Bayesian network based general correspondence retrieval method for depth sensing with single-shot structured light

This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.     

Film and television industry cloud exhibition design based on 3D imaging and virtual reality

The three-dimensional virtual scene can provide users with a visual three-dimensional virtual environment, with various multimedia channels such as sound, video, force feedback equipment, etc., to bring users a completely immersive interactive experience. This paper introduces 3D imaging and virtual reality technology in the film and television industry cloud exhibition, and develops a virtual display platform. First of all, this paper divides the registration into two processes: camera calibration and joint calibration of the camera and laser scanner based on the calibration results. Camera calibration can determine the plane model of the calibration board in the camera coordinate system, and the joint calibration uses the RANSAC algorithm to extract the point cloud of the plane model of the calibration board, and then optimizes the distance between the points in the plane model point cloud and the corresponding plane in the camera coordinate system Find the optimal transformation between the two sets of data, and then calculate the registration relationship between the point cloud and the image. Secondly, this article conducts a demand analysis of the film and television industry cloud exhibition platform based on virtual reality technology, including the business goals set by the platform, platform system analysis, overall design, and system operating environment and configuration requirements. This model provides a feasible solution for the visual interaction of the cloud exhibition design of the film and television industry.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

成像卫星星地联合成像调度

成像卫星星地联合调度问题,涉及调度对象众多,约束条件复杂,需要考虑任务的观测、回传2个过程,是一个具有两层时间窗口约束的双层优化问题,统一建模困难。根据问题的特点,采用基于阶段优化的方式,降低了问题的复杂性。把问题分为观测调度阶段和数据回传调度阶段,分别给出了优化目标和约束条件,建立了基于阶段优化的成像卫星星地联合调度模型,实现了从任务观测到数据回传的全过程调度。仿真实例表明,该方法能够有效解决多星多站的协同任务调度问题。     

分布式PACS通信服务中间层构造方法研究

基于目前分布式PACS中存在的问题,提出一个通信服务中间层的构造方法,分别进行了基于设计模式通信框架的构造、DICOM图像互操作和分布式PACS可复用构件的研究.通信服务中间层的构造不仅提高了分布式PACS在网络异构环境中的可移植性、可扩展性以及相关QoS性能,还满足了PACS今后发展中提出的较高的互连互操作性的要求.     

基于运动误差约束的平面定位平台拓扑优化设计

针对传统平面定位平台拓扑优化设计过程中无法预先了解输入输出位移映射矩阵的问题,基于固体各向同性材料惩罚法,采用遗传算法优化得到具有最大运动空间时的平面并联机构雅克比矩阵作为平面定位平台输入输出期望位移映射矩阵,在相同输入条件下,建立以机构实际运动与期望运动间误差最小为目标函数和以机构体积为约束函数的平面定位平台拓扑优化模型并求解。通过3D打印方式得到实验样品,实验与仿真分析表明:采用运动误差约束的平面定位平台拓扑优化设计方法,在一定偏差范围能够得到输入输出位移映射矩阵与给定期望映射矩阵相一致的平面定位平台,表明该方法对平面定位平台设计具备有效性,为其后续研究提供基础。     

共晶自生复合材料的晶形转变

自生复合材料的晶形转变受到成分过冷与曲面过冷两个因素的控制.非金属相具有较大的熔化熵且易形成小平面是改变领先相的因素,而领先相的改变则可能是晶形转变的根本原因.     

Detection of water ingress in composite sandwich structures: a magnetic resonance approach

Water ingress inside honeycomb sandwich panels during service has been linked to in-flight failure in some aircraft. There is an ongoing effort to develop nondestructive testing methods to detect the presence of water within the panels. Magnetic resonance (MR) represents an attractive approach in that it is sensitive to moisture. Using a unilateral MR sensor, testing can be applied directly to the surface of the panel. The viability of MR is demonstrated through laboratory imaging of both water within sandwich panels, as well as the adhesive itself. The detection of water using a one-sided handheld MR sensor is presented. It is shown that simple detection, as well as spatial localization of water within sandwich panels is possible.     

A NEW ELECTROMAGNETIC IMAGING SCHEME

ＡＮＥＷＥＬＥＣＴＲＯＭＡＧＮＥＴＩＣＩＭＡＧＩＮＧＳＣＨＥＭＥ①ＺｈａｎｇＲｏｎｇｆｅｎｇ，ＨｅＪｉｓｈａｎＣｏｌｅｇｅｏｆＲｅｓｏｕｒｃｅ，ＥｎｖｉｒｏｎｍｅｎｔａｎｄＣｉｖｉｌＥｎｇｉｎｅｒｉｎｇ，ＣｅｎｔｒａｌＳｏｕｔｈＵｎｉｖｅｒｓｉｔｙｏ...     

利用ADCP回波强度估算河流悬移质含沙量的应用研究

通过简要介绍利用声学多普勒水流剖面仪(ADCP)回波强度估算悬移质含沙量的基本原理,结合梧州水文站的应用实例,利用ADCP的回波强度数据和现场采集的水样含沙量建立相关关系,根据相关关系由回波强度反演估算的水体悬移质含沙量验证了利用ADCP的回波强度估算悬移质含沙量的可行性。应用于梧州水文站的实践表明,左河道推算的系统误差为-0.1%,右河道推算的系统误差为1.5%。这种方法具有不扰动流场、测量剖面同步性较好、数据空间分辨率较高的优点,与传统的取样过滤称量法相比,计算效率显著提高。