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Flávio Neves Junior

Researcher at Federal University of Technology - Paraná

Publications -  10
Citations -  88

Flávio Neves Junior is an academic researcher from Federal University of Technology - Paraná. The author has contributed to research in topics: Ultrasonic sensor & Mobile robot. The author has an hindex of 4, co-authored 10 publications receiving 77 citations.

Papers
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Journal ArticleDOI

A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.

TL;DR: In this paper, an image reconstruction algorithm based on regularized least squares using a l 1 regularization norm was proposed to reconstruct an image of a point-like reflector, using both simulated and real data.
Journal ArticleDOI

Navigation's Stabilization System of a Magnetic Adherence-Based Climbing Robot

TL;DR: A climbing robot based on wheel locomotion and magnetic adherence based on active gravitational compensation system and adherence stabilization system to perform internal/external inspection in liquefied petroleum gas storage tanks and other industrial storage structures is presented.
Book ChapterDOI

Localization and Navigation of a Climbing Robot Inside a LPG Spherical Tank Based on Dual-LIDAR Scanning of Weld Beads

TL;DR: In this article, the weld beads are detected by filtering and processing techniques applied to raw signals from the LIDAR (Light Detection And Ranging) sensors and a specific classification technique is used to sort data between noises and weld beads.
Book ChapterDOI

A Decomposition Approach for the Long-Term Scheduling of a Single-Source Multiproduct Pipeline Network

TL;DR: A decomposition approach combining heuristic algorithms and Mixed Integer Linear Programming (MILP) models to solve the long-term scheduling of a multiproduct pipeline connecting a single-source to multiple distribution centers was proposed.
Proceedings ArticleDOI

Heterogeneous measurement system based on optical fiber and ultrasonic sensors to determine ethanol concentration

TL;DR: In this article, an artificial neural network was used to correlate the sensor responses and determine the ethanol concentration in ethanol-water blends, and the results demonstrate that the heterogeneous measurement system can predict, without ambiguity for a range between 0 and 100 % v/v, ethanol concentration with maximum absolute error of 0.55 % v /v and 0.14 % V /v mean squared error, in validation step.