A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

Optical and photocatalytic properties of the Fe-doped TiO2 nanoparticles loaded on the activated carbon

In this work, Fe-doped (1?wt%) TiO₂ loaded on the activated carbon nano-composite was prepared using a sol-gel method. A prepared nano-composite was characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), BET surface area, Fourier transform infrared spectroscopy (FTIR), photoluminescence (PL) spectroscopy and UV–Vis diffuse reflectance spectroscopy (DRS). The photocatalytic activity of the nano-composite was evaluated through degradation of synthetic textile wastewater, reactive red 198, under visible light irradiations. The XRD result indicated that the TiO₂ nano-composite contained only anatase phase. The surface area of the TiO₂ increased from 48?m²/g to 100?m²/g through the fabrication of the nano-composite. The FE-SEM results indicate that the TiO₂ particles with an average particle size of 35–70?nm can be deposited homogeneously on the activated carbon surface. DRS showed that the Fe doping in the TiO₂ -activated carbon nano-composite induced a significant red shift of the absorption edge and then the band gap energy decreased from 3.3 to 2.9?eV. Photocatalytic results indicated that the photocatalytic activity of the Fe doped TiO₂ increased under visible light irradiation in the presence of the activated carbon.     

Electrochemical pseudocapacitors of different compounds and nanocomposite of manganese: an introduction and review to it in recent studies

Journal of Materials Science: Materials in Electronics - This review gives an overview of the synthesis, surface, and electrochemical investigations over Mn-based compounds in the development of...     

Photo-induced superhydrophilicity of nanocomposite TiO2-SiO2 thin film

In this work, TiO₂ and TiO₂-SiO₂ thin films on glass substrates were prepared by the sol-gel dip coating process. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) were used to evaluate the structural and chemical properties of the films. The super-hydrophilicity was assessed by water contact angle measurement. XRD measurements confirmed the presence of polycrystalline anatase and rutile phases in the films. The water contact angle measurements showed that addition of SiO₂ has a significant effect on the super-hydrophilicity of TiO₂ thin films, especially if they are stored in a dark place.     

Simulation analysis of lot streaming in job shops with transportation queue disciplines

In this paper we study a multi-product lot streaming problem in a stochastic job shop with equal and discrete sublots. The problem involves splitting order quantities of different products into different number of equal sublots (NES) and analyzing the effects of sublot-related transportation queue disciplines (TRQD) for different performance measures. Our simulation results show that some combination of TRQD and NES alternatives are appropriate for some performance measures. We also propose a simple heuristic to determine the NES of each product type to further improve the values of performance measures.     

Voice activity detection based on using wavelet packet

This paper, presents a robust voice activity detection (VAD) technique based on wavelet packet. In this technique sub-bands and their amplitudes are represented as the vectors for each sample time in order to find a new feature from the frequency and amplitude changes. On the other hand, the multi-resolution analysis property of the wavelet packet transform (WPT), the voiced, unvoiced, and transient components of speech can be distinctly discriminated. Then, a new feature extraction method is implemented based on observations of the angles between vectors. This feature extraction method retains most unvoiced sounds in a voice active frame. Experimental results show that the proposed WT feature parameter can extract the speech activity under poor SNR conditions and that it is also insensitive to variable-level of noise.     

Automatic information timeliness assessment of diabetes web sites by evidence based medicine

Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites.     

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R ²) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.