scholarly journals Augmented Reality with Industrial Process Tomography: To Support Complex Data Analysis in 3D Space

2021 ◽  
Author(s):  
Adam Nowak ◽  
Yuchong Zhang ◽  
Andrzej Romanowski ◽  
Morten Fjeld
Keyword(s):  
Data Analysis ◽  
Augmented Reality ◽  
Industrial Process ◽  
Complex Data ◽  
3D Space ◽  
Process Tomography

scholarly journals Supporting Visualization Analysis in Industrial Process Tomography by Using Augmented Reality—A Case Study of an Industrial Microwave Drying System

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6515
Author(s):  
Yuchong Zhang ◽  
Adel Omrani ◽  
Rahul Yadav ◽  
Morten Fjeld
Keyword(s):  
Augmented Reality ◽  
Time Reversal ◽  
Industrial Process ◽  
Mobile App ◽  
Microwave Drying ◽  
Process Data ◽  
Significant Information ◽  
Process Tomography ◽  
Volumetric Images ◽  
Drying System

Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case—microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms.

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

scholarly journals A Comprehensive Survey on Local Differential Privacy

2020 ◽  
Vol 2020 ◽  
pp. 1-29 ◽  
Author(s):  
Xingxing Xiong ◽  
Shubo Liu ◽  
Dan Li ◽  
Zhaohui Cai ◽  
Xiaoguang Niu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Learning ◽  
Data Privacy ◽  
Statistical Estimation ◽  
Differential Privacy ◽  
Future Research ◽  
Reference Source ◽  
Complex Data ◽  
Comprehensive Survey

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

Development of an automated source port in IoT for application in industrial process tomography

2021 ◽  
Vol 9 (1A) ◽  
Author(s):  
Diego Vergaças de Sousa Carvalho ◽  
Carlos Henrique de Mesquita ◽  
Gustavo Martinez Gregianin
Keyword(s):  
Industrial Process ◽  
Process Tomography

scholarly journals Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

2017 ◽  
Author(s):  
Baekdoo Kim ◽  
Thahmina Ali ◽  
Carlos Lijeron ◽  
Enis Afgan ◽  
Konstantinos Krampis
Keyword(s):  
Data Analysis ◽  
Cloud Service ◽  
Application Programming Interface ◽  
Single Step ◽  
Easy Access ◽  
Complex Data ◽  
The Galaxy ◽  
Ngs Data Analysis ◽  
Single Data ◽  
Ngs Data

ABSTRACTBackgroundProcessing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.FindingsWe present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a “meta-script” that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is post-processed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.ConclusionsThe goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider,. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

Ultrasonic Sonic Imaging for a Two Phase System Based on Support Vector Machine Classifier

2014 ◽  
Vol 1016 ◽  
pp. 273-278
Author(s):  
Mohd Faizal Mat Desa ◽  
Muhammad Naufal Mansor ◽  
Ahmad Kadri Junoh ◽  
Amran Ahmed ◽  
Wan Suhana Wan Daud ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Lamb Waves ◽  
Industrial Process ◽  
Observation Time ◽  
Support Vector ◽  
Phase System ◽  
Ultrasonic Tomography ◽  
Two Phase ◽  
Horizontal Pipe ◽  
Process Tomography

Multiphase flow characterization is an important task for monitoring, measuring or controlling industrial processes. This can be done by means of process tomography. The use of tomographic techniques has been used within the oil industry. One of the potential applications is flow visualization and measurement in producing wells. Research on industrial process tomography consists in obtaining estimated images of a cross section of a pipe or vessel containing or carrying the substances of the process. One category of process tomography is ultrasonic tomography technique. A simple tomography can be built by mounting a number of sensors around the circumference of a horizontal pipe. This includes acquiring and processing ultrasonic signals from the transducers to obtain the information of the spatial distributions of liquid and gas in an experimental column. Analysis on the transducers’ signals will be carrying out to distinguish between the observation time and the Lamb waves. The information obtained from the observation time is useful for further development of the image reconstruction. To obtain the time easily, the time will be calculated from the starting pulse of transmitter signal until the starting peak of receiver signal. Finally Support Vector Machine (SVM) was employed to distinguish of each phase between water and gas.

The Use of Augmented Reality as a Teaching Tool

2019 ◽  
pp. 405-449
Author(s):  
Ilana de Almeida Souza Concilio ◽  
Beatriz de Almeida Pacheco ◽  
Ana Grasielle Dionísio Corrêa
Keyword(s):  
Augmented Reality ◽  
Young People ◽  
Learning Process ◽  
Teaching And Learning ◽  
Three Dimensional ◽  
Teaching Tool ◽  
Complex Data ◽  
Motivation To Work

Augmented reality (AR) has shown to be a facilitating tool and motivation to work with children, young people, and adults in times of recreation (entertainment) and also in classrooms (formal spaces of education). Augmented reality provides a different way of learning with the support of different technologies such as computers, tablets, and smartphones. It allows easy visualization and manipulation of the study object, reproducing the complex data in the form of objects and three-dimensional texts, increasing the student's ability to perceive, which is stimulated by the possibility of interaction with the interface. This chapter aims to present the different augmented reality technologies used in education and also to discuss methodologies for the use of augmented reality applications to improve the teaching and learning process.

Kernel Generative Topographic Mapping of Protein Sequences

2012 ◽  
pp. 195-208
Author(s):  
M.I. Cardenas ◽  
A. Vellido ◽  
I. Olier ◽  
X. Rovira ◽  
J. Giraldo
Keyword(s):  
Data Analysis ◽  
Kernel Method ◽  
Protein Sequences ◽  
Topographic Mapping ◽  
Complex Data ◽  
Generative Topographic Mapping ◽  
Protein Amino Acid ◽  
Genomics And Proteomics ◽  
The World ◽  
Symbolic Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

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