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Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

The purpose of this paper is to introduce a significant modification of the sensitivity maps calculation process using electric field distribution analysis. A sensitivity matrix is typically a crucial part of a deterministic image reconstruction process in a three-dimensional capacitance tomography (3D ECT) and strictly decides about a final image quality. Commonly used sensitivity matrix computation methods mostly provide acceptable results and additionally allow to perform a recalculation of sensitivity maps according to the changing permittivity distribution.

The new “tunnel-based” algorithm is proposed which traces the surfaces constructed along the electric field lines. The new solution is developed and tested using experimental data.

To fully validate the new technique both linear and non-linear image reconstruction processes were performed and the criteria of image error estimation were discussed. This paper discusses some preliminary results of the image reconstruction process using the new proposed algorithm. As a result of this research, an increased accuracy of the new method is proved.

The presented results of image reconstruction with new sensitivity matrix in comparison with the classic matrix proved that the new solution is able to improve the convergence and stability of image reconstruction process for 3D ECT imaging.

There are many problems in civil or mechanical engineering related to structural design. In such a case, the solution techniques which lead to deterministic results are no longer valid due to the heuristic nature of design problems. The purpose of this paper is to propose a computational tool based on genetic algorithms, applied to the optimal design of cross-sections (solid tubes) of 3D truss structures.

The main feature of this genetic algorithm approach is the introduction of a selective-smart method developed in order to improve the convergence rate of large optimization problems. This selective genetic algorithm is based on a preliminary sensitivity analysis performed over each variable, in order to reduce the search space of the evolutionary process. In order to account for the optimization of the total weight, the displacement (of a specific section) and the internal stresses distribution of the structure a multiobjective optimization function was proposed.

The numerical results presented in this paper show a significant improvement in the convergence rate as well as an important reduction in the relative error, compared to the exact solution.

The variables sensitivity analysis put forward in this approach introduces a significant improvement in the convergence rate of the genetic algorithm proposed in this paper.

This paper aims to quantify the dependence relationship of bat algorithm’s (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities.

This paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA.

The paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA.

This paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.

Pattern recognition applied to control charts centers around the development and assessment of automated algorithms for detecting non‐random or unnatural patterns in observations collected from a production process. The work presented here marks the first examination of enhancements to an existing algorithm, of investigations into sensitivity analysis issues, of development of standard performance metrics, and of a comparative performance with the traditional Western Electric Run tests. The simulation results of the research presented here indicate that the modified algorithm performs markedly better than the original algorithm, is only slightly sensitive to the selection of the user specified algorithm parameters, and competes favorably with the Western Electric Run Tests especially when detecting repetitive patterns like cycles.

A local optimisation method for obtaining material parameters in finite element simulations has been developed. The method is based on the minimisation of an error function which reflects the accuracy of a numerical prediction with respect to the results of simple specimen tests. The experimental data were obtained from high strain rate tensile tests on the alloy 90 per cent titanium – 6 per cent aluminium – 4 per cent vanadium (Ti6Al4V) using the tensile split‐Hopkinson pressure bar. The behaviour of the tensile specimen was monitored during the test using high‐speed photography and transient recorders. Finite element simulations were performed using ABAQUS/Explicit employing the Zerilli‐Armstrong material model for bcc metals).

Green space is one of the main measures to alleviate urban heat islands (UHI). The transformation mechanism of daytime and nighttime scale sensitivity of vegetation coverage to reduce the UHI effect in a daily cycle has been unclear. As a result, we propose a scale sensitivity measurement algorithm to study the spatial and temporal response relationship between UHI and green coverage. Based on the scale theory of landscape ecology and the method of geostatistical analysis, we adopted ArcGIS, MATLAB, SPSS, and other data processing software as well as a large amount of measured and high-resolution satellite imagery data of Beijing and Tianjin to quantitatively study their spatial scale sensitivity and daily variation features of urban green spaces to reduce summer UHI. The results show that first, the green coverage rate and the UHI intensity experience positive and negative correlations during the daytime, and negative correlations at night. When the correlation coefficient is significant, there is a linear relationship between the UHI intensity and the core green rate. Second, the reduction of the UHI by green spaces displays spatial and temporal change scale sensitivity characteristics. The radius scale of daytime sensitivity is 15m, and the radius scale of nighttime sensitivity is 60m. The study's conclusion enriches the theoretical parameters of landscape ecological scales and patterns, and provides spatial and temporal scales for systematic planning of green space to reduce UHI.

The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.

The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.

Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.

The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.

The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.

Design sensitivities of plates and shells under transient dynamic loads with constraints on displacements and stresses are likely to be highly erroneous if proper care is not taken in selecting appropriate finite element mesh and time step size to be used in the analysis. An accurate value of design derivative is assured if an optimal mesh coupled with a reasonably fine time step size is used. The optimal mesh can be obtained iteratively and a number of examples are solved to demonstrate the importance of controlling discretization errors in space and time.