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Global optimization in electrical engineering using stochastic methods requires usually a large amount of CPU time to locate the optimum, if the objective function is calculated either with the finite element method (FEM) or the boundary element method (BEM). One approach to reduce the number of FEM or BEM calls using neural networks and another one using multiquadric functions have been introduced recently. This paper compares the efficiency of both methods, which are applied to a couple of test problems and the results are discussed.

This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).

The Lozi map is used to generate new individuals in the framework of ES algorithms. A quasi‐Newton (QN) method is also used within the iterative loop to improve the solution's quality locally.

It is shown that the combined use of chaotic sequences and QN methods can provide high‐quality solutions with small standard deviation on the selected benchmark problem.

Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems.

This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.

With the rise of alternate discovery services, such as Google Scholar, in conjunction with the increase in open access content, researchers have the option to bypass academic libraries when they search for and retrieve scholarly information. This state of affairs implies that academic libraries exist in competition with these alternate services and with the patrons who use them, and as a result, may be disintermediated from the scholarly information seeking and retrieval process. Drawing from decision and game theory, bounded rationality, information seeking theory, citation theory, and social computing theory, this study investigates how academic librarians are responding as competitors to changing scholarly information seeking and collecting practices. Bibliographic data was collected in 2010 from a systematic random sample of references on CiteULike.org and analyzed with three years of bibliometric data collected from Google Scholar. Findings suggest that although scholars may choose to bypass libraries when they seek scholarly information, academic libraries continue to provide a majority of scholarly documentation needs through open access and institutional repositories. Overall, the results indicate that academic librarians are playing the scholarly communication game competitively.

The purpose of this paper is to demonstrate improvement of the accuracy of electrical tomography reconstruction by incorporation of a priori knowledge into the inverse problem solution.

The fusion of two different inversion algorithms capable of real‐time operation is discussed, namely a non‐iterative monotonicity‐based approach, determining the a priori knowledge and an iterative Gauss‐Newton (GN)‐based reconstruction algorithm. Furthermore, the method is compared with the unmodified algorithms themselves by means of reconstructions from simulated inclusions at different noise levels.

The accuracy of the inverse problem reconstructions, especially at the boundary regions of the unknown inclusions, benefit from the investigations of incorporating a priori knowledge about material values and can be considerable improved. The monotonicity method itself, which has low complexity, provides remarkable reconstruction results in electrical tomography.

The paper is applied to simulated discrete two‐phase scenarios, e.g. gas/oil mixtures. In a further step the method would be tested with measured data. Moreover, investigations have to be carried out in order to make the monotonicity‐based reconstruction principle more robust against disturbing artifacts.

The fusion of the non‐iterative monotonicity‐based method with the GN‐based algorithm demonstrates a novel approach of improving the reconstruction accuracy in electrical tomography.

Decision-making has long been recognized as being at the core of organizational life. Yet, the cognitive mechanisms by which managers make decisions represent a critical field of exploration. In this context, business models (BMs) are cognitive representations of organizational architectures that managers use to orient their firms in the business environment. While BMs – as managerial schemas – have been extensively studied for their beneficial applications at the strategic level, scholarly attention has rarely focused on their dark side. In this chapter, we point out that BM thinking – that focuses excessively on established schemas – might narrow managerial cognition in the process of fine-tuning the current BM; in the process, opportunities for more radical BM innovation can be overlooked. We systematize March and Simon’s contribution on managerial cognition into a more comprehensive conceptual framework by integrating the perspectives of Kahneman, Baron, and Gollwitzer. The result is an epistemologically coherent framework for managerial cognition and decision-making that focuses on how managers can overcome cognitive biases that derive from a reliance on established BMs as schemas. We close this chapter with directions for further research.

The investigation of the influence of the modeling error on the solution of the inverse problem given uncertain measured data in electrical capacitance tomography (ECT).

The solution of the nonlinear inverse problem in ECT and hence, the obtainable accuracy of the reconstruction result, highly depends on the numerical modeling of the forward map and on the required regularization. The inherent discretization error propagates through the forward map, the solution of the inverse problem, the subsequent calculation of process parameters and properties and may lead to a substantial estimation error. Within this work different finite element meshes are compared in terms of obtainable reconstruction accuracy. In order to characterize the reconstruction results, two error measures are introduced, a relative integral error and the relative error in material fraction. In addition, the influence of the measurement noise given different meshes is investigated from the statistical point of view using repeated measurements.

The modeling error, the degree of regularization, and measurement uncertainties are the determining and limiting factors for the obtainable reconstruction accuracy of electrical tomography systems. The impact of these key influence factors on the calculation of process properties given both synthetic as well as measured data is quantified. Practical implications – The obtained results show that especially for measured data, the variability in calculated parameters strongly depends on the efforts put on the forward modeling, i.e. on an appropriate finite element mesh size. Hence, an investigation of the modeling error is highly recommended when real‐world tomography problems have to be solved.

The results presented in this work clearly show how the modeling error as well as inherent measurement uncertainties influence the solution of the inverse problem and the posterior calculation of certain parameters like void fraction in process tomography.

The conventional starting point for the design of an electrical machine (or any low-frequency electromagnetic device) is known as “sizing”. In this process, a simple magnetic circuit is used to estimate the main geometric parameters. This does not work for many devices, particularly where eddy currents and non-linearity dominate. The purpose of this paper is to investigate an approach using a neural network trained on a large database of existing designs as a general sizing system.

The approach is based on a combination of a radial basis function neural network and a database of stored performances of electrical machines. The network is trained based on a set of typical performance requirements for a machines design problem. The resulting design is analyzed using finite elements to determine if the design performance is acceptable.

The number of neurons in the network was varied to determine the approximation and generalization capabilities. The finite element analysis showed that the network produced initial design parameters which resulted in an appropriate performance.

The research has looked at only one class of machine. Further work is needed on a range of machines to determine how effective the approach can be.

The approach can provide a good initial design and thus can reduce overall design time significantly.

The paper proposes a novel, fast and effective generalized approach to sizing low frequency electromagnetic devices.