Data analysis and error detection in assembly lines using a three-dimensional visualization technique

2017 ◽  
Author(s):  
Christian Sand ◽  
Patricia Schuh ◽  
Tobias Lechler ◽  
Jorg Franke
Keyword(s):  
Data Analysis ◽  
Error Detection ◽  
Three Dimensional ◽  
Assembly Lines ◽  
Visualization Technique

scholarly journals Pengaruh Penggunaan Power Point Berbasis Animasi terhadap Hasil Belajar Siswa pada Materi Dimensi Tiga

2020 ◽  
Vol 1 (2) ◽  
pp. 75-82
Author(s):  
Salmun K. Nasib ◽  
Abas Kaluku ◽  
Abdul Wahab Abdullah
Keyword(s):  
Data Analysis ◽  
Experimental Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Descriptive Analysis ◽  
Three Dimensional ◽  
Sampling Technique ◽  
Student Learning Outcomes ◽  
Power Point ◽  
Cluster Random Sampling

This article discusses the use of PowerPoint animation in learning with the aim of knowing the differences in learning outcomes of students whose learning uses power points and without using power points in three-dimensional topics. The method used is an experimental design with a True Experimental Design, namely Posttest-Only Design. The sampling technique used cluster random sampling. Student learning outcomes data were obtained through the learning outcome test instrument in the form of essays. Data analysis using descriptive analysis techniques and inferential data analysis. Hypothesis testing using a parametric analysis t-test. The results of the analysis show that the average learning outcomes of students who are taught using power points are higher than those of students taught conventionally. One of the factors that support the improvement of student learning outcomes is a learning approach to geometric shapes that involves interesting visualization. Interesting visualization makes students not just imagine something abstract but can directly observe the object being studied.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Data Analysis ◽  
Binary Classification ◽  
Brain Mri ◽  
Three Dimensional ◽  
Structural Variations ◽  
Automatic Feature Extraction ◽  
Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

scholarly journals Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2

2020 ◽  
Vol 92 (10) ◽  
pp. 6958-6967
Author(s):  
Dillen Augustijn ◽  
Alina Kulakova ◽  
Sujata Mahapatra ◽  
Pernille Harris ◽  
Åsmund Rinnan
Keyword(s):  
Data Analysis ◽  
Error Detection ◽  
Chemical Denaturation ◽  
Error Detection And Correction ◽  
Analysis Error

The Application of a Unique Flow Modeling Technique to Complex Combustion Systems

1986 ◽  
Author(s):  
J. Waslo ◽  
T. Hasegawa ◽  
M. B. Hilt
Keyword(s):  
Image Processing ◽  
Fluid Flow ◽  
Light Scattering ◽  
Three Dimensional ◽  
Modeling Technique ◽  
Flow Modeling ◽  
Visualization Technique ◽  
Gas Turbine Combustor ◽  
Complex Fluid ◽  
Additional Image

This paper describes the application of a unique three-dimensional water flow modeling technique to the study of complex fluid flow patterns within an advanced gas turbine combustor. The visualization technique uses light scattering, coupled with realtime image processing, to determine flow fields. Additional image processing is used to make concentration measurements within the combustor.

Magnetic Resonance Thermometry: An Emerging Three-Dimensional Temperature Diagnostic Technique

2019 ◽  
Author(s):  
Michael J. Benson ◽  
Mattias Cooper ◽  
Bret P. Van Poppel ◽  
Christopher J. Elkins
Keyword(s):  
Temperature Field ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Data Processing ◽  
Water Channel ◽  
Three Dimensional ◽  
Diagnostic Technique ◽  
The Other ◽  
Temperature Measurements ◽  
Magnetic Resonance Thermometry

Abstract Magnetic Resonance Thermometry (MRT) is a developing diagnostic technique that leverages advanced medical technologies to accurately measure the temperature of a fluid flow within and around complex geometries. The full three-dimensional temperature field obtained by MRT can be used to analyze heat transfer characteristics and potentially investigate thermal boundary layers near arbitrarily complex surfaces. This technique requires neither optical nor physical accessibility, thereby enabling a wide range of engineering applications. This paper describes the current state of the art for MRT measurement, detailing turbulent water channel tests, materials selection, scanning parameters, data analysis of time-averaged temperature measurements, and uncertainty estimates. The purpose of this work was to evaluate and refine the MRT technique to increase the accuracy of temperature measurements and minimize the error in fully turbulent flow measurements. In the present study, a plate with a vertical cylinder extending from both of its sides was placed between two channels, and a diagonal hole was drilled through the cylinder from one side of the plate to the other. This enabled fluid from one channel to mix with the fluid in the other. This experiment studied the mixing of two fluids at different temperatures. The upstream temperatures of each fluid were measured with thermocouples. Both flows were fully turbulent, and the colder temperature channel had a Reynolds number of 11,800. Tests were run with four different fluid temperatures for calibration and to determine any temperature dependence of measurements. Three-dimensional temperature field measurements are reported and details about data processing and procedures to conduct the experiments are provided. This work resulted in several notable improvements to MRT experimental methods. The test section and water channel were designed to limit the effects of thermal expansion in the stereolithography materials used for manufacturing the complex internal flow geometry. Multiple echo scans were used to minimize the effects of magnetic field drift commonly observed in extended scanning periods in MRI systems. Data analysis techniques were used to quantify expansion effects for both hot and cold flow cases. To quantify measurement uncertainty, the standard deviation of the mean was calculated at each data point across different scan numbers and confidence intervals established using a student t-test. An improved data processing code was used to filter data resulting in increased measurement accuracy and reduced uncertainty to less than 1 °C for most of the domain. Future work will further refine the experimental techniques to improve scanning procedures, employ high conductivity ceramics and larger geometries with relevant applications, and simplify data processing methods to generate full-field flow temperature data.

Towards a Scalable ESPINA for Neuroscience Data Analysis

2011 ◽  
Author(s):  
Juan Morales ◽  
Jorge G. Pen˜a ◽  
Jaime Ferna´ndez ◽  
Angel Rodri´guez
Keyword(s):  
Image Segmentation ◽  
Data Analysis ◽  
Response Times ◽  
Three Dimensional ◽  
3D Models ◽  
Volume Management ◽  
Data Volume ◽  
Computational Processes ◽  
Microscopy Images

ESPINA is an image segmentation tool designed to analyse microscopy images in order to identify neuronal structures and to produce 3D models of these structures. This tool allows to display three-dimensional volumes using auto-stereoscopic monitors. It was initially designed for workstations, but when data volume management or its processing complexity makes unfeasible the implementation of the new tools on these computers, it is necessary to resort to computing servers that delimit response times or by means of scalable solutions and algorithmic optimizations. This paper analyses the migration of this tool from the original implementation to a scalable solution and describes the experience achieved during the development of the workstation version. The proposed alternative is a distributed version of the tool that delegate heavy-computational processes to a cluster, improving the performance of the system in a master/slave architecture.

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