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Machining precision components involves challenging distortion issues that entail high costs and material and energy waste to the industry. In parallel, advanced control of production processes is a rapidly growing field because of its unique capabilities to solve multi-agent nonlinear problems and develop control actions based on knowledge and experience. Despite the several studies carried out on the subject, research keeps fragmenting distortion issues in different niches of components, and comprehensive reviews considering distortion as a cross-cutting technical hitch have never been reported. In this paper, a study compiling recent advances in machining distortion control from a holistic perspective is presented. For the first time, distortion understanding is unified, offering a new perspective, more practical and comprehensive, which includes intelligent systems. This novel way of attaining the research on distortion distinguishes three interconnected pillars: distortion source identification and quantification, distortion simulation model development, and control strategies drafting and application. The paper guides the reader through several distortion investigations of different kinds and provides classifications never addressed in the field with which a profound understanding of the issue can be achieved. Finally, future trends and key enabling technologies to drive the advanced control and minimization of machining distortions are outlined.

The distortion in machining aeronautical aluminum alloy parts (AAAPs) is one of the serious challenges in the aviation industry, and the residual stresses produced in multimanufacturing steps are the main cause. In order to get a comprehensive understanding of the problems about residual stresses and distortion in machining AAAPs, the state-of-the-art in several aspects including the generation reasons of residual stresses, the factors influencing distortion during machining, the measurement methods of residual stresses, the prediction and controlling methods of distortion are summarized in this paper. The generation mechanism of the bulk residual stress inner materials and the machining-induced residual stresses, as well as the factors affecting two kinds of residual stresses are stated. Also, the influences of residual stresses and machining process conditions on distortion are analyzed. Furthermore, the common residual stress measurement methods and its application scope are summarized. Significantly, the differences, advantages, and disadvantages of various prediction methods are analyzed. The methods of controlling distortion before and after machining are summarized. Finally, the paper gives out further research on the distortion in machining AAAPs in aeronautical manufacturing.

Accurate correction of radial lens distortion is a fundamental requirement in computer vision and photogrammetry, as geometric inaccuracies directly affect 3D reconstruction, mapping, and geospatial measurements, particularly in high-precision imaging systems. In this study, we propose a fully analytical, non-iterative method for truncated inverse modeling of radial lens distortion, applicable to general radial distortion polynomials that contain constant terms. Unlike classical truncated Lagrange series reversion, which relies on recursive expansion and combinatorial series construction, the proposed formulation determines inverse distortion coefficients directly through a system of constrained algebraic inverse polynomials. This enables deterministic computation of inverse parameters without iterative refinement, numerical root finding, or combinatorial complexity. The method was evaluated using ultra-wide-angle smartphone camera imagery exhibiting severe barrel distortion modeled by an eighth-degree depressed radial distortion polynomial. Its performance was compared with a commonly used iterative inverse modeling approach. The analytical formulation demonstrated improved numerical stability and substantially reduced reprojection errors when correcting highly nonlinear distortion profiles, achieving sub-pixel accuracy in image rectification. In contrast, the iterative approach exhibited instability and significantly larger reprojection errors under identical conditions. These results demonstrate that the proposed framework provides a general, robust, and repeatable solution for inverse radial distortion modeling, particularly for high-order polynomial models. The method offers clear practical advantages for camera calibration pipelines in photogrammetry, remote sensing, robotics, and other applications requiring high-fidelity imaging.

Geometric distortions in digital images, caused by factors such as lens defects and changes in camera angles, substantially influence the fidelity of the image by altering pixel positions and shapes. Current geometric distortion correction methods, focusing on specific types of distortions and relying on high computational resources, face limitations in universality and practicality across diverse real-world applications. We propose here a two-stage distortion correction method that integrates deep learning with traditional image registration algorithms for correcting multiple types of geometric distortion. Compared to state-of-the-art correction methods, our proposed method demonstrates flexibility, capable of addressing a wide range of geometric distortions and achieves superior correction results with fewer parameters. In addition, tests performed on synthetic datasets show an improvement of 10.39% for PSNR, 30.42% for SSIM, and 85% for processing speed, compared to the best performing methods to our knowledge. Finally, experiments with handheld medical endoscopic scanners confirm the applicability and robustness of our method in real-world scenarios. Our method offers a versatile and efficient solution for geometric distortion correction, suitable for various applications, including medical imaging and resource-limited embedded systems. Code is available at https://github.com/MaybeRichard/EffiGeoNet

Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method’s ability to accurately identify distortion parameters and remove distortion from images.

In recent modern power systems, the number of renewable energy systems (RESs) and nonlinear loads have become more prevalent. When these systems are connected to the electricity grid, they may face new difficulties and issues such as harmonics and non-standard voltage. The proposed study suggests the application of a whale optimization algorithm (WOA) based on a fractional-order proportional-integral controller (FOPIC) for unified power quality conditioner (UPQC) and STATCOM tools. These operate best with the help of their improved control system, to increase the system’s reliability and fast dynamic response, and to decrease the total harmonic distortion (THD) for enhancing the power quality (PQ). In this article, three different configurations are studied and assessed, namely: (C1) WOA-based FOPIC for UPQC, (C2) WOA-based FOPIC for STATCOM, and (C3) system without FACTS, i.e., base case, to mitigate the mentioned drawbacks. C3 is also considered as a base case to highlight the main benefits of C1 and C2 in improving the PQ by reducing the %THD of the voltage and current system and improving the systems’ voltage waveforms. With C2, voltage fluctuation is decreased by 98%, but it nearly disappears in C1 during normal conditions. Additionally, during the fault period, voltage distortion is reduced by 95% and 100% with C2 and C1, respectively. Furthermore, when comparing C1 to C2 and C3 under regular conditions, the percentage reduction in THD is remarkable. In addition, C1 eliminates the need for voltage sag, and harmonic and current harmonic detectors, and it helps to streamline the control approach and boost control precision. The modeling and simulation of the prepared system are performed by MATLAB/Simulink. Finally, it can be concluded that the acquired results are very interesting and helpful in the recovery to the steady state of wind systems and nonlinear loads, thereby increasing their grid connection capabilities.

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential. •Ultra-high field strengths provide increased sensitivity and specificity for fMRI.•High-resolution fMRI data enables new analysis strategies and challenges.•Advanced preprocessing strategies can help preserve fMRI resolution.•Small-voxels enabled intracortical analyses for laminar and columnar fMRI.

This article introduces a relative study of linearity performance and harmonic distortion among Si junctionless (JL) FinFET, conventional inversion-mode (IM) FinFET, Tunnel FET, and InGaAs MOSFET. For this, a numerical device simulator has been applied. Our investigation discloses that JL device shows best performance for both cases.

Camera or lens-based detector calibration is essential for spatial accuracy in applications like dimensional tomography, optical metrology, and computer vision. Many methods and software exist yet there is still a lack of approaches that achieve both high accuracy and robustness while being easy to use and capable of handling a wide range of distortions. Radial lens distortion is common in high-resolution X-ray detector optics used in parallel-beam tomography at synchrotrons. Achieving sub-pixel accuracy requires calibrating with an optical target image. Although methods for characterizing radial distortion are well established, acquired images often also include perspective distortion and optical center offset. Here, we present our approaches to individually characterize and correct both types of distortion using a single calibration image, implemented in the Discorpy software.

Determination of lattice parameters from 3D electron diffraction (3D ED) data measured in a transmission electron microscope is hampered by a number of effects that seriously limit the achievable accuracy. The distortion of the diffraction patterns by the optical elements of the microscope is often the most severe problem. A thorough analysis of a number of experimental datasets shows that, in addition to the well known distortions, namely barrel-pincushion, spiral and elliptical, an additional distortion, dubbed parabolic, may be observed in the data. In precession electron diffraction data, the parabolic distortion leads to excitation-error-dependent shift and splitting of reflections. All distortions except for the elliptical distortion can be determined together with lattice parameters from a single 3D ED data set. However, the parameters of the elliptical distortion cannot be determined uniquely due to correlations with the lattice parameters. They can be determined and corrected either by making use of the known Laue class of the crystal or by combining data from two or more crystals. The 3D ED data can yield lattice parameter ratios with an accuracy of about 0.1% and angles with an accuracy better than 0.03°.