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We consider here a general nerwork composed by n‐distributed parameters lines (with telegraph‐equations models) and m‐capacitors, all connected by a resistive multiport. An asymptotic stability property drives us to define and evaluate a global parameter (“λ‐delay time”) which describes the speed of signals propagation through the network. Because of its simplicity of calculation and its tightness, the given upper bound of the λ‐delay time is useful in timing analysis of MOS integrated chips.

To explore Finnish experts' perceptions of the forms of digital healthcare that are anticipated to be the most utilised in healthcare in the medium-term future (year 2035) and anticipated healthcare workforce impacts those forms will have.

A total of 17 experts representing relevant interest groups participated in a biphasic online Delphi study. The results for each round were analysed using descriptive statistical methods and inductive content analysis.

The forms of digital healthcare that the experts perceived as most likely to be utilised were those enabling patient participation, efficient organisation of services and automated data collection and analysis. The main impacts on the healthcare workforce were seen as being the redirection of workforce needs within the healthcare sector and need for new skills and new professions. The decrease in the need for a healthcare workforce was seen as less likely. The impacts were perceived as being constructed through three means: impacts within healthcare organisations, impacts on healthcare professions and impacts via patients.

The results are not necessarily transferable to other contexts because the experts anticipated local futures. Patients' views were also excluded from the study.

Healthcare organisations function in complex systems where drivers, such as regional demographics, legislation and financial constraints, dictate how digital healthcare is utilised. Anticipating the workforce effects of digital healthcare utilisation has received limited attention; the study adds to this discussion.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

The gradient recovery method proposed by Zhu and Zienkiewicz for one‐dimensional problems is generalized to two dimensions, using quadrilateral elements. Its performance is compared with that of conventional local smoothing techniques and of direct differentiation of the finite‐element solution, on finite‐element approximations to analytically known polynomial and transcendental functions on a quadrilateral second‐order finite‐element mesh. The new method appears to be reliable and more stable than local smoothing, and to provide better accuracy than direct differentiation, at low computational cost.

The gradient recovery method proposed by Zhu and Zienkiewicz for one‐dimensional problems and extended to two dimensions by Silvester and Omeragi? is generalized to three‐dimensional solutions based on rectangular prism (brick) elements. The extension is not obvious so its details are presented, and the method compared with conventional local smoothing and direct differentiation. Illustrative examples are given, with an extensive experimental study of error. The method is computationally cheap and provides better accuracy than conventional local smoothing, but its accuracy is position dependent.

The paper is concerned with a design and a validation of a neurocontroller for a pulse magnetiser for magnetising permanent magnets. The goal is to register the peak time and crest current in order to pick up an optimal intermittent duty conditions regime for the magnetiser. This is usually done by solving a set of coupled ordinary differential equations describing current waveforms and the temperature rise in the magnetising winding. The neurocontroller is based on a one‐layer feedforward neural network which is trained using the Levenberg‐Marquardt learning rule. We present the results produced by the neurocontroller and we compare them with the numerical and measurement results. The neurocontroller is intended to serve later as a part of a global optimising algorithm.

The purpose of this paper is to study the optimization problem of low‐frequency magnetic shielding using the adjoint variable method (AVM). This method is compared with conventional methods to calculate the gradient.

The equation for the vector potential (eddy currents model) in appropriate Sobolev spaces is studied to obtain well‐posedness. The optimization problem is formulated in terms of a cost functional which depends on the vector potential and its rotation. Convergence of a steepest descent algorithm to a stationary point of this functional is proved. Finally, some numerical results for an axisymmetric induction heater are presented.

Using Friedrichs' inequality, the existence and uniqueness of the vector potential, its gradient and the corresponding adjoint variable can be proved. From the numerical results, it is concluded that the AVM is advantageous if the number of parameters to optimize is larger than two.

The AVM is only faster than conventional methods if the gradients can be calculated with sufficient accuracy.

Theoretical results for eddy currents model are often based on a non‐vanishing conductivity. The theoretical value of this paper is the presence of non‐conducting materials in the domain. From a practical viewpoint, it has been demonstrated that the AVM can yield a significant reduction of computational time for advanced optimization problems.

To present a neural network‐based approach to the design of electromagnetic devices.

A neural model is created which reproduces the relationship between the design parameters of the device and the performance parameters, typically field values.

The neural model is a single hidden layer MLP network, trained by using a set of cases calculated, for example, by means of a finite element analysis. The design problem can be solved by fixing the performance values at the output of the network and by calculating the corresponding input values. The relationship between the input and the output of the neural network is represented by three equations systems. By means of these three systems, we can forward the domain of the input, and we can back propagate the desired output throughout the network layers. In such a way, both the domain of the design parameters and the domain of the desired performances values can be projected in the same space. Whatever point inside the intersection between the two projected domains corresponds to a solution of the design problem.

Presents a procedure which is able to find a point belonging to such an intersection.

The purpose of this paper is to develop a magnetic induction tomography (MIT) system as well as the conductivity reconstruction algorithms (inverse problem).

In order to define and verify the solution of the inverse problem, the forward problem is formulated using mathematical model of the system. The forward problem is solved using the finite element method. The optimization of the excitation unit is based on the numerical solutions of the direct problem. All the dimensions and shape of the excitation system are optimized in order to focus the main part of the magnetic field in the vicinity of the receiver. Finally, two formulations of the inverse problem are discussed: based on the inversion of the Biot‐Savart law; and based on the artificial neural networks.

The formulation of the forward problem of the considered MIT system is given. The construction of the exciter unit that focuses the main part of the magnetic field in the vicinity of the receiver is proposed. Two formulations of the inverse problem are discussed. First using the inversion of the Biot‐Savart law and second using the artificial neural network. The neural networks seem to be promising tools for reconstructing the MIT images.

This paper demonstrates a real‐life MIT system whose performance is satisfactorily predicted by mathematical models. The original design of the exciter is shown. The new approach to the inverse problem in MIT – the use of the artificial neural network – is presented.

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.