scholarly journals Dynamics and Complexity of Computrons

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 150
Author(s):  
Murat Erkurt
Keyword(s):  
Cellular Automata ◽  
Metric Space ◽  
Finite State Machine ◽  
Finite Size ◽  
Basins Of Attraction ◽  
External Input ◽  
Ergodic Decomposition ◽  
Finite State ◽  
General Network

We investigate chaoticity and complexity of a binary general network automata of finite size with external input which we call a computron. As a generalization of cellular automata, computrons can have non-uniform cell rules, non-regular cell connectivity and an external input. We show that any finite-state machine can be represented as a computron and develop two novel set-theoretic concepts: (i) diversity space as a metric space that captures similarity of configurations on a given graph and (ii) basin complexity as a measure of complexity of partitions of the diversity space. We use these concepts to quantify chaoticity of computrons’ dynamics and the complexity of their basins of attraction. The theory is then extended into probabilistic machines where we define fuzzy basin partitioning of recurrent classes and introduce the concept of ergodic decomposition. A case study on 1D cyclic computron is provided with both deterministic and probabilistic versions.

scholarly journals A DESIGN FOR VERIFICATION APPROACH USING AN EMBEDDING OF PSL IN AsmL

2007 ◽  
Vol 16 (06) ◽  
pp. 859-881 ◽  
Author(s):  
AMJAD GAWANMEH ◽  
SOFIÈNE TAHAR ◽  
HAJA MOINUDEEN ◽  
ALI HABIBI
Keyword(s):  
Model Checking ◽  
Design Process ◽  
Finite State Machine ◽  
System Level ◽  
State Machines ◽  
Top Down ◽  
Finite State ◽  
Property Specification ◽  
Large Systems

In this paper, we propose to integrate an embedding of Property Specification Language (PSL) in Abstract State Machines Language (AsmL) with a top–down design for verification approach in order to enable the model checking of large systems at the early stages of the design process. We provide a complete embedding of PSL in the ASM language AsmL, which allows us to integrate PSL properties as a part of the design. For verification, we propose a technique based on the AsmL tool that translates the code containing both the design and the properties into a finite state machine (FSM) representation. We use the generated FSM to run model checking on an external tool, here SMV. Our approach takes advantage of the AsmL language capabilities to model designs at the system level as well as from the power of the AsmL tool in generating both C# code and FSMs from AsmL models. We applied our approach on the PCI-X bus standard, which AsmL model was constructed from the informal standard specifications and a subsequent UML model. Experimental results on the PCI-X bus case study showed a superiority of our approach to conventional verification.

A cellular automata guided two level obfuscation of Finite-State-Machine for IP protection

Integration ◽  
2020 ◽  
Vol 74 ◽  
pp. 93-106
Author(s):  
Rajit Karmakar ◽  
Suman Sekhar Jana ◽  
Santanu Chattopadhyay
Keyword(s):  
Cellular Automata ◽  
Finite State Machine ◽  
State Machine ◽  
Ip Protection ◽  
Finite State

Finite State Machine Assisted Automatic Tonnage Signal Clustering for Multiple-Operation Forging Processes

2013 ◽  
Author(s):  
Yong Lei ◽  
Haibo Xie
Keyword(s):  
Domain Knowledge ◽  
Finite State Machine ◽  
State Machine ◽  
Structural Deformation ◽  
Classification Error ◽  
Working Process ◽  
Finite State ◽  
Minimum Classification Error ◽  
Multiple Operation

Forging is an essential metal-working process in manufacturing. Forging tonnage signals, which are originally used to monitor the structural deformation of the forging machines for safety purpose, had been increasingly used to monitor the performance of the forging processes. Despite recent advances on forging process monitoring, little attention had been paid to expedite the preparation of the data labeling procedure for different fault classes of the forging tonnage signals. Currently, existing data are mostly classified and labeled by experts, which is extremely time consuming and error prone. In this paper, a new forging tonnage signal clustering method is proposed to label the multiple-operation forging tonnage signals automatically. Due to different workload setups of the forging machines, a Finite State Machine (FSM) modeling approach is developed to utilize the domain knowledge of the multiple-operation forging processes. An adaptive iterative learning scheme is deployed to optimize the clustering results based on minimum classification error of the tree-structure classifier based on FSM. Real-world forging tonnage signals are used in the case study to demonstrate the proposed method.

Design and implementation of finite state machine using quantum-dot cellular automata

2019 ◽  
Vol 11 (2) ◽  
pp. 186
Author(s):  
Dakshinamurthy Sungeetha ◽  
G. Keerthana ◽  
K. Vijayakumar
Keyword(s):  
Cellular Automata ◽  
Quantum Dot ◽  
Finite State Machine ◽  
State Machine ◽  
Quantum Dot Cellular Automata ◽  
Design And Implementation ◽  
Finite State

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

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