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As one of the tomographic imaging techniques, electrical capacitance tomography (ECT) is widely used in many industrial applications. While most ECT sensors have electrodes placed around a cylindrical chamber, the planar ECT sensor has been investigated for depth and defect detection. However, the planar ECT sensor has limited height and depth sensing capability due to its single-sided assessment with the use of only a single-plane design. The purpose of this paper is to investigate a dual-plane miniature planar 3D ECT sensor design using the 3 × 3 matrix electrode array.

The sensitivity map of dual-plane miniature planar 3D ECT sensor was analysed using 3D visualisation, the singular value decomposition and the axial resolution analysis. Then, the sensor was fabricated for performance analysis based on 3D imaging experiments.

The sensitivity map analysis showed that the dual-plane miniature planar 3D ECT sensor has enhanced the height sensing capability, and it is less ill-posed in 3D image reconstruction. The dual-plane miniature planar 3D ECT sensor showed a 28% improvement in reconstructed 3D image quality as compared to the single-plane sensor set-up.

The 3 × 3 matrix electrode array has been proposed to use only the necessary electrode pair combinations for image reconstruction. Besides, the increase in number of electrodes from the dual-plane sensor setup improved the height reconstruction of the test sample.

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.

Cities’ population growth goes in hand with the development of new technologies that are becoming the key factor of the Smart City (SC) concept. It allows the implementation of efficient management solutions, operation, and sustainable development of a city to face the challenges of urbanization and improve the services for the citizens and visitors.

The concept of the SC 5.0 was first presented in Svítek, Skobelev, and Kozhevnikov (2020), where the problems of the complexity of current cities due to rigid management processes, variety of infrastructure, and SC modules, systems, subsystems, and applications were described.

To prove the concept, several practical examples were developed to cover the topics: modeling in SCs, practical implementation of multiagent technologies, the approach of creating city ontology and the city knowledge base as the instrument of semantic interoperability, and visualization possibilities of Smart Evropská as a SC Testbed used for teaching purposes.

The new organizational structure is proposed based on knowledge graphs, and practical examples are shown. The applicability of knowledge graphs to be used in combination with data management platforms for monitoring SC key performance indicators (KPIs) and providing interoperability of services is presented.

Circular pipelines are mostly used for pneumatic conveyance in industrial processes. For optimum and efficient production in industries that use a pipeline for conveyance, tomographic image of the transport particles is paramount. Sensing mechanism plays a vital role in process tomography. The purpose of this paper is to present a two‐dimensional (2‐D) model for sensing the characteristics of electrostatic sensors for electrical charge tomography system. The proposed model uses the finite‐element method.

The domain is discretized into discrete shapes, called finite elements, by using a MATLAB. Each of these elements is taken as image pixels, on which the electric charges carried by conveyed particles are transformed into equations. The charges' interaction and the sensors installed around the circumference, at the sensing zone of the conveying pipeline are related by the proposed model equations. A matrix compression technique was also introduced to solve the problem of unevenly sensing characteristics of the sensors due to elements' number's concentration. The model equations were used to simulate the modeled electrostatic charge distribution carried by the particles moving in the pipeline.

The simulated results show that the proposed sensors are highly sensitive to electrostatic charge at any position in the sensing zone, thereby making it a good candidate for tomographic image reconstruction.

Tomographic imaging using finite element method is found to be more accurate and reliable compared to linear and filtered back projection method.

The purpose of this paper is to better understand landscape dynamics in arid and semi‐arid environments. Land degradation has recently become an important issue for land management in western China. The oasis ecosystem is especially sensitive to environmental disturbances, such as abnormal/extreme precipitation events, variations in the water supply from the upper watersheds, fluctuations in temperature, etc. Satellite remote sensing of terrestrial ecosystems can provide us with the temporal dynamics and spatial distributions of green cover over large areas of landscape. Seasonal green cover data are especially important in assessing landscape health (e.g. desertification, rate of urban sprawl, natural disturbances) in arid and semi‐arid regions. In this study, green cover data are derived from vegetation indices retrieved from moderate resolution imaging spectroradiometer (MODIS) sensors onboard the satellite Terra.

Satellite images recorded during the period from April 2000 to December 2005 are analyzed and the spatial distribution and temporal changes of the Ejin Oasis quantified.

This study shows that it is possible to derive important parameters linked to landscape sensitivity from MODIS and the derived imagery, such as normalized difference vegetation index (NDVI) time‐series data. Such a MODIS‐based time‐series monitoring system is particularly useful in arid and semi‐arid environments. The results of landscape sensitivity analysis prove the effectiveness of the method in assessing landscape sensitivity from the years 2001‐2005.

The novel strategy used in this investigation is based on the T‐S fuzzy model, which is in turn based on fuzzy theory and fuzzy operations.

Simulation results based on fuzzy models will help to improve the monitoring techniques used to evaluate land degradation and to estimate the newest tendency in landscape green cover dynamics in the Ejin Oasis.

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

Presents a computational algorithm for the numerical integration of triaxial concrete plasticity formulations. The specific material formulation at hand is the so‐called extended leon model for concrete. It is based on the flow theory of plasticity which entails isotropic hardening as well as fracture energy‐based softening in addition to non‐associated plastic flow. The numerical algorithm resorts to implicit integration according to the backward Euler strategy that enforces plastic consistency according to the closes‐point‐projection method (generalized radial‐return strategy). Numerical simulations illustrate the overall performance of the proposed algorithm and the significant increase of the convergence rate when the algorithmic tangent is used in place of the continuum operator.

The purpose of this paper is to introduce a rapid method for calculating capacitive sensor signal variations for any small permittivity or electrode modifications at any position in space.

When a capacitive sensor is probing its surrounding, the modification of the permittivity, the displacement or the deformation of one or more electrodes induce a signal variation which depends on the position of the modification. Seeing that modification as a small perturbation and using Gauss identity, it is possible to find integral expressions of the sensor sensitivity map.

Capacitive sensor sensitivity map expressions depend only on the perturbation to measure, on the electric field before the perturbation, and on a sensitivity field which is the electric field produced by the sensor when the measuring electrode is held to 1 V while the others, except the floating ones, are grounded. The sensitivity field is a kind of Green's function for the capacitive sensor. The knowledge of the electric and sensitivity fields makes it possible to obtain the whole sensor sensitivity map at once without requiring time consuming parametric calculations.

The sensitivity field calculation provides a simple and direct view of the capacitive sensor capabilities. That should improve capacitive sensor design.

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.

The purpose of this paper is to propose the use of the continuous adjoint method as a tool to identify the appropriate location and “type” (suction or blowing) of steady jets used in active flow control systems.

The method is based on continuous adjoint and covers both internal and external aerodynamics. The adjoint equations, including the adjoint to the SpalartAllmaras turbulence model and their boundary conditions are formulated. At the cost of solving the flow and adjoint equations just once, the sensitivity derivatives of the objective function with respect to hypothetical (normal) jet velocities at all wall nodes are computed. Comparisons of the computed sensitivities with finite differences and parametric studies to assess the present method are included.

Though the sensitivities are computed for zero jet velocities, they adequately support decision making on: the recommended location of jet(s), at boundary nodes with high absolute valued sensitivities; and the selection between suction or blowing jets, based on the sign of the computed sensitivities. Regarding adjoint methods, two important findings of this work are: the role of the adjoint pressure which proves to be an excellent sensor in flow control problems; and the prediction accuracy of the proposed adjoint method compared to the commonly made assumption of “frozen turbulence”.

First use of the continuous adjoint method using full differentiation of the turbulence model, in flow control optimization. A low‐cost design tool for recommending some of the most important jet characteristics.