基于超宽带雷达及支持向量机的灾后人体呼吸信号识别方法与试验研究

与常规雷达相比,超宽带雷达具有距离分辨力高、近距离盲区小、穿透性强、目标识别率高等特点,已被广泛应用于灾后搜寻、救援工作中,以对受困生命体征目标进行生命探测。为实现使用超宽带雷达对受困生命体征目标的识别定位,本研究提出基于信号多特征提取技术及支持向量机模型的人体呼吸信号识别方法。首先,使用经验模态分解、变分模态分解及希尔伯特变换提取雷达探测信号的微多普勒特征,使用傅里叶变换提取宏观频谱特征,使用相关分析获取相关性特征;然后,以提取的信号特征为输入,使用支持向量机模型对信号进行分类,进而对人体呼吸信号进行识别,对人体位置进行定位。不同障碍物场景下的试验结果表明,本方法可有效识别砖墙、建筑楼板等遮挡物下的受困生命体征目标,并提供其位置信息。     

基于SVDD的变形监测数据可靠性分析研究

提出了一种基于支持向量数据描述的异常值检测方法,该方法在检测异常值方面具有快速形象、通用性强、可有效处理小样本数据等优点。实验结果证明当在训练样本中加入异常值时,训练得到的模型具有很高的正常数据识别率和异常数据识别率,因此,该方法用于异常值的检测是完全可行的。     

Building Environmental Literacy through Participation in GIS and Multimedia Assisted Field Research

Abstract

A Learning Support System (LSS) that emphasizes experiential research in natural environments using the cutting-edge technologies of GIS and multimedia has been developed for teaching environmental literacy to undergraduate students at the University of Georgia. Computers are used as cognitive tools to create a context in which students become interns in an ecological research center. Students are instructed to conduct research in the form of two field laboratories (the stream and forest laboratories). They accomplish their tasks by collecting data in the field (the State Botanical Garden of Georgia near the campus). They enter the data in the Learning Support System (LSS), and are guided to formulate hypotheses relating to stream water quality and human impact on forest succession for testing. Students also interact with the Environmental Research Support Site (ERSS) within the LSS for explanations to their findings. A specially customized Arc View GIS program within the LSS provides a tool to students for spatial analysis in the case of the forest laboratory. Students and faculty evaluations as well as final examination results confirmed the receptiveness of students to the LSS approach and its effectiveness in the learning of environmental literacy.     

基坑土钉墙支护施工工艺探究

土钉墙作为一种比较实用的原位岩土加固技术,近年来在深基坑支护工程中得到了广泛的应用。本文简要阐述了基坑土钉墙支护施工的主要步骤及相关技术,分析了深基坑土钉墙支护主要施工工艺。     

利用风廓线雷达资料分析大理机场飑线天气过程的风垂直变化特征

飑线天气现象是航空气象中危险和复杂的天气现象之一,2018年大理机场出现两次飑线天气过程,利用风廓线雷达资料和航空气象地面观测资料,分析两次飑线天气过程的风垂直变化特征。结果表明:(1)在飑线天气过程开始前2～4 h,从底层到高层均出现明显的上升运动,飑现象开始前后,上升运动和下沉运动同时存在,为大气中垂直热交换过程提供了有利条件;(2)飑线天气过程存在垂直风的水平切变及垂直切变,反映了强烈的对流发展,发展到离地高度5000 m以上;(3)飑线天气过程存在径向速度突变增大、谱宽变宽和单波束径向速度折叠现象,说明本场出现的是风雨交加的强对流天气;(4)高空到地面存在湍流运动,能量交换频繁,气流紊乱。     

Evaluation of the parameters affecting the roughness coefficient of sewer pipes with rigid and loose boundary conditions via kernel based approaches

One of the important issues in water transport and sewer systems is determining the flow resistance and roughness coefficient.An accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes,the calculation of water depth and flow velocity,and the accurate characterization of energy losses.The current study,applies two kernel based approaches [Support Vector Machine(SVM) and Gaussian Process Regression(GPR)] to develop roughness coefficient models for sewer pipes.In the modeling process,two types of sewer bed conditions were considered:loose bed and rigid bed.In order to develop the models,different input combinations were considered under three scenarios(Scenario 1:based on hydraulic characteristics,Scenarios2 and 3:based on hydraulic and sediment characteristics with and without considering sediment concentration as input).The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and 2.Also,the sensitivity analysis results showed that Dgr(Dimensionless particle number) for a rigid bed and w_b/y(ratio of deposited bed width,w_b,to flow depth,y) for a loose bed had the most significant impact on the modeling process.     

基于支持向量机的地震体波震相自动识别及到时自动拾取

面对海量地震资料,自动准确地拾取震相并确定其到时的需求非常迫切.基于支持向量机技术,本文提出了使用两个分类器SSD和SPS自动识别地震体波震相并自动拾取其到时的方法.相比于传统的自动拾取方法,本文方法能够更准确地识别震相并区分P波和S波.进一步地,我们提出了利用台阵资料辅助识别震相的方案,有效地提高了地震震相拾取的准确率.     

An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its VerificationEI北大核心CSCD

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved.