Transformation Parameters Research Articles

Georeferencing of terrestrial radar images in geomonitoring using kernel correlation

ABSTRACT Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument’s pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over 1 k m . This is an improvement of almost one order of magnitude compared to what had been achieved using standard approaches and direct observation of the radar instrument’s pose. The proposed method thus contributes to the automation of TRI data processing and improved localization of small-scale deformation areas detected in radar images.

A review of health index-based condition assessment techniques for power and distribution transformers

In this paper, the most significant diagnostic parameters of transformers and the corresponding asset management strategies adopted by the leading power utilities of various countries are discussed. The analytical techniques used include Monte Carlo simulations (MCSs), fuzzy inference systems, neuro-fuzzy systems, regression analysis, multi-criterion analysis (MCA) and so on. These techniques incorporate information related to transformer design and fabrication, operation history and maintenance, visual inspection and various diagnostic tests and measurements. The health assessment methodologies for power and distribution transformers may require improvement. Cost-effective techniques involving few critical parameters have to be used for distribution transformers. Suitable communication technologies need to be deployed for remote monitoring and assessment. Appropriate interfacing software can be developed for analysis. There are many issues and operating conditions associated with Indian transmission and distribution systems, which call for the adaptation of the discussed techniques for their effective use.

Brownian motions and thermophoresis effects in a vertically stretching sheet with micropolar nanofluid flow

This work is about the investigation of the flow of a micropolar nanoliquid over a stretching surface, taking into account the effects of thermal radiation, thermophoresis, and Brownian motion. The study focuses on the impact of these factors on heat and mass transfer rates, with the assumption that the Newtonian heat impact dominates. The homotropy approach is used to generate non-dimensional transformational parameters, which are then used to create a system of nonlinear differential equations. The study includes charts and tables that define transfer rates based on various parameters, and the results suggest that increased radiance levels and Nb parameters lead to improvements in heat and mass convection. The graphics used in the study are accurate and consistent with previous research in the field. Overall, this research provides insights into the complex dynamics of micropolar nanoliquid flow and the factors that impact heat and mass transfer in this system.

Inclined magnetic force impact on cross nanoliquid flowing with widening shallow and heat generating by using artificial neural network (ANN)

Artificial neural networks (ANNs) have a wide range of applications in science and technology. Artificial neural networks (ANNs) widely utilized in image and speech recognition, neural language processing, drug discovery, genomics and bioinformatics, Robotics and control systems, material science and energy and power system. Due to the above applications, the main focus of this article is to scrutinize the impact of inclined magnetic field on a cross nanofluid flow with slip velocity and convective boundary conditions over a variable porosity. Gold (Au) nanoparticles are suspended in base liquid blood. Thermal stratification and heat generation with joule heating impact is taken in the energy equation in order to see the heat transfer. To transform the non-linear partial differential equations (PDE's) into non-linear ordinary differential equations (ODE's) utilized the von-Karman similarity transformation parameters. For the solutions of the non-linear ODE's, the 4th- order Runge-Kutta numerical method is employed. A dataset for the employed neural network back propagated Levenberg-Marquard scheme (NN-BLMS) is generated for various estimations of the embedded parameters like Weissenberg number, magnetic parameter, angle of inclination, porosity and permeability parameters, thermal stratification parameter by utilizing the 4th-order Runge-Kutta numerical scheme. The testing, validation and training methods of NN-BLMS are utilized to scrutinize the approximate solution of cross nanofluid with slip velocity and convective boundary conditions. The performance of the proposed NN-BLMS to successfully solved the cross nanofluid model is endorsed via mean squared error, error histogram and regression analysis. Streamlines and heatlines are plotted for different parameters including in velocity and temperature equations.

SOME ISSUES OF THE CALCULATION PROCESS OPTIMIZATION IN TERRESTRIAL LASER SCANNERS CALIBRATION

A conventional and widely used mathematical model of laser scanners calibration has such an imperfection as correlation between some of parameters during the adjustment process and quite complex numerical solution. A method of avoiding the correlation between scanner and polygon transformation parameters and calibration parameters as well as calibration numerical solution simplification is considered. It is proposed to implement a separate transformation parameters computation by using Kabsch-Umeyama algorithm that can compute Helmert transformation parameters even in case of gross errors present in data set. But direct transformation before adjustment leads to parametric equations and following numerical solution complication. Therefore it is also proposed to conduct a reverse transformation from polygon coordinate system into scanner coordinate system as it simplifies further equations linearization and allows simplifying the numerical solution in general. As it is more than one station to be scanned during the calibration process, one cannot simply swap coordinate systems. The polygon coordinate system remains as original one but reverse Helmert transformation is conducted. To prove this approach is correct, three datasets using different laser scanners and high-precise total station were obtained. Direct and reverse transformation between scanners and total station coordinate systems was made for all datasets and MSE of transformation was calculated for each case. Thus, the possibility of using the reverse transformation to optimize the process of the calibration parameters determining has been experimentally proven.

Model-assisted analysis for tuning anthocyanin composition in grape berries.

Anthocyanin composition is responsible for the red colour of grape berries and wines, and contributes to their organoleptic quality. However, anthocyanin biosynthesis is under genetic, developmental and environmental regulation, making its targeted fine-tuning challenging. We constructed a mechanistic model to simulate the dynamics of anthocyanin composition throughout grape ripening in Vitis vinifera, employing a consensus anthocyanin biosynthesis pathway. The model was calibrated and validated using six datasets from eight cultivars and 37 growth conditions. Tuning the transformation and degradation parameters allowed us to accurately simulate the accumulation process of each individual anthocyanin under different environmental conditions. The model parameters were robust across environments for each genotype. The coefficients of determination (R2) for the simulated versus observed values for the six datasets ranged from 0.92 to 0.99, while the relative root mean square errors (RRMSEs) were between 16.8 and 42.1 %. The leave-one-out cross-validation for three datasets showed R2 values of 0.99, 0.96 and 0.91, and RRMSE values of 28.8, 32.9 and 26.4 %, respectively, suggesting a high prediction quality of the model. Model analysis showed that the anthocyanin profiles of diverse genotypes are relatively stable in response to parameter perturbations. Virtual experiments further suggested that targeted anthocyanin profiles may be reached by manipulating a minimum of three parameters, in a genotype-dependent manner. This model presents a promising methodology for characterizing the temporal progression of anthocyanin composition, while also offering a logical foundation for bioengineering endeavours focused on precisely adjusting the anthocyanin composition of grapes.

Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study

A web mapping XYZ Tile Layer Service, such as Google Earth (GE), provides an amazing resource for the visualization of spatial data against aerial and satellite imagery with global coverage, typically at a resolution finer than 5 m. However, the increasing requirement on spatial accuracy in farm information requires a greater appreciation of the issues involved in the use of such services. Position errors can be created in the georeferencing and orthorectification of images, transformation between reference frames (datums) in map projection, e.g., using a spheroid as compared to an ellipsoid earth model, and tectonic shifts. A review is provided of these issues, and a case study is provided of the horizontal positional accuracy of web map imagery for Australian mango orchards. Positional accuracies varied from 1.804 to 6.131 m across four farms using GE 2021 imagery, between 1.556 and 3.365 m in one farm for the most recent imagery available from each of four web map providers, and from 0.806 m (in 2016) to 10.634 m (in 2003) in one farm for the period of 2003 and 2021 using the historical GE imagery resource. A procedure involving the estimation of four transformation parameters was demonstrated for the alignment of GNSS data with GE imagery. However, as the scale factor was unity and the rotational value was near zero, the use of a simple horizontal mean shift vector was recommended. Further recommendations are provided on (i) the use of web mapping services, with a comparison of the use of UAV survey imagery, and (ii) the need for metadata, particularly the date of collection, on collected position data, in the context of use in farm management information systems.

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images

BackgroundSingle photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. MethodsA total of 254 patients were enrolled, including 226 stress SPECT MPIs and 247 rest SPECT MPIs. Fivefold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced nuclear cardiologist and used as the reference standard. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: (1) optimize the translation parameters while fixing the rotation parameters; (2) optimize rotation parameters while fixing the translation parameters; (3) optimize both translation and rotation parameters together. ResultsIn the test set, the Spearman determination coefficients of the translation distances and rotation angles between the model prediction and the reference standard were 0.993 in X axis, 0.992 in Y axis, 0.994 in Z axis, 0.987 along X axis, 0.990 along Y axis and 0.996 along Z axis, respectively. For the 46 stress MPIs in the test set, the Spearman determination coefficients were 0.858 in percentage of profusion defect (PPD) and 0.858 in summed stress score (SSS); for the 46 rest MPIs in the test set, the Spearman determination coefficients were 0.9 in PPD and 0.9 in summed rest score (SRS). ConclusionsOur deep learning-based LV reorientation method is able to accurately generate the SA images. Technical validations and subsequent evaluations of measured clinical parameters show that it has great promise for clinical use.

Parameters of the Liquid-Gas-State Triangle for Hard-Core Attractive Yukawa Fluids.

We discuss the triangle of the liquid-gas concept within the fluid-lattice gas isomorphism approach and the Zeno-element, taking the hard-core Yukawa fluid (HCAYF) as an example. The applicability of such an isomorphism is demonstrated by the symmetrization of binodals with mapping onto the Ising model coexistence curve. We show that the parameters of the isomorphism transformation reflect the liquid-phase instability for hard-core Yukawa potential and its generalization. We also provide a simple mean-field estimate for the parameter z of the fluid-lattice gas transformation and use it to obtain the locus of the critical point.

Fast registration method for sequential star images.

Registration of sequential star images is an important component of space observation. Especially for onboard devices, the efficiency and robustness of registration algorithms are particularly important. The typical star image registration approach based on star matching has a low tolerance for incorrect matching, and the time cost will increase rapidly as the number of stars increases. Due to the high overlap and rigid transformation of adjacent star images, a proposed method based on star angular distance (SAD) is presented. Stars are easy to locate and extract as natural feature points, and there are a large number of identical stars in adjacent star images. The rotation and translation of the SAD, composed of identical stars in adjacent star images, are the same. Therefore, maximum intersection clustering (MIC) was proposed to cluster rotation and translation, and Gaussian weight iteration (GWI) was proposed to estimate rigid transformation parameters. The use of SAD as a star image feature reduces the complexity of star image features, which can improve the efficiency of the algorithm. MIC can tolerate errors within a certain range, and GWI can lessen their impact on the results, increasing the algorithm's robustness. Experimental results show that the proposed method can improve the trend of rapidly increasing computation as the number of stars increases and avoid the restriction that transformation parameters must be obtained with correctly matching stars. Compared to the typical triangle method and SAD similarity method, the proposed method has higher efficiency under different numbers of stars, and translation, rotation, and location errors.

Self-supervised Multi-view Learning via Auto-encoding 3D Transformations

3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly performed in a supervised fashion, which often requires a large amount of data labels with high costs. In contrast, self-supervised learning aims to learn multi-view feature representations without involving labeled data. To this end, we propose a novel self-supervised framework to learn Multi-View Transformation Equivariant Representations (MV-TER), exploring the equivariant transformations of a 3D object and its projected multiple views that we derive. Specifically, we perform a 3D transformation on a 3D object and obtain multiple views before and after the transformation via projection. Then, we train a representation encoding module to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks and show the generalization to real-world datasets. The code is available at https://github.com/gyshgx868/mvter .

The influence of the chemical composition of the Ti-Hf-Zr-Ni-Cu-Co shape memory alloys on the structure and the martensitic transformations

Twelve senary alloys were manufactured with different concentrations of doping elements (Hf, Zr, Ni, and Cu) that varied from 1 to 17 at%, and the concentration of the Ni equivalent (Ni, Cu, and Co) atoms changed from 49 to 51 at%. It allowed the production of Ti-Hf-Zr-Ni-Cu-Co alloys with various configuration entropies. It was found that, regardless of the entropy value, all alloys crystallised in the B2 phase, in which the lattice parameter increased upon a rise in the doping elements’ concentration (X parameter) and was hardly affected by the Ni group concentration ([Ni] = Ni + Cu + Co). In the alloys with low and medium entropy, the chemical composition of the B2 phase was homogeneous and close to the composition of the alloy. In the alloys with high entropy, the core and edge of dendrite cells in the matrix were characterised by different concentrations of doping elements, while the concentration of the Ni group was constant. Besides the B2 phase, the secondary phase existed in all alloys, whose type [Ti]2[Ni] or [Ni]4[Ti]3 depended on the [Ni] value, as was observed in the binary NiTi alloys. It was found that the Ti-Hf-Zr-Ni-Cu-Co alloys with low and medium entropy underwent the B2 ↔ B19′ martensitic transformation on cooling and heating. An increase in the [Ni] and X values initiated the formation of the strain nanodomains on cooling before the forward transformation. In the high-entropy alloys with X = 10 at%, the strain nanodomains formed on cooling to − 180 °C without the formation of the B19′ phase. In the alloys with X = 17 at%, strain nanodomains, and martensitic transformation were not found on cooling, but the martensitic transformation was initiated by loading at − 100 °C. It was shown that the variation in the Ni group concentration affected the martensitic transformation parameters only in the low-entropy alloys. In the medium- and high-entropy alloys, the concentration of the doping element had a more significant effect on the transformation than the [Ni] value.

Construction of angular-dependent potentials from trigonometric Pöschl-Teller systems within the Dunkl formalism

We generate solvable cases of the two angular equations resulting from variable separation in the three-dimensional Dunkl-Schrödinger equation expressed in spherical coordinates. It is shown that the Dunkl formalism interrelates these angular equations with trigonometric Pöschl-Teller systems. Based on this interrelation, we use point transformations and Darboux-Crum transformations to construct new solvable cases of the angular equations. Instead of the stationary energy, we use the constants due to the separation of variables as transformation parameters for our Darboux-Crum transformations.

Theory and simulation analysis of parallel operation after main transformer replacement

As one of the most important main equipment in the power system, the safe and stable operation of transformer plays a vital role in ensuring the reliability of power supply. Because the three-phase parameters of the main transformer are not the same, the parameters of each phase and the three-phase load of the power grid are also different, resulting in a certain degree of asymmetry between the three-phase current and voltage of the transformer during normal operation. There will be negative sequence and zero sequence current components. The degree of asymmetry is generally small for the high-voltage power grid, but it has a large impact on the low-voltage side of the main transformer. In this paper, the parameters of two main transformers in a substation are quite different, but when they need to run in parallel, whether they can run in parallel, what are the conditions of parallel operation, these problems have been studied in theory and simulation. The results show that the two main transformers can be operated in parallel under suitable conditions. The actual operation result of a substation’s main transformer is normal, which further verifies the correctness of theoretical calculation and simulation.

Parasitic Parameter Prediction for Planar Transformers Based on Neural Network

Parasitic parameters such as leakage inductance and distributed capacitance of planar transformers have a direct impact on the performance and efficiency of transformers. Traditional methods for parasitic parameter prediction are commonly based on empirical formulas or simulation software, but they have problems of high computational complexity, time-consuming and low accuracy. In this paper, a method for predicting parasitic parameters of planar transformers based on a multilayer perceptron (MLP) under a specific winding structure is proposed, which can improve the efficiency of transformer design. The experiments demonstrate that the model can effectively predict the leakage inductance, distributed capacitance, and AC loss of planar transformers.

Oral Leukoplakia: Diagnosis And Management Revisited

The definition of oral leukoplakia has not much changed during the past five decades and is still a definition by exclusion of ‘known’ lesions. Therefore, a diagnosis of leukoplakia is not always a straightforward one for the clinicians and, to some extent, also for the pathologists. The traditional clinical classification in homogeneous and nonhomogeneous leukoplakia may just be simplified into leukoplakia (thin and thick/verrucous) and erythroleukoplakia. In spite of numerous reported predictive molecular and genetic parameters of malignant transformation, the presence and grade of epithelial dysplasia as assessed by histopathological examination is still the most important one. Of the various treatment modalities, surgery and CO2 laser evaporation are still the most common ones. Treatment may delay or prevent recurrence, but does not seem to prevent malignant transformation or the occurrence of cancer development elsewhere in the mouth or the head and neck region or beyond. There is a strong need for randomized prospective studies and uniform reporting of treatment results.

A new method for analyzing the impact of winding mechanical defects on the equivalent circuit parameters of power transformers

AbstractCondition monitoring of the power transformer winding and diagnose of the fault in its incipient stages can prevent the spread of defect, and as a result, the unexpected exit of the transformer. Correct and accurate perception of the effects of each defect or winding deformation on the detailed or any other model parameters is essential. In this article, a software based on finite element method is used, in order to increase accuracy of the study to model the transformer in healthy and defected cases. Three types of winding mechanical deformation, that is, axial displacement, radial deformation, and disc space variation , have been simulated for different locations and extents. From the simulation of the finite element model in the healthy and other defected cases, R, L, and C matrices are obtained for each case. Each of the above matrices in defected cases has been compared with the corresponding matrix of the healthy case. The amount and manner of the above matrices’ element variations have been investigated in detail, might help the fault classification in terms of the type, extent and location, and risk assessment of using the transformer in the current defective situation.

Time domain differential protection scheme applied to power transformers

This paper presents a new differential protection scheme for power transformers based on Intrinsic Time-Scale Decomposition (ITD) and Ensemble of Bagged Tree (EBT) as a classifier. The ITD decomposes the input nonstationary signal into Proper Rotation Components (PRCs) and a monotonic trend signal. In the proposed scheme, statistical time-domain features extracted from the ITD output are fed to the EBT classifier, which classifies different events, including internal fault, internal fault combined with inrush current, different inrush currents (energizing transformer, sympathetic, and recovery inrush current), and external fault. The combination of ITD and EBT tools requires only a limited sample of post-fault data to protect the transformer. The proposed scheme is tested under various operating conditions to confirm its accuracy, and the effect of current transformer (CT) saturation on the scheme's performance is evaluated. The scheme is immune to noisy conditions and does not require transformer parameters. The power systems were simulated using PSCAD/EMTDC and MATLAB software.

CT2X-IRA: CT to x-ray image registration agent using domain-cross multi-scale-stride deep reinforcement learning

Objective. In computer-assisted minimally invasive surgery, the intraoperative x-ray image is enhanced by overlapping it with a preoperative CT volume to improve visualization of vital anatomical structures. Therefore, accurate and robust 3D/2D registration of CT volume and x-ray image is highly desired in clinical practices. However, previous registration methods were prone to initial misalignments and struggled with local minima, leading to issues of low accuracy and vulnerability. Approach. To improve registration performance, we propose a novel CT/x-ray image registration agent (CT2X-IRA) within a task-driven deep reinforcement learning framework, which contains three key strategies: (1) a multi-scale-stride learning mechanism provides multi-scale feature representation and flexible action step size, establishing fast and globally optimal convergence of the registration task. (2) A domain adaptation module reduces the domain gap between the x-ray image and digitally reconstructed radiograph projected from the CT volume, decreasing the sensitivity and uncertainty of the similarity measurement. (3) A weighted reward function facilitates CT2X-IRA in searching for the optimal transformation parameters, improving the estimation accuracy of out-of-plane transformation parameters under large initial misalignments. Main results. We evaluate the proposed CT2X-IRA on both the public and private clinical datasets, achieving target registration errors of 2.13 mm and 2.33 mm with the computation time of 1.5 s and 1.1 s, respectively, showing an accurate and fast workflow for CT/x-ray image rigid registration. Significance. The proposed CT2X-IRA obtains the accurate and robust 3D/2D registration of CT and x-ray images, suggesting its potential significance in clinical applications.

Efficient single-pixel imaging encrypted transmission based on 3D Arnold transformation

Single-pixel imaging (SPI) can transform 2D or 3D image data into 1D light signals, which offers promising prospects for image compression and transmission. However, during data communication these light signals in public channels will easily draw the attention of eavesdroppers. Here, we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission. This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection. Then the transformation parameters serve as the secret key, while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism. Compared with existing encryption schemes, both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules. Additionally, this approach solves the problem of secure key transmission, thus ensuring the security of information and the quality of the decrypted images.