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Mathiyalagan, Ramya,Subramaniyam, Sathiyamoorthy,Kim, Yeon Ju,Natarajan, Sathishkumar,Min, Jin Woo,Kim, Se Young,Yang, Deok Chun Japan Society for Bioscience, Biotechnology, and A 2014 Bioscience, Biotechnology, and Biochemistry Vol.78 No.3
<P>The ginsenosides in Panax ginseng have vast structural and pharmacological efficacies. We covalently conjugated polyethylene glycol on the surface of CK (PEG-CK) through an acid-labile ester-linkage that showed increased solubility of CK. HPLC analysis showed that the release of CK was enhanced at acidic pH 5, whereas it was dramatically decreased at physiological pH 7.4. This might enhance the efficacy of CK.</P>
Mathiyalagan, K.,Park, J.H.,Sakthivel, R. Elsevier [etc.] 2015 Applied mathematics and computation Vol.259 No.-
In this paper, we formulate and investigate the impulsive synchronization of memristor based bidirectional associative memory (BAM) neural networks with time varying delays. Based on the linear matrix inequality (LMI) approach, the impulsive time dependent results are derived for the exponential stability of the error system, which guarantees the exponential synchronization of the BAM model by means of master-slave synchronization concept. Different from the existing models, an observer (slave system) for the considered BAM neural network in this paper is modeled with time-varying and random impulse moments. Some sufficient conditions are obtained to guarantee the exponential synchronization of the BAM model is derived by using the time-varying Lyapunov function. Simple LMI expressions are proposed to find the feedback controller gains at impulse instants. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results.
Mathiyalagan, K.,Park, J.H.,Sakthivel, R. Elsevier Ltd 2015 NONLINEAR ANALYSIS HYBRID SYSTEMS Vol.17 No.-
<P>This paper is concerned with the problem of passivity-based H-infinity controller design for a class of networked cascade control systems (NCCSs) with random packet dropouts. The NCCS under consideration is modeled by using state feedback controllers and the network-induced imperfections like packet dropouts and time-varying delays. The model is defined with a stochastic packet-dropout case by using the Bernoulli distributed white sequence with time-varying probability measures. The probability-dependent conditions for stabilization of NCCSs are established to guarantee the resulting closed-loop system to be stochastically stable and achieve a prescribed mixed H-infinity and passivity performance. The Lyapunov stability theory and linear matrix inequality (LMI) approach are used to derive criteria for the existence of the state feedback controllers. The proposed probability-dependent gain scheduled controller can be designed by solving the convex optimization problem by means of a set of LMIs, which can be easily solved by using some standard numerical packages. Finally, a practical application is presented to illustrate the effectiveness and potential of the proposed results. (C) 2015 Elsevier Ltd. All rights reserved.</P>
Finite-time boundedness and dissipativity analysis of networked cascade control systems
Mathiyalagan, K.,Park, J. H.,Sakthivel, R. Springer Science + Business Media 2016 Nonlinear dynamics Vol.84 No.4
<P>In this paper, finite-time boundedness and dissipativity analysis for a class of networked cascade control systems (NCCSs) is investigated. The NCCS is defined with network-induced imperfections such as packet dropouts and time delays, and Bernoulli distributed white sequence is used to model a stochastic packet dropout case. Using the Lyapunov stability theory and linear matrix inequality (LMI) approach, we propose the sufficient conditions for finite-time boundedness and finite-time dissipativity of NCCS. Finally, the LMI-based conditions are applied on a practical power plant boiler-turbine system to show the effectiveness and applicability of the achieved results.</P>
Novel results on robust finite-time passivity for discrete-time delayed neural networks
Mathiyalagan, K.,Park, Ju H.,Sakthivel, R. Elsevier 2016 Neurocomputing Vol.177 No.-
<P><B>Abstract</B></P> <P>This paper presents some novel results on robust finite-time passivity for a class of uncertain discrete-time neural networks (DNNs) with time varying delays. Using the Lyapunov theory together with the zero inequalities, convex combination and reciprocally convex combination approaches, we propose the sufficient conditions for finite-time boundedness and finite-time passivity of DNN for all admissible uncertainties. The results are achieved by using a new Lyapunov-Krasovskii functional (LKF) with novel triple summation terms, several delay-dependent criteria for the DNN are derived in terms of linear matrix inequalities (LMIs) which can be easily verified via the LMI toolbox. Finally, numerical example with simulation scheme have been presented to illustrate the applicability and usefulness of the obtained results.</P>