Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

Evaluating the use of three-dimensional reconstruction visualization technology for precise laparoscopic resection in gastroesophageal junction cancer

BACKGROUND Laparoscopic gastrectomy for esophagogastric junction(EGJ)carcinoma enables the removal of the carcinoma at the junction between the stomach and esophagus while preserving the gastric function,thereby providing patients with better treatment outcomes and quality of life.Nonetheless,this surgical technique also presents some challenges and limitations.Therefore,three-dimensional reconstruction visualization technology(3D RVT)has been introduced into the procedure,providing doctors with more comprehensive and intuitive anatomical information that helps with surgical planning,navigation,and outcome evaluation.AIM To discuss the application and advantages of 3D RVT in precise laparoscopic resection of EGJ carcinomas.METHODS Data were obtained from the electronic or paper-based medical records at The First Affiliated Hospital of Hebei North University from January 2020 to June 2022.A total of 120 patients diagnosed with EGJ carcinoma were included in the study.Of these,68 underwent laparoscopic resection after computed tomography(CT)-enhanced scanning and were categorized into the 2D group,whereas 52 underwent laparoscopic resection after CT-enhanced scanning and 3D RVT and were categorized into the 3D group.This study had two outcome measures:the deviation between tumor-related factors(such as maximum tumor diameter and infiltration length)in 3D RVT and clinical reality,and surgical outcome indicators(such as operative time,intraoperative blood loss,number of lymph node dissections,R0 resection rate,postoperative hospital stay,postoperative gas discharge time,drainage tube removal time,and related complications)between the 2D and 3D groups.RESULTS Among patients included in the 3D group,27 had a maximum tumor diameter of less than 3 cm,whereas 25 had a diameter of 3 cm or more.In actual surgical observations,24 had a diameter of less than 3 cm,whereas 28 had a diameter of 3 cm or more.The findings were consistent between the two methods(χ^(2)=0.346,P=0.556),with a kappa consistency coefficient of 0.808.With respect to infiltration length,in the 3D group,23 patients had a length of less than 5 cm,whereas 29 had a length of 5 cm or more.In actual surgical observations,20 cases had a length of less than 5 cm,whereas 32 had a length of 5 cm or more.The findings were consistent between the two methods(χ^(2)=0.357,P=0.550),with a kappa consistency coefficient of 0.486.Pearson correlation analysis showed that the maximum tumor diameter and infiltration length measured using 3D RVT were positively correlated with clinical observations during surgery(r=0.814 and 0.490,both P<0.05).The 3D group had a shorter operative time(157.02±8.38 vs 183.16±23.87),less intraoperative blood loss(83.65±14.22 vs 110.94±22.05),and higher number of lymph node dissections(28.98±2.82 vs 23.56±2.77)and R0 resection rate(80.77%vs 61.64%)than the 2D group.Furthermore,the 3D group had shorter hospital stay[8(8,9)vs 13(14,16)],time to gas passage[3(3,4)vs 4(5,5)],and drainage tube removal time[4(4,5)vs 6(6,7)]than the 2D group.The complication rate was lower in the 3D group(11.54%)than in the 2D group(26.47%)(χ^(2)=4.106,P<0.05).CONCLUSION Using 3D RVT,doctors can gain a more comprehensive and intuitive understanding of the anatomy and related lesions of EGJ carcinomas,thus enabling more accurate surgical planning.

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

An Exact Solution for Two-Dimensional Frictionless Motion in the Atmosphere

A solution of the nonlinear problem for determining the wind velocity in frictionless atmosphere (the gradient wind) under given geopotential (pressure) field is proposed. The approach is analytical and is based on quadratic polynomial approximation of the geopotential field and linear approximation of the wind velocity field with respect to x and y, the coefficients of the expansions being functions of the time t. The derived system of ordinary nonlinear differential equations is analyzed as a dynamical system. Exact analytical solutions are found for some particular cases. Some of their properties bear a resemblance to those or really existing atmospheric vortices (cyclones and anticyclones).

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motioncompensated reconstruction, biomechanical modeling and deep learning

4-Dimensional cone-beam computed tomography(4D-CBCT)offers several key advantages over conventional 3DCBCT in moving target localization/delineation,structure de-blurring,target motion tracking,treatment dose accumulation and adaptive radiation therapy.However,the use of the 4D-CBCT in current radiation therapy practices has been limited,mostly due to its sub-optimal image quality from limited angular sampling of conebeam projections.In this study,we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement,and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction(SMEIR).Based on the original SMEIR scheme,biomechanical modeling-guided SMEIR(SMEIR-Bio)was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs.To improve the efficiency of reconstruction,we recently developed a U-net-based deformation-vector-field(DVF)optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs(SMEIR-Unet),without explicit biomechanical modeling.Details of each of the SMEIR,SMEIR-Bio and SMEIR-Unet techniques were included in this study,along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs.We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy,and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

Using Pure Translation to Get Euclidean Reconstruction

A technique for getting Euclidean reconstruction from two images of the same scene taken by a single moving camera, which undergoes a pure translation, is presented. Euclidean reconstruction of the scene up to three scale factors can be obtained by using this special but still realistic motion when the skew factor of the cam- era is zero; otherwise Euclidean reconstruction of the depth up to one scale factor can be achieved. The only assumption is that the camera intrinsic parameters are constant. Using this special but still realistic motion to do the reconstruction has the advantage that no projective reconstruction is needed and the Euclidean reconstruction is computed directly from the point correspondences in the two images.

ACCURATE DETECTION OF HIGH-SPEED MULTI-TARGET VIDEO SEQUENCES MOTION REGIONS BASED ON RECONSTRUCTED BACKGROUND DIFFERENCE

The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.

Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Neural hand reconstruction using an RGB image

Background This study presents a neural hand reconstruction method for monocular 3D hand pose and shape estimation.Methods Alternate to directly representing hand with 3D data,a novel UV position map is used to represent a hand pose and shape with 2D data that maps 3D hand surface points to 2D image space.Furthermore,an encoder-decoder neural network is proposed to infer such UV position map from a single image.To train this network with inadequate ground truth training pairs,we propose a novel MANOReg module that employs MANO model as a prior shape to constrain high dimensional space of the UV position map.Results The quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.

Super resolution reconstruction of moving objects from low resolution surveillance video

Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based super resolution reconstruction scheme was proposed,in which a six-parameter affine model-based object tracking and registration method was first used to segment and match objects among a se-quence of low resolution frames.The motion model was then further extended to the traditional maximuma posterior(MAP)super resolution algorithm.The proposed object tracking and registration method wasevaluated by both simulated and real acquired sequences.The results have demonstrated the high accura-cy of the proposed object based method and the enhanced reconstruction performance of the extended ap-proach.

动态场景的三维重建研究综述

随着静态场景三维重建算法的不断成熟,动态场景三维重建算法成为近年来的研究热点和研究难点。现有的静态场景三维重建算法对静止的对象有较好的重建效果,一旦场景中对象出现变形或者是相对运动,其重建效果不太理想,因此发展对动态场景的三维重建研究工作是相当重要的。简要介绍三维重建的相关概念及基本知识、静态场景三维重建和动态场景三维重建的研究分类及研究现状;全面总结了动态场景三维重建研究最新进展,将动态场景三维重建按照基于RGB数据源的动态三维重建和基于RGB-D数据源的动态三维重建进行分类,其中RGB数据源下又可划分为基于模板的动态三维重建、基于非刚性运动恢复结构的动态三维重建和RGB数据源下基于学习的动态三维重建,RGB-D数据源下主要总结归纳基于学习的动态三维重建,对各类典型重建算法进行了介绍和对比分析;介绍了动态场景三维重建在医学、智能制造、虚拟现实与增强现实、交通等领域的应用;提出了动态场景三维重建的未来研究方向,并对这个快速发展领域中的各个方向研究进行了展望。

肌内效贴对前交叉韧带重建后康复疗效的Meta分析

目的:一些研究显示肌内效贴在提升前交叉韧带重建后患者肌肉力量、改善关节稳定性、减轻疼痛及水肿方面具有积极效应,然而现有研究关于肌内效贴的临床疗效存在相互矛盾的结果。文章采用Meta分析方法,系统评价肌内效贴对前交叉韧带重建术后康复疗效的影响。方法:应用计算机检索PubMed、Web of Science、Embase、The Cochrane Library、EBSCO、中国知网、万方、维普数据库,搜集有关肌内效贴对前交叉韧带重建后患者影响的随机对照试验,检索时限均从各数据库建库至2022-12-06,结局指标包括股四头肌力量、腘绳肌力量、膝关节肿胀、膝关节活动度、Lysholm膝关节功能评分、目测类比评分6个连续型变量。运用EndNote X9.1筛选文献,采用Cochrane风险偏倚评估工具和Jadad量表评估纳入文献质量,采用RevMan 5.3软件进行Meta分析。结果:①共纳入6项随机对照试验,包括252例前交叉韧带重建后患者,其中对照组126例,肌内效贴组126例;②Meta分析结果显示,与对照组相比,肌内效贴组患者腘绳肌力量显著增加[SMD=0.68,95%CI(0.12,1.23),P=0.02]、目测类比评分显著降低[MD=-0.56,95%CI(-1.04,-0.08),P=0.02],两组患者间股四头肌力量、膝关节肿胀、膝关节活动度及Lysholm膝关节功能评分比较差异均无显著性意义(P>0.05)。结论:当前证据显示,肌内效贴可能有助于提升前交叉韧带重建后患者腘绳肌力量、减轻患者疼痛,然而并不能显著改善患者股四头肌力量、膝关节肿胀、膝关节活动度和功能评分。

机械臂环境三维重建与避障算法研究

为了解决机器人在未知环境中的避障规划难题,提出了一种基于视觉的三维重建与避障规划方法。通过深度相机(RealSense D435i)与机械臂组成手眼系统,首先,根据目标物体与ArUco码的相对关系,通过相机获取的ArUco标记上的特征点坐标,求解出目标物姿态;然后,改进三维重建方法,通过融合机械臂工具末端位姿和多帧点云数据,对环境进行较高精度的三维重建,作为后续规划的障碍空间;最后,应用了快速扩展随机树(RRT)的改进算法进行机械臂的避障运动规划。研究搭建了仿真及控制平台,来进行验证。实验表明:环境三维建模的精度维持在8mm以内,避障成功率为96.5%,平均规划时间为1.2s,验证了方法的可行性。

机器视觉中角点检测算法研究

角点检测是运动检测、图像匹配、视频跟踪、三维重建和目标识别等必不可少的关键步骤,角点检测的准确性直接影响实验结果;为了更好地了解角点检测技术的发展现状,根据3种现有的角点检测方法分类对角点检测方法及相关改进进行了总结分析,并选择了FAST、SUSAN、SIFT、Shi-Tomas这几种较为典型的角点检测算法进行了实验对比,并给出了实验结果;不同的实际应用对角点检测的要求不同,不同的角点检测算法也可以相互结合,通过对现有角点检测技术的总结分析为在实际应用中对角点检测技术的选择和改进方向提供了借鉴和参考。

融合深度特征的无人机影像SfM重建

可靠特征匹配是无人机影像运动恢复结构(SfM)的重要环节。近年来,深度学习被用于特征提取和匹配,在基准数据集表现优于SIFT等手工特征。但是,公开模型往往采用互联网照片进行训练和测试,鲜有用于无人机影像SfM三维重建的性能评价。利用多组不同特点的无人机数据集,本文对比分析手工特征和深度学习特征在无人机影像特征匹配和SfM三维重建的综合性能。试验结果表明,利用公开的预训练模型,结合手工特征的高精度定位和深度学习的特征描述能力,可实现更准确和完整的特征匹配,并在SfM三维重建中取得与SIFT等手工特征相当,甚至更优的性能。

基于水下机器人的水下混凝土结构表观病害三维重建方法

利用水下机器人进行水下结构病害检测能大幅提升检测效率、降低检测成本,然而利用水下机器人拍摄得到的水下结构平面图像仅包含结构表面的有限信息。通过对水下结构三维重构可以直观地显示结构的空间构形和特征,可以多视角、全方位获取结构的表观缺陷信息。该文提出了基于水下机器人拍摄平面图像的水下混凝土结构三维重建方法,通过水下折射摄像机成像模型、SIFT特征提取和匹配算法以及点云重建的方式对水下带病害的混凝土结构表面进行三维重建。实验表明:该文提出的水下混凝土结构三维重建方法能实现病害三维立体展示,从多视角清晰获取水下结构表面病害信息。

基于光线重排的印制电路板高速扫描成像迭代去模糊方法研究

自动光学检测(automated optical inspection,AOI)相机运动速度过高,拍摄印制电路板(printed circuit board,PCB)图像会产生严重模糊。如能正确恢复因此而产生的退化,可提高相机的运动速度,进而提高AOI的检测效率。受代数迭代重建算法(algebraic reconstruction technique,ART)启发,提出一种基于光线重排的图像非盲去模糊算法。该方法在模糊核已知的情况下,基于快速迭代收缩阈值算法(fast iterative shrinkage thresholding algorithm,FISTA)迭代重建算法,运用Nestrov加速和光线重排提高了迭代收敛速度,运用数据正则抑制图像的各类加性噪声和伪影,较好地重建出原本的清晰图像。结果表明,该方法对噪声具有较好的抑制能力,对AOI相机运动曝光引起的严重模糊,较常用的传统逆滤波方法具有更好的图像恢复效果。

基于多视图前景分割的电网设施三维数字化重建

电网设施高保真数字化建模和可视化是构建数字孪生电网,推动新型电力系统建设的重要步骤。为解决手工建模方式的诸多弊端,满足数字孪生电网设施三维可视化需求,提出了一种基于多视图前景分割的电网设施三维数字化重建方法。基于深度学习网络结合前景分割算法,构建电网设施检测定位与前景分割模型,将相机原始多视图转化为待建设施前景图像,减少图像复杂背景带来的特征误匹配、模型体外噪声增多等问题的影响,提高重建视觉效果。以运动恢复结构为基础原理,提出基于改进加速稳健特征–随机抽样一致性(speeded-up robust features-random sample consensus,SURF-RANSAC)算法的高质量相机位姿估计方法与稀疏点云稠密化方案,最终形成了一种面向分布式电网设施的统一结构化静态模型重建方式。通过实拍图像构建设施重建数据集,分别对电力变压器、变压器绕组、高压真空交流断路器3种典型电网设施进行仿真重建,均取得良好重建视觉效果和较低的重投影误差,验证了方法的通用性和有效性。所提方法不仅为分布式设施在虚拟空间的统一表征、状态监测提供三维模型支撑,也为供需侧电网设施的跨时空互动及供需平衡调节实现提供可能。

低轨互联网星座发展研究

近年来,随着互联网用户激增,SpaceX、OneWeb等创新型企业纷纷计划打造低轨互联网星座,引发全球低轨卫星互联网星座的发展热潮。因此,研究国内外低轨互联网星座的发展情况及当前面临的技术难题有着很重要的意义。基于此,介绍了国外具有代表性的3个低轨互联网星座计划(Starlink、OneWeb、Lightspeed)及国内星座的最新发展情况;根据低轨互联网星座的特点,着重分析了低轨互联网星座面临的五大技术挑战:星座相对运动演化预报、星座自主导航与定轨、星座构型重构的路径规划、星座自组织协同构型控制、星座通信与网络服务;根据当前低轨互联网星座的发展趋势,对中国在低轨互联网星座的发展给出了建议。