智慧轨道交通——更透彻的感知

智慧轨道交通不仅是当前轨道交通行业的新理念,同时也是该行业进一步发展所面临的重大技术课题。提升现代轨道交通系统的智慧水平,首先必须使轨道交通系统对自身和外部环境具有“更透彻的感知”能力,使系统内的“人、机、物”的信息具有更广泛的互联互通的能力,使系统具有更加深入的智能化的信息处理能力,从而达到系统的高度智能化,实现系统与行业的运作“智慧化”。分析了智慧轨道交通中“更透彻的感知”的发展方向与趋势,从“敏锐”、“快捷”、“可靠”、 “高效”、“全面”和“ 智能”多个角度来探讨如何在智慧轨道交通行业运作和智能轨道交通系统中实现“更透彻的感知”。     

融入类别信息的图像标注概率主题模型

基于概率主题模型的图像标注方法旨在通过学习图像语义进行图像标注,近年来倍受研究人员关注。考虑到类别对图像标注可提供有价值的信息,例如,“高楼”类图像,出现“天空”、“摩天楼”的可能性大于“海水”和“沙滩”。而“海岸”类图像出现“海水”、“沙滩”的可能性要大于“天空”和“摩天楼”。在Corr-LDA模型的基础上利用图像类别来改进图像的标注性能,提出了一个融入类别信息的图像标注概率主题模型。为该模型推导了一个基于变分EM的参数估计算法,并给出了使用该模型标注图像的方法。在LabelMe和UIUC-Sport两个真实数据集上验证了提出模型的标注性能要高于其他相比较模型。     

基于认知属性库的原型范畴研究

原型范畴是认知科学研究中的重要理论,使用属性来区分范畴中心成员及边缘成员有着较强的解释力,但该理论一直缺乏基于频率信息的属性数据支撑。该文借助认知属性库的23万条数据,对原型理论研究中经常讨论的“鸟”、“水果”、“交通工具”等范畴的典型成员和非典型成员进行分析验证。认知属性库的数据显示,在汉语中,“鸟”的典型成员是“麻雀”、“燕子”等,和“鸟”具有较多的共同属性;而“企鹅”、“鸵鸟”则只共享了“鸟”很少的属性,且缺少关键的属性“飞”。大体上验证了原型理论的观点。同时,我们也发现“小鸟”的属性特别丰富,具有典型成员的特性。在进一步观察了“水果”和“交通工具”两个范畴后,我们探讨了范畴的跨类现象,进而从数学模型上区分了树结构的层次分类体系和图结构的范畴化体系。     

基于遥感与GIS的民勤盆地荒漠垦殖研究

通过对民勤盆地1959年航片,1973年MSS影像,1987、1994、1998和2001年TM影像的人机交互目视解译结合自动分类,制作了6个典型年代的绿洲分布图。利用GIS软件进行迭加运算处理并进行面积统计,获得民勤盆地40年来荒漠垦殖位置变化图和面积变化数据。结果表明,40年来民勤盆地荒漠垦殖发展迅速,其中尤以70年代和90年代增加显著;荒漠垦殖范围扩展的方式主要有两种:一种是原有绿洲的“摊饼式”扩展,一种是向荒漠腹地的“跳跃式”推进;荒漠垦殖扩展的原因,早期主要受“以粮为纲”政策的影响,晚期在于绿洲生态环境的恶化和经济利益的驱动。荒漠垦殖的迅猛发展极大地破坏绿洲外围的生态环境,加速了荒漠化进程。     

基于非结构化P2P技术的资源信息服务

因特网资源的“分布性”、“自治性”和“多样性”等自然特性给资源的有效共享和综合利用带来了巨大的挑战。本文基于非结构化P2P技术提出了一种虚拟计算环境（iVCE）中的可扩展资源信息服务（SRIS）,并对SRIS的应用实例进行了模拟分析。     

论智慧轨道交通及其系统架构

提倡用“智慧轨道交通”来描述未来的“高效、便捷、安全、可视、可预测、环保和智慧”的现代轨道交通行业和宏观系统的发展远景视图,并提出了一个“智慧轨道交通系统架构”。该架构以“智慧轨道交通智能化地面基础设施”（SRT-IGI）和智慧列车为基础实现更透彻的感知;以“轨道交通全联网”(SRT-IoT)作为信息交换与共享平台实现更广泛的互联互通;以“与人类融合智慧管理、决策与指挥”（HCA-IMDC）应用系统为顶层结构。三部分组成结构及分布其中的“智能化”功能共同实现整个交通行业的“智慧化”。同时分别对三部分的技术内涵作了进一步的探讨。鉴于笔者尚未发现从信息技术（IT）角度全面探讨“智慧轨道交通”的文献,所以希望本框架的提出能够为从信息技术（IT）角度全面研究智慧轨道交通提供参考,进一步推动智慧轨道交通在技术上的跨越式发展,使我国轨道交通以“更透彻的感知、更广泛的互联互通、更深入的智能化处理能力”的雄姿,为国家经济建设发展和人性化地为人民服务。     

网络CSCW建组协商中的Meta-协商研究和应用

自动协商的广泛应用激发了CSCW(ComputerSupportedCooperativeWork)建组的新思路.独特的在建组成员间的“Meta 协商”使得建组协商不同于普通的购买协商。从分析“Meta 协商”存在性出发,提出了描述临时组结构的“组对象”概念, 将“群决策”理论中“投票”方法用作组策略的核心思想,实现了决策群体动态变动的Meta 协商机制。在“限制空间”基础上,讨论了协商中解空间和组空间的变化情况,结合网络应用的需要设计出减少因网络拥塞延迟造成建组失败的实现方案。     

图形演化下的图学学科

本文回顾了人类在适应自然、改造自然的过程中对图形的需求以及图形的发展对 社会进步的作用,从图形引出文字、图形引出数字的本源出发,追溯图之源。用一个“形” 、 “意” 、 “元” 、“用”四维结构演绎图形的属性、地位和作用,并从这 4 个维度分析图形的演化过程, 揭示了图形在“形”维度的高维化、“意”维度的深度化、“元”维度的融合化和“用”维度的 渗透化的发展趋势。阐述了在这个图形演化趋势下的图学科学的形成、变革与发展,揭示图学 科学的内涵。在此认知下给出了一个基于图形要素的图学学科框架体系。     

微波月亮-人类对月球的全新视角——中国“嫦娥一号”卫星微波探测仪若干探测结果

中国“嫦娥一号”探月卫星自2007年10月24日成功发射并于同年11月7日进入其工作轨道。在轨工作一年多,完成了全部使命,期间获取了大量的科学数据。其中“嫦娥一号”月球微波探测仪(Chang’e-1 Lunar Microwave Sounder-CELMS)已多次覆盖全月表面,首次获取了全月微波亮温分布数据,创建了“微波月亮”(Microwave Moon-MicM)。“微波月亮”的建立为月球科学研究、宇宙科学研究、月球资源研究及应用、未来月球基地的建立等带来了全新的信息,与“可见月亮”、“红外月亮”及其它相关探测结果(如X、γ谱仪,中子谱仪)、地基探测及未来月球轨道上观测和就位探测等多方信息的融合、分析,将大大提升人类对太空、月球及宇宙起源、生命起源等问题的认识和研究水平,在人类探月活动中具有里程碑意义。
在“嫦娥一号”卫星微波探测仪绕月探测之前,从来没有从月球轨道对全月球进行微波探测的活动。很多涉及月球微波特征研究,如月表微波亮温分布、月壤厚度及氦-3资源量分布信息、涉及月球历史等的研究多数是靠Apollo、Luna的落月点实测数据为依据,加上其它探测(如光学等)结果融合分析并逻辑延伸而得来的,因此其结果存在相当的多解或不确定性,这就使我们对月球微波辐射特性的真实情况了解很少,甚至可能有偏差。着重讨论“微波月亮”的含义,其相关信息内涵,几种特征区、点的分析等。根据微波探测仪的数据,获得了全月月壤厚度分布、氦-3资源量评估、全月亮温及其变化规律等研究结果,得出了一些与现今其它研究结果不同的结论。     

遥感技术在海洋应用研究中的新进展

概要介绍了国家“七五”重点科技攻关项目“中国近海表层环境因子测报”、“海洋航空遥感数据处理技术研究”的进展和项目推广应用情况。     

奥运火炬塔自动化控制系统

北京第29届奥运会主火炬是历届奥运会构思最独特、造型最完美、史无前例的火炬塔,集中体现了中华名族的智慧,展现的是中华民族屹立于世界东方的风采。自2001年7月13日申办成功以来,北京奥运会一直是大家共同关注的一件大事,火炬塔的点燃圆了中华民族百年的奥运梦想,对整个中国必将产生巨大的社会效益;火炬塔自动化控制系统包括水平移动的变频控制和火炬塔举升的液压控制,由总装备部设计研究院做一次设计,北京首钢自动化信息技术有限公司做二次设计、设备制造、安装、调试及操作任务,本文对火炬塔制作工艺、自动化控制系统硬件及软件配置、关键技术做了较详细的阐述。     

基于Sentinel-2和全卷积网络的北京六环内高层建筑区提取与分析

高层建筑区是我国城镇化进程的重要标志,具有重要的社会经济功能和独特的几何形态.应用全卷积网络和Sentinel-2多光谱数据,提取北京六环内高层建筑区,并结合环线、乡镇边界和轨道交通数据,对高层建筑区的空间分布和交通可达性进行分析.结果表明:实验提出的全卷积网络方法能够实现北京高层建筑区快速精确提取,总体精度90％以上...     

Monitoring of urban heat island effect in Beijing combining ASTER and TM data

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.     

基于深度学习的村镇砖(混)木房屋识别

破坏性地震发生后, 相较按照抗震设防标准建设的城市公共设施和居民住宅, 广大无抗震设防的村镇居民自建房屋, 更易发生倒塌甚至完全损毁. 以往地震灾情预评估、地震灾害风险调查、地震重点危险区调研, 依靠专家现场踏勘, 确定不同结构类型建筑物数量及所占比例. 本研究借助深度学习和倾斜摄影技术, 进行砖(混)木结构房屋识别, 郯庐断裂带山东境内砖(混)木房屋影像制作数据集, 训练得到Faster R-CNN模型, 该区域内砖(混)木房屋识别平均精度为91.868%. 结果表明, 本文方法能够对砖(混)木房屋进行有效检测, 可应用于地震行业开展震前、震后各类现场工作, 提高政府部门应急管理能力.     

Trend analysis of building height and total floor space in Beijing,China using ICESat/GLAS data

GLA14, one of the products of the spaceborne Light Detection and Ranging (LiDAR) sensor Geoscience Laser Altimeter System (GLAS), provides six Gaussian decomposition waveforms that represent different vertical layers of the ground target in a laser spot. In this article, we have extracted the relative height of ground targets from peak positions of the GLAS waveform, carried out the field validations, analysed the trend of building height in Beijing and then multiplied the building height and the percentage of building area within a pixel of the land-use/land-cover classification map to get the annual change of total floor space of buildings in Beijing. Based on the total floor space of buildings (TFSB) released by the National Bureau of Statistics of China (NBSC), we have established a linear regression model between the GLAS-estimated total floor space in Beijing and the data provided by NBSC. The results show that the building height and (TFSB) in Beijing increased from 2003 to 2008. The method proposed in this article expands research on urban change from a two-dimensional plane to a three-dimensional space to improve research accuracy, and is complementary to current remote-sensing methods.     

On High Dimensional Projected Clustering of Data Streams

The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams.In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.Charu C. Aggarwal received his B.Tech. degree in Computer Science from the Indian Institute of Technology (1993) and his Ph.D. degree in Operations Research from the Massachusetts Institute of Technology (1996). He has been a Research Staff Member at the IBM T. J. Watson Research Center since June 1996. He has applied for or been granted over 50 US patents, and has published over 75 papers in numerous international conferences and journals. He has twice been designated Master Inventor at IBM Research in 2000 and 2003 for the commercial value of his patents. His contributions to the Epispire project on real time attack detection were awarded the IBM Corporate Award for Environmental Excellence in 2003. He has been a program chair of the DMKD 2003, chair for all workshops organized in conjunction with ACM KDD 2003, and is also an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal. His current research interests include algorithms, data mining, privacy, and information retrieval.Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. He has been working on research into data mining, data warehousing, stream and RFID data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook “Data Mining: Concepts and Techniques” (Morgan Kaufmann, 2001).Jianyong Wang received the Ph.D. degree in computer science in 1999 from the Institute of Computing Technology, the Chinese Academy of Sciences. Since then, he ever worked as an assistant professor in the Department of Computer Science and Technology, Peking (Beijing) University in the areas of distributed systems and Web search engines (May 1999–May 2001), and visited the School of Computing Science at Simon Fraser University (June 2001–December 2001), the Department of Computer Science at the University of Illinois at Urbana-Champaign (December 2001–July 2003), and the Digital Technology Center and Department of Computer Science and Engineering at the University of Minnesota (July 2003–November 2004), mainly working in the area of data mining. He is currently an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China.Philip S. Yuis the manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. The current focuses of the project include the development of advanced algorithms and optimization techniques for data mining, anomaly detection and personalization, and the enabling of Web technologies to facilitate E-commerce and pervasive computing. Dr. Yu,s research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, disk arrays, computer architecture, performance modeling and workload analysis. Dr. Yu has published more than 340 papers in refereed journals and conferences. He holds or has applied for more than 200 US patents. Dr. Yu is an IBM Master Inventor.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He will become the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering on Jan. 2001. He is an associate editor of ACM Transactions of the Internet Technology and also Knowledge and Information Systems Journal. He is a member of the IEEE Data Engineering steering committee. He also serves on the steering committee of IEEE Intl. Conference on Data Mining. He received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts”. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.     

基于USB的飞机EICAS系统研究

介绍了飞机的EICAS系统的模拟,并提出了简单而现实的硬件设计方法,实现了对飞机各种主要报警参数的前级采集.USB的应用实现了模型和计算机接口的设计,并通过它进行数据的转换和传输,以便后级的数字信号的输出和计算机软件的显示.     

Leakage current estimation of CMOS circuit with stack effect

Leakage current of CMOS circuit increases dramatically with the technology scaling down and has become a critical issue of high performance system. Subthreshold, gate and reverse biased junction band-to-band tunneling (BTBT) leakages are considered three main determinants of total leakage current. Up to now, how to accurately estimate leakage current of large-scale circuits within endurable time remains unsolved, even though accurate leakage models have been widely discussed. In this paper, the authors first dip into the stack effect of CMOS technology and propose a new simple gate-level leakage current model. Then, a table-lookup based total leakage current simulator is built up according to the model. To validate the simulator, accurate leakage current is simulated at circuit level using popular simulator HSPICE for comparison. Some further studies such as maximum leakage current estimation, minimum leakage current generation and a high-level average leakage current macromodel are introduced in detail. Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage current estimation methods are very accurate and efficient.     

基于兴趣点的北京市住宅生活便利度指数研究

住宅的生活便利度指数指住宅居民在一定范围内可利用的设施种类与数量,是城市宜居度的重要指标.兴趣点(point of interest,POI)数据是一组包含物理实体属性的地理坐标点,具有数据种类详尽、粒度精细、范围广和时效性强的优点.基于高德POI数据,对北京市城区住宅的生活便利度指数进行研究,提出面向地铁出行的各向异...     

面向北京2022年冬奥会水下火炬传递的新型机器人

面向北京2022年冬奥会水下火炬传递应用需求,设计水下变结构机器人和两栖机器人,并提出水下火炬传递控制方法。克服机械臂（含火炬）运动和野外流场的复合扰动是火炬传递控制的关键问题,尤其是手持燃烧火炬的机械臂对浮游模式水下机器人的影响更加显著。针对以上问题,本文提出一种基于自适应控制策略的水下火炬传递控制方法,在线辨识静力学和流场水动力参数,预先补偿控制扰动,实现水下机器人的高精度姿态控制、位置控制。最终,在北京2022年冬奥会上成功完成奥运史上首次机器人间水下火炬接力。