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

Sistem Informasi Pengolahan Data pada Koperasi Rezeky

Koperasi adalah sebuah kegiatan bisnis yang bertujuan untuk meningkatkan keuntungan dari anggota koperasi. Pada Perusahaan diperlukan sistem yang bisa membantu menghasilkan serta untuk mengetahui informasi tentang data dan anggota pinjaman. Dalam penulisan ini memiliki tujuan perangkat pengolahan data dari sistem informasi dan anggota yang menggunakan bahasa pemrograman java dan mysql sebagai databasenya. Dengan menerapkan sistem pengolahan data informasi, Rezeky Koperasi diharapkan akan meningkatkan efektivitas dan efisiensi karyawan dalam pengolahan data anggota dan dapat menghasilkan informasi secara cepat dan akurat, membantu koperasi untuk mengambil keputusan dalam penentuan kebijakan kepemimpinan

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

Dual EMAT and PEC non-contact probe: applications to defect testing

For many non-destructive testing (NDT) applications, more information and greater reliability can be gained by using different techniques for defect detection, especially when the methods are particularly sensitive to different types of defects. However, this will often lead to a much longer and more expensive test and is not always practical due to time and cost constraints. We have previously discussed initial experiments using a new dual-probe combining electromagnetic acoustic transducers (EMATs) generating and detecting ultrasonic surface waves, and a pulsed eddy current (PEC) sensor 1. This enables more reliable detection and sizing of surface and near-surface defects, with a reduced testing time compared to using two \{NDT\} techniques separately. In this paper, we present experiments using the dual-probe on samples which are more representative of real defects, for example testing for surface defects in rails. Several aluminium calibration samples containing closely spaced and angled slots have been measured, in addition to rail samples containing manufactured and real defects. The benefits of using the dual-probe are discussed

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

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

Comparing dogs and great apes in their ability to visually track object transpositions

Knowing that objects continue to exist after disappearing from sight and tracking invisible object displacements are two basic elements of spatial cognition. The current study compares dogs and apes in an invisible transposition task. Food was hidden under one of two cups in full view of the subject. After that both cups were displaced, systematically varying two main factors, whether cups were crossed during displacement and whether the cups were substituted by the other cup or instead cups were moved to new locations. While the apes were successful in all conditions, the dogs had a strong preference to approach the location where they last saw the reward, especially if this location remained filled. In addition, dogs seem to have especial difficulties to track the reward when both containers crossed their path during displacement. These results confirm the substantial difference that exists between great apes and dogs with regard to mental representation abilities required to track the invisible displacements of objects

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

Segmentation of Lumbar Spine MRI Images for Stenosis Detection using Patch-based Pixel Classification Neural Network

This paper addresses the central problem of automatic segmentation of lumbar spine Magnetic Resonance Imaging (MRI) images to delineate boundaries between the anterior arch and posterior arch of the lumbar spine. This is necessary to efficiently detect the occurrence of lumbar spinal stenosis as a leading cause of Chronic Lower Back Pain. A patch-based classification neural network consisting of convolutional and fully connected layers is used to classify and label pixels in MRI images. The classifier is trained using overlapping patches of size 25x25 pixels taken from a set of cropped axial-view T2-weighted MRI images of the bottom three intervertebral discs. A set of experiment is conducted to measure the performance of the classification network in segmenting the images when either all or each of the discs separately is used. Using pixel accuracy, mean accuracy, mean Intersection over Union (IoU), and frequency weighted IoU as the performance metrics we have shown that our approach produces better segmentation results than eleven other pixel classifiers. Furthermore, our experiment result also indicates that our approach produces more accurate delineation of all important boundaries and making it best suited for the subsequent stage of lumbar spinal stenosis detection