Misalignment Issues Research Articles

Application of fuzzy PID control algorithm in hypersonic vehicle transpiration cooling control

As an efficient active cooling method, transpiration cooling is employed for thermal protection of blunt nose cones. However, most current systems utilize open-loop control for coolant supply. This study establishes a dynamic model of a one-dimensional blunt nose cone transpiration cooling system that concurrently considers aerodynamic heating, internal heat transfer in porous media, and the thermal insulation process of the air film layer formed by injected coolant. The findings indicate that the coolant film's thermal insulation effect significantly impacts the nose cone cooling system, with flow in the porous media causing a time-delay in the dynamic insulation effect. After implementing a closed-loop feedback controller, the fuzzy PID control algorithm demonstrates superiority over the conventional PID control algorithm in mitigating positive and negative feedback misalignment issues caused by time-delay, resulting in reduced temperature oscillation time and amplitude. Additionally, the fuzzy PID control algorithm achieves faster response and shorter stabilization time when external interference from varying Mach numbers occurs.

Development of Automobile Wheel Smart Alignment Monitoring System

This study addresses the pressing issue of ineffective traditional methods for monitoring wheel alignment in vehicles. The motivation stems from the limitations of manual inspection and the need for a more efficient and accurate solution. The aim is to develop a Smart Alignment Monitoring System that can provide real-time monitoring of alignment parameters. The objective is to enhance vehicle safety and performance by detecting misalignment issues early. The proposed solution involves the use of advanced sensor technologies integrated with vehicles' tire position. The results demonstrate the effectiveness of the Smart Alignment Monitoring System in reducing maintenance costs, improving fuel efficiency, and enhancing vehicle safety and performance. This study highlights the significance of adopting advanced technologies like Smart Alignment Monitoring Systems in the automotive industry to ensure safer and more efficient vehicles.

TrajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning

：Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Existing models, such as ConvLSTM, TrajGRU, and PredRNN, exhibit limitations in capturing fine-grained appearances due to insufficient memory units or addressing positional misalignment issues, thereby compromising the accuracy of model predictions. In this study, we propose trajPredRNN+, an innovative approach that integrates the trajectory segmentation model and the PredRNN deep learning model to address both limitations in nowcasting precipitation using weather radar echo images. By incorporating attention mechanisms, the model demonstrates an enhanced focus on short-term and imminent heavy rainfall events. To ensure improved stability during training, a residual network is introduced. Lastly, a more rational and effective training loss function is proposed, encompassing weight mechanism, SSIM index, and GAN loss. To validate the proposed model, we conducted a comparative experiment and an ablation experiment using the radar echo map dataset obtained from the Shenzhen Meteorological Bureau. The results of these experiments demonstrate that our model has achieved significant improvements across multiple key performance indicators.

Oxidized Ethylene Glycol/ZrO2-coated NiTi Orthodontic Arch Wires: Surface Characterization and Electrochemical and Corrosion Studies

Background: Orthodontic arch wires, typically made of Nickel Titanium (NiTi), are widely utilized in dental procedures for correcting teeth misalignment and jaw issues due to their favorable mechanical attributes and cost-effectiveness. However, these NiTi wires are prone to corrosion in the oral environment, leading to diminished mechanical stability, compromised aesthetics, and potential health concerns for patients. Objective: There is a growing demand to augment the corrosion resistance and stability of orthodontic wires. Hence, this study aimed to address these issues. Herein, zirconium dioxide (ZrO2) and oxidized ethylene glycol (OEG) films were deposited onto NiTi wires to improve the corrosion resistance and stability. Methods: NiTi wires were modified by a two-step process involving electrodeposition of ZrO2 and oxidized ethylene glycol (OEG) film. The surface characterizations of coated material (OEG/ZrO2/NiTi) were carried out by using Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray Spectroscopy (EDS), and Electron Microprobe Analysis (EMap) to confirm the elemental composition of the coated NiTi wire. Results: The OEG/ZrO2/NiTi wire exhibited a potentiodynamic polarization resistance of 547037 Ω and higher stability than the bare NiTi wire (396421 Ω). The corrosion rate for OEG/ZrO2/NiTi wire was found to be 0.040 mm/year, which was comparatively lower than a bare NiTi wire (0.069 mm/year). Due to the formation of OEG/ZrO2 film, NiTi wire became electrically insulative and showed a higher impedance than bare NiTi wire. Conclusion: The bilayer coating of ZrO2 and OEG has proven to significantly improve the corrosion resistance and stability of the wires. Thus, these materials can be considered for coating orthodontic arch wires with improved corrosion stability.

Enhanced recognition of insulator defects on power transmission lines via proposal-based detection model with integrated improvement methods

Deep learning-driven transmission line inspection is a critical area for smart power grid development. Despite advances in deep learning for insulator defect detection, challenges remain in model robustness and adaptability for the varying real-world adaptability, especially for insignificant defects in complex backgrounds. This study presents a comprehensive improvement strategy for detecting insulators and cross-scale broken defects on transmission lines, employing a proposal-based detection model. The model introduces a holistic pipeline of improved methods, including backbone modification, anchor box scale recalibration, and improvements in Region of Interest (RoI) downsampling alignment and Intersection over Union (IoU) loss function. Various backbone networks, including convolutional network (ConvNet) and Vision Transformer (ViT) structures, are constructed and integrated with attention modules, specifically designed to amplify the perception of insulators and defective regions. The geometric scale of anchor boxes is reconstructed using a developed clustering algorithm, considering the elongated characteristics of insulator strings to improve the adaptability of anchor boxes. Bilinear interpolation is utilized to mitigate spatial misalignment issues during the downsampling process of Region Proposal Network (RPN)-based proposals. The experimental results indicate that the improved models with the Swin Transformer (Swin-T) backbone framework achieve the mean Average Precision (mAP)@0.5 of 88.42% and mAP@0.7 of 60.52%, with a defect recall rate of 81.94%. Additionally, the improved IoU loss function contributes to the performance of the model at higher IoU thresholds. The results of this study contribute to the further development of defect detection frameworks for power vision applications.

Sustained Area-Selectivity in Atomic Layer Deposition of Ir Films: Utilization of Dual Effects of O3 in Deposition and Etching.

Area-selective deposition (ASD) based on self-aligned technology has emerged as a promising solution for resolving misalignment issues during ultrafine patterning processes. Despite its potential, the problems of area-selectivity losing beyond a certain thickness remain critical in ASD applications. This study reports a novel approach to sustain the area-selectivity of Ir films as the thickness increases. Ir films are deposited on Al2O3 as the growth area and SiO2 as the non-growth area using atomic-layer-deposition with tricarbonyl-(1,2,3-η)-1,2,3-tri(tert-butyl)-cyclopropenyl-iridium and O3. O3 exhibits a dual effect, facilitating both deposition and etching. In the steady-state growth regime, O3 solely contributes to deposition, whereas in the initial growth stages, longer exposure to O3 etches the initially formed isolated Ir nuclei through the formation of volatile IrO3. Importantly, longer O3 exposure is required for the initial etching on the growth area(Al2O3) compared to the non-growth area(SiO2). By controlling the O3 injection time, the area selectivity is sustained even above a thickness of 25nm by suppressing nucleation on the non-growth area. These findings shed light on the fundamental mechanisms of ASD using O3 and offer a promising avenue for advancing thin-film technologies. Furthermore, this approach holds promise for extending ASD to other metals susceptible to forming volatile species.

Area-Selective Atomic Layer Deposition of Ru Using Carbonyl-Based Precursor and Oxygen Co-Reactant: Understanding Defect Formation Mechanisms.

Area selective deposition (ASD) is a promising IC fabrication technique to address misalignment issues arising in a top-down litho-etch patterning approach. ASD can enable resist tone inversion and bottom-up metallization, such as via prefill. It is achieved by promoting selective growth in the growth area (GA) while passivating the non-growth area (NGA). Nevertheless, preventing undesired particles and defect growth on the NGA is still a hurdle. This work shows the selectivity of Ru films by passivating the Si oxide NGA with self-assembled monolayers (SAMs) and small molecule inhibitors (SMIs). Ru films are deposited on the TiN GA using a metal-organic precursor tricarbonyl (trimethylenemethane) ruthenium (Ru TMM(CO)3) and O2 as a co-reactant by atomic layer deposition (ALD). This produces smooth Ru films (<0.1 nm RMS roughness) with a growth per cycle (GPC) of 1.6 Å/cycle. Minimizing the oxygen co-reactant dose is necessary to improve the ASD process selectivity due to the limited stability of the organic molecule and high reactivity of the ALD precursor, still allowing a Ru GPC of 0.95 Å/cycle. This work sheds light on Ru defect generation mechanisms on passivated areas from the detailed analysis of particle growth, coverage, and density as a function of ALD cycles. Finally, an optimized ASD of Ru is demonstrated on TiN/SiO2 3D patterned structures using dimethyl amino trimethyl silane (DMA-TMS) as SMI.

End-to-end multiview fusion for building mapping from aerial images

In the domain of photogrammetry, the fusion of information from multiple views holds the potential to significantly enhance the accuracy and robustness of building mapping. While multiview observation and stereoscopic imaging form the bedrock of photogrammetric projects, current deep learning methodologies predominantly focus on orthophotos and digital surface models (DSMs), often sidelining the rich multiview information inherent in original images. Addressing this gap, we present Multiview Mapper (MVMapper), an end-to-end learning framework explicitly crafted to harness and fuse the rich semantic information from original multiview images with object-space features. MVMapper uses stereo labels for supervised building segmentation in a dual-space, encompassing both image and object domains. Additionally, it incorporates a novel piecewise affine projection method for ensuring a robust image-to-object feature transformation. Experimental results on an aerial photogrammetric dataset with a resolution of 30 cm demonstrate MVMapper’s superiority over state-of-the-art multiview data fusion methods, yielding significant improvements in segmentation and contour accuracy. Notably, the proposed piecewise affine projection method mitigates misalignment issues caused by DSM noise, enabling the effective fusion of multiview features with object-space features. Further experimentation on a separate open-source dataset demonstrates MVMapper’s substantial advantages in transferability to other contexts.

Governing the global fisheries commons

Despite significant advances in understanding the biophysical and institutional causes of overfishing, we have yet to make progress in addressing the depletion of our global fisheries stock. Investigations of potential solutions tend to be too broad (mischaracterizing global fisheries as a singular commons problem to be addressed at the supranational level) or too narrow (focusing on improving management of small fisheries at the micro level). This article attempts to bridge the gap between our scientific understanding of our collective dilemmas and their pragmatic solutions. Building on insights from Nobel laureate Elinor Ostrom, we frame the depletion of global fisheries as a nested set of diverse and interconnected collective action problems organized at different horizontal and vertical levels, where decisions and actions of one jurisdictional unit reinforce and amplify problems (and solutions) for other units. We examine features of the global fisheries system, such as nonstationarity, nestedness, and prohibitive transaction costs. Then, we explore some potential solutions. The success of our conservation goals depends on our ability to craft institutional rules at the lower levels that are adaptive to local conditions, address incentive misalignment issues, and allow for the transfer of positive externalities to adjacent and higher levels.

A Low-Profile Circularly Polarized Millimeter-Wave Broadband Antenna Analyzed with a Link Budget for IoT Applications in an Indoor Scenario.

Broadband antennas with a low-profile generating circular polarization are always in demand for handheld/ portable devices as CP antennas counter multipath and misalignment issues. Therefore, a compact millimeter-wave antenna is proposed in this article. The proposed antenna structure comprises two circular rings and a circular patch at the center. This structure is further embedded with four equilateral triangles at a 90° orientation. The current entering the radiator is divided into left and right circular directions. The equilateral triangles provide the return path for current at the differential phase of ±90°, generating circular polarization. Structural development and analysis were initially performed through the characteristic mode theory. It showed that Modes 1 to 4 generated good impedance matching from 20 to 30 GHz and Modes 1 to 5, from 30 to 40 GHz. It also demonstrated the summation of orthogonal modes leading to circular polarization. The antenna-measured reflection coefficient |S11| > 10 dB was 19 GHz (23-42 GHz), and the axial ratio at -3 dB was 4.2 GHz (36-40.2 GHz). The antenna gain ranged from 4 to 6.2 dBi. The proposed antenna was tested for link margin estimation for IoT indoor conditions with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The communication reliability with co- and cross-polarization was also studied under these conditions, and the results proved to be satisfactory.

AlignYOLO: A feature-aligned network for object detection

Aggregating features at various levels or scales has been empirically demonstrated to enhance feature representations in object detection. However, existing approaches tend to aggregate features or embed contextual information indiscriminately through simple concatenation or addition, which disregards the misalignment resulting from repeated sampling operations. This paper proposes a feature-aligned network based on YOLOv5 to address the misalignment issues, namely AlignYOLO. The network consists of three primary modules: the self-attention convolution (SAC) module, the feature aggregation and alignment (FAA) module, and the multiscale aligned channel attention (MSACA) module. Firstly, the SAC module comprehensively extracts information by simultaneously employing both convolution and self-attention. Secondly, the FAA module aggregates features across layers and aligns them through the adoption of a learnable interpolation strategy. Lastly, the MSACA module employs multiscale convolution to capture contextual information. The in-layer features are aligned with the learnable interpolation strategy. Additionally, channel attention is leveraged to enhance feature representations. Extensive experiments are conducted on benchmark datasets to evaluate the effectiveness of the proposed method, where AlignYOLO outperforms state-of-the-art detectors.

SGR: An Improved Point-Based Method for Remote Sensing Object Detection via Dual-Domain Alignment Saliency-Guided RepPoints

With the advancement of deep neural networks, several methods leveraging convolution neural networks (CNNs) have gained prominence in the field of remote sensing object detection. Acquiring accurate feature representations from feature maps is a critical step in CNN-based object detection methods. Previously, region of interest (RoI)-based methods have been widely used, but of late, deformable convolution network (DCN)-based approaches have started receiving considerable attention. A significant challenge in the use of DCN-based methods is the inefficient distribution patterns of sampling points, stemming from a lack of effective and flexible guidance. To address this, our study introduces Saliency-Guided RepPoints (SGR), an innovative framework designed to enhance feature representation quality in remote sensing object detection. SGR employs a dynamic dual-domain alignment (DDA) training strategy to mitigate potential misalignment issues between spatial and feature domains during the learning process. Furthermore, we propose an interpretable visualization method to assess the alignment between feature representation and classification performance in DCN-based methods, providing theoretical analysis and validation for the effectiveness of sampling points. In this study, we assessed the proposed SGR framework through a series of experiments conducted on four varied and rigorous datasets: DOTA, HRSC2016, DIOR-R, and UCAS-AOD, all of which are widely employed in remote sensing object detection. The outcomes of these experiments substantiate the effectiveness of the SGR framework, underscoring its potential to enhance the accuracy of object detection within remote sensing imagery.

Digital Market: Tool for Poverty Alleviation

This study delves into the intricate relationship between digital market solutions and poverty alleviation, specifically focusing on underserved communities in the global South. The persistent poverty traps prevalent in these regions necessitate integrated interventions that foster economic inclusion, sustainable livelihoods, and self-sufficient prosperity. The advent of digital technology adoption presents an infrastructure connectivity and widespread mobile unprecedented opportunity to overcome traditional barriers and uplift marginalized populations. The study argues that digital connectivity serves as a powerful vehicle for empowerment, dissolving geographical barriers and facilitating access to essential information, financial tools, and employment opportunities. It advocates for a market-based approach that recognizes the inherent talents for enterprise within impoverished communities, empowering individuals to direct solutions that meaningfully improve their circumstances. The digital divide dilemma is addressed, acknowledging that the impact data often focuses on those with basic digital literacy, leaving out those at the extreme margins without access to mobile phones or the internet. Design misalignment issues, including language barriers and cumbersome interfaces, are highlighted as potential obstacles to the effectiveness of digital interventions. The study emphasizes the importance of capacity building and localization, advocating for skills training and leadership development within communities to enable them to direct solutions aligned with their priorities. In conclusion, the study asserts that thoughtfully developed digital platforms have the potential to disrupt endemic poverty cycles. However, realizing this potential requires equitable access policies, sustained design optimizations, and participatory capacity building

A Systematic Review on Rigid Exoskeleton Robot Design for Wearing Comfort: Joint Self-Alignment, Attachment Interface, and Structure Customization.

Exoskeleton robots enable individuals with impaired physical functions to perform daily activities and maintain independence. However, the discomfort experienced by users when using these devices may limit the application scope of exoskeleton robots. Therefore, this paper systematically defines and analyzes the key design factors affecting the wearing comfort of rigid exoskeleton robots by differentiating and discussing the characteristics of traditional exoskeleton robots and exoskeleton robots equipped with the self-alignment mechanism based on addressing misalignment issues. Furthermore, the various structural configurations of the Physical Human-Robot Attachment Interface (PHRAI) and related quantitative evaluation indicators are explored in depth, and the advantages and limitations of structural customized design methods combining parametric design, Three-Dimensional (3D) scanning, and 3D printing technology are evaluated. Finally, the current concerns in the research field and potential solution strategies are proposed, aiming to provide directional guidance to optimize future exoskeleton robots. The research findings are of significant value for enhancing the comfort of wearing exoskeleton robots and provide valuable theoretical and practical references for future research.

Modified Coupler with Reduced Misalignment Issues for Wireless Power Transfer System

Modified Coupler with Reduced Misalignment Issues for Wireless Power Transfer System

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images.

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.

Symptoms of sacral fractures after posterior spinal instrumented fusion: A case series

BackgroundImprovements in spinal fusion devices and techniques have enabled stronger spinal fusion, resulting in excellent clinical outcomes. Nevertheless, complications associated with implants, such as screw misalignment, screw lubrication, cage dislocation, and skin issues, might occur. This study aimed to investigate the characteristics and symptoms of sacral fractures after spinal instrumented fusion. MethodsThis case series retrospectively examined the medical records of eight patients (one man and seven women; mean age: 74 years) diagnosed with sacral fractures after undergoing posterior spinal instrumented fusion from February 2015 to March 2022. ResultsThe average number of fusion levels in all patients was 3.5 (range, 1–10). The lowest instrumented vertebrae (LIV) ranged from L5 to the ilium. Sacral fractures were diagnosed at 18.8 (range, 0.5–84) months postoperatively. The average time from consultation to diagnosis was nine days (range, 0–25 days). Two patients had subclinical fractures, two had H-shaped fractures with the LIV at L5, and four had U-shaped fractures, including screw holes. Buttock pain and lower extremity pain, the most commonly reported symptoms, were observed in seven patients each. There were also instances of leg numbness, muscle weakness, and unilateral leg pain that may be related to L5 or S1 radiculopathy. In all patients, leg and buttock pain were worse during movement and in the sitting position, and better while resting and in the supine position. Three patients were treated conservatively, and five were treated with extended fixation to the ilium. ConclusionsSacral fractures following posterior spinal fusion can cause radiculopathy and buttock pain. Symptoms are especially severe when instability occurs in the pelvic region, such as during movements or sitting. As atypical radiculopathy may lead to delays in diagnosis, spine surgeons should recognize the symptoms of this condition.

Spatio-Temporal Alignment and Track-To-Velocity Module for Tropical Cyclone Forecast

The prediction of a tropical cyclone’s trajectory is crucial for ensuring marine safety and promoting economic growth. Previous approaches to this task have been broadly categorized as either numerical or statistical methods, with the former being computationally expensive. Among the latter, multilayer perceptron (MLP)-based methods have been found to be simple but lacking in time series capabilities, while recurrent neural network (RNN)-based methods excel at processing time series data but do not integrate external information. Recent works have attempted to enhance prediction performance by simultaneously utilizing both time series and meteorological field data through feature fusion. However, these approaches have relatively simplistic methods for data fusion and do not fully explore the correlations between different modalities. To address these limitations, we propose a systematic solution called TC-TrajGRU for predicting tropical cyclone tracks. Our approach improves upon existing methods in two main ways. Firstly, we introduce a Spatial Alignment Feature Fusion (SAFF) module to address feature misalignment issues in different dimensions. Secondly, our Track-to-Velocity (T2V) module leverages time series differences to integrate external information. Our experiments demonstrate that our approach yields highly accurate predictions comparable to the official optimal forecast for a 12 h period.

PAR2Net: End-to-end Panoramic Image Reflection Removal.

In this paper, we investigate the problem of panoramic image reflection removal to relieve the content ambiguity between the reflection layer and the transmission scene. Although a partial view of the reflection scene is attainable in the panoramic image and provides additional information for reflection removal, it is not trivial to directly apply this for getting rid of undesired reflections due to its misalignment with the reflection-contaminated image. We propose an end-to-end framework to tackle this problem. By resolving misalignment issues with adaptive modules, high-fidelity recovery of the reflection layer and the transmission scenes are accomplished. We further propose a new data generation approach that considers the physics-based formation model of mixture images and the in-camera dynamic range clipping to diminish the domain gap between synthetic and real data. Experimental results demonstrate the effectiveness of the proposed method and its applicability for mobile devices and industrial applications.

Output feedback attitude control of flexible spacecraft under actuator misalignment and input nonlinearities

The attitude tracking control problem of flexible spacecraft subjected to parameter uncertainties, time-dependent external disturbances, actuator input nonlinearity, and input actuator misalignment is investigated in this paper. Explicitly, the proposed strategy addresses the input actuator misalignment and dead-zone issues that increase the controller design difficulties. Initially, a new second-order sliding mode observer (SoSMO) using an extended state approach is developed by adding a correction function to improve observer performance to estimate unwanted system perturbations. Then, a distinct SoSMO-based integral-type sliding mode control (ISMC) structure is designed in a unified manner to guarantee the asymptotic stability of the closed-loop system. Comparative numerical simulations under input actuator misalignment, the dead-zone nonlinearity, external disturbance, and inertia uncertainty are performed to illustrate the effectiveness of the proposed controller.