Multiobjective analysis of chaotic dynamic systems with sparse learning machines

Sparse learning machines provide a viable framework for modeling chaotic time-series systems. A powerful state-space reconstruction methodology using both support vector machines (SVM) and relevance vector machines (RVM) within a multiobjective optimization framework is presented in this paper. The utility and practicality of the proposed approaches have been demonstrated on the time series of the Great Salt Lake (GSL) biweekly volumes from 1848 to 2004. A comparison of the two methods is made based on their predictive power and robustness. The reconstruction of the dynamics of the Great Salt Lake volume time series is attained using the most relevant feature subset of the training data. In this paper, efforts are also made to assess the uncertainty and robustness of the machines in learning and forecasting as a function of model structure, model parameters, and bootstrapping samples. The resulting model will normally have a structure, including parameterization, that suits the information content of the available data, and can be used to develop time series forecasts for multiple lead times ranging from two weeks to several months.     

基于CART集成学习的城市不透水层百分比遥感估算

利用Landsat ETM^＋遥感数据，提出了一种基于CART集成学习的ISP遥感亚像元估算方法，将Boosting重采样技术引入CART分析中，用于提高ISP估算的精度。实验结果表明，该方法的ISP估算性能优于传统的单一CART学习算法，从ETM^＋影像中估算的ISP值与真实值之间的相关系数达到0．91，平均偏差为11．16％。     

投影寻踪学习网络的遥感影像分类

采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

成人英语学习动机行为调查与教学对策研究

动机是激励人们行动的心理动因.英语学习动机是推动英语学习的内部动因,成人英语学习动机是复杂的.了解他们的英语学习动机特点有利于教师改进教学手段和策略,激发与培养其学习的主动性和积极性,发挥其学习潜能,从而提高英语学习的效果.运用定量研究的方法,在年轻干部学员中进行学习动机调查,分析了其学习动机的特点,并提出成人英语教学的对策.     

基于高清工业相机的盾构隧道裂缝智能识别算法分析

裂缝一直是隧道病害的重点检测对象,但传统人工巡检仅能通过肉眼发现后记录,人工识别精准度与效率完全取决于个人经验判断,无信息化手段辅助,作业效率识别精度亟待提升。针对以上问题,本文借助高清工业相机成像分辨率高、采集速度快等特点,将高清工业相机部署于轨道车上获取隧道表面裂缝病害信息,大幅提高了隧道裂缝识别效率,将识别精度提升至0.2 mm,同时融入优化的Cascade R-CNN算法,在有监督情况下训练隧道裂缝样本,最终实现了隧道裂缝病害的高效提取,同时研发了一套包含硬件数据采集、数据处理软件、数据管理平台的裂缝病害识别路线,真正意义上破除了识别慢、精度低、靠经验、难管理的技术壁垒。     

建筑物立面分类与DIM点云滤波

针对目前基于近景摄影测量方法构建建筑物立面模型过程中因密集影像匹配（DIM）点云噪声所引起的建筑物立面TIN网格模型畸变问题,本文借鉴机器学习中样本学习的思想,对建筑物立面进行了分类并对DIM点云提出了相应的滤波方法,以达到去除DIM点云噪声和改善其TIN网格模型畸变的目的。其中,针对平面结构立面,采取先对点云样本进行学习计算构建数学立面模型所需参数,再对该立面模型设定阈值并对其点云进行滤波处理的方法;针对曲面结构立面,则结合DIM点云特性先将点云样本分类标记归为立面点与非立面点,再进行样本特征值学习,使用Logistic回归算法迭代计算求解最佳回归系数,从而构建滤波分类器的方法对立面点云进行滤波处理。试验结果表明,本文滤波处理方法能将立面DIM点云噪声有效识别并去除,而且使用该方法处理后所得点云构建的建筑物立面TIN网格模型精细化程度得到有效提高,模型质量得到明显改善。     

基于卷积神经网络的无人机影像违章建筑检测应用

无人机航拍影像具有分辨率高、回访周期短等特点,利用无人机遥感技术手段对城市范围的建设进行动态监测,可及时、有效地发现涉嫌违法的建设活动。本文结合实际项目需求,研究通过卷积神经网络方法进行违章建筑的自动检测,替代过去靠大量人力检查的模式,目前测试区域无人机影像试验取得了较好的效果,在样本数据不足5000份的情况下,准确率和召回率分别达到了71%和88%。随着样本数据的不断增多,基于该深度学习方法将较大程度上持续提升检测准确率和召回率,能够更精准地发现违法活动,具有较大的实际应用价值及潜力。     

深度学习PaddlePaddle框架支持下的遥感智能视觉平台研究与实现

百度深度学习PaddlePaddle框架支持下的遥感智能视觉平台,能够运用深度学习技术实现遥感影像的智能建模、训练和解译。本文通过深入分析PaddlePaddle图像分割模型库PaddleSeg的图像处理深度学习算法模型DeepLabV3+、U²-Net及RetinaNet,开发设计了遥感智能视觉平台,实现了遥感影像的地块分割、变化检测和斜框检测等专业功能。研究表明：遥感智能视觉平台提取的图斑总面积是目视解译的80%、有效图斑比例为76%、错误图斑比例为18%,实现了快速有效的遥感图像智能处理。     

深度学习支持下的丘陵山区耕地高分辨率遥感信息分区分层提取方法

耕地是丘陵山区稀缺的土地资源,具有地形条件复杂、种植结构多样的特点,导致了山地耕地信息难以快速、准确获取,并且基于传统的遥感数据及遥感监测方法开展山区耕地信息快速自动提取比较困难。针对这一问题,本文以西南山区贵州省息烽县作为试验区,根据地理空间异质性特征,提出分区控制、分层提取的耕地形态信息提取思路,构建了一种地貌单元约束条件下的分区分层耕地形态信息的提取方法。该方法首先根据地貌-植被特征将试验区划分为平坝区、山坡区、林草区3类地理分区;然后在每类分区基础上,根据耕地所呈现的视觉特征划分为不同的类型,对不同类型的耕地分别设计不同的深度学习模型进行分层提取。试验结果证明,该方法对山区复杂地形背景噪声具有较好的抑制作用,所提取的耕地地块信息相比于传统方法更符合实际耕地的实际分布形态,有效地减少了漏提率和错提率。