Fuzzy system for seismic signal classification

A new efficient technique for the classification of signals, in the form of earthquake-induced ground-acceleration time histories, according to the damage that they cause in buildings, is presented for the first time. A training set of real seismic accelerograms with well-known damage effects is utilised and fuzzy representations of prototype signals are extracted. These prototypes are selected with respect to the architectural and structural damage caused by the seismic-acceleration time histories. The classification of the unknown accelerograms takes place through a fuzzy comparison with the prototypes and each is classified to the most similar prototype. Real, seismic time-acceleration records were used for testing the algorithm and the high percentage of the correctly recognised signals prove the effectiveness of the algorithm. Correct classification rates of up to 84% are achieved.     

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

A two-dimensional (2-D) cellular automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. In this paper, the study is focused on the optimal parameterisation of the model introducing the use of genetic algorithm (GA). The optimisation of the CA model parameterisation, by applying a standard GA, extends its ability to study various hypotheses concerning the seismicity of the region under consideration. The GA evolves an initially random population of candidate solutions of model parameters, such that in time appropriate solutions to emerge. The quality criterion is realised by taking into account the extent that the simulation results match the Gutenberg-Richter (GR) law derived from recorded data of the area under test. The simulation results presented here regard regions of Greece with different seismic and geophysical characteristics. The results found are in good quantitative and qualitative agreement with the GR scaling relations.     

Comparison between cellular automata and linear feedback shift registers based pseudo-random number generators

A detailed comparison between pseudo-random number generators (PRNGs) based on cellular automata (CA) and linear feedback shift registers (LFSRs) is presented in this paper. Various statistical tests have been applied in order to reveal the advantages and disadvantages of both approaches. Both LFSRs and hybrid additive cellular automata (HACA) produce satisfactory PRNGs. HACA operate at higher speeds than LFSRs with the same characteristic polynomials. Regarding the silicon area, direct comparisons between the two approaches cannot be made since it depends on the PRNG length. However, the inherent modularity of HACA reduces the silicon area occupied by them and, when long feedback paths are used, the silicon area occupied by LFSRs increases.     

Soft Morphological Filtering Using a Fuzzy Model and its Application to Colour Image Processing

Abstract Soft morphological filters form a class of filters with many desirable properties. They were introduced to improve the behaviour of standard morphological filters in detail preservation and noise elimination. In this paper, a framework for soft morphological colour image processing using a fuzzy model is introduced. This extends the standard colour morphological operators in the same way that soft greyscale morphology extends the standard greyscale morphology theory. The primary and secondary operations of the new soft morphological approach are defined. The proposed operators are less sensitive to image distortion and to small variations in the shape of the objects, and perform significantly better in impulse noise removal problems, compared to standard morphological operators. Experimental results of the application to real colour images demonstrate these advantageous characteristics of the new operators. Additionally, illustrative examples that exhibit the applicability of the proposed methodology to edge detection problems are also included.An erratum to this article can be found at     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

On stochasticity and turbulence in the federal funds market

This paper uses various tests from statistics and dynamical systems theory to support stochastic models to explain the behaviour of the federal funds rate. In particular, a fractional Brownian motion is supported by the power spectrum of the series, the structure function test and the Hurst test. A coloured noise model is supported by the signal differentiation test and the space-time separation test. Evidence against the turbulent behaviour hypothesis is also provided.     

Chaotic analysis of US money and velocity measures

This paper uses tools from dynamical systems theory to investigate the properties of US money and velocity series. Comparisons are made between simple-sum, Divisia and currency equivalent aggregates (of M1, M2, M3, and L), using the Anderson et al. monthly data (from January 1960 to June 1996).     

The effect of the parasitic current on the Direct Ethanol PEM Fuel Cell Operation

In the present work the effect of the parasitic or leakage current, I_p, which is the result of the ethanol crossover through the polymer electrolyte membrane (PEM) from the anode to the cathode side of the cell, on both the cathode activation overpotential and the fuel cell operation is investigated. A one-dimensional (1-D), isothermal mathematical model is developed in order to describe the operation of a Direct Ethanol PEM Fuel Cell (DE-PEMFC) in steady state. The equations used describe the mass transport of both ethanol and humidified oxygen at the anode and the cathode compartment of the cell respectively. The mathematical model is validated against experimental data and a relatively good agreement between the model predictions and the experimental results is found. The direct correlation that exists between the ethanol crossover rate and the parasitic current formation is graphically depicted. Moreover, when the parasitic current is enabled and disabled, the calculation of the cathode activation overpotential shows that the mixed overpotential for a DE-PEMFC poses a serious problem hindering the fuel cell operation. According to the model results, the parasitic current is greater at low current density values due to the greater amounts of the crossovered ethanol. Finally, the effect of both the oxygen feed concentration and the parasitic current formation on the fuel cell operation is also presented and discussed.     

New fuzzy model for morphological colour image processing

Mathematical morphology is a powerful tool for image processing and analysis of binary, greyscale and colour images. An efficient new fuzzy model for morphological colour image processing is introduced. A new vector-ordering scheme that uses fuzzy if-then rules is proposed, and then the basic morphological operations of erosion and dilation are defined. The morphological operators presented, which are vector preserving, are less sensitive to image distortion and perform significantly better in noise removal problems than other reported morphological operators. Experimental results demonstrate these advantageous characteristics on real images. Additionally, illustrative examples that exhibit the applicability of the proposed framework to edge-detection problems are given.