基于钻进状态监测的智能工况识别          下载免费PDF全文

范海鹏  吴敏  曹卫华  赖旭芝  陈略峰  陆承达 《探矿工程》2020,47(4):106-113

钻进过程状态监测旨在实时描述钻进工况，判断运行性能优劣程度进行非优追溯，及时指导司钻人员调整作业操作,保证钻进过程安全、高效、稳定开展。钻进工况是钻进系统运行状态的反映，因此开展面向状态监测技术的钻进工况识别研究具有重要的理论和应用价值。本文针对钻进工况识别问题，基于状态监测数据，建立基于支持向量机的钻进工况识别模型，对钻进工况进行识别。综合工况识别结果，对钻进效率进行评估，并对影响钻进效率的因素进行讨论，寻找提升钻进效率的手段。最后，采用钻进现场实钻数据进行仿真实验,验证所提方法的可行性和有效性。  相似文献   

基于随机森林的光子计数激光雷达点云滤波     

陈博伟  庞勇  李增元  卢昊  梁晓军 《地球信息科学学报》2019,21(6):898-906

新一代星载激光雷达卫星ICESat-2首次采用了微脉冲光子计数激光雷达技术,由于单光子探测的灵敏性导致数据在大气和地表下层产生了大量噪声,因此对光子计数激光雷达点云数据实现信号和噪声的分离是开展进一步应用研究的前提和基础。本文选择美国俄勒冈州和弗吉尼亚州2个研究区,采用MATLAS数据,根据光子点云数据的特点构造了12个光子点云特征,对所构造的特征利用随机森林进行变量筛选,用机器学习方法对光子点云进行分类,并将建立好的模型推广到整个研究区。研究结果表明,本文构建的分类器分类总精度达到了96.79%,Kappa系数为0.94,平均生产者精度和用户精度分别为97.1%和96.8%。在相对弱噪声、平坦地形区域和强噪声、复杂地形区域都取得较好的分类结果。本文结果显示了基于少量样本通过机器学习的方法构建模型,可以推广到较大范围区域的光子点云分类应用中。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

基于离散Gabor变换的磁暴识别     

王静  贺巍  汪伟明 《地震地磁观测与研究》2020,41(4):64-69

以地磁秒数据为研究对象，通过离散Gabor变换将时域的地磁数据转换至二维时频面，提取Gabor变换谱图的均值和方差作为特征值，使用支持向量机实现地磁正常数据与磁暴干扰数据的自动分类识别。对5个地磁台的200组地磁秒数据进行计算分析，结果表明该方法对测试样本数据的识别率可达94%。  相似文献   

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer   总被引：1，自引：1，他引：0  

Basant Yadav  Shashi Mathur  Sudheer Ch  Brijesh Kumar Yadav 《水文科学杂志》2018,63(2):210-226

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.  相似文献   

基于PSR-WSVM模型的边坡位移预测     

李建新  刘小生  肖钢  周文  刘仁志 《大地测量与地球动力学》2020,40(6):577-580

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。  相似文献   

基于计算机视觉的头足类角质颚特征研究Ⅰ:轮廓与特征点提取     

贺芊菡  孙翁杰  刘必林  孔祥洪  林龙山 《海洋与湖沼》2020,51(6):1493-1500

轮廓与特征点研究是头足类角质颚形态特征鉴别的基本方法，对于轮廓与特征点的提取最常用的方法是手动描绘与标定，利用计算机视觉进行轮廓与特征点的提取，不仅可以降低手动提取带来的误差、提高准确性，而且更加快速、便捷。文章将利用计算机视觉提取头足类角质颚的轮廓与特征点，首先将自制装置拍摄得到的角质颚三视图放入MTALAB软件中进行编程处理，然后利用Canny算法提取角质颚轮廓，最后根据地标点的定义标定特征点位置并建立空间坐标系得到角质颚的特征点坐标。研究结果显示，利用计算机视觉提取角质颚的轮廓图像以及特征点坐标是可行的，当标准差σ取值为0.1时角质颚轮廓图效果最佳，在得到的轮廓图上进行特征点的标定，通过迭代遍历轮廓图获得各个特征点的空间坐标。研究分析认为，将计算机视觉应用于头足类角质颚形态学的研究可以提高研究的便捷性，同时也为后续的研究提供了新的实验思路和方法。  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   

Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine     

Jian Jin  Lei Li  Huan Xu  Guang Lin 《水文科学杂志》2020,65(3):415-426

ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.  相似文献