2,307 research outputs found
Process Mining of Programmable Logic Controllers: Input/Output Event Logs
This paper presents an approach to model an unknown Ladder Logic based
Programmable Logic Controller (PLC) program consisting of Boolean logic and
counters using Process Mining techniques. First, we tap the inputs and outputs
of a PLC to create a data flow log. Second, we propose a method to translate
the obtained data flow log to an event log suitable for Process Mining. In a
third step, we propose a hybrid Petri net (PN) and neural network approach to
approximate the logic of the actual underlying PLC program. We demonstrate the
applicability of our proposed approach on a case study with three simulated
scenarios
COMPUTER SIMULATION AND COMPUTABILITY OF BIOLOGICAL SYSTEMS
The ability to simulate a biological organism by employing a computer is related to the
ability of the computer to calculate the behavior of such a dynamical system, or the "computability" of the system.* However, the two questions of computability and simulation are not equivalent. Since the question of computability can be given a precise answer in terms of recursive functions, automata theory and dynamical systems, it will be appropriate to consider it first. The more elusive question of adequate simulation of biological systems by a computer will be then addressed and a possible connection between the two answers given will be considered. A conjecture is formulated that suggests the possibility of employing an algebraic-topological, "quantum" computer (Baianu, 1971b)
for analogous and symbolic simulations of biological systems that may include chaotic processes that are not, in genral, either recursively or digitally computable. Depending on the biological network being modelled, such as the Human Genome/Cell Interactome or a trillion-cell Cognitive Neural Network system, the appropriate logical structure for such simulations might be either the Quantum MV-Logic (QMV) discussed in recent publications (Chiara, 2004, and references cited therein)or Lukasiewicz Logic Algebras that were shown to be isomorphic to MV-logic algebras (Georgescu et al, 2001)
Learning, Generalization, and Functional Entropy in Random Automata Networks
It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that
feedforward Boolean networks can learn to perform specific simple tasks and
generalize well if only a subset of the learning examples is provided for
learning. Here, we extend this body of work and show experimentally that random
Boolean networks (RBNs), where both the interconnections and the Boolean
transfer functions are chosen at random initially, can be evolved by using a
state-topology evolution to solve simple tasks. We measure the learning and
generalization performance, investigate the influence of the average node
connectivity , the system size , and introduce a new measure that allows
to better describe the network's learning and generalization behavior. We show
that the connectivity of the maximum entropy networks scales as a power-law of
the system size . Our results show that networks with higher average
connectivity (supercritical) achieve higher memorization and partial
generalization. However, near critical connectivity, the networks show a higher
perfect generalization on the even-odd task
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
The Kinetic Basis of Self-Organized Pattern Formation
In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that
different spatio-temporal patterns can arise due to instability of the
homogeneous state in reaction-diffusion systems, but at least two species are
necessary to produce even the simplest stationary patterns. This paper is aimed
to propose a novel model of the analog (continuous state) kinetic automaton and
to show that stationary and dynamic patterns can arise in one-component
networks of kinetic automata. Possible applicability of kinetic networks to
modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the
Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted
09.05.201
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