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This paper proposes the PD-type iterative learning control (ILC) for multiple time-delays systems with polytopic parameter uncertainty. Based on repetitive process framework, the system under study is equivalently converted into a class of uncertain repetitive processes with multiple time-delays. This approach accounts for effective inclusion of both time and trial domain objectives and hence some requirements on transient dynamics and trial-to-trial error convergence are incorporated for robust design procedures. Additionally, this approach can easily avoid the need for computation with very large dimensioned matrices as it is required for the lifting approach. Also, the proposed controller is designed with the generalized Kalman-Yakubovich-Popov lemma to ensure the monotonic trial-to-trial error convergence in finite frequency domain. This allows us to reduce the conservatism inherent to entire frequency range approaches since the reference signal spectrum reside in a known frequency range. Moreover, the sufficient conditions for the convergence of the resulting scheme are expressed by linear matrix inequalities and hence they are amenable to effective algorithmic solution. Finally, numerical simulations of different scenarios are presented to illustrate the effectiveness of the proposed method. In particular, to highlight the potential interest in PD-type ILC the robust tracking performance is compared with the results for P and D types of ILC.

Iterative learning control (ILC) is a high-performance technique for repeated control tasks with design postulates on a fixed reference profile and identical initial conditions. However, the tracking performance is only critical at few points in point-to-point tasks, and their initial conditions are usually trial-varying within a certain range in practice, which essentially degrades the performance of conventional ILC algorithms. Therefore, this study reformulates the ILC problem setup for point-to-point tasks and considers the effort of trial-varying initial conditions in algorithm design. To reduce the tracking error, it proposes a worst-case norm-optimal problem and reformulates it into a convex optimisation problem using the Lagrange dual approach. In this sense, a robust ILC algorithm is derived based on iteratively solving this problem. The study also shows that the proposed robust ILC is equivalent to conventional norm-optimal ILC with trial-varying parameters. A numerical simulation case study is conducted to compare the performance of this algorithm with that of other control algorithms while performing a given point-to-point tracking task. The results reveal its efficiency for the specific task and robustness against trial-varying initial conditions.

In recent years, the technique of machine learning or deep learning has been employed in intelligent fault diagnosis methods to achieve much success using massive labeled data. However, it is generally difficult or expensive to label the monitoring data in practical engineering due to its complex working conditions. Therefore, an unsupervised fault diagnosis method is proposed in this paper for rolling bearings, which incorporates short-time Fourier transform (STFT) as well as categorical generative adversarial networks (CatGAN). The proposed method first adopts STFT to transform raw 1-D vibration signals into 2-D time-frequency maps to serve as the input of CatGAN. Then, it obtains a CatGAN model via an adversarial training process to generate fake samples with a similar distribution to the maps extracted by STFT and cluster the input samples into certain categories. Furthermore, the performance of the proposed ST-CatGAN method is verified using a classic rotating machinery dataset, and the experimental results demonstrate its high diagnosis accuracy and strong robustness against the motor load changes.

For a class of uncertain differential spatially interconnected systems in the particular case of active electrical ladder circuits, the problem of iterative learning control with stability and robust performance specifications in finite frequency ranges is investigated in this paper. Firstly, the dynamics are converted to an equivalent differential linear repetitive process. Then, based on the Kalman–Yakubovich–Popov Lemma, a control law design algorithm is presented in the form of the corresponding linear matrix inequalities. Since the system parameters are norm-bounded uncertain, an extension to robust control law is also discussed. The resulting dynamics satisfy the robust performance specifications, and the error monotonically converges in finite frequency ranges with the robust iterative learning control law. Finally, a control simulation of an active electrical ladder circuit is presented to illustrate the advantages of the proposed algorithm.

The subject area considered is discrete linear time delay systems operating repetitively on a finite time interval with actuator faults, where the system resets at the end of each operation. Regulation of the dynamics is by iterative learning control and performance goals imposed over finite frequency intervals for the case of uncertainty in the dynamic model. To derive the results, the generalized Kalman–Yakubovich–Popov lemma is used. A simulation based case study is also given to demonstrate the applicability of the new results.

This paper uses repetitive process stability theory to design robust iterative learning control law for linear discrete systems with multiple time-delays and polytopic uncertainty. Both dynamic and static forms of the control law are considered and used when designing robust iterative learning control schemes. Also, based on the generalized Kalman-Yakubovich-Popov Lemma, the proposed design procedures a required frequency attenuation over a finite frequency range and the monotonic trial-to-trial error convergence. Moreover, linear matrix inequality techniques are applied to formulate the convergence conditions and to obtain formulas for the control law designs. Finally, an illustrative numerical simulation example is given and concludes the paper.

A series of (Z)-3-(2-(1,3,4-thiadiazol-2-yl)hydrazono)-1-substituted indolin-2-ones derivatives (3a-3m) were designed and synthesized. All newly synthesized compounds were evaluated for their a-glucosidase inhibitory activity with resveratrol as positive control in vitro. Except for 3i and 3j, all of the compounds showed a potent inhibitory activity against a-glucosidase with IC50 values in the range of 3.12 ± 1.25 to 45.95 ± 1.26 μM and the purity of these compounds was greater than 95%. The IC50 values were being compared to the standard resveratrol (IC50 = 22.00 ± 1.15 μM) and it was found that compounds 3b, 3d-3h were found to be more active than resveratrol. Specifically, (Z)-3-(2-(1,3,4-thiadiazol-2-yl)hydrazono)-1-(4-chlorobenzyl)indolin-2-one (3d) exhibited the most potent a-glucosidase inhibitory activity with IC50 value of 3.12 ± 1.25 μM. The kinetic analysis revealed that compound (3d) is noncompetitive inhibitor. Structure activity relationship has been established for all compounds. Furthermore, the binding interactions of compound 3d with the active site of a-glucosidase were confirmed through molecular docking. This study has identified a new class of potent a-glucosidase inhibitors for further investigation.

A closed-loop iterative learning fault-tolerant control scheme is proposed for batch process with actuator faults, in which the system parameters have uncertainties simultaneously. Firstly, the batch fault-tolerant controller is designed based on the two-dimensional (2D) system theory, and the batch process of iterative learning control is transformed to an equivalent 2D Roesser model. Then the sufficient conditions for the existence of the controller are analyzed in terms of linear matrix inequality (LMI) technique, and the control gain matrices are derived from the convex optimization problems with LMI constrains. Under these conditions of all additive uncertainties on system parameters and admissible failure, the proposed controller can ensure the closed-loop fault-tolerant performance along time. Finally, the simulation on injection molding nozzle pressure control simulation indicate that the proposed method achieve the design objective well, and show excellent fault tolerance performance.

This paper considers the design of iterative learning control laws for classes of nonlinear dynamics. In particular, a new Newton method design is developed for discrete nonlinear systems in the presence of input constraints, where such constraints will arise in applications. The new design is based on the use of a penalty function and an iterative method for solving an unconstrained nonlinear optimization problem with an algorithm that has monotonic and super linear convergence characteristics. In this new algorithm the input inequality constraints are transformed into equality form by adding auxiliary variables. A cost function is then minimized to produce the new iterative learning control law design. Finally, a simulation based case study is given to illustrate the performance of the new design.

We improved the ultraviolet (UV)/O3-based method for measuring chemical oxygen demand (COD) in water. An on-line COD monitoring device was developed and the UV/O3 method was used to oxidize sample solutions. A model was established by using support vector machines (SVM) algorithm to estimate dissolved oxygen and CO2 in solutions. Based on the measured data by each sensor during the oxidation process and the estimated dissolved oxygen and CO2, the UV/O3-based COD test accuracy was improved. This approach overcomes many problems associated with the conventional COD determination techniques such as long analysis time, consumption of expensive and toxic reagents, and production of secondary toxic waste. The effect of important parameters on the measurement of COD was systematically investigated. The improved method was successfully applied to determine the COD of real samples from environmental water. Compared with the standard dichromate method, our UV/O3-based COD test method is more effective. The assay time of 10–15 min/sample can be readily achieved. A practical detection limit of 0.89 mg/L COD with a linear range of 1–300 mg/L was achieved under the normal conditions.

For a class of single-input single-output (SISO) dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D) repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs) technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method.

This paper develops a fault-tolerant iterative learning control law for a class of differential time-delay batch processes with actuator faults using the repetitive process setting. Once the dynamics are expressed in this setting, stability analysis and control law design makes use of the generalized Kalman–Yakubovich–Popov (KYP) lemma in the form of the corresponding linear matrix inequalities (LMIs). In particular, sufficient conditions for the existence of a fault-tolerant control law are developed together with design algorithms for the associated matrices. Under the action of this control law the ILC dynamics have a monotonicity property in terms of an error sequence formed from the difference between the supplied reference trajectory and the outputs produced. An extension to robust control against structured time-varying uncertainties is also developed. Finally, a simulation based case study on the model of a two-stage chemical reactor with delayed recycle is given to demonstrate the feasibility and effectiveness of the new designs.

This paper considers the identification problem of non-uniformly sampled-data (NUSD) systems with asynchronous input and output data. By using the lifting technique, the lifted transfer function (L-TF) model of the asynchronous NUSD systems is derived. Furthermore, an auxiliary model based recursive least squares (AM-RLS) algorithm is developed to directly identify the L-TF model. In order to avoid the causality constraint problem and improve the computational efficiency, a coupled AM-RLS algorithm is proposed to identify the subsystems of the L-TF model. The effectiveness of the proposed identification algorithms is validated by two simulation examples.

Keloid, marked by excessive deposition of extracellular matrix components, usually occurs after cutaneous trauma. The molecular mechanism involved in the etiology of keloid remains largely unknown in spite of extensive studies presented on the mechanism of Keloid. NEDD4 has come to be recognized as a potential mediator implicated in inflammation and keloid that could chronically develop. Despite this, the working mechanism of NEDD4 involved in keloid remains unclear. In our present report, STAT3 was identified as a novel transcriptional factor that can diametrically regulate the transcription of NEDD4 and the translation that ensues. The regulation by STAT3 over NEDD4 can be abolished as long as the p-STAT3 was inactivated in the presence of Niclosamide, a kind of inhibitors that work specifically on STAT3 signaling pathway. In turn, silencing of NEDD4 was also shown to be able to down-regulate the expression of p-STAT3. No direct protein-protein interactions between STAT3 and NEDD4 can be identified in our setting. The data we provided herein enrich the knowledge regarding the molecular mechanism of NEDD4 involved in the pathogenesis of keloid, defining a new regulatory role for STAT3 in keloid.

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances, an iterative learning fault diagnosis algorithm is proposed. Firstly, in order to measure the impact of fault on system between every consecutive output sampling instants, the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem, then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method. Afterwards, an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault, and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials, so the algorithm can detect and estimate the system faults adaptively. Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

Based on the lifting technique, the lifted state space model and the lifted transfer function model can be derived to describe non-uniformly sampled-data (NUSD) systems. However, the lifted models are inconvenient for both identification and control purposes due to the causality constraint and the model complexity. To solve this problem, a novel model of NUSD systems is proposed by introducing a time-varying backward shift operator. The proposed model is more concise in structure and has fewer parameters than the lifted models, using which the traditional identification methods and control strategies of single-rate systems can be easily extended to NUSD systems. The advantages and effectiveness of the proposed model are well illustrated by a simulation example.

This paper considers iterative learning control for a class of uncertain multiple-input multiple-output discrete linear systems with polytopic uncertainties and actuator faults. The stability theory for linear repetitive processes is used to develop control law design algorithms that can be computed using linear matrix inequalities. A class of parameter-dependent Lyapunov functions is used with the aim of enlarging the allowed polytopic uncertainty range for successful design. The effectiveness and feasibility of the new design algorithms are illustrated by a gantry robot case study. Copyright © 2016 John Wiley & Sons, Ltd.

This paper puts forward a PD-type iterative learning control algorithm for a class of discrete spatially interconnected systems with unstructured uncertainty. By lifting and changing the variable of discrete space model, the uncertain spatially interconnected systems is converted into equivalent singular system, and the general state space model is derived in view of singular system theory. Then, the state error and output error information are used to design the iterative learning control law, transforming the controlled system into an equivalent repetitive process model. Based on the stability theory of repetitive process, sufficient condition for the stability of the system along the trial is given in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed algorithm is verified by the simulation of ladder circuits.

2-(2,6-bis((E)-4-(diphenylamino)styryl)-1-methylpyridin-4(1 H)-ylidene)malononitrile (TPA-TCMP) offering aggregation-induced emission(AIE) with practical fluorescence efficiency was easily transformed from 2-(2,6-bis(4-(diphenylamino)stryryl-4H-pyranylidene)malononitrile (TPA-DCM) which suffers from aggregation-caused quenching (ACQ) by replacing oxygen atom with methylamine group. Subsequently, a hybrid fluorescent nanoporous material (THPP) was prepared by Friedel-Crafts reaction of TPA-TCMP with octavinylsilsesquioxane, [vinylSiO1.5]8 (OVS). THPP possesses a good porosity with a high surface area of 620 m2 g−1, a large pore volume of 0.54 cm3 g−1. It also displays a high thermal stability with 5 % mass loss thermal decomposition temperature above 400 °C in N2 atmosphere and a good pH stability ranging from 4–9. More importantly, it offers a large Stokes shift of 180 nm. The adsorption of Ru3+ on THPP dramatically quenches the fluorescence emission allowing the efficient detection of Ru3+with high selectivity, and the detection limit (LOD) is calculated to be 5.2 × 10−6 mol L−1. Moreover, THPP can adsorb Ru3+ with an equilibrium adsorption capacity of 208 mg g−1.

This paper studies a robust iterative learning control design for discrete linear repetitive processes in the finite frequency domain. Firstly, the state-space model of the iterative learning process is deduced. Then the dynamic performance condition of the control system in the finite frequency domain is derived by combining it with the stability theory of discrete linear repetitive processes. The system performances in the finite frequency domain are then transformed into the corresponding solutions of the linear matrix inequality by using the generalised KYP lemma. Finally, an integrated state feedback PD-type iterative learning control strategy is proposed. The robust control problem with norm-bounded uncertainty and convex polyhedral uncertainty are also considered in this paper. The simulation of the injection velocity in injection molding verified that the proposed methods in this paper are more effective than the P-type state feedback iterative learning control algorithm.

The synthesis of a hybrid nanoporous material is achieved by following Friedel-Crafts reaction between octavinylsilsesquioxane and pyrene using AlCl3 as the catalyst. The hybrid nanoporous material is characterized by IR, solid 13C and 29Si NMR. In addition, nitrogen adsorption/desorption measurement at 77K reveals that the hybrid nanoporous material possesses a high surface area of 1300 m2 g−1 and a bimodal pore distribution on the order of 1.41 and 4.15 nm, respectively. The macroscopic structure is amorphous under PXRD while FE-SEM and HR-TEM micrographs feature aggregates of nanosize particles. The efficacy of Py-HPP has been studied for the detection and removal of antibiotics, namely, berberine chloride hydrate (BCH), tetracycline hydrochloride (TH), and mafenide hydrochloride (MH) using fluorescence spectroscopy. The fluorescence quenching phenomenon of the primitive material (λem = 450 nm; λex = 372 nm), upon interaction with the analytes separately, allows to delineate and compare the adsorption kinetics and adsorption capacity. The study reveals that Py-HPP possesses the highest and lowest adsorption capacity for BCH (330 mg/g) and MH (165 mg/g) while that of TH lies in the intermediate range (195 mg/g). The result suggests its potential for the remediation of antibiotic contaminants from water.

We improved the ultraviolet (UV)/O

The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A (BPA). For online estimation of the catalytic activity, a catalytic deactivation model is studied for a production plant of BPA, state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance. A hybrid model of state equation is constructed for improving estimation precision. The unknown parameters in observation equation are calculated with sample data. The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity. The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.

The Bisphenol A concentration at the reactor outlet is a direct response to the production quality index, and online soft measurement of Bisphenol A concentration is very necessary. Based on the mechanism of Bisphenol A condensation process, the mechanism equations describing the dynamic behavior of reactor are established, and the state equation and observation equation of Bisphenol A are derived through simplification and derivation of the soft measurement model, and then an Unscented Kalman Filtering is adopted to estimate the Bisphenol A concentration. Simulation results confirm that the method is feasible and effective.

Traditional kernel principal component analysis (KPCA) based process monitoring method generally depends on a global monitoring model, however, because of the complex relationships among numerous variables in modern industrial processes, KPCA monitoring technique may not function well. Inspired by the recognition of this shortcoming, a novel totally data-driven multi-block statistical process monitoring method (MBSPM) based on evaluating nonlinear relations between variables is proposed. In MBSPM, a new nonlinearity correlation coefficient is firstly employed to divide process variables into several individual subspaces and each subspace shows strong nonlinear characteristic. Then, to cope with nonlinear correlations, KPCA is adopted to build process monitoring models respectively, thus, the constructed monitoring models in different subspaces can reflect the local behavior of certain faults. The results in all subspaces are combined together to create a final statistic by Bayesian inference. The feasibility and validity of the proposed method is finally demonstrated through comparison studies on the Tennessee Eastman process.

Macrophages, differentiation from monocytes infiltrated in the wound, have been suggested to be involved and to play an important role in the pathogenesis of wound healing. Nevertheless, no evidence has been established regarding M1 and M2 type macrophages in Keloid. To understand the status of M1 and M2 type macrophages in keloid, immunohistochemistry was performed on 30 cases of Keloid tissues and normal controls, with CD68, typical surface marker for M1 and CD163, well-accepted marker for M2 being immunostained. Meanwhile, the glucocorticoid receptor NR3C1 was also detected. As further confirmation, quantitative real-time PCR was utilized to verify the expression of CD68, CD163 and NR3C1 on mRNA level. It was consistently shown that infiltrated M2 macrophages pronouncedly outnumbered M1 macrophages in the dermis of keloids; and that NR3C1 expression was significantly up-regulated in keloids than that in normal controls. In addition, there was a marked correlation between CD163 and NR3C1 expression. Our results suggest that the number of infiltrated M2 macrophages in the dermis of keloids may be linked to the responsiveness to glucocorticoids in the pathogenesis of keloid.

This article proposed a novel method to determine the number of principal components and the optimal values of tuning factors for kernel principal component models. Existing work predominantly relies on ad-hoc rules or cross-validatory approaches to estimate. To guarantee statistical independence, the proposed technique incorporates a two-fold cross-validatory approach by omitting one variable in turn, which is predicted by the remaining ones. For these regressions, the number of principal components varies. This finally yields an optimum selection for the parameters, which application and the analysis of recorded industrial data from a glass melter process confirm.

In this work, we propose a probabilistic just-in-time (PJIT) modeling methodology with nonlinear feature extraction for estimating quality variables of interest. In literature, deterministic nonlinear feature extraction methods have been employed to deal with high dimensional input data. However, these methods require prespecifying the latent dimensions, which often results in overfitting. To circumvent this issue, we employ the Bayesian Gaussian process latent variable model (BGPLVM) to extract nonlinear latent variables and determine their dimensions automatically. Owing to the probabilistic framework, the proposed approach involves computing the variational distribution of latent variables for the query sample as well as historical samples, and selecting relevant samples based on a distribution measure for building a local Gaussian process model to predict the quality variable. Furthermore, the applicability of the proposed approach to missing data and multi-rate data is discussed. Two case studies are presented to demonstrate the efficacy of the proposed PJIT model.

In this paper, we analyse a delayed Holling-II predator–prey system with stage-structure for the prey. At first, we study the stability and the existence of periodic solutions via Hopf bifurcation with respect to both delays at the positive equilibrium by analysing the distribution of the roots of the associated characteristic equation. Then, the explicit formula that determine the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions from the Hopf bifurcation are established by using the normal form method and centre manifold argument. Finally, some numerical simulations are carried out to support the main theoretical results.

The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron's outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm.

Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phenomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.

This paper addresses the problem of robust adaptive iterative learning control for a chain of uncertain integral nonlinear systems, whose aim is to stabilize the tracking error of the system and improve convergence speed in the presence of uncertainties. In response to unknown bounded disturbances, a continuous second-order sliding mode adaptive iterative learning control scheme is proposed, in which an integral term is to attenuate the effects of the disturbances and achieve fast convergence performance. By designing a suitable controller and composite energy function, it is proved that the tracking error along iterative learning horizon will converge to a small neighborhood of zero. Numerical examples are provided to validate the efficacy of the proposed method.

A predator-prey system with two delays and stage-structure for both the predator and the prey is considered. Sufficient conditions for the local stability and the existence of periodic solutions via Hopf bifurcation with respect to both delays are obtained by analyzing the distribution of the roots of the associated characteristic equation. Specially, the direction of the Hopf bifurcation and the stability of the periodic solutions bifurcating from the Hopf bifurcation are determined by applying the normal form theory and center manifold argument. Some numerical simulations for justifying the theoretical analysis are also provided.

Most of the existing underdetermined blind source separation (BSS) approaches assume that the source signals are strictly or partially sparse. This paper, however, presents a new BSS method in underdetermined mixing situation for non- sparse signals. The proposed method first introduces the local mean decomposition algorithm into the BSS problem to rebuild some extra mixing signals. Such signals are then combined with the initial mixtures such that the underdetermined BSS problem is transformed to a determined one and the difficulty of the deficiency of the mixtures is overcome. For the rebuilt mixtures and the newly formed determined BSS problem, the minimum mutual information principle is employed as the BSS cost function. A conjugate gradient learning algorithm is then derived for training the separating matrix. In each update step of the algorithm, the term of score function is estimated by a kernel function estimation algorithm. The simulation results have demonstrated the efficacy of the proposed underdetermined BSS method.  Index Terms—Blind source separation, underdetermined model, local mean decomposition, conjugate gradient

This paper is concerned with a delayed predator-prey system with modified Leslie-Gower and Holling type III schemes. By analyzing the associated characteristic equation, its local stability and the existence of periodic solutions via Hopf bifurcation with respect to both delays are obtained. Based on the normal form method and center manifold theorem, the formulaes for determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are derived. Finally, some numerical simulations to illustrate the theoretical analysis are also carried out.

The motivation for this article comes from our development of soft sensors for chemical processes where several challenges are encountered. For example, quality variables in chemical processes are often measured off-line through laboratory analysis. Collection of samples and subsequent analyses inevitably introduce uncertain time delays associated with the irregularly sampled quality variables, which add significant difficulty in identification of process with multirate (MR) data. Considering the MR system with random sampling delays described by a finite impulse response (FIR) model, an Expectation–Maximization (EM)-based algorithm to estimate its parameters along with the time delays is developed. Based on the identified FIR model, two algorithms are proposed to recover the approximate output error (OE) or transfer function model. Two simulation examples as well as a pilot-scale experiment are provided to illustrate the effectiveness of the proposed methods. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4124–4132, 2013

Hopf bifurcation of a delayed predator-prey system with prey infection and the modified Leslie-Gower scheme is investigated. The conditions for the stability and existence of Hopf bifurcation of the system are obtained. The state feedback and parameter perturbation are used for controlling Hopf bifurcation in the system. In addition, direction of Hopf bifurcation and stability of the bifurcated periodic solutions of the controlled system are obtained by using normal form and center manifold theory. Finally, numerical simulation results are presented to show that the hybrid controller is efficient in controlling Hopf bifurcation.

A delayed three-species food chain system with two types of functional response, Holling type and Beddington-DeAngelis type, is investigated. By analyzing the distribution of the roots of the associated characteristic equation, we get the sufficient conditions for the stability of the positive equilibrium and the existence of Hopf bifurcation. In particular, using the normal form theory and center manifold theorem, the properties of Hopf bifurcation such as direction and stability are determined. Finally, numerical simulations are given to substantiate the theoretical results.

In this paper, we report on the development of a simple, fast, and environment-friendly UV/O3-based method as an improved alternative to the conventional chemical methods using dichromate or permanganate for determining chemical oxygen demand (COD) in water. In the method through the continuous monitoring of O3 and CO2 (concentration and flow rate) before and after reaction, COD can be accurately determined. During the experiment, sample solutions with known COD concentration of 25, 12.5, 5, 2.5, and 1 ppm were first used to validate the feasibility of this new technique. These samples were treated under ambient temperature and pressure for 15 min before the complete digestion time for each sample was measured by analyzing the produced CO2 concentration. After digestion, residual O3 dissolved in solution was quantified by the indigo method. A linear relationship between the O3 consumption and COD value was observed, and the slope of calibration curve was determined to be 0.34 with a R 2 of 0.991. Detection limit of the current experimental setup is 0.81 ppm with a measurement range of 1–25 ppm. The precision of the COD measurement is within 5% of the actual concentration. This developed UV/O3 method demonstrates viability in being applied to fast, reliable, and accurate COD monitoring.

The lifted state space model and the corresponding lifted transfer function model have been widely adopted to describe non-uniformly sampled-data (NUSD) systems. However, the lifted models are too complex and involve a large number of parameters, which bring a great challenge to NUSD systems identification and control. Motivated by this fact, we propose a novel input-output representation of general NUSD systems by introducing a time-varying backward shift operator. Based on the novel model, the traditional identification methods and control strategies of single-rate systems can be easily extended to general NUSD systems. The advantages and effectiveness of the novel model are well illustrated by a simulation example.