Coping with Uncertainty in Map Learning

In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate approaches to learning maps under such conditions based on Valiant's probably approximately correct learning model. We focus on the problem of coping with accumulated error in combining local measurements to make global inferences. In one approach, the effects of accumulated error are eliminated by the use of local sensing methods that never mislead but occasionally fail to produce an answer. In another approach, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. We also suggest some insights into why certain existing techniques for map learning perform as well as they do. The learning problems explored in this paper are quite different from most of the classification and boolean-function learning problems appearing in the literature. The methods described, while specific to map learning, suggest directions to take in tackling other learning problems.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

First and second order dynamics in a hierarchical SOM system for action recognition

Human recognition of the actions of other humans is very efficient and is based on patterns of movements. Our theoretical starting point is that the dynamics of the joint movements is important to action categorization. On the basis of this theory, we present a novel action recognition system that employs a hierarchy of Self-Organizing Maps together with a custom supervised neural network that learns to categorize actions. The system preprocesses the input from a Kinect like 3D camera to exploit the information not only about joint positions, but also their first and second order dynamics. We evaluate our system in two experiments with publicly available datasets, and compare its performance to the performance with less sophisticated preprocessing of the input. The results show that including the dynamics of the actions improves the performance. We also apply an attention mechanism that focuses on the parts of the body that are the most involved in performing the actions.     

基于ZedGraph和Google Maps的地应力分析系统设计

随着信息技术的不断发展,计算机技术和网络技术在各行各业中日益被广泛应用。以往的一些科学计算,数据分析都是依靠工作人员的高负荷重复劳动来实现,随着网络开发技术、数据库技术、电子地图等一系列相关技术的相继出现,利用计算机来分析和处理各种序列数据越来越受到相关技术人员的青睐。利用ZedGraph图表控件,实现对地应力监测数据的动态曲线分析,结合Google Maps来实现对监测点的直观呈现,极大地方便了研究人员对地震形成的检测和分析研究。     

追根溯源探析地域文化的时代传承之路——文山“壮乡苗岭”主题居住区概念规划

随着国内住宅地产的发展愈加成熟,人们对居住空间的关注不仅仅停留在理性需求层面,更多情感诉求的满足成为居住区规划的关键,“主题居住社区”的概念也随之应运而生,该文通过对“壮乡苗岭”大型居住区规划设计及思考过程的回顾,提出并探讨了基于地域文化进行主题社区规划设计的思路,这既是对使用者精神需求的关注,也是对地域文化的时代传承.     

基于ARM的手机位置采集及跟踪研究

针对特殊群体的实时位置跟踪需求,研究了GPS位置数据获取算法和数据采集时间间隔更新算法,设计实现了基于ARM和Google Maps的手机位置跟踪系统。用户只需随身携带GPS手机,监护人即可通过嵌入式移动终端在地图上实时查看其位置信息,并在用户接近和进入危险区域时报警,可应用于老人、小孩、残疾人等特殊群体的保护。基于ARM的嵌入式位置信息数据库保存了用户出行数据,为智能手机定位的大数据研究提供支持。通过对GPS数据重复性、位置信息数据库以及ARM位置跟踪系统的性能测试,验证了算法的有效性及设计思路的可行性。     

基于矿山GIS的煤矿矿图维护与管理

在煤矿生产中，矿山地学信息时刻都处于时间和空间的动态变化之中。煤矿生产中使用的矿图是为了满足煤矿生产的需要，因此矿图的内容几乎天天都在变动，矿图的及时维护与更新十分重要。针对矿山生产的实际情况，并基于矿山GIS(MGIS)，论述了矿山空间数据的GIS管理、矿图的绘制与更新。     

热断层(TTM)技术对睡眠质量的评估与探讨

本文的目的是探讨利用热断层(TTM)技术对睡眠质量进行评估的方法及其应用价值。采用的方法是对281位体检客人进行睡眠质量评估,以客人详细主述记录为对照。结果,被TTM诊断为有睡眠障碍的有119例,有主诉睡眠障碍的有122例,检出率为97．5％。由此可得出这样的结论:TTM对睡眠质量的评估简单快速,准确率高,具有直观实用的诊断价值。     

The performance of TiN-coated high speed steel tool inserts in turning

While it is well known that thin, hard coatings can reduce tool wear and improve tool life and productivity, there is still little consensus over the degree of advantage coated tools have over their uncoated counterparts. This paper compares the behaviour of titanium nitride- (TiN-) coated and uncoated high speed steel (HSS) tool inserts during turning. Wear maps describing the crater wear characteristics of these tools are used to show that the extent of tool wear reduction due to the coatings depends strongly on the cutting speed and feed rate. The maps also demonstrate that the benefits of TiN coatings on HSS tools may be easily realized over a wide range of machining conditions.