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The purpose of this paper is to evaluate the rate of discrepancy between the group of expected project results and the group of corresponding results realized preliminarily at the design stage.

The authors formulate and solve the problem to determine the values of two assemblies of independent variables guaranteeing the project with the best (X→^*) and as well this one with the worst (X→^**) tolerance field of the expected Z_x realization. The problem is formulated as an optimization one in nonlinear mathematical programming and it is solved by applying the flexible tolerance method.

By the use of basic relations in statistical science an expression of standard deviation σ_Zx of Z_x is found and proposed. The limits in which are expected to be change in the deviations between the main project results and the corresponding ones of the implemented design, mathematically defined previous to its realization.

The accent is put on the cases when the device characteristics are presented as polynomials, obtained on the bases of the design of experiments (DOE) and the response surface methodology. An arbitrary technical characteristic Z_x=Z(x→) is presented.

A researcher could confine numerically the tolerance field between project results and expected realized ones in the whole investigated region.

The paper is characterized by an original approach and a new expression of standard deviation is proposed which will be of interest to designers.

The purpose of this paper is to introduce a method for evaluating the production tolerances influence on the practically realized optimal solution of electrotechnical devices. The influence is estimated by the optimal solution range defined with a given probability.

Because of the tolerances nature, the paper is in probabilistic categories. The accent is put on the cases when the mathematical description of the cost function is analytical, for example polynomial found on the basis of the design of experiments and response surface methodology. The optimal solution range is defined with a given probability. The governing equation is Chebychev's inequality. In some cases, Chebychev's inequality would be rather weak but the advantage is that it is valid for all kinds of probabilistic distributions.

A numerical example – an electrical machine – is considered with respect to variances in the magnetic characteristics of the stator and rotor core electrotechnical steel and tolerances in the geometrical dimensions of the machine. An analytical expression for the variance of the optimal solution is obtained in the case of a second order polynomial cost function. It is found that the energetic characteristic of the realized optimal design is expected to be negligibly different from its value in the proposed optimal project.

Although the example concerns the field of electrical machines, the methodology can be of interest for other domains and for different electrotechnical devices.

The inverse problem related to eddy current testing (ECT) is often formulated as a shape optimization problem. The purpose of this paper is to propose a methodology for determining the optimal parameters of a sensor system for more accurate reconstruction of the crack shape.

In this paper, an objective function is formulated using the shape sensitivity information computed from the ECT data. The design of a non‐destructive testing (NDT) sensor is carried out through optimizing the sensor parameters under such a criterion.

The methodology proposed results in modifications to the original sensor geometry which makes it more sensitive to the depth changes in a crack. A square wave form of excitation is used in order to provide more information on the size of the crack at different depths, essentially through the superposition of a range of excitation frequencies, each of which has a different depth of penetration. The newly designed ECT sensor system is suitable for dealing with the natural crack problem.

While the methodology is general and has been shown to work in a simulated environment, the result is not verified by the experiments because the newly designed device has not actually been fabricated.

This paper has demonstrated the possibility of designing a sensor probe using computer aided design tools without extensive physical testing. The design process is novel and based on a sensitivity approach. This is shown to be very efficient and effective and the solution of the inverse problem demonstrates a very fast convergence.

The purpose of this paper is to develop an inverse approach for 3D thermal sources determination.

The developed approach is based on the Green's function for Poison's equation. Forward and inverse couple electromagnetic‐thermal field problems are formulated. Finite elements models are built and applied. Thermal field data are acquired by thermo vision camera. The thermal field sources are determined inside of the investigated inaccessible volume object using modeled and measured data with the developed approach.

The presented method and implemented examples demonstrate the possibilities of the developed approach for inverse source problem solution and determination of thermal field distributions of electrical devices.

The proposed inverse method uses the Green's function for Poison's equation for solution of thermal field problem taking into account the couple electromagnetic‐thermal problems. Proposed inverse method is very fast, accurate and can be used in many practical activities for electrical current determination and visualization in inaccessible regions only by measured external thermal field. Thermal field data needed for the method are easily acquired by thermo vision camera.

The purpose of this paper is to consider the optimization of an 8‐electrode cylindrical electrical capacitance tomography (ECT) sensor. The aim is to obtain maximum uniformity and value of the sensitivity distribution of the sensor, while keeping the mutual capacitances between the electrodes above a predefined level.

The optimization methods that have been used are response surface methodology, genetic algorithm and a combination of both.

As results, optimum dimensions for the gap, mounting pipe, shield and insulation are determined, which ensure more uniform distribution of sensitivity in the sensing area.

The optimization strategies used – RSM and the combined RSM+GA – make the optimization of ECT sensors feasible. The results show the effectiveness of the RSM+GA strategy which could also be used for optimization of 3D multilayer ECT sensors.

The purpose of this paper is to propose a method, reducing the order of the analysis problem, to help the solution of optimal shape design problems in electricity and magnetism.

The proposed method, the background of which relies on the generalized Thévenin theorem, exploits a principle of field diakoptics.

After assessing the proposed method on a benchmark of inverse magnetostatics, the shape optimisation of the stress‐control ring in a power transformer is considered as the case study.

The proposed method makes it possible to reduce the cost of an optimisation procedure, when the direct problem is solved by means of finite‐element analysis.

The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.

The flock‐of‐starlings optimization (FSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the BCA has been used to refine the FSO‐found solutions, thanks to its better performances in local search.

A good solution of the 8‐th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO+BCA algorithms.

The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.

The paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

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.