Multiple Hysteresis Jump Resonance in a Class of Forced Nonlinear Circuits and Systems

In this paper, a new class of systems with nonclassical jump resonance behavior is presented. Although jump resonance has been widely studied in the literature, this contribution refers to systems presenting a multiple hysteresis jump resonance phenomenon, meaning that the frequency response of the system presents more hysteresis windows nested within the same range of frequency. The analytical conditions for observing this type of behavior are derived and a design strategy to obtain multiple hysteresis jump resonance in circuits and systems presented

One Day Ahead Prediction of Wind Speed Class

This paper deals with the problem of clustering daily wind speed time series based on two features referred to as Wr and H, representing a measure of the relative daily average wind speed and the Hurst exponent, respectively. Daily values of the pairs (Wr ; H) are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes, allows performing some useful statistics. Further, the problem of predicting 1-step ahead the class of daily wind speed is addressed by introducing NAR sigmoidal neural models into the classification process. The performance of the prediction model is finally assessed

Interactive Real-Time Control System for The Artificial Hand

In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand

Automation of the Leonardo da Vinci Machines

Leonardo da Vinci inventions and projects represent an intriguing starting point to remark the concept that innovation must be considered as a continuous route towards evolution in history. Some of the particular ideas and innovations presented by Leonardo da Vinci led us to formulate a link with automatic control. Selected models of the Leonardo da Vinci machines are presented in this paper, taking strictly into account the original mechanical schemes and working principles, but introducing modern low-cost control equipment, emphasizing the role of automatic control and that of electronic control devices, such as microcontrollers, sensors, and communication devices, to completely automate the Leonardo da Vinci machines. The approach outlined in the paper can be applied not only to other Leonardo machines but also to other mechanical equipment not necessarily designed by Leonardo da Vinci. Moreover, it is useful to remark that the approach followed in this paper can be very important also to introduce students, leading by example, to concepts typical of automation and for assisting in learning, keeping in mind the practical applications of advanced automation principles. The main research task of this paper is proving the efficacy of modern digital control techniques and teleoperation in strongly classical mechanical Leonardo machines, remarking that the projects of Leonardo are prompt to be efficiently controlled. This task could not be explored by Leonardo himself due to the lack of control technology. Moreover, the paper is addressed also to stimulate the young generations of engineers in joining classical mechanics with advanced technology. Therefore, the paper is also devoted to give focus on the fact that the Leonardo machines encompass all the key aspects of modern system engineering

Motion control of a population of Artemias

In this work, the collective behavior of Artemia Salina is studied both experimentally and theoretically. Several experiments have been designed to investigate the Artemia motion under different environment conditions. From the results of such experiments, a strategy to control the direction of motion of an Artemia population, by exploiting their sensitivity to light, has been derived and then implemented

Chaos Addresses Energy in Networks of Electrical Oscillators

In this paper, a new application of active chaos is presented. It will be shown how chaotic behavior allows to address the energy transport in networks of linear oscillators. The paper is focused on RLC oscillators coupled in a network through links implemented by capacitors. The capacitances are subjected to an external input which leads to fluctuations in their values. This creates the conditions for the onset of decoherence, a situation in which a phenomenon called environment-assisted quantum transport occurs in quantum mechanics. From numerical and experimental observations, the use of active chaos to alter the value of the coupling capacitances reveals to be more effective than the use of random fluctuations. This implies that an energy control strategy for linear systems based on chaos can be outlined

Integrated inversion of ground deformation and magnetic data at Etna volcano using a genetic algorithm technique

Geodetic and magnetic investigations have been playing an increasingly important role in studies on Mt. Etna eruptive processes. During ascent, magma interacts with surrounding rocks and fluids, and inevitably crustal deformation and disturbances in the local magnetic field are produced. These effects are generally interpreted separately from each other and consistency of interpretations obtained from different methods is qualitatively checked only a posteriori. In order to make the estimation of source parameters more robust we propose an integrated inversion from deformation and magnetic data that leads to the best possible understanding of the underlying geophysical process. The inversion problem was formulated following a global optimization approach based on the use of genetic algorithms. The proposed modeling inversion technique was applied on field data sets recorded during the onset of the 2002-2003 Etna flank eruption. The deformation pattern and the magnetic anomalies were consistent with a piezomagnetic effect caused by a dyke intrusion propagating along the NE direction