Structural Setting of the South-West Cameroon Using Satellite Potential Field Derived from SGG-UGM-2 Gravity Data

This study aims to improve knowledge of the structure of southwest Cameroon based on the analysis and interpretation of gravity data derived from the SGG-UGM-2 model. A residual anomaly map was first calculated from the Bouguer anomaly map, which is strongly affected by a regional gradient. The residual anomaly map generated provides information on the variation in subsurface density, but does not provide sufficient information, hence the interest in using filtering with the aim of highlighting the structures affecting the area of south-west Cameroon. Three interpretation methods were used: vertical gradient, horizontal gradient coupled with upward continuation and Euler deconvolution. The application of these treatments enabled us to map a large number of gravimetric lineaments materializing density discontinuities. These lineaments are organized along main preferential directions: NW-SE, NNE-SSW, ENE-WSW and secondary directions: NNW-SSE, NE-SW, NS and E-W. Euler solutions indicate depths of up to 7337 m. Thanks to the results of this research, significant information has been acquired, contributing to a deeper understanding of the structural composition of the study area. The resulting structural map vividly illustrates the major tectonic events that shaped the geological framework of the study area. It also serves as a guide for prospecting subsurface resources (water and hydrocarbons). .

Spectral-spatial target detection based on data field modeling for hyperspectral data

Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.

CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA

The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.

基于信息采纳模型的机器学习领域科学数据重用影响因素研究

本文基于信息采纳模型构建了机器学习领域科学数据重用影响因素模型,并选择了开放科学社区平台和科学数据共享平台两种类型的代表性平台进行数据采集,实证了数据集内容质量和数据源可信性对科学数据重用的影响,以及平台类型在数据源可信性对科学数据重用影响中的调节作用。本研究对完善科学数据平台建设、促进科学数据重用具有重要意义。

一种基于径向基隐式曲面的地质三维建模方法

针对目前基于钻孔数据生成地质三维模型过程繁琐、算法复杂、中间数据庞大的弊端,本文使用钻孔点和径向基隐函数模型描述地层曲面,以多边形标量场提取曲面函数模型的等势面生成地质曲面进行可视化;再提取曲面的边框,根据网格大小长度提取边框的转折点数据;根据上下相邻两个地层曲面的边框转折点,绘制侧立面;合并侧立面与上下相邻两地质层曲面,生成地质三维模型。并使用郑州市高新区地质钻孔数据作为数据源进行实验,快速完整地生成了研究区的地质体三维模型。

基于隐式神经表示的模型隐写方案

可视数据(图像、视频、3D模型)的隐式表达已经成为当前计算机视觉研究的热点,提出了一种新的基于隐式神经表示的模型隐写方案,消息发送方利用神经辐射场(neural radiance fields,NeRF)新视角合成的特性,通过引入一个视角作为密钥,由NeRF模型生成秘密视角图像作为后门,然后利用过拟合的方法训练一个消息提取器,以建立秘密消息和秘密视角图像的一一映射。发送方将训练好的NeRF模型和消息提取器通过公开信道传递给接收方,接收方利用双方共享的密钥,由NeRF模型获得秘密视角下的渲染图像,再通过消息提取器获得秘密消息。而攻击方因无法准确掌握该视角信息,从而无法窃取秘密消息。实验结果证明:所训练的消息提取器实现大容量快速隐写,消息嵌入量达100%,同时NeRF巨大的视角密钥空间保证了该方案的隐蔽性。

改进增广最小二乘的轧机垂振系统参数辨识

数字孪生是物理实体的数字化表现,它通过数学模型实时监测和控制物理实体,因此对数学模型的精度要求非常高。针对轧机垂振系统模型精度低的问题,提出了改进增广最小二乘法对轧机垂振系统进行辨识。建立了垂振系统数学模型,并通过系统离散化推导出动力学方程,确定了辨识模型的结构。以现场采集的数据为基础,采用改进最小二乘法对模型参数进行辨识。实验结果表明,实测振幅与机理模型输出振幅的均方根误差为0.82,而与辨识模型输出振幅的均方根误差只有0.62,有效提高了模型的精度。

Evaluation of a Micro-scale Wind Model's Performance over Realistic Building Clusters Using Wind Tunnel Experiments

The simulation performance over complex building clusters of a wind simulation model（Wind Information Field Fast Analysis model, WIFFA） in a micro-scale air pollutant dispersion model system（Urban Microscale Air Pollution dispersion Simulation model, UMAPS） is evaluated using various wind tunnel experimental data including the CEDVAL（Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models） wind tunnel experiment data and the NJU-FZ experiment data（Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data）. The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier–Stokes equations, and can produce very high-resolution（1–5 m） wind fields of a complex neighborhood scale urban building canopy（～ 1 km ×1km） in less than 3 min when run on a personal computer.

基于无人机倾斜摄影测量的大比例尺地形图测绘方法研究

无人机倾斜摄影测量技术的日益发展,为大比例尺地形图测绘提供了一种高效、精准的手段。借助无人机的灵活性和机动性,结合倾斜摄影单头或多头相机的应用,可以获取具有较高分辨率和精度的地面影像数据,为地形图的测制提供强有力的支持。基于此,文章讨论了基于无人机倾斜摄影测量的大比例尺地形图测绘优势、存在的问题,深入探讨测绘方法,以供参考。

基于数据挖掘的输变电野外勘测数据识别及存储方法

可靠存储野外勘测数据是保证输变电合理规划、安全运行的基础,为此提出基于数据挖掘的输变电野外勘测数据识别及存储方法。通过移动端采集野外数据,将勘测相关数据传送至服务器端,并在云端引入边缘计算,形成云边协同模型,通过该模型识别以及压缩输变电野外勘测数据,通过动态局部敏感哈希算法和加权k近邻算法选择缓存数据,实现野外勘测数据复用。测试结果显示:输变电野外勘测数据传输时延结果稳,时延时间短,存储后野外勘测数据可用性高,可以为输变电规划提供可靠数据依据。

Signal classification method based on data mining formulti-mode radar

For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.

Improvement of wind field hindcasts for tropical cyclones

This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the cross-calibrated multi-platform （CCMP） reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.

Predicting formation lithology from log data by using a neural network

In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.

融合相似性原理的涡轮叶型流场预测方法研究

计算流体力学(CFD)方法是涡轮叶片等设计阶段性能评估的重要手段.然而,基于CFD的数值仿真方法通常比较耗时,难以满足涡轮叶型设计阶段快速迭代的需求.为实现快速性能评估并克服纯数据驱动预测模型泛化能力不足的问题,受到物理增强的机器学习思路的启发,将相似性原理与深度学习模型相结合,提出了一种泛化能力强的涡轮叶型流场预测新方法.以涡轮叶片表面等熵马赫数分布预测为例,提出采用相似性原理对叶型几何变量和气动参数进行归一化,进而在归一化参数空间构建训练样本集与深度学习预测模型,由此建立统一的流场预测模型,对几何尺寸、边界条件差异较大的叶型气动性能进行评估.在完成模型训练后,对归一化条件下不同工况/不同形状叶型的流场、真实环境下不同工况/不同尺寸叶型的流场以及GE-E3低压涡轮不同截面叶型的流场进行预测,结果表明预测结果的分布曲线与CFD评估结果吻合良好,平均相对误差在1.0%左右,由此验证了所提出的融合相似性原理的流场预测模型的精度与泛化能力.

Case studies of ERS-1 altimeter data applicating in China Sea

The impact of ERS-1 altimeter significant wave height on analysis of wave field and wave pre- dictions is tested through analysis of selected cases. Application of the altimeter data may modifg initial tield and thus 24-hour prediction of significant wave height. However the variations in initial wave field almost make no effect on 48-hour predictions.

基于北京市区实测数据的雷暴风风场时空统计特性研究

雷暴风引发的结构破坏、财产损失和人员伤害屡有发生,其风场特性的认识和研究是结构抗雷暴风设计的基础。该文基于北京市区325 m高气象塔实测数据识别的70个雷暴风事件,采用经典雷暴风模型对其平均风及脉动风时空特性等进行了统计分析,研究结果表明:雷暴风频发于夏季,其峰值风速主要出现在北京时间的下午及傍晚时分且较易发生于西北方向;在峰值风速发生时段,平均风速剖面均值呈现轻微的鼻形特征;雷暴风湍流强度和阵风因子剖面与大尺度稳态强风特性存在较大差异,且雷暴风统计结果均大于大尺度稳态强风的对应值;北京市结构抗雷暴风设计中可考虑应用Von Karman谱计算雷暴风荷载。该研究结果可为雷暴风风场统一模型的建立和结构抗雷暴风设计提供参考。

基于近似模型的装夹应力场虚拟映射方法研究

切削加工过程中材料内部残余应力是影响加工质量的关键因素之一,在虚拟环境实现应力场演化过程的可视化能有效监测其演化规律,有助于提高零件加工质量。为实现应力场在虚拟环境的实时映射,依据数字孪生技术开发了一种装夹应力场实时映射方法。首先,阐明了切削加工系统数字孪生层级。然后,提出了加工过程残余应力场在虚拟环境的映射方案,具体为:基于有限元仿真方法,采用RBF神经网络建立了应力场仿真近似模型;以孪生几何模型为载体,采用数据驱动方法实现了近似模型仿真结果在虚拟环境的映射。最后,以工件装夹残余应力场分布情况为例进行验证。结果表明,近似模型实时仿真结果与有限元仿真结果的最大偏差为7.7%,验证了该应力场虚拟映射方法的可行性与有效性,为切削加工过程数字孪生提供了技术支撑。

基于有效积温模型的草地贪夜蛾田间种群动态分析

为了解湖北省局部地区草地贪夜蛾田间种群动态特征及影响因素,采用抽样调查和模型分析相结合进行了研究。2020年玉米田间抽样调查结果显示:当阳、仙桃、武穴、红安4个样点的幼虫首见时间分别为6月4日、6月1日、5月27日和6月9日,幼虫发生一直持续到10月底;幼虫百株虫量的全年峰值发生在9月上中旬,峰值分别为21、36、23、10头,与基于有效积温模型的模拟结果基本吻合。模型分析显示:草地贪夜蛾全年发生4~5代;首次见卵时间越早,则全年峰值时间越早,而初始卵密度越高,则全年峰值越高;温度上升幅度越高,则全年峰值时间越早、峰值越高;产卵期越集中,则全年峰值时间越早、峰值越高。结果表明:首次见卵时间、初始卵密度、温度、产卵年龄分布均是影响草地贪夜蛾田间种群动态的重要因素。结果为进一步研究草地贪夜蛾的区域种群动态及迁飞条件对区域种群动态的影响打下基础。

用于变压器温度预测的基于物理场数据增强的深度网络

深度网络是预测变压器温度以进行火灾预防的常用方法,但标准训练数据的不足将限制预测模型的有效性。文中提出了一种用于变压器温度预测的基于物理场数据增强的深度网络。首先分析了铁心损耗原理,建立了损耗计算模型。然后探讨了变压器内部的热传递机理,并建立了热传递机制模型。将损耗计算模型和热传递机制模型相结合构建出物理场模型。通过对不同参数下变压器温度变化过程进行模拟,收集得到的数据可以用于增强深度网络的训练数据。最后,使用增强后的数据训练深度网络CAT-LSTM以进行温度预测。实验结果表明,该深度网络具有较高的预测精度。

智能气田一体化模型数据共享平台建设与实践

一体化仿真模型是智能气田建设的重要基础,如何利用数据驱动模型高效运转已成为亟待解决的关键难题。针对数据源分散、可用性差及与模型交互程度低等问题,开展了以下技术攻关:(1)结合数据服务总线(DSB)与PI OLEDB接口技术,实现多源结构化数据的有效集成与实时抽取,满足一体化模型对不同时间维度生产数据的业务需求;(2)建立数据质量规则库和自动扫描制度,实现数据质量的有效管控,确保一体化模型的正常运行;(3)开发基于数据流引擎技术的数据交互工具,实现了综合数据库与仿真模型的自动交互,其优势在于应用场景改变时,能够应用灵活编排基础服务的方法高效复用前期数据建设成果,大幅提高开发效率。研究表明,与国内外同类技术相比,应用数据流引擎技术突破了一体化模型与外部数据资源的交互壁垒,打破了该领域数据应用定制化开发的服务模式,为智能气田建设奠定了技术基础。