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Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

• The original identification model is decomposed into several sub models according to the dimension of output and different forms of parameters. • To solve the unmeasurable noise terms in the information matrices, we construct some auxiliary models based on the obtained parameter estimates. • To cut down the redundant estimates and solve the associate terms , a partially coupled nonlinear parameter optimization algorithm is proposed. A key to the analysis and design of a dynamic system is to establish a suitable mathematical model of the system. This paper investigates the parameter optimization problem of a class of radial basis function-based multivariate hybrid models. Taking into account the high dimensions of the models and different forms of the parameters, the original identification model is separated into several regressive sub-identification models according to the characteristics of model outputs. Some auxiliary models are constructed to solve the unmeasurable noise terms in the information matrices. For the purpose of eliminating the redundant computation and to deal with the associate terms caused by the model decomposition, inspired by the coupling concept, a partially-coupled nonlinear parameter optimization algorithm is proposed for the multivariate hybrid models. Through the computational efficiency analysis and numerical simulation verification, it is shown that the proposed algorithm has low computational complexity and high parameter estimation accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Spare parts and maintenance are closely related logistics activities where maintenance generates the need for spare parts. When preventive maintenance is present, it may need more spare parts at one time because of the planned preventive maintenance activities. This paper considers the joint optimisation of three decision variables, e.g., the ordering quantity, ordering interval and inspection interval. The model is constructed using the well-known Delay-Time concept where the failure process is divided into a two-stage process. The objective function is the long run expected cost per unit time in terms of the three decision variables to be optimised. Here we use a block-based inspection policy where all components are inspected at the same time regardless of the ages of the components. This creates a situation that the time to failure since the immediate previous inspection is random and has to be modelled by a distribution. This time is called the forward time and a limiting but closed form of such distribution is obtained. We develop an algorithm for the optimal solution of the decision process using a combination of analytical and enumeration approaches. The model is demonstrated by a numerical example. [Copyright &y& Elsevier]

Abstract: Expensive optimization aims to find the global minimum of a given function within a very limited number of function evaluations. It has drawn much attention in recent years. The present expensive optimization algorithms focus their attention on metamodeling techniques, and call existing global optimization algorithms as subroutines. So it is difficult for them to keep a good balance between model approximation and global search due to their two-part property. To overcome this difficulty, we try to embed a metamodel mechanism into an efficient evolutionary algorithm, low dimensional simplex evolution (LDSE), in this paper. The proposed algorithm is referred to as the low dimensional simplex evolution extension (LDSEE). It is inherently parallel and self-contained. This renders it very easy to use. Numerical results show that our proposed algorithm is a competitive alternative for expensive optimization problems. [Copyright &y& Elsevier]

Abstract: The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms. [Copyright &y& Elsevier]

Abstract: Total variation minimisation is a well-established method for digital image restoration. Its implicit preservation of edges permits the derivation of anisotropic models for a qualitative improvement at corners. This paper is a synopsis of anisotropic models with state-of-the-art insights into the numerics of isotropic models. We generalise two representative models from both branches of research. This formulation leads to a general convergent algorithm and a general highly efficient algorithm which apply for both cases. A transfer of the discretisation from the anisotropic model to the isotropic setting results in an improvement of rotational invariance. [ABSTRACT FROM AUTHOR]

Abstract: This paper formulates and solves a lane-based evacuation network optimization problem that integrates lane reversal and crossing elimination strategies. To tackle the combinatorial complexity arising from this combined treatment of discrete network capacity and connectivity settings, an integrated Lagrangian relaxation and tabu search solution method is developed. The method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. Numerical results from optimizing a regional evacuation network for a nuclear power plant illustrate the validness and usefulness of the modeling and solution methodology in evacuation planning practice. [ABSTRACT FROM AUTHOR]

Abstract: Active shape models (ASMs) are an effective boundary segmentation technique currently applied to a variety of image analysis problems, they are fast and accurate if the initial model pose and shape are close enough to the object boundary. An ASM consists essentially of a statistical shape model (point distribution model (PDM)), and a local search method along normal pixel profiles, which are located at each point of the PDM. In this paper a new ASM fitting algorithm which incorporates the optimization of an objective function together with the local search currently used in ASMs is reported. The objective function was constructed as the mean Mahalanobis distance of all the pixel profiles of each point of the PDM, to the corresponding mean profile of a training set, and is optimized using simplex search, which provides a fast numerical optimization. Our ASM-simplex algorithm increases significantly the range of initial model poses (scale (s 0), rotation (θ 0), and translations (Tx0, Ty0)) which results in more accurate boundary segmentation, without the need for any additional model training. Evaluation was performed using the shape models of the prostate and the left hand. The mean maximum error of prostate segmentation (for a range of initial rotations of [θ 0 −72, θ 0 +72] degrees) in ultrasound images, decreased 16% for the ASM-simplex algorithm with respect to the original ASM. The following reductions were obtained on photographic images of the left hand for each initial pose parameter: 39% for [Tx0 −48, Tx0 +48] pixels, 38% for [Ty0 −48, Ty0 +48] pixels, 9% for [s 0 −0.25, s 0 +0.25] and 42% for [θ 0 −30, θ 0 +30] degrees. [Copyright &y& Elsevier]

Abstract: An identification algorithm for a power system load model is proposed in this paper. The overall non-convex identification problem is separated into convex and non-convex subproblems, allowing for a global optimum to be found. Numerical experiments using data from both simulated and physical systems illustrate the accuracy of the proposed algorithm. Experiments are performed to investigate the robustness of the algorithm. [Copyright &y& Elsevier]