Eddy current generation enhancement using ferrite for electromagnetic acoustic transduction

Eddy currents are generated in an electrically conducting surface as a step in electromagnetic acoustic transduction (EAT). In eddy current testing, wire coils are often wound onto a ferrite core to increase the generated eddy current. With EAT, increased coil inductance is unacceptable as it leads to a reduction in the amplitude of a given frequency of eddy current from a limited voltage source, particularly where the current arises from capacitor discharge. The authors present a method for EAT where ferrite is used to increase the eddy current amplitude without significantly increasing coil inductance or changing the frequency content of the eddy current

A system identification based approach for pulsed eddy current non-destructive evaluation

This paper is concerned with the development of a new system identification based approach for pulsed eddy current non-destructive evaluation and the use of the new approach in experimental studies to verify its effectiveness and demonstrate its potential in engineering applications

A Comparative Study Installation Arrangement of Primary Flight Display (PFD) in the Flight Deck's Regional Passenger Transport Aircraft

The Flight Deck or cockpit is designed to support the prosecution of aircraft mission. For aircraft with commercial purpose such as passenger transport aircraft, there must be good “fit” between the human, the machine which composed of various aircraft system and the aircraft mission; and these are what the Flight Deck design process needs to ensure but not limited to display design, aircraft control, automation, HCI on the Flight Deck and pilot's view to outside through cockpit windows namely external vision. The external vision must satisfy regulatory requirements which intended to ensure that the view is adequate for pilots to operate the aircraft safely and gives them a reasonable opportunity to see and avoid other aircraft that pose a collision threat. Concurrently during critical periods of flight, it is important that the flight crew access information in front of his view with minimal head rotation. Cockpit-Displays with critical flight information should then be located to these locations. Compromising both external vision and internal vision as Pilot's visibility should be attained

Markerless detection of fingertips of object-manipulating hand

Most reported works on fingertip detection focus on extended fingers where the hand is not occluded by another object. This paper proposes a machine-vision-based technique exploiting the contour of the hand and fingers for detecting the fingertips when the hand is manipulating a ball, which means that the fingers are closed and the hand is partially occluded. The preliminary result of our on-going research is promising where it can be used to generate a more objective performance indicator for monitoring the progress during hand therapy by using a digital webcam. Being markerless and contactless, the proposed technique will require minimal preparation prior to the therapy

Visual-based fingertip detection for hand rehabilitation

This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands. © 2018 Institute of Advanced Engineering and Science. All rights reserved

PRELIMINARY GEOMECHANICAL ANALYSIS ON LIMESTONES IN PPSDM GEOMINERBA CAMPUS, PADALARANG, WEST JAVA

The research location is in the PPSDM Geominerba field campus. The campus is located in Padalarang, West Java that is surrounded by the open-pit mining of limestone and marble. This limestone was formed in Oligo-Miocene of Rajamandala Formation. The research objective was to determine the condition of the slopes around the campus based on geomechanical characteristics. Based on field observations, the slope angle in the area is dominated by steep slopes. The rock hardness level is dominated by hard rock with a hardness ranging from 50-100 MPa. Rock Mass Rating shows that the area is dominated by good rocks. While the Slope Mass Rating calculation show that the maximum slope angle is between 52-75°. Level of deformation and intensive weathering process will reduce the strength of the rock in the future. Several rock fall occurrences on this research area support this assumption. Yet, some local open pit mining area activity near the toe hill of the area need to be concerned regarding the effect of the local rock fall occurrences

Pulsed Eddy Current signal processing using wavelet scattering and Gaussian process regression for fast and accurate ferromagnetic material thickness measurement

Testing the structural integrity of pipelines is a crucial maintenance task in the oil and gas industry. This structural integrity could be compromised by corrosions that occur in the pipeline wall. They could cause catastrophic accidents and are very hard to detect due to the presence of insulation and cladding around the pipeline. This corrosion manifests as a reduction in the pipe wall thickness, which can be detected and quantified by using Pulsed Eddy Current (PEC) as a state-of-the-art Non-Destructive Evaluation technique. The method exploits the relationship between the natural log transform of the PEC signal with the material thickness. Unfortunately, measurement noise reduces the accuracy of the technique particularly due to its amplified effect in the log-transform domain, the inherent noise characteristics of the sensing device, and the non-homogenous property of the pipe material. As a result, the technique requires signal averaging to reduce the effect of the noise to improve the prediction accuracy. Undesirably, this increases the inspection time significantly, as more measurements are needed. Our proposed method can predict pipe wall thickness without PEC signal averaging. The method applies Wavelet Scattering transform to the log-transformed PEC signal to generate a suitable discriminating feature and then applies Neighborhood Component Feature Selection method to reduce the feature dimension before using it to train a Gaussian Process regression model. Through experimentation using ferromagnetic samples, we have shown that our method can produce a more accurate estimation of the samples’ thickness than other methods over different types of cladding materials and insulation layer thicknesses. Quantitative proof of this conclusion is provided by statistically analyzing and comparing the root mean square errors of our model with those from the inverse time derivative approach as well as other machine learning models

Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

Artificial Intelligence through supervised machine learning remains an attractive and popular research area in medical image processing. The objective of such research is often tied to the development of an intelligent computer aided diagnostic system whose aim is to assist physicians in their task of diagnosing diseases. The quality of the resulting system depends largely on the availability of good data for the machine learning algorithm to train on. Training data of a supervised learning process needs to include ground truth, i.e., data that have been correctly annotated by experts. Due to the complex nature of most medical images, human error, experience, and perception play a strong role in the quality of the ground truth. In this paper, we present the results of annotating lumbar spine Magnetic Resonance Imaging images for automatic image segmentation and propose confidence and consistency metrics to measure the quality and variability of the resulting ground truth data, respectively