Search results

Gives a bibliographical review of the error estimates and adaptive finite element methods from the theoretical as well as the application point of view. The bibliography at the end contains 2,177 references to papers, conference proceedings and theses/dissertations dealing with the subjects that were published in 1990‐2000.

To define a methodology for comparing sensor arrays for solving magnetostatic linear inverse problems.

A singular value decomposition related projection method is used for comparing sensor arrays and we applied it to a biomagnetic inverse problem, as an example. Furthermore, a theoretical reference sensor system is introduced and used as a benchmark for the analysed sensor arrays.

The method has turned out to be effective in comparing three different theoretical sensor arrays, showing the superiority of the two arrays constituted by three‐axial sensors.

The method has been applied only to the case of over‐determined problems. The underdetermined case will be considered in future work.

From the applicative point of view, the illustrated methodology is useful when one has to choose between existing sensor arrays or in the design phase of a new sensor array.

A new methodology is proposed for comparing sensor arrays. The advantage of the methodology are to take into account the regularization in the solution of the inverse problem and to be general, not depending on a particular source configuration.

Inverse problems in electromagnetism, namely, the recovery of sources (currents or charges) or system data from measured effects, are usually ill-posed or, in the numerical formulation, ill-conditioned and require suitable regularization to provide meaningful results. To test new regularization methods, there is the need of benchmark problems, which numerical properties and solutions should be well known. Hence, this study aims to define a benchmark problem, suitable to test new regularization approaches and solves with different methods.

To assess reliability and performance of different solving strategies for inverse source problems, a benchmark problem of current synthesis is defined and solved by means of several regularization methods in a comparative way; subsequently, an approach in terms of an artificial neural network (ANN) is considered as a viable alternative to classical regularization schemes. The solution of the underlying forward problem is based on a finite element analysis.

The paper provides a very detailed analysis of the proposed inverse problem in terms of numerical properties of the lead field matrix. The solutions found by different regularization approaches and an ANN method are provided, showing the performance of the applied methods and the numerical issues of the benchmark problem.

The value of the paper is to provide the numerical characteristics and issues of the proposed benchmark problem in a comprehensive way, by means of a wide variety of regularization methods and an ANN approach.

The purpose of this paper is to propose a planning strategy for the radio frequency ablation (RFA) treatment of hepatic tumors. The goal is to give to the surgeon the opportunity of controlling the shape and the size of the treated volume and preserving the healthy tissues.

A FEM model of the human torso is built from radiographic and MRI scans of the patients, and then the RFA treatment “dynamically optimized” by controlling currents in multiple external electrodes, in such a way to drive currents in the desired regions, burning the tumor while trying to preserve healthy regions. A suitable cellular death model is considered in order to achieve an effective description of the biological modifications in the tumor volume.

A numerical method to plan the RFA treatment of hepatic tumors has been defined, aiming to preserve as much as possible healthy tissues.

The method depends on the knowledge of inner structure and properties of the patient's torso; while the structure of tissues can be determined by TAC or MRI scans, the physiological properties are much more uncertain.

The proposed approach allows optimized RFA treatments to be designed, allowing reduction of damage to healthy tissues deriving from application of the treatment.

The problem of optimal design of RFA treatments has been previously tackled in literature, but in this paper, dynamical optimization techniques and a cell death rate model have been included.

The purpose of this paper is to analyze the impact of different current representation models in the high field magnets characterization. Inverse source methodology used for current reconstruction is discussed. The regularizing effect of successive field map computation in different regions is also assessed.

Under suitable hypotheses, the resulting inverse source problem is linear, and different current representation bases are used to assemble different matrices. Properties of matrices are then assesses using SVD. The following field computation problem is also formulated using a projection matrix, and the properties of combined matrix operators are analyzed and compared to the inversion matrix.

The characteristics of the inverse matrix depend on the choice of the current representation basis, but in any case the application of the further projection matrix has a relevant regularizing effect.

The method is intrinsically tied to the linearity assumption, and the regularizing effect of the projection operator is stronger for further field regions.

The accuracy in the current reconstruction procedure can be reduced if data will be used only to compute field in distant regions.

The paper casts the problem of field computation in distant regions from magnetic measurements in the language of direct and inverse operators, allowing to assess its properties and fine tune the procedure parameters to achieve satisfactory results with minimum effort.

The paper aims to go through signage management from the standpoint of the user in a service quality perspective and by three principal means. First, it gives a literature review on the theme. Second, it identifies the needs that users expect to be satisfied when using signage. Third, it proposes a conceptual framework that encompasses user assessment determinants on signage management quality (SMQ).

Conceptual analysis is based on extensive research through literature on signage management contained in contributions on the theme of physical surroundings, atmospherics and servicescape.

Users, when they enjoy use of signage, may find satisfaction of implicit, expected and unexpected needs. User assessment of SMQ depends on the discrepancy between expected and perceived wayfinding. In particular, personal needs (cognitive-rational and psycho-emotional) affect expected wayfinding. User interactions with servicescape components, including signage, impact on perceived wayfinding.

The paper is conceptually developed, but lacks empirical validation. This study does not pretend to be thorough in any way. It leaves several questions open for future research.

Service managers must understand, from user behavior, how the signage is used, where users make their decisions and where the key points in the servicescape are located. The goal is to permit the user to develop a wayfinding process through the signage components he encounters in the servicescape. Signage should be designed and managed from the standpoint of attractive quality.

Previous research has essentially analyzed signage in terms of design. Signage management has received little and fragmented consideration in a service quality literature. The paper fills this gap. It is one of the first attempts to analyze signage management in a service quality perspective.

To present a robust optimal design technique in the presence of system parameters uncertainties.

The properties of normally distributed random variables are exploited, together with surface response fitting techniques, with the aim to reduce the computational cost in assessing the effect of uncertainties.

A fast approximate method for computing statistical average is presented together with its implementation for the design of magnets for magnetic resonance imaging.

Future research will be focused to multi‐dimensional problems and to the best choose of closed form expressions to evaluate statistical moments fitting.

Robust optimal design methodologies are receiving an increasing interest in both academic and industrial research, due to their capability of coping with construction uncertainties and tolerances.

The effectiveness of the simplified method has been demonstrated for an analytical example and on a simplified superconducting magnet design. The proposed strategy is quite general and it can be applied to a wide class of optimal design problems.

Aims to present a novel approach for the worst‐case, robust design (RD) of an electromagnetic system able to overcome some discretionary aspects of the available methods.

The proposed methodology is based on a suitable application of the interval arithmetic and considers the width of the interval Taylor extension (WITE) of the performance function (PF) as a valuable robustness index (RI). In particular, the most robust stationary point is obtained by looking for the minimum of a continuous, non‐differentiable function thanks to the properties of WITE.

The method furnishes a reliable classification of the robust solutions with PFs expressed by either mono or multi‐dimensional polynomial forms and provides the over‐bounding of the PF without additional computational efforts.

Limitations concern the necessity of an analytic formulation of the system performance. The IA approach may lead to lose some robust solution.

A RI can be assigned in analytic form and it can be suitably used as a figure of merit in a multi‐objective optimisation problem.

The use of the interval analysis for the worst‐case, RD of an electromagnetic system is original. The system designer may find the most robust stationary solution by means of a continuous, non‐differentiable function.

Inductances of complex coils, in the presence of linear materials only, can be computed by discretizing coils into simpler elements, whose magnetic behavior is analytically expressible, and suitably combining elementary contributions. Reliable results require high numbers of elements. In such cases, advantages can be taken from Graphic Processor Unit (GPU) capabilities of dealing efficiently with high numbers of repeated simple computational tasks. The purpose of this paper is to set up a fast and prompt numerical procedure to cope with the above described task.

The coils are first decomposed into current segments, taking into account accuracy, relative position and shape of coils to determine the number of segments. An analytical formula is then used to compute elementary contributions using GPUs to speed up the process, and finally superposition is used to recover the result.

The main advantages of the proposed approach are first demonstrated using simple examples, with analytical solutions, to validate the method accuracy and promptness, then more complex cases are taken to demonstrate its generality.

The method is intrinsically limited by the linearity assumption, excluding the presence of magnetic materials. The adopted formulas require in addition that coils must lie in free space.

The proposed method can help in the design of complex coils or coils systems, where the performance depends on total magnetic energy or magnetic forces among coils.

The paper presents an original implementation in GPU-based computational environment of a procedure to compute inductances, based on the superposition of a high number of current segments. The procedure includes an original method to self-adaptively define number and position of current segments used in the coils discretization.

An effective approach to the optimal design of electromagnetic devices should take into account the effect of mechanical tolerances on the actual devices performance. A possible approach could be to match a Pareto optimality study with a Monte Carlo analysis by randomly varying the constructive parameters. In this paper it is shown how such an analysis can be used to allow an expert designer to select among different Pareto optimal designs.