浙江省岩石地层清理成果简介

本文介绍浙江省岩石地层单位清理成果。按照岩石地层单位划分出原则，对浙江省境域内出露的元古界—第四系进行全面清理：划分出岩石地层单位109个，其中群级11个、组级94个；在清理中新建3个组，启用6个群19个组，引用外省12个组，未采用2个群27个组。并明确各岩石地层单位的含义及时代。     

中丹水平子午环DCMT光栏的自动控制

本文指出了子午环光栏自动控制系统研制的必要性，并简要介绍了控制原理．结果表明，该控制部分设计合理大大简便了操作．     

自动气象观测站远距离数据采集方法

应用以太网技术实现自动气象观测站采集器与上位计算机的数据通信,解决超远距离数据采集,对调整站网布局、特殊环境下自动气象观测站的布设提供了一种解决方法。     

建立地方实时气象信息网站的可行性初探

介绍地方实时气象信息网站的结构、功能和实现方法,为实现类似系统提供了思路。     

地下水饮用水水源地保护区划分关键技术研究

依据《中华人民共和国水污染防治法》和《饮用水水源保护区划分技术规范》(HJ/T338-2007),对饮用水源保护区进行科学合理划分是实现水资源综合开发、合理利用、积极保护、科学管理的基础工作,也是运用法律、行政、经济手段强化水资源管理的根本保证,是防治水污染、保护水资源的重要措施。饮用水源保护区划分涉及经济社会发展、人类活动影响,地表、地下水资源数量,地表、地下水资源水质,陆地水文、工程地质水文,以及水利、环保等学科内容,是一项技术性较强,涉及学科较多的技术工作。通过饮用水源保护区划分涉及关键技术研究和保护区划分工作的开展,合理确定水源的保护范围和保护目标,为地表、地下水资源的合理开发、保护、利用提供科学依据,以保障供水安全、生态与环境安全。     

智能驾驶培训和考试服务系统

介绍利用空间信息技术、传感检测技术、惯性导航技术、IT技术等研发机动车驾驶培训和考试服务系统,实现高精度、智能化自动评判驾驶学员的培训和考试成绩,保障了驾考过程管理的公平、公正,有助于提升驾驶培训机构的服务质量。     

Results of the ISPRS benchmark on urban object detection and 3D building reconstruction

For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community by ISPRS WGIII/4. Researchers were encouraged to submit their results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.     

Automatic 3D modelling of metal frame connections from LiDAR data for structural engineering purposes

The automatic generation of 3D as-built models from LiDAR data is a topic where significant progress has been made in recent years. This paper describes a new method for the detection and automatic 3D modelling of frame connections and the formation of profiles comprising a metal frame from LiDAR data. The method has been developed using an approach to create 2.5D density images for subsequent processing using the Hough transform. The structure connections can be automatically identified after selecting areas in the point cloud. As a result, the coordinates of the connection centre, composition (profiles, size and shape of the haunch) and direction of their profiles are extracted. A standard file is generated with the data obtained from the geometric and semantic characterisation of the connections. The 3D model of connections and metal frames, which are suitable for processing software for structural engineering applications, are generated automatically based on this file. The algorithm presented in this paper has been tested under laboratory conditions and also with several industrial portal frames, achieving promising results. Finally, 3D models were generated, and structural calculations were performed.     

基于声暴露级向量的海洋工程噪声信号自动提取方法

为从海量的海洋原始噪声数据中快速和准确地提取海洋工程噪声信号,完善大型海洋工程的海洋噪声评估方法,文章提出基于声暴露级向量的海洋工程噪声信号自动提取方法,并进行实验验证。研究结果表明:基于声暴露级向量的信号自动提取方法实现的核心是计算疑似信号与样本信号声暴露级向量(包括声暴露级和有效时间长度)的标准化欧氏距离,结合变步长截取分析、半波包络双阈值触发和中心位置触发等方法,提高信号提取的效率和准确率;与传统的人工提取方法相比,该自动提取方法具有稳健、准确和高效的性能,可替代人工提取方法并广泛应用。     

Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series

We present the current capabilities of a software tool to automatically detect coronal mass ejections (CMEs) based on time series of coronagraph images: the solar eruptive event detection system (SEEDS). The software developed consists of several modules: preprocessing, detection, tracking, and event cataloging. The detection algorithm is based on a 2D to 1D projection method, where CMEs are assumed to be bright regions moving radially outward as observed in a running-difference time series. The height, velocity, and acceleration of the CME are automatically determined. A threshold-segmentation technique is applied to the individual detections to automatically extract an approximate shape of the CME leading edge. We have applied this method to a 12-month period of continuous coronagraph images sequence taken at a 20-minute cadence by the Large Angle and Spectrometric Coronagraph (LASCO) instrument (using the C2 instrument only) onboard the Solar and Heliospheric Observatory (SOHO) spacecraft. Our automated method, with a high computational efficiency, successfully detected about 75% of the CMEs listed in the CDAW CME catalog, which was created by using human visual inspection. Furthermore, the tool picked up about 100% more small-size or anomalous transient coronagraph events that were ignored by human visual inspection. The output of the software is made available online at . The parameters of scientific importance extracted by the software package are the position angle, angular width, velocity, peak, and average brightness. Other parameters could easily be added if needed. The identification of CMEs is known to be somewhat subjective. As our system is further developed, we expect to make the process significantly more objective.