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In this paper, we discuss the Krylov subspace-based model order reduction methods of second-order systems with time delays, and present two structure-preserving methods for model order reduction of these second-order systems, which avoid to convert the second-order systems into first-order ones. One method is based on a Krylov subspace by using the Taylor series expansion, the other method is based on the Laguerre series expansion. These two methods are used in the multi-order Arnoldi algorithm to construct the projection matrices. The resulting reduced models can not only preserve the structure of the original systems, but also can match a certain number of approximate moments or Laguerre expansion coefficients. The effectiveness of the proposed methods is demonstrated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with designing fault detection filters for networked control systems subject to limited communication capacity and a class of sensor stuck faults. By taking into account the communication limitations (e.g. measurement quantisation, signal transmission delays and data packet dropouts) and all possible sensor stuck faults, a unified mathematical model is first presented. Based on this framework, a full-order fault detection filter is designed such that the residual system is asymptotically stable with the prescribed attenuation level in the generalisedH∞sense. In order to further improve the detection performance, an optimisation algorithm is proposed to minimise the threshold. Finally, a spring-mass-damper system is utilised to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective. [ABSTRACT FROM PUBLISHER]

This article considers the periodic review stochastic inventory models with service level constraint to provide an improved solution procedure. The previous researchers assumed that the objective function is concave down in the lead time so that the minimum must occur on the boundary points of each sub-domain. In this article, we will show that their assumption is questionable since the minimum might not occur at the boundary points of each sub-domain. In a recent paper in International Journal of Systems Science, Ouyang and Chuang studied this problem. However, their solutions contained questionable results and their algorithm might not find the optimal solution due to flaws in their solution procedure. We develop some lemmas to reveal the parameter effects and then present our improved solution procedures for finding the optimal solution for periodic review stochastic inventory models in which the lead time demand is a normal distribution. The savings are illustrated by solving the same examples from Ouyang and Chuang's paper to demonstrate the improvement using our revised algorithm. In the direction of future research, we discuss the comparison between the reordered point being fixed and the reordered point as a new variable. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of computing an approximation system for a given system with time-varying delay such that the energy-to-peak gain of the error system is less than a prescribed scalar. First, a delay-dependent boundedness condition of energy-to-peak gain is given in terms of linear matrix inequalities (LMIs), which recovers the delay-independent case. Then, based on the established delay-dependent boundedness condition of energy-to-peak gain, a sufficient condition to characterize the approximation system is obtained to solve the energy-to-peak model approximation problem in the form of LMIs with inverse constraints. A number of delay-independent energy-to-peak model approximation cases are special cases of a delay-dependent approximation. An efficient algorithm is derived to obtain the approximation models. Finally, examples are employed to demonstrate the effectiveness of the model approximation algorithm. [ABSTRACT FROM AUTHOR]

The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers’ satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers’ characteristics and the similarity of customers’ satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer. [ABSTRACT FROM AUTHOR]

As a special type of bilinear systems, K-power bilinear systems have a special coupled structure that should be preserved in the process of model reduction. We investigate moment matching methods for K-power systems and extract structure-preserved reduced models from the perspective of bilinear systems and coupled systems. The optimalH2reduction is also considered for K-power systems. We prove that there exist reduced models satisfying the optimality conditions and meanwhile preserving the coupled structure of the original models. Furthermore, such reduced models can be produced by an iterative algorithm, or alternatively by a subsystem-iteration algorithm with less computational effort and faster convergence rate. Simulation results show that the proposed iterative algorithms possess superior performance in contrast to moment matching methods. [ABSTRACT FROM PUBLISHER]

This article considers that the number of defective units in an arrival order is a binominal random variable. We derive a modified mixture inventory model with backorders and lost sales, in which the order quantity and lead time are decision variables. In our studies, we also assume that the backorder rate is dependent on the length of lead time through the amount of shortages and let the backorder rate be a control variable. In addition, we assume that the lead time demand follows a mixture of normal distributions, and then relax the assumption about the form of the mixture of distribution functions of the lead time demand and apply the minimax distribution free procedure to solve the problem. Furthermore, we develop an algorithm procedure to obtain the optimal ordering strategy for each case. Finally, three numerical examples are also given to illustrate the results. [ABSTRACT FROM PUBLISHER]

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes to deploy a group of mobile sensor agents to cover a polluted region so that they are able to retrieve the pollutant distribution. The deployed mobile sensor agents are capable of making point observation in the natural environment. There are two approaches to modelling the pollutant distribution proposed in this article. One is a model-based approach where the sensor agents sample environmental pollutant, build up an environmental pollutant model and move towards the region where high density pollutant exists. The modelling technique used is a distributed support vector regression and the motion control technique used is a distributed locational optimising algorithm (centroidal Voronoi tessellation). The other is a model-free approach where the sensor agents sample environmental pollutant and directly move towards the region where high density pollutant exists without building up a model. The motion control technique used is a bacteria chemotaxis behaviour. By combining this behaviour with a flocking behaviour, it is possible to form a spatial distribution matched to the underlying pollutant distribution. Both approaches are simulated and tested with a group of real robots. [ABSTRACT FROM AUTHOR]

In this article, we consider an inventory model for items that are stored in two-warehouses when demand is a general ramp-type function of time. Shortages are allowed and a constant fraction of shortages is backlogged. The existence and uniqueness of optimal solution is proved for both - the single-warehouse and the two-warehouse models. An algorithm is developed to facilitate the choice between the two-warehouse and the single-warehouse systems and hence to obtain the optimal replenishment policy. Numerical examples are presented. Sensitivity analysis with respect to the parameters of the model is performed. [ABSTRACT FROM AUTHOR]

In this article, a mathematical model is developed to formulate optimal ordering policies for retailer when demand is practically constant and partially dependent on the stock, and the supplier offers progressive credit periods to settle the account. The notion of progressive credit period is as follows: If the retailer settles the outstanding amount by M, the supplier does not charge any interest. If the retailer pays after M but before second period N offered by the supplier, then the supplier charges the retailer on the unpaid balance at the rate Ic1. If the retailer settles the account after N, then he will have to pay an interest rate Ic2 on the unpaid balance (Ic2 > Ic1). The cost minimization is considered to be an objective function. An algorithm is given to find the flow of optimal ordering policy. A numerical illustration is given to study the effect of various parameters on ordering policy and total cost of an inventory system. [ABSTRACT FROM AUTHOR]

In 1978, Barbosa and Friedman established a ‘general root law’ for power-form demand rate. Shortages are prohibited. However, in reality, shortages may occur and the planning time horizon is usually finite as the product life cycle is short. Therefore, the author here extends their inventory lot-size model to allow for shortages during a finite planning time horizon and propose an alternative algorithm in a backward manner to determine the optimal replenishment timing without using iterative search schemes. The study not only shows that the total relevant cost is a strictly convex function of the replenishment number, but also provides the condition for determining the optimal replenishment number. Finally, a numerical example is given for illustration. [ABSTRACT FROM AUTHOR]