Transformation Parameters Research Articles

Effect of the Addition of High-Protein Hydrolyzed Flour from Oncorhynchus mykiss Byproducts on the Properties of an Extruded Feed.

This work aims to evaluate the effect of the addition of a high-protein hydrolyzed (HPH) flour from the chemical silage of trout (Oncorhynchus mykiss) residues on the parameters of the extrusion system physicochemical transformations and the microstructure of the extrudate. During the extrusion process, the materials used for the study were the HPH flour obtained from trout by chemical silage, fishmeal, and cassava starch. The extrudate’s microstructural changes were evaluated by determining the porosity, scanning electron microscopy, the chemical changes, the amino acid profile, residual formic and lactic acid content, the molecular mass profile, the grade of hydrolysis, and in vitro digestibility. The results showed pellets with high durability due to the cohesiveness of the hydrolyzed protein flour but at the same time with low hardness due to the high porosity achieved. The monitoring carried out to the changes in the protein, such as the degree of hydrolysis, water-soluble protein, and molecular mass profile, verify the binding effect of the high-protein hydrolyzed flour during the extrusion process. Finally, the high-resolution optical microscopy methodology presented a high correlation with the phenomena presented in the experiment.

Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.

A Multiscale Framework With Unsupervised Learning for Remote Sensing Image Registration

Registration for multisensor or multimodal image pairs with a large degree of distortions is a fundamental task for many remote sensing applications. To achieve accurate and low-cost remote sensing image registration, we propose a multiscale framework with unsupervised learning, named MU-Net. Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from the image pairs to their transformation parameters. MU-Net stacks several deep neural network (DNN) models on multiple scales to generate a coarse-to-fine registration pipeline, which prevents the backpropagation from falling into a local extremum and resists significant image distortions. We design a novel loss function paradigm based on structural similarity, which makes MU-Net suitable for various types of multimodal images. MU-Net is compared with traditional feature-based and area-based methods, as well as supervised and other unsupervised learning methods on the optical-optical, optical-infrared, optical-synthetic aperture radar (SAR), and optical-map datasets. Experimental results show that MU-Net achieves more comprehensive and accurate registration performance between these image pairs with geometric and radiometric distortions. We share the code implemented by Pytorch at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yeyuanxin110/MU-Net</uri> .

PMU-Based Estimation of Systematic Measurement Errors, Line Parameters, and Tap Changer Ratios in Three-Phase Power Systems

The availability of accurate data is fundamental for several monitoring and control applications of modern power grids. Nevertheless, the knowledge of critical data, such as transmission line and transformer parameters, is often affected by uncertainty. This can lead to important problems in the correct management of the power systems. In spite of a monitoring infrastructure that is being renewed due to new generation devices providing synchronized measurements, the actual values of line parameters and tap changer ratios are still affected by uncertainty sources that need to be properly considered. The behavior of all the elements involved in the measurement chain must be duly modeled. This article proposes an improved method to carry out the simultaneous estimation of line parameters, tap changer ratios, and systematic measurement errors for a three-phase power system. The proposed method is based on the suitable modeling of the measurement chain and three-phase constraint equations (voltage drop and current balance) of all the components involved. Its effectiveness is confirmed by tests performed on an IEEE 14-bus test system reproduced as a three-phase system under different operative conditions.

Registration of Airborne LiDAR Bathymetry and Multibeam Echo Sounder Point Clouds

Airborne light detection and ranging (LiDAR) bathymetry (ALB) and multibeam echo sounder (MBES) are both active remote sensing technologies that are complementary in terms of survey scope. The registration of ALB and MBES data can provide complete overwater and underwater geoinformation on a measurement target. However, in the overlapping area of the ALB and MBES data, there are different point densities and few identifiable structure features. Although the existing multiplatform registration strategies can provide good results for overwater datasets, they are difficult to adapt for the registration of ALB and MBES data. Therefore, to address these problems, a new registration method for ALB and MBES datasets is proposed in this letter. First, a triangulated irregular network (TIN) is constructed with control points extracted from the MBES data. Then, the features of the TIN facets are extracted to identify the data gaps. Finally, the transformation parameters are iteratively calculated by minimizing the distances between the ALB points and MBES TIN facets. Five samples with different characteristics captured around Yuanzhi Island in the South China Sea are selected to evaluate the performance of the proposed method. The mean root mean square error (RMSE) of the five samples is approximately 0.2 m. The results indicate that the proposed method performs well for the registration of ALB and MBES datasets, with advantages in accuracy and robustness.

Підходи та вимоги до моделювання структури напівпровідникового шару сонячного елемента

The existing models of the solar cell are considered and analyzed. It was established that the existing models investigate: the dependence of the normalized current density on the voltage when the thickness of the photoelectric layer of the photoelectric converter, photoelectric characteristics of the photoelectric converter depending on the temperature coefficient and different light conditions. The models show that with an increase in temperature, the value of the value of the utility coefficient, current, the fill coefficient of the volt-amperage characteristic is reduced.The analysis of models showed that a calculation over of basic electrophysics parameters (current of short circuit, tension of idling) was brought without the account of changes of size of area active to the perceiving surface of relief of semiconductor layer of photo-electric transformer. It substantially influences on the calculation of size of initial power and output-input ratio, and also the volt-ampere и volt of wadding reduces exactness and stability descriptions of sunny element. For the calculation of size of the real area of perceiving surface of semiconductor layer of photo-electric transformer, it offers to use the changes of properties of underlying structure of semiconductor layer of photo-electric transformer. A study of model of sunny photo-electric transformer is undertaken taking into account the general geometrical area of superficial semiconductor layer of photo-electric transformer. For research of influence of properties of the real perceiving surface on the initial parameters of photo-electric transformer, it offered to use a model a general geometrical area is taken into account in that. On results the design of photo-electric transformer by means of the program MathCAD, built by a volt-ampere descriptions of photo-electric transformer with a geometrical area and with the real areas of surface of photo-electric transformer. It is educed, that curves of volt-ampere description for the real areas of perceiving surface, differ from a curve for a geometrical topological area.It is proposed to calculate the area of the active receiving surface of the relief of the semiconductor layer of a photoelectric converter, based on the simulation of processes in the structure of the semiconductor layer of a photoelectric converter.

Revealing the Semantics of Data Wrangling Scripts With COMANTICS.

Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop COMANTICS, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a "slot filling" strategy. We design experiments to evaluate the performance of COMANTICS. Further, we assess its flexibility using three example applications in different domains.

Lorentz Transformation Leads to Invariance of the Difference between the Electric and Magnetic Field Intensity

In course of a direct calculation we demonstrate the activity of parameters of the Lorentz transformation entering the original electric and magnetic field vectors E and H. The validity of the transformation is shown with the aid of the relation E 2- H2 = E'2- H'2 which holds for any suitable pair of the vectors E, H and E', H'. No special geometry of the vector pairs entering (E, H) and (E ', H') is assumed. The only limit applied in the paper concerns the velocity ratio betweeen v and c which should be smaller than unity.

Electron Diffraction Study of the Temperature --- Time Dependences of Crystallization of Nano thick amorphous CuIn-=SUB=-5-=/SUB=-Te-=SUB=-8-=/SUB=- Films Sublimated Under Normal Conditions and under the Action of an External Electric Field

Kinematic electron diffraction was used to study the crystallization processes of amorphous CuIn5Te8 films obtained under normal conditions and under the influence of an external electric field with a strength of 500 V · cm-1. Isothermal curves of phase transitions are examined and the kinetic parameters of phase transformations are determined: the dimensions of crystal growth and of activation energies are established. It is shown that during the crystallization of amorphous CuIn5Te8 films, the rates of nucleation and their further growth follow the Arrhenius relations. Keywords: electronographic structural analysis, thin films, crystallization isotherms, activation energy.

FIRM: Framework for Image Registration Using Multistage Feature Detection and Mode-Guided Motion Smoothness Keypoint Optimization

Remote-sensing image registration is a pivotal preprocessing step for earth observation data analytics. In this regard, georeferencing corrects the systematic geometric degradation in the image. However, it is difficult to achieve subpixel geometric accuracy across multitemporal scenes. This article focuses on hindmost part of geometric correction that uses reference layer and feature detection in hierarchical stages to improve the geometric fidelity of images at subpixel level. The methodology developed is based on patch affine-oriented fast and brief with mode-guided tiled scale invariant feature transform (MT-SIFT) techniques in a coordinate manner at a multistage processing architecture, which we refer to as FIRM. Motion smoothness constraint (MSC) keypoint correspondence optimization is used in FIRM to remove the outliers at gross stage and estimate segmented affine transformation parameters at finer stage. The automatic coregistration pipeline is evaluated in Indian Resourcesat multispectral camera images covering diverse landscapes. The capability of the designed framework is demonstrated to handle relatively large geometrical error. With more than a decade difference in acquisitions, multitemporal images are superimposed over each other and compared with state-of-the-art feature-based methods. The potential of the proposed approach FIRM is assessed on multisatellite imagery acquired from Resourcesat-2 and Landsat −8. It is observed that the root mean square error (RMSE) between coregistered images is 0.12 pixel at a spatial resolution of 5 m.

Combined Measurement Based Wing-Fuselage Assembly Coordination via Multiconstraint Optimization

In the field of aircraft manufacturing, the demand for high aerodynamic performance and long fatigue life is growing. This makes high-precision assembly of large components and assembly coordination increasingly important. This is particularly prominent for wing-fuselage joining due to the large size, measurement difficulty, and multi-constraint coordination. In this paper, we propose a novel optimization method for the wing-fuselage assembly coordination problem, as well as an assembly gap measurement approach. First, a gap measurement framework combining laser tracker, 3D scanner and photogrammetry is adopted to solve the occlusion problem that makes measurement difficult in traditional methods. A compatible tooling is utilized to transform the scanned point cloud data in the local coordinate system to the global aircraft assembly coordinate system. Based on this, a comprehensive wing-fuselage pose coordination model is then established, which takes engineering constraints into account to realize assembly gap redistribution for wing-fuselage joining. The transformation parameters of pose adjustment are solved by the proposed multi-constraint optimization algorithm based on the Gauss-Newton method. Finally, experiments on synthetic data and actual data have verified the effectiveness of the coordination method. Results show that our method significantly optimizes the gap distribution within the given tolerance and outperforms traditional methods in both accuracy and efficiency. With advantages of high efficiency and accuracy, our method can be well applied to the wing-fuselage joining and satisfy the requirement of the large component assembly.

Using 2-Lines Congruent Sets for Coarse Registration of Terrestrial Point Clouds in Urban Scenes

The registration of terrestrial point clouds is a crucial technique to obtaining the entire scene geometry. However, urban scenes, characterized by repetitive components, symmetrical structures, and object occlusion, pose ambiguity challenge of matches in the accurate registration of terrestrial point clouds. This article proposes a coarse registration approach using 2-lines congruent sets (2LCS) for urban point clouds. 3-D line features are extracted in the point clouds and classified into two categories according to directions. With that, a pair of 3-D line features from different categories is selected in the target point cloud to construct the 2-lines base with significant geometric characteristics. Next, the congruent 2-lines bases are identified in the source point cloud using geometric constraints, leading to the 2LCS. The 2LCS are then used to estimate the rigid motion between pairwise point cloud, leading to a set of candidate transformation parameters. The largest common point (LCP) set and maximum line structure consistency (MLSC) are integrated to evaluate the transformations. Finally, the procedure is repeated based on the RANSAC scheme to achieve optimal transformation values. We applied the proposed method to nine urban scenes with varying conditions and compared its performance with other state-of-the-art methods. The results show that our proposed approach is more robust for registering terrestrial point clouds in urban scenes with repetitive components, symmetrical structures, and limited overlap, leading to optimal or near-optimal results after fine registration optimization.

Automatic marker-free registration of single tree point-cloud data based on rotating projection

Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research. Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information. However, the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult. In this study, an automatic coarse-to-fine method for the registration of point-cloud data from multiple scans of a single tree was proposed. In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans. Corresponding feature-point pairs are then extracted from these series of 2D images. In fine registration, point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters. To evaluate the accuracy of registration results, we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans. For accurate evaluation, we conducted experiments on two simulated trees and six real-world trees. Average registration errors of the proposed method were 0.026 m around on simulated tree point clouds, and 0.049 m around on real-world tree point clouds.

Geometric Correction Analysis of Highly Distortion of Near Equatorial Satellite Images Using Remote Sensing and Digital Image Processing Techniques

The Near-equatorial orbit (NEqO) satellite represents a new generation of optical satellite images characterized by nonlinear distortion when captured. Conventional modeling techniques are insufficient to overcome the geometric distortion in these satellite images. This study proposes a new methodology for overcoming the geometric distortion of the NEqO images. The data used are obtained from RazakSAT and SPOT-5 satellite images in Malaysia. The method starts with applying the RI-SIFT algorithm to extract control points (CPs) automatically. These CPs are used to solve for the transformation parameters of the geometric correction model by applying spline transformations. The result is verified through statistical comparison: 1) geometric correction on the RazakSAT image is performed with Spot satellite image with using first-order polynomial trans-formation. 2) Then calculate the root mean square error (RMSE). 3) Compare the calculated RMSE with that obtained from the first step with that of the proposed method. The RMSE value of the geometric corrections using the proposed method was 7.08 × 10−9 m. The proposed method provides promising results.

Micro-Doppler Extraction of Radar Targets With Translational Motion Based on Spatial Transformer Network

The micro-Doppler (MD) signature, an effect that is induced by micro-motion, characterizes the rich motion information of targets, making it a valuable tool for target classification and recognition. However, the MD structure is always destroyed for radar targets with translational motion. In this letter, we propose an end–to–end translational motion compensation network based on a spatial transformer network (STN). First, the effect of the translational motion on the radar echo and its time-frequency graph (TFG) is modeled, so that translational motion compensation is regarded as an image spatial transformation task. Then, a two-branch convolutional network based on residual modules is designed to locate the transformation parameters, and the TFG containing only MD is obtained using a grid generator and sampler. Finally, the simulation results demonstrate that the proposed algorithm has high accuracy and stability.

Диэлектрическая спектроскопия нанокристаллических пленок VO2

The article describes the first reported case of applying dielectric spectroscopy to study nontrivial details of size dependences of phase transformation parameters in nanocrystallites of VO2 films. The method allows to identify the electrophysical parameters of nanocrystalline sets of different sizes and random location on the VO2 substrate. The article investigates the martensitic nature of the Mott-Peierls semiconductor-metal phase transition in crystalline VO2 films.

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity. Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments. Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation. Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals. Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1. Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems. To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. The comparsions show that SCBBO&#x2019;s optimization results on these dimensions are basically the same. SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.

Study on the Transient Characteristic Measurement System of DC Current Transformer

Nowadays, thirteen ultra-high voltage direct current (UHVDC) transmission projects have commissioned in China, including Jinping-Sunan, Fulong-Fengxian, Yibin-Jinhua project, and so forth. Meanwhile, another two UHVDC transmission projects are under construction. The highest UHVDC voltage level is ±1100kV. The HVDC transmission is superior in high capacity and long transmission distance, and its control response put forward higher requirements for the transient characteristics of DC current transformer. At present, there is no test method and equipment for testing the transient characteristics of the DC current transformer. Therefore, mastering the transient response characteristics of operating DC current transformer remains to be a challenge. In this paper, a transient test system of DC current transformer is developed, which includes a step current output unit, a high precision acquisition unit and a calibrator algorithm. The system was used to test the rising time, maximum overshoot and transient delay of the DC current transformers installed on the pole line and valve outlet. This study can clarify the influence factors of the transient characteristics of DC transformer on the control and protection system of UHVDC, and can define the principle of selecting the transformer parameters to meet the requirements of the control and protection system of UHVDC. The results of this study have great significance to improve the performance of UHVDC control and protection system.

Visual servoing based self-calibration of robotic inspection system using rigid body transformation parameters

Visual servoing based self-calibration of robotic inspection system using rigid body transformation parameters

Visual servoing based self-calibration of robotic inspection system using rigid body transformation parameters

Visual servoing based self-calibration of robotic inspection system using rigid body transformation parameters