Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

高校办公自动化课程教学方法的研究与实践

办公自动化课程是我院针对中国汉语言文学专业高级涉外文秘方向学生开设的特色课程,该课程开设至今已取得一定的成绩,但如何进一步对该课程进行教学改革,使学生的实践技能与综合素质得到更大提升,是目前我们每一名任课教师必须面对和思考的问题。该文针对这一问题从教学内容、教学方法及考核方式几方面进行了深入探讨。     

易燃性智能测试仪软件系统的设计与实现

基于玩具安全标准对玩具易燃性的要求,结合PLC控制技术、网络技术和软件技术,对研制的智能燃烧测试仪的工作原理、硬件结构设计、功能特性进行了详细的论述,并重点对软件系统中的主控程序、人机界面、通信程序的设计与实现进行了阐述,指出此测试仪是一台软件与硬件相结合的设备,其软件系统的实现极大地提高了测试仪的自动化水平.     

计算机控制实验内容设计及实验装置研制

文中介绍了一种既能体现工业控制计算机的特点,又面向自动化工程实际应用的计算机控制实验教学内容的设计方案,阐述了与实验教学内容相适应的计算机控制实验装置的研制指导思想和主要功能。     

数据中心的航空公司数据仓库的设计

为了适应管理人员对决策支持系统的需要,许多企业纷纷建造自己行业的数据仓库来存储、使用、分析海量的历史数据。结合数据仓库的理论,提出了一种基于数据中心平台的航空公司数据仓库的基本体系架构,并就航空公司数据仓库的设计和相关技术等问题进行了探讨。     

EPA组态软件工程管理模块的设计与实现

在EPA组态软件的设计中,工程管理模块贯穿着整个软件的运行。针对EPA网络的特点,将在线的EPA设备作为一个EPA工程,并通过该工程管理设备的运行状态。实验结果表明,该设计方法能有效地控制现场的EPA设备。     

PLC技术在生活污水处理及回用系统中的应用

阐述了采用PLC技术在生活污水处理及回用系统中的应用实例,介绍了该系统的工艺流程及控制要求,并针对该控制要求设计了PLC控制系统的硬件构成和软件流程,说明了PLC技术在生活污水处理及回用系统中实现自动化控制的可行性。     

融合课程思政的“自动化综合实验”教学探索

为将思政教育融入课程教学中,培养德能兼备的自动化专业优秀人才。在《自动化综合实验》课程中,首先明确提出了教学的专业目标和思政目标,然后将教学环节分为课程介绍、选题汇报、学生实践以及项目展示和答辩环节,挖掘出丰富的思政元素并融入到课程教学的每个环节,并选取典型案例,通过思维导图法剖析了课程思政的融入策略,最后介绍了课程思政考核模式和取得的初步成效。该课程思政教学模式可以为“专业实验课程-思政教育体系”的打造提供参考。     

全矿井综合自动化系统服务级别协议的探究

针对目前全矿井综合自动化系统服务级别混乱和服务周期长的问题,提出采用服务级别协议的方式来分类分级管理售中和售后服务的方案,从安装调试类服务、系统推广类服务、定制升级类服务、技术支持服务和日常运营服务5个方面详细介绍了服务级别协议的分类管理及内容。服务级别协议将IT服务管理的思想贯彻到了全矿井综合自动化系统的合同签订及实施过程中,对合同的实施周期、项目的验收和合同回款都将起到显著的作用。     

煤楼运输自动化控制系统的应用

根据某煤矿的现状,提出了利用自动化控制系统对煤楼运输系统进行改造的方案;介绍了煤楼运输自动化控制系统的选型原则,给出了系统的组成、主要配置及其在某矿煤楼运输系统中的应用。实际应用表明,该煤楼运输自动化控制系统实时性好、可靠性高、故障率低,提高了生产效率,保障了安全生产。