eLearning

Application of Artificial Intelligence Technology in Asphalt Concrete Mixer Control System

Ming-shan Liu; Yuan Zhou Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007

Analysis the working process of the asphalt concrete mixer control system, this article introduces a new type of intelligent control system based on the fuzzy expert control technology. To minimize the control error caused by nonlinearity and long time delay in the working process, the advance technology of fuzzy control, expert system and digital signal processing are adopted in the ...

Forecast: a neural system for diagnosis and control of highway surfaces

A. Luchetta; S. Manetti; F. Francini IEEE Intelligent Systems and their Applications, 1998

Forecast is a system that predicts the temperature of road surfaces, using a neural net with backpropagation learning algorithms and online parameter tuning. Forecast is a part of the Icenet monitoring system, which warns highway authorities about impending subzero road conditions

Temperature Control Strategies for Microwave Hot In-Place Recycling of Asphalt Pavements

Tongsheng Sun; Jinfei Shi; Zhisheng Zhang; Jianyong Zheng 2009 International Workshop on Intelligent Systems and Applications, 2009

In order to realize the optimal temperature control for microwave hot in-place recycling of asphalt pavements, the electric field in heating process was solved based on the radio propagation principle, and the equation of heat flow control was established by the theory of permeability plate. The temperature control model was built through coupling between the two equations. The exact solution ...

Recent Progress In Mobile Robot Harunobu(2) - Moving Obstacle Detection And Mobile Robot Application

H. Mori; S. Kotani Intelligent Vehicles '93 Symposium, 1993

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Effects of Aging on Static Viscoelastic Performance and Parameters of Asphalt Binder

Jin Lei; Zheng Jianlong 2010 International Conference on Intelligent System Design and Engineering Application, 2010

The aging of asphalt binder is an important factor to asphalt pavement service property, the paper applied short-term age to asphalt binder by using RTFO, and then aged the residue various hours by using PAV. The authors studied the effects of aging on static viscoelastic performance of asphalt binder through bending beam rheometer tests on the PAV residue.

IEEE-USA E-Books

• 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

• 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.