Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

Preparation and characterization of new photoactive polyamides containing 4‐(4‐dimethylaminophenyl)urazole units

4‐(4‐dimethylaminophenyl)‐1,2,4‐triazolidine‐3,5‐dione ( DAPTD ) was prepared from 4‐dimethylaminobenzoic acid in five steps. The compound DAPTD was reacted with excess acetyl chloride in N,N‐dimethylacetamide (DMAc) solution and gave 1,2‐bisacetyl‐4‐[4‐(dimethylaminophenyl)]‐1,2,4‐triazolidine‐3,5‐dione as a model compound. Solution polycondensation reactions of monomer with succinyl chloride (SucC), suberoyl chloride (SubC), and sebacoyl chloride (SebC) were performed under conventional solution polymerization techniques in the presence of triethylamine and pyridine as a catalyst in N‐methylpyrrolidone (NMP) and led to the formation of novel aliphatic polyamides. These novel polyamides have inherent viscosities in the range of 0.09–0.21 dL/g in N,N‐dimethylformamide (DMF) at 25°C. Fluorimetric studies of the model compound as well as polymers were performed. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 947–954, 2007     

Synthesis of Photoactive Polyureas Derived from 4-(4-Dimethylaminophenyl)-1,2,4-triazolidine-3,5-dione and Diisocyanates

Summary 4-(4-Dimethylaminophenyl)-1,2,4-triazolidine-3,5-dione (DAPTD) was prepared from 4-dimethylaminobenzoic acid in five steps. The reaction of monomer DAPTD with n-isopropylisocyanate was performed at room temperature in N,N-dimethylacetamide (DMAc) solution and the resulting bis-urea derivative was obtained in high yield and was finally used as a model compound for polymerization reaction. The step-growth polymerization reactions of monomer with hexamethylene diisocyanate (HMDI), isophorone diisocyanate (IPDI) and toluene-2,4-diisocyanate (TDI) were performed in DMAc solution in the presence of pyridine, triethylamine or dibutyltin dilurate as catalysts. Some physical properties and structural characterization of these novel polyureas are reported. Fluorimetric studies of the model compound as well as polymers were performed.     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

A bio-inspired gelatin-based pH- and thermal-sensitive magnetic hydrogel for in vitro chemo/hyperthermia treatment of breast cancer cells

Gelatin (Gel)-based pH- and thermal-responsive magnetic hydrogels (MH-1 and MH-2) were designed and developed as novel drug delivery systems (DDSs) for cancer chemo/hyperthermia therapy. For this goal, Gel was functionalized with methacrylic anhydride (GelMA), and then copolymerized with (2-dimethylaminoethyl) methacrylate (DMAEMA) monomer in the presence of methacrylate-end capped magnetic nanoparticles (MNPs) as well as triethylene glycol dimethacrylate (TEGDMA; as crosslinker). Afterward, a thiol-end capped poly(N-isopropylacrylamide) (PNIPAAm-SH) was synthesized through an atom transfer radical polymerization technique, and then attached onto the hydrogel through “thiol-ene” click grafting. The preliminary performances of developed MHs for chemo/hyperthermia therapy of human breast cancer was investigated through the loading of doxorubicin hydrochloride (Dox) as an anticancer agent followed by cytotoxicity measurement of drug-loaded DDSs using MTT assay by both chemo- and chemo/hyperthermia-therapies. Owing to porous morphologies of the fabricated magnetic hydrogels according to scanning electron microscopy images and strong physicochemical interactions (e.g., hydrogen bonding) the drug loading capacities of the MH-1 and MH-2 were obtained as 72 ± 1.4 and 77 ± 1.8, respectively. The DDSs exhibited acceptable pH- and thermal-triggered drug release behaviors. The MTT assay results revealed that the combination of hyperthermia therapy and chemotherapy has synergic effect on the anticancer activities of the developed DDSs.     

Relay-Assisted Wireless Energy Transfer for Efficient Spectrum Sharing in Harsh Environments

International Journal of Wireless Information Networks - With exponential growth in the number of wireless devices and limited available spectrum, the problem of spectrum sharing remains in...     

Liquid–liquid equilibria of aqueous two-phase systems composed of TritonX-100 and sodium citrate or magnesium sulfate salts

Liquid–liquid phase diagrams of surfactant-based aqueous two-phase systems (ATPS) composed of TritonX-100, as a non-ionic surfactant, and two different salts have been studied at 298.15 K. The salts used were an inorganic salt, magnesium sulfate (MgSO₄), and an organic salt, sodium citrate (Na₃C₆H₅O₇). The results show that the salt MgSO₄ is more capable of inducing ATPS formation than the salt Na₃C₆H₅O₇. The experimental liquid–liquid equilibrium data were correlated using a modified virial model. Good agreement was obtained with the experimental data.     

A genetic programming model to generate risk-adjusted technical trading rules in stock markets

Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.