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This article investigates a non-equilibrium chaotic system in view of commensurate and incommensurate fractional orders and with only one signum function. By varying some values of the fractional-order derivative together with some parameter values of the proposed system, different dynamical behaviors of the system are explored and discussed via several numerical simulations. This system displays complex hidden dynamics such as inversion property, chaotic bursting oscillation, multistabilty, and coexisting attractors. Besides, by means of adapting certain controlled constants, it is shown that this system possesses a three-variable offset boosting system. In conformity with the performed simulations, it also turns out that the resultant hidden attractors can be distributively ordered in a grid of three dimensions, a lattice of two dimensions, a line of one dimension, and even arbitrariness in the phase space. Through considering the Caputo fractional-order operator in all performed simulations, phase portraits in two- and three-dimensional projections, Lyapunov exponents, and the bifurcation diagrams are numerically reported in this work as beneficial exit results.
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In this paper, we study the postural behaviour of two categories of people: Post-CVA subjects suffering from cerebrovascular accident syndromes and healthy individuals under several levels of anterior-posterior and medial-lateral sinusoidal disturbances (0.1-0.5 Hz). These perturbations were produced from an omnidirectional platform called Isiskate. Afterwards, we have quantified seventy postural parameters, they were combined of linear stabilometric parameters and non-linear time dependent stochastic parameters using stabilogram diffusion analysis and some spectral attributes using power spectral density. The aim of our analysis is to reduce data dimensionality using principal component analysis (PCA). Furthermore, we proposed a new PCA-related criterion named: criterion of contribution in order to evaluate the contribution of every variable in the resulted system structure, and thus to eliminate the redundant postural characteristics. Afterwards, we highlighted some interesting distinctive parameters. The selected parameters were used thereafter in comparison between the studied groups. Finally, we created a classification model using support vector machines to distinguish stroke patients. Our proposed techniques help in understanding the human postural dynamics and facilitate the diagnosis of pathologies related to equilibrium which can be used to improve the rehabilitation services.
Asunto(s)
Postura , Análisis de Componente Principal , Accidente Cerebrovascular/patología , Accidente Cerebrovascular/fisiopatología , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Máquina de Vectores de Soporte , Adulto JovenRESUMEN
This work aims to achieve an optimization of the TiO(2) and PMAPTAC concentrations in a chemical resistive-type humidity sensing mechanism (RHSM). Our idea is based primarily on the modeling of the sensing mechanism. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO(2) and PMAPTAC effects concentrations on the humidity sensing properties in our model. Secondly, we used the Matlab environment to create a database for an ideal model for the sensing mechanism, where the response of this ideal model is linear for any value of the above parameters. We have done the training to create an analytical model for the sensing mechanism (SM) and the ideal model (IM). After that, the SM and IM models are established on PSPICE simulator, where the output of the first is identical to the output of the RHSM used and the output of the last is the ideal response. Finally a "DIF bloc" was realized to make the difference between the SM output and the IM output, where this difference represents the linearity error, we take the minimum error, to identify the optimal TiO(2) and PMAPTAC concentrations. However, a compromise between concentrations, humidity and temperature must be performed. The simulation results show that in low humidity and at temperature more than 25 °C, sample 1 is the best (in alumina substrate). However, the sample 9 represents the best sensor (in PET substrate) predominately for the lowest humidity and temperature.
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An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods.
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Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects ). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor's response characteristics and eliminate its dependency on the environmental parameters. The corrector's responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor's responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.