eLearning

FDTD simulation of radio wave propagation at intersection surrounded by compound walls in residential area for inter-vehicle communications using 720 MHz band

Kenji Taguchi; Ryosuke Aoyama; Suguru Imai; Tatsuya Kashiwa Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, 2012

In Japan, an inter-vehicle communication system using the 720 MHz band has been proposed to prevent car crashes at intersections. This study analyzes the propagation characteristics in an intersection surrounded by compound walls in a residential area. We investigated the qualitative impact of the wall thickness and height and the ground on the propagation characteristics for the first time by ...

An Optimalizing Threshold Segmentation Algorithm for Road Images Based on Mathematical Morphology

Xu Guo-bao; Yin Yi-xin; Xie Shi-yi 2009 Third International Symposium on Intelligent Information Technology Application, 2009

Considering the requirements of real time and robustness for the segmentation algorithms for mobile robots, an optimal threshold segmentation algorithm based on mathematical morphology was studied. Two improved measures, i.e., introducing an improved combination filter of median and multi-scale, and increasing morphological opening and closing operation, were proposed for the general optimal threshold segmentation algorithm. The experimental results indicated that ...

Recycling characteristics of polymer modified asphalt

Jiuguang Geng; Yanping Sheng; Haibin Li; Qian Zhang 2011 International Conference on Electric Technology and Civil Engineering (ICETCE), 2011

The aged polymer modified asphalt (abbreviated as PMA) was recycled with two methods which were adding fresh PMA and adding recycling agent to investigate the recycling characteristics of PMA. The performance of the recycled PMA was evaluated systematically with a variety of indices, including penetration, softening point, ductility, viscoelasticity and stiffness. The results indicate that the performance of recycled PMA ...

Parameterized Radiation Transport Model for Neutron Detection in Complex Scenes

C. M. Lavelle; D. Bisson; J. Gilligan; B. M. Fisher; R. M. Mayo IEEE Transactions on Nuclear Science, 2013

There is interest in developing the ability to rapidly compute the energy dependent neutron flux within a complex geometry for a variety of applications. Coupled with sensor response function information, this capability would allow direct estimation of sensor behavior in multitude of operational scenarios. In situations where detailed simulation is not warranted or affordable, it is desirable to possess reliable ...

Analysis of classification algorithms applied to road surface recognition

Aleksandr Bystrov; Mohammad Abbas; Edward Hoare; Thuy-Yung Tran; Nigel Clarke; Marina Gashinova; Mikhail Cherniakov 2015 IEEE Radar Conference (RadarCon), 2015

The development of remote surface recognition systems is an important step in ensuring road safety. This paper examines the performance of surface classification algorithms, used for the analysis of backscattered microwave and ultrasonic signals. The novelty of our research is the joint use of data obtained from sonar and multifrequency polarimetric radar. The results demonstrate the feasibility of reliable surface ...

IEEE-USA E-Books

• Dimensionality Reduction Using Automatic Supervision for Vision-Based Terrain Learning

  This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot's mechanical sensors as supervision. We present a probabilistic framework in which the visual information and the mechanical supervision interact to learn the available terrain types. Within this framework, a novel supervised dimensionality reduction method is proposed, in which the automatic supervision provided by the robot helps select better lower dimensional representations, more suitable for the discrimination task at hand. Incorporating supervision into the dimensionality reduction process is important, as some terrains might be visually similar but induce very different robot mobility. Therefore, choosing a lower dimensional visual representation adequately is expected to improve the vision-based terrain learning and the final classification performance. This is the first work that proposes automatically supervised dimensionality reduction in a probabilistic framework using the supervision coming from the robot's sensors. The proposed method stands in between methods for reasoning under uncertainty using probabilistic models and methods for learning the underlying structure of the data. The proposed approach has been tested on field test data collected by an autonomous robot while driving on soil, gravel and asphalt. Although the supervision might be ambiguous or noisy, our experiments show that it helps build a more appropriate lower dimensional visual representation and achieves improved terrain recognition performance compared to unsupervised learning methods.

• Description of Stator Items

  This chapter contains sections titled: Cleanliness of Bore Air/Gas Ducts Clogged/Unclogged Iron Oxide Deposits Hardware Condition High-Voltage Bushings Stand-Off Insulators Bushing Vents Greasing/Red Oxide Deposits on Core Bolts and Spring Washers Space Heaters Fan-Baffle Support Studs Heat Exchanger Cleanliness and Leaks Hydrogen Desiccant/Dryer Core-Compression (ldquo;Bellyrdquo;) Belts Bearing Insulation (at Pedestal or Babbitt) Coil Cleanliness (including water cooled windings) Blocking Condition Ties Between Coils Ties to Surge-Rings Surge-Ring Insulation Condition Surge-Rings Support Assembly Additional End-Winding Support Hardware RTDs and TCs Wiring Hardware Asphalt Bleeding/Soft Spots Tape Separation/Girth Cracking Insulation Galling/Necking Beyond Slot Insulation Bulging into Air Ducts Insulation Condition Circumferential Bus Insulation Corona Activity Wedges Condition Wedges Slipping Out Fillers Slipping Out Bars Bottomed in Slot Laminations Bent/Broken in Bore Laminations Bulging into Air Ducts Terminal Box Current Transformer (CT) Condition Bushing-Well Insulators and H2 Sealant Condition End-Winding Expansion-Bearing-Bolts Condition (Round-Rotor Mach ines) This chapter contains sections titled: References