Sharif Digital Repository

A Magneto rheological (MR) rotary brake as a prosthesis knee is addressed here. To the gait of the amputee, the brake, automatically adapts knee damping coefficient using only local sensing of the knee torque and position. It is difficult to design a model-based controller, since the MR knee system has nonlinear and very complicated governing mathematical equations. Hence, a Hybrid self-organizing fuzzy controller and multidimensional wavelet neural network (HSFCMWNN) is proposed here to control the knee damping coefficient using of the inverse dynamics of the MR rotary damper. A Self-organizing fuzzy controller (SOFC) is also proposed and during the control process, the SOFC continually... 

A chemical kinetic model for i-butane and n-butane catalytic cracking over synthesized HZSM-5 zeolite, with SiO 2/Al 2O 3 = 484, and in a plug flow reactor under various operating conditions, has been developed. To estimate the kinetic parameters of catalytic cracking reactions of i-butane and n-butane, a lump kinetic model consisting of six reaction steps and five lumped components is proposed. This kinetic model is based on mechanistic aspects of catalytic cracking of paraffins into olefins. Furthermore, our model takes into account the effects of both protolytic and bimolecular mechanisms. The Levenberg-Marquardt algorithm was used to estimate kinetic parameters. Results from statistical... 

Spinal posture including thorax/pelvis orientations as well as loads on the intervertebral discs are crucial parameters in biomechanical models and ergonomics to evaluate the risk of low back injury. In vivo measurement of spinal posture toward estimation of spine loads requires the common motion capture techniques and laboratory instruments that are costly and time-consuming. Hence, a closed loop algorithm including an artificial neural network (ANN) and fuzzy logic is proposed here to predict the L5–S1 segment loads and thorax/pelvis orientations in various 3D reaching activities. Two parts namely a fuzzy logic strategy and an ANN from this algorithm; the former, developed based on the... 

One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning... 

In the last years, an appreciable effort has been directed toward developing empirical models to link the relative permeability of gas condensate reservoirs to the interfacial tension and velocity as well as saturation. However, these models suffer from non-universality and uncertainties in setting the tuning parameters. In order to alleviate the aforesaid infirmities in this study, comprehensive modeling was carried out by employing numerous smart computer-aided algorithms including Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), and Gene Expression Programming... 

In the current study, CuO nanoparticles were dispersed in a mixture of Ethylene Glycol-Water (60/40 wt. %) to prepare stable nanofluid in different concentrations (0.05 − 0.8 vol. %). The samples were used as the coolant fluid in a specific car radiator to evaluate the thermal performance of nanofluid and base fluid in the system. Five different and novel Machine-learning methods were applied over experimental data to predict the Nusselt number and output temperature of the coolant in the system. These methods are M5 tree regression, Linear and Cubic Multi-Variate Adaptive Regression Splines (MARS), Radial Basis Function (RBF), and Artificial Neural Network-Levenberg Marquardt Algorithm... 

In this article, a feed-forward neural network is explored to reconstruct the performance map of an axial compressor through the utilization of a limited number of experimental data. The Levenberg-Marquardt algorithm with Bayesian regularization method is used to adjust the weights and biases of the network. The proposed technique is utilized to estimate the mass flowrate, the pressure ratio, the shaft speed, and the efficiency in regions where no experimental data are available. The surge line is predicted and the line of maximum efficiencies is determined. The results are compared with experimental data  

Vacuum tubes are widely used for various applications. These vacuum tubes are supplied by high voltage power supplies. The amount of delivered energy from the high voltage power supply to the vacuum tube is an important issue during the vacuum arc in the tube. The protection mechanism consists of a shunt crowbar which diverts the fault current from the tube to itself as a parallel path. Detection of the vacuum arc is crucial and only one sensor is usually employed to detect the vacuum arc. This characteristic intensifies the interference susceptibility of the vacuum arc diagnosis system in a noisy environment. As a result of the noise, the arc detection system can report false alarms. False... 

Global Motion Estimation (GME) has many important roles in numerous applications, such as video compression, image stabilization, video-object segmentation, and etc. One well-known GME method is the gradient-based technique. This method uses optimization techniques, like the Levenberg-Marquardt algorithm, to minimize estimation error. Such algorithms require an initial value for the initializing step. In this paper, we propose a simple and reliable GME structure with a new predictor. This structure uses a three-step search and a predictor for the initializing step. It is also incorporated with a fast GME method that uses pixel subsampling. This incorporation reduces the computational... 

Global motion estimation (GME) is an important technique in image and video processing. Whereas the direct global motion estimation techniques boast reasonable precision they tend to suffer from high computational complexity. As with indirect methods, though presenting lower computational complexity they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, motion vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the... 

In this paper we present a method of localizing multiple wideband acoustic sources based on both signal attenuation pattern and delays in the reception of the signal. Moreover, an additional order of flexibility is added to the problem by considering the attenuation model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to when only the signal attenuation data or the time delays are used. A cost function is then formed in terms of the sources locations and the attenuation model parameters. Having different types of unknowns in the cost function, convergence concerns, and the...