Alternating Direction Finite Volume Element Methods for Three-Dimensional Parabolic Equations

This paper presents alternating direction finite volume element methods for three-dimensional parabolic partial differential equations and gives four computational schemes, one is analogous to Douglas finite difference scheme with second-order splitting error, the other two schemes have third-order splitting error, and the last one is an extended LOD scheme. The L2 norm and H1 semi-norm error estimates are obtained for the first scheme and second one, respectively. Finally, two numerical examples are provided to illustrate the efficiency and accuracy of the methods.

Temporally Consistent Depth Map Estimation for 3D Video Generation and Coding

In this paper, we propose a new algorithm for temporally consistent depth map estimation to generate three-dimensional video. The proposed algorithm adaptively computes the matching cost using a temporal weighting function, which is obtained by block-based moving object detection and motion estimation with variable block sizes. Experimental results show that the proposed algorithm improves the temporal consistency of the depth video and reduces by about 38% both the flickering artefact in the synthesized view and the number of coding bits for depth video coding.

THE CHARACTERISTIC FINITE ELEMENT ALTERNATING-DIRECTION METHOD AND ANALYSIS FOR THREE-DIMENSIONAL NUMERICAL RESERVOIR SIMULATION

Petroleum science has made remarkable progress in organic geochcmistry and in the research into the theories of petroleum origin, its transport and accumulation. In estimating the oil-gas resources of a basin, the knowledge of its evolutionary history and especially the numerical computation of fluid flow and the history of its changes under heat is vital. The mathematical model can be described as a coupled system of nonlinear partial differentical equations with initial-boundary value problems. This thesis, from actual conditions such as the effect of fluid compressibility and the three-dimensional characteristic of large-scale science-engineering computation, we put forward a kind of characteristic finite element alternating-direction schemes and obtain optimal order estimates in L^2 norm for the error in the approximate assumption.

THE CHARACTERISTIC FINITE DIFFERENCE METHOD FOR THREE-DIMENSIONAL MOVING BOUNDARY VALUE PROBLEM

The software for oil-gas transport and accumulation is to describe the history of oil-gas transport and accumulation in basin evolution. It is of great value in rational evaluation of prospecting and exploiting oil-gas resources. The mathematical model can be discribed as a coupled system of nonlinear partial differential equations with moving boundary value problem. For a generic case of the three-demensional bounded region, bi this thesis, the effects of gravitation、buoyancy and capillary pressure are considered, we put forward a kind of characteristic finite difference schemes and make use thick and thin grids to form a complete set, and of calculus of vaviations, the change of variable, the theory of prior estimates and techniques, Optimal order estimates in l^2 norm are derived for the error in approximate assumption, Thus we have completely solved the well-known theoretical problem proposed by J. Douglas, Jr.

基于骨架序列的工序动作识别与分析

为了解决工业工程领域传统制造过程中工序动作分析方法存在的耗时耗力与依赖经验的问题,运用动作识别技术替代传统的人工分解动作方法,提出一种基于骨架序列的工序动作智能检测方案。使用2D相机与MediaPipe框架搭建人体姿态估计模型以获取骨架序列,引入相关评估指标进行工序动作量化分析。利用骨架数据构建基于卷积门控循环单元CNN-GRU的动作分类模型,面向自建工序动作数据集进行实验验证。结果表明,所提出的CNN-GRU模型在参数量更少的情况下具有更高的准确率,相较于LSTM模型和GRU模型表现更优。在动作识别的基础上,将所得推理结果与标准作业程序进行对比得出异常动作,为工序动作识别与分析提供有效的解决方案,有助于规范生产操作和提升生产效率。

Soft wearable flexible bioelectronics integrated with an anklefoot exoskeleton for estimation of metabolic costs and physical effort

Activities and physical effort have been commonly estimated using a metabolic rate through indirect calorimetry to capture breath information.The physical effort represents the work hardness used to optimize wearable robotic systems.Thus,personalization and rapid optimization of the effort are critical.Although respirometry is the gold standard for estimating metabolic costs,this method requires a heavy,bulky,and rigid system,limiting the system’s field deployability.Here,this paper reports a soft,flexible bioelectronic system that integrates a wearable ankle-foot exoskeleton,used to estimate metabolic costs and physical effort,demonstrating the potential for real-time wearable robot adjustments based on biofeedback.Data from a set of activities,including walking,running,and squatting with the biopatch and exoskeleton,determines the relationship between metabolic costs and heart rate variability root mean square of successive differences(HRV-RMSSD)(R=−0.758).Collectively,the exoskeleton-integrated wearable system shows potential to develop a field-deployable exoskeleton platform that can measure wireless real-time physiological signals.

Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF

The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study proposed a novel approach for the skeleton extraction and pose estimation of piglets.First,an improved Zhang-Suen(ZS)thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons.Then,body nodes were extracted on the basis of the improved DeepLabCut(DLC)algorithm,and a part affinity field(PAF)was added to realize the connection of body nodes,and consequently,construct a database of pig behavior and postures.Finally,a support vector machine was used for pose matching to recognize the main behavior of piglets.In this study,14000 images of piglets with different types of behavior were used in posture recognition experiments.Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation,medial axis transformation,morphology refinement,and the traditional ZS algorithm.The node tracking accuracy reached 85.08%,and the pressure test could accurately detect up to 35 nodes of 5 pigs.The average accuracy of posture matching was 89.60%.This study not only realized the single-pixel extraction of piglets’skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets.Furthermore,this study established a database of pig posture behavior,which provides a reference for studying animal behavior identification and classification and anomaly detection.

Study on Estimation Method of Rock Mass Discontinuity Shear Strength Based on Three-Dimensional Laser Scanning and Image Technique

The estimation of shear strength of rock mass discontinuity is always a focal, but difficult, problem in the field of geotechnical engineering. Considering the disadvantages and limitation of exist- ing estimation methods, a new approach based on the shadow area percentage （SAP） that can be used to quantify surface roughness is proposed in this article. Firstly, by the help of laser scanning technique, the three-dimensional model of the surface of rock discontinuity was established. Secondly, a light source was simulated, and there would be some shadows produced on the model surface. Thirdly, to obtain the value of SAP of each specimen, the shadow detection technique was introduced for use. Fourthly, compared with the result from direct shear testing and based on statistics, an empirical for- mula was found among SAP, normal stress, and shear strength. Data of Yujian （~ River were used as an example, and the following conclusions have been made. （1） In the case of equal normal stress, the peak shear stress is positively proportional to the SAP. （2） The formula for estimating was derived, and the predictions of peak-shear strength made with this equation well agreed with the experimental re- suits obtained in laboratory tests.

Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information

In this Letter, we propose a novel three-dimeusional （3D） color microscopy for microorganisms under photon- starved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori infor- mation. In photon counting integral imaging, 3D images can be visualized using maximum likelihood estimation （MLE）. However, since MLE does not consider a priori information of objects, the visual quality of 3D images may not be accurate. In addition, the only grayscale image can be reconstructed. Therefore, to enhance the visual quality of 3D images, we propose photon counting microscopy using maximum a posteriori with adaptive priori information. In addition, we consider a wavelength of each basic color channel to reconstruct 3D color images. To verify our proposed method, we carry out optical experiments.

基于AlphaPose模型的远距离行人头部姿态估计算法

在公共场所的监控视频中,远距离的行人目标头部区域占比往往较小并且头部区域分辨率较低,仅靠目标检测算法和头部姿态估计模型检测并分类头部特征来判定头部姿态或注视方向存在较大难度.考虑到目前不同分辨率下的人体骨骼关键点估计算法越来越成熟,本研究提出一种利用骨骼关键点和头部姿态之间的关系来进行远距离行人的头部姿态估计算法.该算法首先利用AlphaPose模型检测出二维人体骨骼关键点的全局坐标值,然后根据部分骨骼关键点的坐标值计算出头部朝向象限角度,最后根据预设角度范围计算并可视化出视线落点.目前,尚没有公开发布的可用于远距离行人头部姿态估计的数据集,因此本文建立了一个远距离行人头部姿态系统,以对现有的数据集进行标注,并利用标注的数据集对提出的方法进行测试.结果表明,本文提出的算法可以较精准地判定到远距离行人的头部姿态和注视方向,准确率达到69.7%.

基于TSSI和STB-CNN的跌倒检测算法

跌倒行为会给老人特别独居老人带来严重伤害,准确识别跌倒并及时报警可以有效降低这种危险。本文提出一种基于树结构骨架图像(Tree Structure Skeleton Image,TSSI)和可学习时空块卷积神经网络(Spatio-temporal Block Convolution Neural Network,STB-CNN)的跌倒检测方法。首先使用三维姿态估计算法提取人体关节点,进而获得骨架序列;然后利用基于深度优先搜索(Depth First Search,DFS)算法将骨架序列编码为TSSI;最后构建由时空差分模块、可学习时空框架和时空多分支卷积模块组成的可学习STB-CNN网络,实现跌倒检测。该方法在公开数据集和自建数据集上进行仿真实验分别取得98.6%和98.3%的准确率,优于其他相关算法。

一类三维趋化–斯托克斯方程组正则化问题解的先验估计

本文研究了一类带流体耦合的三维趋化–斯托克斯方程,该模型刻画了流体环境中的生物趋化现象。大量的生物实验观测表明,在趋化流体模型中,重力对细胞运动的影响和趋化本身对流体的交互作用都应该放在方程中同时考虑。本文将在 的条件下利用权函数的方法建立正则化问题解的先验估计,为进一步研究解的定性理论做好准备。

基于Alphapose的跑步动作标准化评估

不合理的健身动作可能会带来关节磨损,肌肉拉伤,运动效果下降等负面效果。采用Alphapose人体姿态估计算法,获取标准动作序列和测试动作序列的人体骨骼关键点的位置,并采用DTW算法求解动作相似度。结果表明:在比较标准跑步动作序列与测试跑步动作序列时,结果有较好的区分度,而两条标准序列间的比较时差距较小,从而验证了该算法的有效性。

^（134）Cs在大鼠整体、骨骼和睾丸中转运滞留及剂量估算

研究了裂片１３４Ｃｓ内污染时在整体水平、骨骼和睾丸的转运、滞留，及其相应的吸收剂量估算。用整体测量装置探讨１３４Ｃｓ在整体的滞留，拟合的滞留方程为：Ｒ（ｔ）＝１８．０４ｅ－９．３１７５ｔ＋４５．１３ｅ－０．０４２３ｔ在整体滞留过程中包括两个滞留半减期，其中快组分Ｔ１＝０．０７ｄ，而慢组分Ｔ２＝１６．３８ｄ。并估算了其吸收剂量．根据１３４Ｃｓ在骨骼和睾丸中７周内的转运和滞留动态变化，拟合了１３４Ｃｓ在骨组织的滞留方程为：Ｒ（ｔ）＝０．００２９ｅ－０．１２１ｔ，观察到在骨中的滞留半减期只有５．７３ｄ。而拟合的１３４Ｃｓ在睾丸中的滞留方程为：Ｒ（ｔ）＝０．００４７ｅ－０．１３３ｔ，即在睾丸中的滞留半减期亦只有５．２１ｄ．可见随着１３４Ｃｓ内污染后时间的延长，其在骨骼和睾丸中的滞留均可迅速下降，而其吸收剂量呈缓慢增长。

基于线性近似的微分等效介质方程解耦方法

岩石物理理论在弹性模量与速度估算中发挥着重要作用。干岩石骨架弹性模量是计算饱和岩石弹性模量和速度的关键参数,微分等效介质理论(DEM)是计算干岩石骨架等效模量的理论依据,但由于其方程是耦合的,很难得到精确解析解,通常只能求取近似数值解。基于前人研究成果,假设干岩石骨架模量比是孔隙度的线性函数,利用极化因子表达式,将耦合微分等效方程解耦为常微分方程,构建了一种新的干岩石骨架等效模型;与微分方程数值解做对比,可知本文导出的解析解能有效计算干岩石骨架等效模量;利用Han实验数据和实际井资料做测试,结果表明用该模型可有效估算岩石纵横波速度。

基于改进CPMs和SqueezeNet的轻量级人体骨骼关键点检测模型

针对目前的人体骨骼关键点检测模型参数多、训练时间长和检测速度慢的问题,提出了一种将人体骨骼关键点检测模型CPMs与小型卷积神经网络模型SqueezeNet相结合的检测方法。首先,采用4个Stage的CPMs(CPMsStage4)对人物图像进行关键点检测;然后,在CPMs-Stage4中引入SqueezeNet的Fire Module网络结构,利用Fire Module结构大大压缩模型参数,得到一种新的轻量级人体骨骼关键点检测模型SqueezeNet15-CPMs-Stage4。在扩展的LSP数据集上的验证结果显示,与CPMs相比,SqueezeNet15-CPMs-Stage4模型在训练时间上减少86.68%,在单张图像检测时间上减少44.27%,准确率达到90.4%;与改进的VGG-16、DeepCut和DeeperCut三种参照模型相比,SqueezeNet15-CPMs-Stage4模型在训练时间、检测速度和准确率方面均是最优的。实验结果表明,所提模型不仅检测准确率高,而且训练时间短、检测速度快,能够有效降低人体骨骼关键点检测模型的训练成本。

基于SMPL模型的人体姿态估计

针对目前人体形变模型中姿态估计算法容易出现误差、信息缺失等问题,提出一种利用深度相机获取的人体三维信息来优化模型的方法。通过深度相机Kinect获取的三维骨架信息,与SMPL模型进行配准,修正原始的模型姿态,得到一个接近人体真实姿态的模型。实验结果表明,融合人体三维信息后,模型的准确性得到一定程度上的提高。

融合层级注意力机制的二维骨架动作识别方法

为了解决传统的基于RGB的动作识别方法易受到光照、遮挡等背景因素的影响,降低了识别准确率和实时性,提出了一种融合层级注意力机制的二维骨架动作识别方法。使用人体姿态估计算法OpenPose提取视频中的人体骨架关节点,对所提取的骨架数据中缺失点和异常点通过均值补全法和指数平滑法进行数据预处理,构建了基于CNN-LSTM与层级注意力机制融合的网络模型CNN-HALSTM实现动作分类。结果表明:本文方法在交互运动数据集和KTH数据集上的识别准确率分别为96.73%和98.35%,模型参数量显著减少,具备实时性优势,优于其他同类型方法。

基于空间骨架时序图的舞蹈特定动作识别方法

随着机器视觉技术的发展,人体动作识别逐渐成为热门的研究方向,但其在舞蹈动作识别领域的应用研究则处于起步阶段。文中将基于深度学习的人体动作识别技术引入此领域,以实现辅助训练的智能化。文中首先分析最先进的人体动作识别算法,在计算机视觉方面的优势与在特定领域的局限性。在此基础上,提出一种基于空间骨架时序图的舞蹈动作识别算法。该算法先使用PAFs对舞蹈视频中的舞蹈演员提取骨架节点,再生成骨架序列。最后,结合LSTM对其进行动作识别。测试与分析结果表明,该算法可针对Balletto舞蹈视频数据库进行识别,且可有效识别舞蹈动作,实现对舞蹈演员的动作纠正。

CT技术在骨骼年龄推断研究中应用的进展

法医人类学应用诸多学科的研究方法和先进技术,对个体骨骼识别进行广泛的研究。年龄推断是法医人类学领域中一项重要的研究课题。随着影像学技术的迅速发展,法医人类学家开展了将计算机断层扫描(CT)技术应用于法医人类学领域的研究。CT具有连续快速扫描的成像能力以及强大的图像后处理功能,能够提供多种模式的高分辨骨骼重组图像,对于活体和遗骸的法医人类学鉴定都具有重要意义。本文将综述近年来CT在骨骼年龄推断研究中应用的进展。