Autonomous Underwater Vehicle to Inspect Hydroelectric Dams

Driven by the rising demand for underwater operations in the fields of dam structure monitoring, ecosystems of reservoir lakes from Hydropower Plants (HPP) and mining and oil, underwater robotics is increasing rapidly. The increase in exploration, prospecting, monitoring and security in lakes, rivers and sea, both in commercial applications such as scientific applications, has led large companies and research centers to invest in the development of underwater vehicles. The purpose of this work is to develop and evaluate the performance of a dedicated expert system for an Autonomous Underwater Vehicle (AUV) to inspect hydroelectric dams, focusing efforts on mechatronic project based on dimensioning structural elements and machinery and elaborating the sensory part, which includes navigation sensors and sensors of environment conditions, as well as its vision system to detect and measure cracks on hydroelectric dams. The integration of sensors in an intelligent platform provides a satisfactory control of the vehicle, allowing the movement of the submarine on the three spatial axes. Because of the satisfactory fast response of the sensors, it is possible to determine the acceleration and inclination besides his attitude in relation to the trajectory instantaneously taken, and geometry and depth of the cracks. This vehicle will be able to monitor the physical integrity of dams, making acquisition and storage of environment parameter such as temperature, dissolved oxygen, pH and conductivity as well as document images of the biota from reservoir lakes HPP, with minimized cost, high availability and low dependence on a skilled workforce to operate it.

http://www.ijcaonline.org/archives/volume101/number11/17728-8801

An OCR System for Numerals Applied to Energy Meters

This work describes a prototype of an OCR (Optical Character Recognition) system designed for reading digits of power measurement devices, using Artificial Neural Networks. The motivation for this work is the implementation of an alternative automatic measurement system to be used in a prepaid power system - SEPPRA. Prototype software using Computer Vision techniques and pattern recognition through Neural Networks is implemented in the C++/Windows platform. Considering this application, adaptive threshold methods are compared in order to choose the more appropriated algorithm of binarization. Several algorithms are implemented and evaluated under different conditions of zoom and camera focus. The system works satisfactorily and can be carried to other platforms, making possible its production in commercial scale. 

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