Electrical Tomography Method Research Articles

Research on capacitance-coupled electrical impedance imaging fusion algorithm based on wavelet transform.

Capacitively Coupled Electrical Impedance Tomography (CCEIT), as a new electrical tomography method, effectively overcomes the drawbacks of traditional electrical resistance tomography that requires direct contact with liquids, so as to achieve non-contact measurement. In order to make full use of the complete electrical impedance information (real and imaginary) obtained by CCEIT and further improve the quality of image reconstruction, an image fusion algorithm based on wavelet transform was proposed. First, the Landweber algorithm is used to image the real part information and the imaginary part information, and the wavelet transform algorithm is used to decompose the two images at multiple scales, in which the low-frequency coefficient is fused by the improved logic filter algorithm based on the adaptive threshold, the high-frequency coefficient is selected based on the absolute value maximum method for fusion, and finally, the fused image is obtained by wavelet reconstruction. Numerical simulations and experiments verify the feasibility of the proposed method, and the results show that the relative error is reduced by up to 17% and the correlation coefficient is increased by up to 10%.

Geoelectric Zoning and Chemical Composition of the Molybdenum Deposit Tailings (Village of Vershino-Shakhtaminsky, Trans-Baikal Territory)

A study of the Shakhtaminskoe tailings dump of the molybdenum deposit (village of Vershino-Shakhtaminsky, Trans-Baikal Territory) was carried out using the electrical tomography method with verification by geochemical testing. It has been shown that waste corresponds to a medium with a resistance of 1–300 Ohm·m. The location of the fault zone at the base of the storage facility has been clarified. The body of the technogenic system has been contoured, the resources of valuable and toxic elements are calculated.

Geophysical electrical potential inversion for grouting remote monitoring of coal mine double-layered wellbores

This work aims to remotely monitor the grouting process of double-layered wellbores in deep coal mining area by proposing a new geophysical electrical potential inversion imaging method. The proposed geophysical method is able to evaluate the grouting quality between the inner and outer walls of the wellbores by inverting the geologic electrical potential. Th challenge lies here in the potential inversion is that the thickness of the grouting space is extremely narrow when comparing with the wellbore diameter. To solve this problem, a constrained inversion model is developed to precisely generate the three-dimensional (3D) resistivity distributions of the double-layered wellbore based on the measured geologic electrical potential. A special designed test platform is constructed and experimental tests have been conducted to examine the performance of the proposed geophysical inversion imaging method. The geologic electrical potential of the grouting space is measured and remotely transmitted to the ground processing center; and the constrained inversion model is applied to the geologic signals to calculate the resistivity distributions at different grouting stages and produce the 3D geophysical images. The analysis results suggest that the present geophysical monitoring method can effectively visualize the grouting to evaluate the construction quality of the wellbore.

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

Evaluation of wet front under foundation at “El Chocón dam” using electrical tomography

El Chocón dam is located on Limay River in the province of Neuquén (Argentina) and is one of the most important in the country and in Latin America due its reservoir capacity. Currently, the dam is at full operation stage, while its structural health is monitored through a significant number of instruments installed in the dam body and its foundation. In the sector of the closure of the dam on its left margin, the measurements of some piezometers that monitor the deep rock foundation, showed some changes that gave rise to the hypothesis of the advance of wet front, despite fact that the work has practically 50 years from commissioning. To confirm or rule out this hypothesis, research work was started using the 2D electrical tomography method. The methodology was selected based on the good correlation that exists between the electrical resistivity and the volumetric content of water in a rock formation. Tomographic image obtained made it possible to determine different sectors in which wet front is detected and to define its extension, also evidencing a good correlation with what was recorded in the rock auscultation instruments, confirming the slow advance of wet front to downstream.

The application of a wavelet filtering method in magneto-acousto-electrical tomography

Magneto-acousto-electrical tomography (MAET), which couples ultrasound imaging with electrical impedance tomography, is an electrical property imaging method which is expected to have a wide range of clinical applications, including the early detection of breast and liver cancers. Obviously, as a coupled imaging method, how to improve the signal-to-noise ratio (SNR) is a key issue in the imaging process. In this paper, a wavelet filtering method is introduced into MAET, which includes the filtering effect of the db6 wavelet, and its filtering effect at different decomposition levels. At the same time, based on the Lorentz reciprocity theorem, the wave equation satisfied by the detected voltage obtained by electrode was deduced. We also built an experimental platform to acquire signals by keeping the position of the target unchanged and moving the ultrasound transducer along the trajectory of a circular arc. The experimental results show that the wavelet filtering scheme proposed in this paper improves the SNR of the detected signal of 15.1 dB, and the images of electrical properties of the phantom and pork from isolated tissues were realized by the filtered signal of the db6 wavelet and time reversal method, which reflects the interface of electrical conductivity change of tissues. This scanning method, of fixing the target body and rotating the transducer, can effectively reduce the error and noise caused by the movement of the detection electrodes in the experiment. The filtering technique and imaging algorithm proposed in this paper have improved the SNR and contrast of the images. Thus, the images of the low conductivity phantom with 0.2 S m−1 and isolated tissue were obtained, which indicates that the MAET has good prospects in clinical applications.

Improved Magneto-Acousto-Electrical Computed Tomography (MAE-CT) With Multi-Angle Plane Wave Excitation.

As a tissue conductivity imaging method, magneto-acousto-electric tomography (MAET) has the advantage of high axial spatial resolution compared with traditional electrical impedance imaging methods. However, it has the problems of difficulty in imaging targets with irregular conductivity distribution and poor lateral spatial resolution. Although the rotation-based MAET method can partly solve the irregular target problem, there is still a poor imaging signal-to-noise ratio (SNR) problem. Our previous study established a framework of an innovative MAET method, which has a very similar imaging theory and reconstruction algorithm to those of computed tomography (CT). Therefore, we name the method magneto-acoustic-electric computed tomography (MAE-CT). This paper proposes an improved implementation of MAE-CT based on multi-angle plane wave excitation. This method combines the electronic steering of the linear array transducer with the mechanical rotation to increase the number of projection angles while keeping the imaging complexity. In this study, we first established a finite element simulation model to verify the method's feasibility. Then phantom experiments were conducted to systematically investigate the performance of the proposed method. Finally, in vitro liver tissue experiment was conducted to further explore the feasibility of the method. The experimental results show that our method improves both the SNR and spatial resolution of the reconstructed image. For the phantom results, this method can detect conductivity of 0.67 S/m in an area with a size of 2 mm. To the best of our knowledge, this is the best result of spatial resolution available for MAET.

Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review

The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.

The Study of Inspection on Thin Film Resistance Strain Gauge Contact Failure by Electrical Excitation Thermal-Wave Imaging

Contact failure as one of primary failures, which occurs between thin film resistance strain gauge (FRSG) and metal measured objective substrate, seriously influences the measurement validity and precision of the strain gauge. In this article, an electrical excitation thermal-wave imaging (EE-TWI) method is proposed for the inspection of contact failure. Equivalent circuits of the contact resistance under an external electrical source are used to identify the location of the contact failure. Long pulse thermography and lock-in thermography (LIT) are experimentally studied on the detection of contact failure. Pulsed phase thermography and principal component thermography in long pulse thermography as well as the amplitude, quadrature component in LIT are desirable to form the EE-TWI feature images, respectively. A graph-based segmentation method is employed to distinguish and localize the contact failure from EE-TWI images. EE-TWI provides a powerful tool for the contact failure evaluation of integrated thin film resistance strain gauge (FRSG) and has a significant potential to ensure the thin FRSG sticking performance and quality with application of mechanical properties measurements.

Landslide vulnerability of the Debre Sina-Armania road section, Central Ethiopia: Insights from geophysical investigations

Geophysical investigations using 2D electrical imaging and magnetic methods have been conducted to study the landslide problem on the road located at Dokakit Kebele near Sar Amba Kidane Mhiret locality between Debre Sina and Armania towns in Central Ethiopia. From field observations, the landslide problem in the Debre Sina-Armania road section at Dokakit is due to a combination of factors, the most important of which are topography, the high degree of weathering, hydrology of the area, and human activities. Among these factors, human activities that include deforestation for housing, agricultural activities, search for building materials, and road construction are generally assumed to be the triggering factors for slope failure in the area. From the interpretation of the geophysical results, the near-surface geology of the study area is found to be composed of massive deposits of fragmented basalts and structurally disturbed layers together with expansive clay soils, which considerably contribute to the sliding problem. Another contributing factor to the subsurface instability is the high gradient slope in the area and the lack of vegetation/wooded tree cover. From the geophysical survey results, it has further been possible to map zones of weakness in the subsurface over which the geologic layers slide as well as their vertical and lateral extents. Areas of active sliding surfaces and extensive subsurface cracks, especially at the two ends of the profiles, were mapped. A combination of numerous weak zones, deep weathering conditions of the lithologic units, the action of rainfall, springs, and the high slope gradient of the study area were the contributing factors for this sliding problem. Remedial measures to stabilize a section of the affected area and construction of a detour road are proposed as mitigation measures for the road segment's recurrent and rather expensive failure over the locality.

Advances on acute respiratory distress syndrome based on imaging: from morphology to function

<p indent=0mm>Controversy and challenges have accompanied acute respiratory distress syndrome (ARDS) since it was proposed <sc>50 years</sc> ago. The progress of lung imaging has witnessed the development of ARDS. From the earliest chest X-ray to chest computed tomography (CT), to non-radioactive, bedside chest ultrasound, electrical impedance imaging and other methods, lung imaging plays an important role in the diagnosis and treatment of ARDS and the research of pathophysiological mechanism. The development of ARDS imaging progresses from whole to local, from pulmonary ventilation to blood perfusion, from simple respiratory assessment to combination with circulatory assessment, and from morphological analysis to functional assessment. Till now, ARDS imaging is not only used in diagnosis, but also in bedside continuous evaluation to guide treatment. This article systematically reviews the progress of clinically available imaging evaluation of ARDS, hoping to better understand the morphological and functional changes of ARDS, to guide clinical treatment, and to improve the prognosis of these patients.

Identification of Brain Damage after Seizures Using an MR-Based Electrical Conductivity Imaging Method.

Previous imaging studies have shown the morphological malformation and the alterations of ionic mobility, water contents, electrical properties, or metabolites in seizure brains. Magnetic resonance electrical properties tomography (MREPT) is a recently developed technique for the measurement of electrical tissue properties with a high frequency that provides cellular information regardless of the cell membrane. In this study, we examined the possibility of MREPT as an applicable technique to detect seizure-induced functional changes in the brain of rats. Ultra-high field (9.4 T) magnetic resonance imaging (MRI) was performed, 2 h, 2 days, and 1 week after the injection of N-methyl-D-aspartate (NMDA; 75 mg/kg). The conductivity images were reconstructed from B1 phase images using a magnetic resonance conductivity imaging (MRCI) toolbox. The high-frequency conductivity was significantly decreased in the hippocampus among various brain regions of NMDA-treated rats. Nissl staining showed shrunken cell bodies and condensed cytoplasm potently at 2 h after NMDA treatment, and neuronal cell loss at all time points in the hippocampus. These results suggest that the reduced electrical conductivity may be associated with seizure-induced neuronal loss in the hippocampus. Magnetic resonance (MR)-based electrical conductivity imaging may be an applicable technique to non-invasively identify brain damage after a seizure.

2d elektrometrijsko skeniranje na neolitskom nalazištu "Pločnik"

At the Neolithic settlement "Pločnik" near Prokuplje in Serbia, the electrical imaging method (ERT) was deployed. At the sounding site S-3, 1D electrical survey revealed a low resistivity layer with a thickness of 0.8 meters and a resistivity of about 30 Ωm. A sharp and steep boundary divides the high resistivity medium and the low resistivity medium, according to the 2D electrical survey. The resolution of the ERT method was tested using 3D forward and inverse modeling to see whether it could distinguish the high resistivity pit from the surrounding high resistivity gravel.

Ionic liquid based distributed touch sensor using electrical impedance tomography

Inspired by the human skin sensory mechanism, there are growing interests in creating a sense of touch in robotics. This work describes a new impedance based design to create an artificial tactile sensing skin. It has demonstrated that the electrical impedance tomography imaging technique allows for detecting the pressure distribution in a large area by a distributed touch sensor. The sensor is fabricated by filling a circular shaped phantom with liquid conductor and covering with an elastic shell on the top. The proposed sensor can detect the pressure applied to the elastic top using electrical impedance tomography imaging method. The sensor can therefore operate as a touch sensor mimicking a piezo-impedance operation in a simple fashion. The new sensor can differentiate between various force levels and their locations and thus produces a distribution of pressure. Such a simple sensor can function as a large area skin, enabling smarter human-machine interactions in emerging augmented reality and robotic applications.

EXPERIENCE OF USING ELECTRICAL TOMOGRAPHY, RADON SURVEY AND MICROSEISMIC SOUNDING IN SEARCH FOR PIPES CONTROLLED BY FAULT ZONES

The complex of geophysical methods was successfully applied in the Alakit-Markha kimberlite field of the Yakutsk diamondiferous province. A pipe was identified in the local forecast area specified within the field at the previous stage of prospecting. The studies using electric tomography (ET), radon survey (RS) and microseismic sounding (MS) covered an area of ≈1000⨯500 m. Based on the jointly processed ET and RS plan images and MS profiles, a tubular body was detected in the sedimentary cover. It is confined to a fault node and stands out among the host rocks by low electrical resistivity, increased soil radon concentrations and high spectral ratios of the horizontal and vertical components of microseisms. Its complex shape is manifested at the ground surface by two isometric structures (each being several hundred meters long), which jointly form a dike-like body at a depth of ≈40 m, as shown by the electrical tomography images. According to the MS data, its root part is detected to comprise one or two fractured narrow zones that are traceable to a depth of 2 km and below. The structure of the identified body and its chemical composition will be determined after exploratory drilling is complete. Today there are grounds to conclude that using ET, RS and MS methods jointly as a complex described in the article proved effective for identifying pipes controlled by fault zones in the sedimentary cover areas.

A robust electrical conductivity imaging method with total variation and wavelet regularization

PurposeThis study aims to develop and evaluate a robust conductivity imaging method that combines total variation and wavelet regularization to enhance the accuracy of conductivity maps. Theory and methodsThe proposed approach is based on a gradient-based method. The central equation is derived from Maxwell's equation and describes the relationship between conductivity and the transceive phase. A linear system equation is obtained via a finite-difference method and solved using a least-squares method. Total variation and wavelet transform regularization terms are added to the minimization problem and solved using the Split Bregman method to improve reconstruction stability. The proposed approach is compared with conventional and gradient-based methods. Numerical simulations are performed to validate the accuracy of the developed method, and the effects of noise are determined. Phantom and in vivo experiments are conducted at 3 T to verify the clinical applicability of the proposed method. ResultsNumerical simulations show that the proposed method is more robust than other methods and can suppress the effects of noise. The quantitative conductivity value of the phantom experiment agrees with the measured value. The in vivo experiment results present a clear structure, and the conductivity value of the tumor region is significantly higher than that around healthy tissues. ConclusionThe proposed electrical conductivity imaging method can improve the quality of conductivity reconstruction, and thus, has future clinical applications.

Geophysical Investigation of the Triassic Salt Material Hazard: El Fahs Case Example (Northern Tunisia)

The Tunisian territory (area of diapirs) is exposed to the risks of ground movements linked to water, some of which are related to the phenomenon of dissolution of gypsum, allowing the appearance of underground cavities which present natural risks and set people in danger. The analysis of the hazard was determined by the field study coupled with the application of geophysical methods to locate and map the cavities and identify their dimensions and their positions in the subsoil. In the region of El Fahs (40 km NW of Tunis): we used a non-destructive method, georadar (GPR) with a 200 MHz antenna band-width, and an electric method, by using the electric tomography of which we have applicated the sequence of dipole-dipole measurement. The results ob-tained were examined and interpreted according to 2D profiles. The geophysi-cal methods of GPR and electrical tomography aim to detect many calvities in different depths. However, the geological radar was able to identify several cavities and the zones of dissolution whose investigation depth did not exceed the first 3 meters. On the contrary, the electric tomography method allowing the presence of several deeper underground cavities with larger dimensions ex-tended to 64 meters. To conclude, the geological and hydrogeological contexts of the terrain studied and on the state of the soil and subsoil allow us to better understanding the mechanisms of the establishment of the dissolution phe-nomenon and the appearance of cavities in the basement. The analysis of the predisposing factors present in the study area shows that the geodynamic con-text of the Triassic ascent takes place according to a precise thermodynamic process that favors the phenomenon of dissolution of the gypsum.

Modification of relativistic beam fields under the influence of external conducting and ferromagnetic flat boundaries

AbstractWe derive analytical expressions for external fields of a relativistic bunch of charged particles with a circular and an elliptical cross section under different boundary conditions and interaction of the fields with an accelerator structural elements. The particle density in the bunch is assumed to be uniform as well as non-uniform. At distances far apart from the bunch, in free space the field reduces to the relativistic modified Coulomb form for a pointlike charge and at small distances the expressions reproduce the external fields of a continuous beam. In an ultra-relativistic limit the longitudinal components of the internal and external electric fields of the bunch are strongly suppressed by the Lorentz factor. If the bunch is surrounded by conducting surfaces, the bunch self-fields are modified. Image fields generated by a bunch between two parallel conducting plates are studied in detail. Exact summation of the electric, $E_y$, and magnetic, $B_x$, image field components allows the infinite series to be represented in terms of elementary trigonometric functions. The new expressions for modified fields are applied to study image forces acting on the bunch constituents and the bunch as a whole. The coherent and incoherent tune shifts for an arbitrary bunch displacement from the midplane are calculated in the framework of an improved linear theory, for both infinite and finite parallel flat surfaces. Moreover, the developed method allows us to generalize the Laslett image coefficients $\epsilon_1$, $\epsilon_2$, $\xi_1$, $\xi_2$ to the case of an arbitrary bunch offset and reveal relationships between these coefficients. Appendix C provides a brief historical background of the development of the method of electrical images.

Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

Contactless electrical impedance and ultrasonic tomography: correlation, comparison and complementarity study

Electrical tomography (ET) and ultrasonic tomography (UT) techniques are effective and promising super-sensing tools with uses in many industrial process applications. They can create internal mapping images of both electrical and mechanical properties from measurements at the exterior boundaries of domains of interests. There are several different types of ET methods and different modes of UT imaging. Here we focus on contactless ET and contactless UT imaging for liquid masses, enabling fully non-intrusive, integrated mechanical and electrical imaging because direct contact to the process material is often a major limiting factor. ET is sensitive to the distribution of dielectric parameters inside the region of interest, and the highest sensitivity often lies near the outer surface of the boundary. UT has very good responses to the intersections of different phases of materials and has the highest resolution in the central area. Capacitively coupled electrical impedance tomography (CCEIT) is proposed as a contactless ET technique. This work investigates CCEIT based on phase measurements of the electrical impedance between transmitting and receiving electrodes, and UT based on the transmission mode, measuring the time-of-flight between the transmitted signal and the first received signals. A combined sensor which comprises a 16-electrode CCEIT array and a 16-transducer UT array is developed. Experimental results show the performances of the two tomography systems and their dual modality combination. This work highlights various aspects of the correlation, comparison and complementarity between these two contactless imaging techniques. Inclusion material characterization and identification is demonstrated using this novel dual modality.