel way that enables the decoupling of complexity and observation length. Starting from these expressions, a family of suboptimal (practical) algorithms is motivated, based on forward/backward adaptive processing with linear complexity in N. Previously proposed A-SISO algorithms, as well as existing adaptive hard decision algorithms are interpreted as special cases within this framework. Using a representative application-joint iterative equalization-decoding for trellis-based codes over frequency-selective channels-several design options are compared and the impact of parametric uncertainty on previously established results for ID with perfect channel state information is assessed.

Self‐checking Synchronous FSM Network Design with Low Overhead

A method of a self‐checking synchronous Finite State Machine (FSM) network design with low overhead is developed. Checkers are used only for FSMs, which output lines are at the same time output lines of the network. The checkers observe output lines of these FSMs. The method is based on reducing the problem to a self‐checking synchronous FSM design. The latter is provided by applying a special description of FSM namely, so‐called unate Programmable Logic Array (PLAu) description. Single stuck‐at fault on the FSM poles and gate poles are considered. PLAu realization of FSM allows a factorized or multilevel logic synthesis. They both provide a unidirectional manifestation of the above mentioned faults on the output lines of the corresponding FSMs. This realization also gives rise to a transparency of each component FSM of the network for the faults. PLAu realization is derived from the State Transition Graph (STG) description of FSMs with using the m‐out‐of‐n encoding of its states and insignificant expanding the products of STG. The problem of replacing an arbitrary synchronous FSM network for the self‐checking one with low overhead is discussed.

FSM Decomposition and Functional Verification ofFSM Networks

Here we present a new method for the decomposition of a Finite State Machine (FSM) into a network of interacting FSMs and a framework for the functional verification of the FSM network at different levels of abstraction. The problem of decomposition is solved by output partitioning and state space decomposition using a multiway graph partitioning technique. The number of submachines is determined dynamically during the partitioning process. The verification algorithm can be used to verify (a) the result of FSM decomposition on a behavioral level, (b) the encoded FSM network, and (c) the FSM network after logic optimization. Our verification technique is based on an efficient enumeration‐simulation method which involves traversal of the state transition graph of the prototype machine and simulation of the decomposed machine network. Both the decomposition and verification/simulation algorithms have been implemented as part of an interactive FSM synthesis system and tested on a set of benchmark examples.

LISAS — Simulation tool for regular networks of finite state machines

LISAS, a simulator for the development and analysis of regular networks of finite state machines is introduced. Designed to manage, understand, and visualize complex flow of data of systolic architectures and algorithms, it was recently adapted to simulate cellular automata. Broad applicability and a convenient graphical user-interface are major characteristics of the tool. In this paper its simulation concepts and interactive usability are outlined, which rendered successful simulation of systems for cryptographic and signal processing applications.

LISAS — Simulation tool for regular networks of finite state machines

LISAS, a simulator for the development and analysis of regular networks of finite state machines is introduced. Designed to manage, understand, and visualize complex flow of data of systolic architectures and algorithms, it was recently adapted to simulate cellular automata. Broad applicability and a convenient graphical user-interface are major characteristics of the tool. In this paper its simulation concepts and interactive usability are outlined, which rendered successful simulation of systems for cryptographic and signal processing applications.

Cascaded Finite-State Machines

In this paper, networks of finite-state machines, rather than individual machines, are discussed. The investigation centers around cascade networks, where the output of one machine serves as an input to another. It is shown how, by means of connection matrices, the characteristics of such a network can be obtained from those of the component machines, and how a specified machine can be decomposed into a number of cascaded components. The advantages of such a decomposition, as well as some of the problems that remain to be solved in this area, are discussed.