The real-time dynamic liquid level calculation method of the sucker rod well based on multi-view features fusion

In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

基于Multi-View模型的敏捷虚拟企业知识共享模式研究

敏捷虚拟企业的核心在于实现全球信息经济时代的知识共享、综合利用和优化配置及合理流动,其主要特征在于所处的信息化环境和知识协同集成与共享性。敏捷虚拟企业Multi-View维度即对敏捷虚拟企业过程、功能、组织、资源、知识等系统进行描述,各视图之间实现知识共享与关联。基于Multi-View模型分析,敏捷虚拟企业需要构建知识共享环境与过程,并基于分布式对象技术实现敏捷虚拟企业知识共享建模。

Interactive transport of multi-view videos for 3DTV applications

The authors propose a novel method for transporting multi-view videos that aims to keep the bandwidth requirements on both end-users and servers as low as possible. The method is based on application layer multicast, where each end point re- ceives only a selected number of views required for rendering video from its current viewpoint at any given time. The set of selected videos changes in real time as the user’s viewpoint changes because of head or eye movements. Techniques for reducing the black-outs during fast viewpoint changes were investigated. The performance of the approach was studied through network experiments.

Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

Multi-view video color correction using dynamic programming

Color inconsistency between views is an important problem to be solved in multi-view video systems. A multi-view video color correction method using dynamic programming is proposed. Three-dimensional histograms are constructed with sequential conditional probability in HSI color space. Then, dynamic programming is used to seek the best color mapping relation with the minimum cost path between target image histogram and source image histogram. Finally, video tracking technique is performed to correct multi-view video. Experimental results show that the proposed method can obtain better subjective and objective performance in color correction.

Frame-layer bit allocation for multi-view video coding based on frame complexity estimation

Current multi-view video coding （MVC） reference model in joint video team （JVT） does not provide efficient rate control schemes. This paper presents a rate control algorithm for MVC by improving the quadratic rate-distortion （R-D） model. We reasonably allocate bit-rate among views based on the correlation analysisl The proposed algorithm consists of three levels to control the rate bits more accurately, of which the frame layer allocates bits according to the frame complexity and the temporal activity. Extensive experiments show that the proposed algorithm can control the bit rate efficiently.

Multi-view ladar data registration in obscure environment

Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.

基于WorldView-3卫星多视角数据的三维实景建模技术研究

目前亚米级立体卫星影像因其不受空域限制且重访周期短,已逐步成为航空摄影的有益补充,用于满足城市大比例尺地形图更新测绘的需要。而随着航空倾斜摄影测量技术在三维实景建模中的广泛应用,卫星影像平台的发展也让高分辨率卫星大倾角数据进入到我们的视野,研究基于高分辨率、多视角的立体卫星影像在三维实景建模应用的原理和方法具有实际意义。本文首次定制获取了三景不同倾角的WorldView-3卫星数据,其中两景是常规小倾角0.31 m多光谱立体影像一景是大于45°的0.5 m大倾角、多光谱卫星影像。本文对其三维实景建模的工艺和关键技术包括对基于卫星影像的纹理提取和自动映射技术、不同地物的采集方法进行了探讨,并提出星载高分立体多视角影像在三维建模方面的优劣势。

一种基于改进型Multi-Agent多分类器融合的乳腺钼靶肿块分类算法

乳腺钼靶摄影(mammography)是目前诊断乳腺癌的一种重要手段,肿块是钼靶图像中表征乳腺病变的主要病灶之一。文中提出了一种基于改进型Multi-Agent(MA)多分类器融合算法,用来对乳腺肿块的良恶性进行分类。它在经典MA算法思想的基础上,对各单分类器的分类效果、识别性能有所差别的情况进行针对性修正;同时在统计训练数据时,不再依靠分类结果的标签,而是用统计分类结果的置信度来代替;而当算法的迭代训练超过一定次数仍未能趋向稳定时,采用概率加权平均代替原来的简单平均进行整体决策,并用整体溯源情况调整决策结果,从而帮助算法收敛。此方法在公共数据库DDSM上做了测试,实验结果表明,在单视角下,改进型MA融合算法的分类精度达到了95.63%,优于经典的MA融合算法,其稳定性也较大多数单分类器以及多分类器融合算法要好;在双视角下,改进型MA融合算法的分类精度达到了97.79%,相较其他分类方式也具有一定的优势。

一种基于证据多视角的模糊C-means聚类算法

针对传统多视角聚类算法难以准确识别噪声和有效划分类间重叠区域样本的问题,提出一种基于证据多视角的模糊C均值(evidential multi-view fuzzy C-means,EMVFCM)聚类算法。首先,在证据推理框架下,研究一种改进的模糊C-means多视角聚类算法,通过优化改进的目标函数获得待测样本属于单类和噪声的信任值,从而识别出噪声数据。然后,由于重叠区域的样本不能被准确地划分类别,所以将其划分到相对应的复合类,这不仅能够表征数据样本类别的不精确性,还能降低错误分类的风险。最后,通过人工数据集和UCI数据集验证本文算法的性能并与相关算法对比。实验结果表明,本文算法较传统多视角聚类算法能更有效地处理数据中的噪声和重叠样本难以准确划分的问题。

基于改进的GoogleNet-ResNet算法的路基病害智能分类方法

针对路基病害分类算法存在的复杂病害辨识难度大、多视图雷达图像特征利用不充分等问题,提出一种基于改进的GoogleNet-ResNet算法的路基病害智能分类方法;首先,引入坐标注意力和改进的Inception模块对GoogleNet网络结构进行优化;然后,利用改进的GoogleNet学习c-scan数据特征剔除非目标病害,实现病害目标的粗分类;最后,将分类成病害的b-scan数据输入基于迁移学习的ResNet50,实现病害的细分类;实验表明,改进的GoogleNet进行病害粗分类的准确率可达到98.2%,检测速度可达90.9 fps;基于迁移学习的ResNet50进行病害细分类的准确率可达90.5%,检测速度可达52.6 fps;该算法的准确率比单独的改进的GoogleNet网络高10.1%,比单独的ResNet50网络高7.4%,有效地提高了道路路基病害的识别精度与效率。

多视图融合DJ-TextRCNN的古籍文本主题推荐研究

传统编目分类和规则匹配方法存在工作效能低、过度依赖专家知识、缺乏对古籍文本自身语义的深层次挖掘、编目主题边界模糊、较难实现对古籍文本领域主题的精准推荐等问题。为此,本文结合古籍语料特征探究如何实现精准推荐符合研究者需求的文本主题内容的方法,以推动数字人文研究的进一步发展。首先,选取本课题组前期标注的古籍语料数据进行主题类别标注和视图分类;其次,构建融合BERT(bidirectional encoder representation from transformers)预训练模型、改进卷积神经网络、循环神经网络和多头注意力机制的语义挖掘模型;最后,融入“主体-关系-客体”多视图的语义增强模型,构建DJ-TextRCNN(DianJi-recurrent convolutional neural networks for text classification)模型实现对典籍文本更细粒度、更深层次、更多维度的语义挖掘。研究结果发现,DJ-TextRCNN模型在不同视图下的古籍主题推荐任务的准确率均为最优。在“主体-关系-客体”视图下,精确率达到88.54%,初步实现了对古籍文本的精准主题推荐,对中华文化深层次、细粒度的语义挖掘具有一定的指导意义。

Multi-features Based Approach for Moving Shadow Detection

In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, also called the Guo Shou Jing Telescope) is a special reflecting Schmidt telescope. LAMOST’s special design allows both a large aperture (effective aperture of 3.6 m–4.9 m) and a wide field of view (FOV) (5°). It has an innovative active reflecting Schmidt configuration which continuously changes the mirror’s surface that adjusts during the observation process and combines thin deformable mirror active optics with segmented active optics. Its primary mirror (6.67m×6.05 m) and active Schmidt mirror (5.74m×4.40 m) are both segmented, and composed of 37 and 24 hexagonal sub-mirrors respectively. By using a parallel controllable fiber positioning technique, the focal surface of 1.75 m in diameter can accommodate 4000 optical fibers. Also, LAMOST has 16 spectrographs with 32 CCD cameras. LAMOST will be the telescope with the highest rate of spectral acquisition. As a national large scientific project, the LAMOST project was formally proposed in 1996, and approved by the Chinese government in 1997. The construction started in 2001, was completed in 2008 and passed the official acceptance in June 2009. The LAMOST pilot survey was started in October 2011 and the spectroscopic survey will launch in September 2012. Up to now, LAMOST has released more than 480 000 spectra of objects. LAMOST will make an important contribution to the study of the large-scale structure of the Universe, structure and evolution of the Galaxy, and cross-identification of multiwaveband properties in celestial objects.

基于单-多视图优化的足球球员三维姿态和体型估计

足球比赛场景的三维重建有助于观众自由切换视角,增加了互动性和沉浸感。针对足球比赛场景中的足球球员,提出一种三维姿态和体型估计方法。对球员的多视图图像使用训练好的部分注意力回归的三维人体估计(PARE)模型生成初始的三维姿态和体型估计,并使用人工标注的二维关节点作为优化目标。单-多视图优化操作利用蒙皮多人线性模型(SMPL)和正交投影的可微性,将球员的三维姿态和体型参数映射到二维关节点,计算其与人工标注之间的差异,再使用神经网络的反向传播算法更新三维姿态和体型参数,持续这些过程直到差异最小化。在自建的足球球员多视图数据集上的实验结果表明,该方法能够有效估计足球球员的三维姿态和体型,与人体网格恢复、在循环中优化SMPL、PARE等方法相比,二维关节点精度在单视图上提高了9.2%~37.5%,在多视图交叉验证中提高了34.9%~54.1%。

基于YOLOv5与多视图几何联合的动态V-SLAM

目的为了解决传统视觉SLAM(simultaneous localization and mapping,SLAM)系统在动态环境下容易受运动物体干扰而无法实现准确定位与建图的问题,方法基于传统ORB_SLAM2算法,提出一种YOLOv5与多视图几何联合的动态V-SLAM算法。首先,在视觉SLAM系统的前端加入一个目标检测模块,该模块采用深度学习中的目标检测网络YOLOv5,结合多视图几何方法识别并分割动态物体和静态物体;其次,根据目标检测模块检测结果,在系统跟踪线程中进行动态特征点的剔除,仅使用静态特征点进行帧间匹配和位姿估计,同时结合渐进采样一致性算法(progressive sample consensus,PROSAC)剔除误匹配的特征点并求得位姿估计模型;最后,根据剔除动态特征点信息的关键帧完成稠密点云地图构建。为了评估改进算法的有效性,主要基于德国慕尼黑工业大学(TUM)数据集中的动态场景进行实验。结果结果表明,在图像特征匹配实验中,与基于ORB特征粗匹配和随机抽样一致性(random sample consensus,RANSAC)算法对比,提出的算法具有更高的运算效率;在轨迹跟踪实验中,所提算法的定位精度较ORB_SLAM2系统平均提升了96.14%,较ORB_SLAM3系统平均提升了94.52%;在点云建图实验中,本文算法在3种不同运动状态场景下均能构建出与实际场景一致的稠密点云地图。结论改进后的视觉SLAM算法在室内动态场景下具有较高的可靠性和准确度。

A Comparative Study of Acute Hemodynamic Effects of Multi -site Cardiac Pacing in patients with cardiac function NYHA class ⅠtoⅡ without bundle branch block

Objective To compare the acute hemodynamic effects of five different pacing modes in patients with cardiac function NYHA class Ⅰ to Ⅱ without bundle branch block (BBB). Methods This study included 12 patients (SSS 7, Ⅲ°AVB 5) undergoing pacemaker implantation. Right ventricular apex (RVA), right ventricular outflow tract (RVOT), right ventricular bifocal (RV-Bi), left ventricular base (LVB) and bi -ventricular (Bi-Ⅴ) pacing at 60 -80 ppm were done in VVI mode prior to implantation of DDD pacemaker. The cardiac index (CI), mean pulmonary artery pressure (mPAP) and pulmonary capillary wedge pressure (PCWP) were measured with Swan - Ganz thermodilution catheter after 5 minutes of each pacing mode. Results (1) Comparing to pacing at RVA (CI: 2. 41± 0. 38 L/min per m2, PCWP: 16. 7 ±3.3 mmHg), the CI increased and the PCWP decreased significantly in pacing at RVOT(CI: 2. 63 ± 0.46, PCWP: 13. 8±2. 3), LVB(CI: 2. 78±0.52, PCWP: 14. 4±3.1), RV-Bi(CI: 2. 83±0.57, PCWP: 12. 8± 2. 5) and Bi -Ⅴ pacing (CI: 2. 94± 0.60, PCWP: 12. 7±2. 5), P < 0. 01, respectively. (2) The CI of RV-Bi and Bi-Ⅴ pacing was higher than that of RVOT and LVB pacing, the PCWP was lower, P < 0. 05, respectively. (3) There was no significant difference between RV - Bi pacing and Bi-Ⅴ pacing in CI and PCWP. Conclusion There is no significant difference between RV - Bi pacing and Bi -V pacing in the acute hemodynamic effects; however,dual - site pacing is much better than single site pacing in that aspect for patients with cardiac function NYHA class Ⅰ to Ⅱ without BBB. Among single site pacing, the RVOT and LVB pacing is better than RVA pacing in cardiac function.

Non-reference virtual view quality evaluation of MVD

The quality of virtual view based on multi-view video （MVD） plus depth format is often evaluated by PSNR or subjectively judged. However, due to synthesizing arbitrary view images, the virtual view images mostly have no reference images and are only assessed using non-reference. Virtual view images synthesized by depth estimation reference software （DERS） and view synthesis reference software （VSRS） often accompanied with blockiness and other distortions on the edge. In addition, matching level for the depth map and the corresponding texture maps of left and right views also affects the quality of the virtual view. This paper compares the edge similarity of the depth and the corresponding texture maps which generate the intermediate virtual view and combined with the virtual view＇s blockiness which causing blur to evaluate the quality of the virtual view. Experiment results show that the proposed method can reflect the quality of the virtual view better.