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This paper deals with the oscillation problems of delay hyperbolic systems with impulses. Some sufficient conditions for oscillations of impulsive delay hyperbolic systems with Robin boundary conditions are obtained and the criteria of oscillation of the systems are established.

This paper is devoted to the study of an SIRS computer virus propagation model with two delays and multistate antivirus measures. We demonstrate that the system loses its stability and a Hopf bifurcation occurs when the delay passes through the corresponding critical value by choosing the possible combination of the two delays as the bifurcation parameter. Moreover, the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by means of the center manifold theorem and the normal form theory. Finally, some numerical simulations are performed to illustrate the obtained results.

Adipose-derived stem cells (ASCs) have been shown to enhance wound healing by human dermal fibroblasts; however, the interactions between ASCs and fibroblasts during injury remain unclear. Fibroblasts were treated with ASC-conditioned medium (ASC-CM) with and without transforming growth factor-β1 (TGF-β1) stimulation. Fibroblast proliferation, apoptosis, differentiation and expression of extracellular matrix genes and proteins, type I collagen, and type III collagen were measured. Also, wound-healing effect of ASC-CM was verified with in vivo animal study. ASC-CM inhibited proliferation and enhanced apoptosis of fibroblasts under TGF-β1 stimulation. Furthermore, 10% ASC-CM inhibited α-smooth muscle actin expression in fibroblasts, whereas 100% ASC-CM increased collagen, especially type III, expression in fibroblasts. ASC-CM was found to contain more basic fibroblast growth factor than hepatocyte growth factor, and 100% ASC-CM increased hepatocyte growth factor gene expression in fibroblasts. These results suggest ASCs affect fibrogenesis by dermal fibroblasts stimulated with TGF-β1 via paracrine signaling by adipocytokines present in ASC-CM. These results also suggest that higher concentrations of ASC-CM increase collagen production and inhibit fibroblast proliferation to avoid excessive fibrogenesis. We demonstrated that a lower ASC-CM concentration attenuated fibroblast differentiation. Additionally, 100% ASC-CM significantly reduced the wound size in an in vivo wound-healing model. In this study, we provided evidence that ASCs modulate fibrogenesis by fibroblasts via paracrine signaling, suggesting that application of ASCs during wound healing may improve the quality of wound repair.

For a class of nonlinear time-delay repetitive systems with initial deviation, output error, disturbances and arbitrary initial value conditions, the PD type closed-loop iterative learning tracking control problem which satisfies the global Lipschitz conditions is investigated. By defining the bounded desired trajectory, expected control and the tracking error of the iterative learning process, the necessary and sufficient conditions for the existence of the gain of the learning controller is given through the λ norm theory and Bellman-Gronwall inequality, which can guarantee the tracking error of the closed-loop system to be bounded, and the uniform convergence criteria of the control algorithm are also proved. Simulation of the speed control of the injection molding machine demonstrate the proposed method of this paper not only can improve the performance of the system significantly, but also has certain inhibition effect on the bounded disturbance and arbitrary initial problem.

An objective of blind source separation (BSS) is to recover potential source signals from their mixtures without a prior knowledge of the mixing process. In this paper, a new underdetermined blind source separation (UDBSS) approach, based on the local mean decomposition (LMD) method and the AMUSE algorithm, is proposed. To make the UDBSS problem simpler, some extra observation signals are first constructed using the LMD method. Thus the underdetermined blind source separation problem is transformed into an (over-)determined one. Subsequently, the well known AMUSE algorithm is applied to these new observations to estimate the source signals. The proposed method does not resort to the sparsity constraint which is included in most of the former researches. The theoretical analysis and simulation results illustrate the effectiveness of the proposed UDBSS method.

Traditional K-means clustering algorithm easily fall into local extremum. A maximum distances product algorithm is used to optimize the initial clustering centers and a K-means clustering algorithm with simulated annealing (SA) is promoted. The proposed method uses SA to optimize the clustering pattern in clustering analysis which can achieve global optimization. A combination model based on support vector machine (SVM) is established. The method is applied to a soft sensor modeling for the quality index in a Bisphenol A production process. The simulation result shows that the change trend of phenol content is tracked effectively and data classification result is improved by the algorithm. It also shows that the estimation precision of the soft sensor model is improved which demonstrates the potential application in industry field.

Water quality monitoring is a critical part of environmental management and protection, and to be able to qualitatively and quantitatively determine contamination and impurity levels in water is especially important. Compared to the currently available water quality monitoring methods and techniques, laser-induced breakdown spectroscopy (LIBS) has several advantages, including no need for sample pre-preparation, fast and easy operation, and chemical free during the process. Therefore, it is of great importance to understand the fundamentals of aqueous LIBS analysis and effectively apply this technique to environmental monitoring. This article reviews the research conducted on LIBS analysis for liquid samples, and the article content includes LIBS theory, history and applications, quantitative analysis of metallic species in liquids, LIBS signal enhancement methods and data processing, characteristics of plasma generated by laser in water, and the factors affecting accuracy of analysis results. Although there have been many research works focusing on aqueous LIBS analysis, detection limit and stability of this technique still need to be improved to satisfy the requirements of environmental monitoring standard. In addition, determination of nonmetallic species in liquid by LIBS is equally important and needs immediate attention from the community. This comprehensive review will assist the readers to better understand the aqueous LIBS technique and help to identify current research needs for environmental monitoring of water quality.

In this paper, we analyze the dynamics of a delayed food chain system with harvesting. Sufficient conditions for the local stability of the positive equilibrium and for the existence of Hopf bifurcation are obtained by analyzing the associated characteristic equation. Formulas for determining the direction of Hopf bifurcation and the stability of the bifurcating periodic solutions are derived by applying the normal form method and center manifold theorem. Finally, numerical simulation results are presented to validate the theoretical analysis.

By incorporating the time delay due to the period that computers use antivirus software to clean the virus into the SLBRS model a delayed SLBRS computer virus model is proposed in this paper. The dynamical behaviors which include local stability and Hopf bifurcation are investigated by regarding the delay as bifurcating parameter. Specially, direction and stability of the Hopf bifurcation are derived by applying the normal form method and center manifold theory. Finally, an illustrative example is also presented to testify our analytical results.

This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

A four-dimensional recurrent neural network with two delays is considered. The main result is given in terms of local stability and Hopf bifurcation. Sufficient conditions for local stability of the zero equilibrium and existence of the Hopf bifurcation with respect to both delays are obtained by analyzing the distribution of the roots of the associated characteristic equation. In particular, explicit formulae for determining the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are established by using the normal form theory and center manifold theory. Some numerical examples are also presented to verify the theoretical analysis.

A delayed SEIRS epidemic model with vertical transmission in computer network is considered. Sufficient conditions for local stability of the positive equilibrium and existence of local Hopf bifurcation are obtained by analyzing distribution of the roots of the associated characteristic equation. Furthermore, the direction of the local Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by using the normal form theory and center manifold theorem. Finally, a numerical example is presented to verify the theoretical analysis.

This paper is concerned with a computer virus model with two delays. Its dynamics are studied in terms of local stability and Hopf bifurcation. Sufficient conditions for local stability of the positive equilibrium and existence of the local Hopf bifurcation are obtained by regarding the possible combinations of the two delays as a bifurcation parameter. Furthermore, explicit formulae for determining direction of the Hopf bifurcation and stability of the bifurcating periodic solutions are obtained by using the normal form method and center manifold theory. Finally, some numerical simulations are presented to support the theoretical results.

This paper is concerned with a Holling type III predator-prey system with stage structure for the prey population and two time delays. The main result is given in terms of local stability and bifurcation. By choosing the time delay as a bifurcation parameter, sufficient conditions for the local stability of the positive equilibrium and the existence of periodic solutions via Hopf bifurcation with respect to both delays are obtained. In particular, explicit formulas that can determine the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are established by using the normal form method and center manifold theorem. Finally, numerical simulations supporting the theoretical analysis are also included.

A modified Holling-Tanner predator-prey system with multiple delays is investigated. By analyzing the associated characteristic equation, the local stability and the existence of periodic solutions via Hopf bifurcation with respect to both delays are established. Direction and stability of the periodic solutions are obtained by using normal form and center manifold theory. Finally, numerical simulations are carried out to substantiate the analytical results.

An inferential adaptive control algorithm is developed for a class of non-uniformly sampled-data systems with fast and non-uniformly updated inputs and infrequently sampled outputs. The specific approach involves three steps: first, to derive the mathematical relationships between the transfer function model of the measurable output and that of the non-uniform missing outputs; second, to compute the models of the non-uniform missing outputs based on the derived mathematical relationships and the identified model of the measurable output; third, using the computed models to estimate the non-uniform missing outputs and further supply which for feedback control. The proposed control algorithm can generate fast rate control signals and has the property of minimum variance. An example is included and the simulation results illustrate the effectiveness of the proposed inferential control scheme.

The common definition of modal participation factors was intended to provide a measure of participation of modes in states and of states in modes for linear timeinvariant (LTI) systems. In this paper, recent work by the authors that revisited this common definition is extended to yield definitions and calculations for quantifying the relative contribution of system modes in system outputs. When the system outputs are simply the system states, the mode-in-output participation factors are found to reduce to the original modein- state participation factors. A numerical example is given to illustrate the issues addressed and the results obtained.

In this paper, a filtering based recursive least squares algorithm is derived for identification of the no-nuniformly sampled Box-Jenkins systems. The basic idea is to use an estimated noise transfer function to filter the input-ouput data, to obtain two identification models containing the parameters of the system model and the noise model respectively, and to present the filtering based recursive least squares method to identify the parameters of these two models, by replacing the unmeasurable terms in the information vectors with their estimates. Finally, an illustrative example is given to indicate that the proposed algorithm can generate more accurate parameter estimation compared with the auxiliary model based recursive generalized extended least squares algorithm.

This paper converts a Box-Jenkins model into two identification submodels with the system model parameters and the noise model parameters, respectively. However, the information vectors in the submodels contain unmeasurable variables, which leads the conventional recursive least squares algorithm impossible to generate the parameter estimates. In order to overcome this difficulty, the interactive least squares algorithm is derived by using the auxiliary model identification idea and the hierarchical identification principle. The simulation results indicate that the proposed algorithm has less computational burden and more accurate parameter estimation compared with the auxiliary model based recursive generalized extended least squares algorithm.

This paper investigates the delay-dependent exponential stability problem of Takagi-Sugeno (TS) fuzzy Hopfield neural networks (HNNs) with time-varying delay. Based on a fuzzy Lyapunov-Krasovskii functional (LKF), some delay-dependent stability criteria guaranteeing the exponential stability of the fuzzy HNNs are devised by taking the relationship between the terms in the Leibniz-Newton formula into account. Since free weighting matrices are used to express this relationship and the appropriate ones are selected by means of linear matrix inequalities (LMIs), the criteria are less conservative than existing ones reported in the literature for delayed fuzzy neural networks. A simulation example is provided to illustrate the effectiveness of the developed method.

In this paper, we study solutions of general matrix equations by using the iterative method and present gradient-based iterative algorithms by applying the hierarchical identification principle. Convergence analysis indicates that the iterative solutions always converge fast to the exact solutions for any initial values and small condition numbers of the associated matrices. Several numerical examples are included.

Several thresholds were used in wavelet packet shrinkage, and according to the feature that the wavelet coefficients of signals and noises on each scale had different characteristic, SURE threshold, Bayesian threshold, fixed form threshold and the moving minimax threshold were combined during wavelet packet threshold de-noising. Simulation results show that the proposed method indeed has better de-noising effect.

A novel, multi-wavelength, fiberoptic system was constructed, evaluated and implemented to determine internal tissue optical properties at ultraviolet A (UVA) and visible (VIS) wavelengths. Inverse modeling was performed with a neural network to estimate absorption and reduced scattering coefficients based on spatially-resolved reflectance distributions. The model was calibrated with simulated reflectance datasets generated using a condensed Monte Carlo approach with absorption coefficients up to 85 cm(-1) and reduced scattering coefficients up to 118 cm(-1). After theoretical and experimental evaluations of the system, optical properties of porcine bladder, colon, esophagus, oral mucosa, and liver were measured at 325, 375, 405, 445 and 532 nm. These data provide evidence that as wavelengths decrease into the UVA, the dominant tissue chromophore shifts from hemoglobin to structural proteins such as collagen. This system provides a high level of accuracy over a wide range of optical properties, and should be particularly useful for in situ characterization of highly attenuating biological tissues in the UVA-VIS.

The robust synchronization of a class of uncertain chaotic neural networks with parameters perturbation and external disturbances is investigated in this paper. The time-delay feedback control term is introduced in the synchronization controller to achieve the robust synchronization of the drive-response structure of neural networks. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some sufficient conditions are established for the robust synchronization of the uncertain neural networks. An illustrative example is provided to show the effectiveness and feasibility of the proposed method and results.

Information system discretization widens the use of rough set theory. Rough set attribute discretization should maintain the consistency of knowledge base classification with less cut-points. This paper introduces within the context of rough set a novel method of attribute discretization by applying binary particle swarm optimization (PSO). A minimal set of cut-points are determined with PSO, which is especially adept at global search, while the consistency of knowledge base classification is assured from the high quality of clustering results from rough set theory. The effectiveness of this discretization method is verified in a simulated example of evaluating the vibroacoustic diagnosis symptoms.

Based on testing Bayesian information criterion (TBIC), an approach was presented to solve the indeterminacy of structure existing in multi-resolution wavelet network. A method considering both scale and precision of the networks was proposed, which can also result in an optimal network structure under the TBIC rule. Simulation results illustrate that the multi-resolution wavelet network has reasonable scale and good testing performance when the method was applied. Compared with Bayesian information criterion (BIC), the TBIC rule can reflect accurately the trend of precision along with the structure.

According to the requirement for real-time modeling in industrial processes, a rapid and efficient method based on Kalman filter (KF) for training neural networks (NN) was presented. In this algorithm, the weights of hidden-layer were initialized randomly at the beginning of training and left unchanged, while the weights of out-layer were served as the states of an ordinary Kalman filter and adjusted automatically according to real-time input-output data of dynamic systems. It considered NN training as the problem of linear state estimation. Simulation results for a non-linear multi-input and single-output (MISO) system showed that the proposed training algorithm was rapider and more efficient than back-propagation (BP) and extended Kalman filtering (EKF) algorithms. Therefore, the proposed algorithm is more suitable for on-line learning compared with BP and EKF.

The asymptotical properties for the Solutions of neutral distributed parameter systems with Robin boundary conditions are analyzed by using the inequality analysis. Some sufficient conditions for the asymptotical properties for solutions of the neutral distributed parameter systems are analyzed and oscillation criteria for the systems are established.

The variable structure control problems of a class of uncertain distributed parameter systems with nonlinear input under the Neumann boundary conditions are studied by using inequality analysis as the main mathematical tool, and a robust variable structure controller is derived.

In this paper, we mainly use a combination of the kernel density estimation and the conjugate gradient optimisation to solve the overdetermined blind source separation (OBSS) problem. The Informax principle is used as the basis for introducing the contrast function of the OBSS problem with the singular value decomposition (SVD) technology. For practical optimisation of the contrast function, the conjugate gradient optimising algorithm is employed to derive the learning rules for training the separating matrix. Within the training equations, the term of score function is estimated directly by the kernel density estimation method. Experimental results confirm that the proposed OBSS approach works very fast and reliably.

The problem of the exponential stability for a class of delayed Cellular Neural Networks (CNNs) which are subject to large impulses is considered. By using the Lyapunov functional method and applying Young and Halanay inequalities, some sufficient conditions have been derived to ensure the exponential stability of the neural networks. An example is given to show the effectiveness of the presented criteria.

This design uses the ion-selective electrode method to measure the pH, or the ion concentration of a certain solution. The small DC electrical potential signal which is generated by ion selective electrode and reference electrode is put into an amplifier of high input impedance and linear. The MSP430F1611 MCU which is taken as the master control system converts the amplified signal by the 16-bit AD converter. Then the LCD screen shows the electrical potential difference between the two electrodes. A PC display program is written through the LabVIEW to get the curve of the electrical potential difference between the two electrodes at different sampling time. To analyze and process the electrical potential difference between the electrode systems of different solution, and then the pH, or a certain ion concentration of the solution is get. Besides, before the electrical potential signal passing the amplifier, the design adds a dual 8-channel analog switch which can enable the switch between the 8 groups of electrodes and ensure the simultaneous measurement of multiple electrodes.

This paper applies the hierarchical identification principle and the gradient search method to study iterative solutions for a class of general coupled matrix equations with real coefficients. As long as the convergence factors are appropriately chosen, the proposed algorithms for any initial values can provide iterative solutions that are arbitrarily close to the unique solutions of the equations. Two numerical examples are given to demonstrate the effectiveness of the proposed algorithms.