scholarly journals Review of smoothing methods for enhancement of noisy data from heavy-duty LHD mining machines

2018 ◽  
Vol 29 ◽  
pp. 00011 ◽  
Author(s):  
Jacek Wodecki ◽  
Anna Michalak ◽  
Paweł Stefaniak
Keyword(s):  
Data Analysis ◽  
Noisy Data ◽  
Point Of View ◽  
Heavy Duty ◽  
Data Smoothing ◽  
Industrial Data ◽  
Smoothing Methods ◽  
Data Segmentation ◽  
Deterministic Components ◽  
Segmentation Task

Appropriate analysis of data measured on heavy-duty mining machines is essential for processes monitoring, management and optimization. Some particular classes of machines, for example LHD (load-haul-dump) machines, hauling trucks, drilling/bolting machines etc. are characterized with cyclicity of operations. In those cases, identification of cycles and their segments or in other words – simply data segmentation is a key to evaluate their performance, which may be very useful from the management point of view, for example leading to introducing optimization to the process. However, in many cases such raw signals are contaminated with various artifacts, and in general are expected to be very noisy, which makes the segmentation task very difficult or even impossible. To deal with that problem, there is a need for efficient smoothing methods that will allow to retain informative trends in the signals while disregarding noises and other undesired non-deterministic components. In this paper authors present a review of various approaches to diagnostic data smoothing. Described methods can be used in a fast and efficient way, effectively cleaning the signals while preserving informative deterministic behaviour, that is a crucial to precise segmentation and other approaches to industrial data analysis.

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

scholarly journals How Digital Inclusion Increase Opportunities for Young People: Case of NEETs from Bulgaria, Romania and Turkey

2021 ◽  
Vol 13 (14) ◽  
pp. 7894
Author(s):  
Gabriela Neagu ◽  
Muhammet Berigel ◽  
Vladislava Lendzhova
Keyword(s):  
Data Analysis ◽  
Rural Areas ◽  
Secondary Data ◽  
Point Of View ◽  
Multidimensional Analysis ◽  
Theoretical Point ◽  
Digital Inclusion ◽  
Virtual Mobility ◽  
Alternative Research ◽  
High Share

This paper examines the perspectives of rural NEETs in the information society. Our analysis focuses on the situation of three European countries—Bulgaria, Romania, and Turkey—characterized by a high share of rural areas and a population of NEETs. From a methodological point of view, we use alternative research methods (secondary data analysis) with statistical methods (simple linear regression). From a theoretical point of view, we will opt for a multidimensional analysis perspective: the theory of digital divide, digital inclusion, virtual mobility, etc. Through data analysis, we expect to obtain a more complete and detailed picture of the ICT situation in rural areas (level of digital skills, level of digital inclusion) to demonstrate the importance of ICT in optimizing virtual mobility for the living conditions of the population, especially the NEET population.

scholarly journals Approximation of noisy data using multivariate splines and finite element methods

2021 ◽  
Vol 15 ◽  
pp. 174830262110084
Author(s):  
Bishnu P Lamichhane ◽  
Elizabeth Harris ◽  
Quoc Thong Le Gia
Keyword(s):  
Finite Element ◽  
Differential Operators ◽  
Impulsive Noise ◽  
Noisy Data ◽  
Multivariate Splines ◽  
The Finite Element Method ◽  
Data Smoothing ◽  
Multivariate Spline ◽  
Partial Differential Operators ◽  
Mixture Of Gaussian

We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differential operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.

Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

2021 ◽  
Author(s):  
Nikolai West ◽  
Jonas Gries ◽  
Carina Brockmeier ◽  
Jens C. Gobel ◽  
Jochen Deuse
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Quality Criteria ◽  
Industrial Data ◽  
Analysis Quality ◽  
Integrated Data Analysis

Nonlinear Time-Series Prediction with Missing and Noisy Data

1998 ◽  
Vol 10 (3) ◽  
pp. 731-747 ◽  
Author(s):  
Volker Tresp ◽  
Reimar Hofmann
Keyword(s):  
Time Series ◽  
Stochastic Simulation ◽  
Time Series Prediction ◽  
Noisy Data ◽  
Nonlinear Time Series ◽  
Point Of View ◽  
Data Set ◽  
Sunspot Data ◽  
Error Bars ◽  
Probabilistic Point

We derive solutions for the problem of missing and noisy data in nonlinear time-series prediction from a probabilistic point of view. We discuss different approximations to the solutions—in particular, approximations that require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly used heuristics can lead to suboptimal solutions. We show how error bars for the predictions can be derived and how our results can be applied to K-step prediction. We verify our solutions using two chaotic time series and the sunspot data set. In particular, we show that for K-step prediction, stochastic simulation is superior to simply iterating the predictor.

scholarly journals Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

2016 ◽  
Author(s):  
Daniele Oxoli ◽  
Mayra A Zurbarán ◽  
Stanly Shaji ◽  
Arun K Muthusamy
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Point Of View ◽  
Geospatial Data ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Software Environment ◽  
Hotspot Analysis ◽  
Computational Procedures ◽  
Gis Software

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

Data Analysis and Interpretation in Metabolomics

2013 ◽  
pp. 1494-1521
Author(s):  
Jose M. Garcia-Manteiga
Keyword(s):  
Mass Spectrometry ◽  
Nuclear Magnetic Resonance ◽  
Data Analysis ◽  
Systems Biology ◽  
Magnetic Resonance ◽  
Biological System ◽  
Analytical Techniques ◽  
Point Of View ◽  
The Other ◽  
Top Down

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.

Data Analysis and Interpretation in Metabolomics

2011 ◽  
pp. 29-56
Author(s):  
Jose M. Garcia-Manteiga
Keyword(s):  
Mass Spectrometry ◽  
Nuclear Magnetic Resonance ◽  
Data Analysis ◽  
Systems Biology ◽  
Magnetic Resonance ◽  
Biological System ◽  
Analytical Techniques ◽  
Point Of View ◽  
The Other ◽  
Top Down

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.

Cyberbullying Perception and Experience Among the University Students in Bangladesh

2022 ◽  
pp. 248-269
Author(s):  
Aftab Hossain ◽  
Juliana Abdul Wahab ◽  
Md. Rashedul Islam ◽  
Md. Saidur Rahman Khan ◽  
Arif Mahmud
Keyword(s):  
Data Analysis ◽  
University Students ◽  
Research Work ◽  
Point Of View ◽  
Ecological Perspective ◽  
Group Discussions ◽  
Social Ecological ◽  
Global North ◽  
The University ◽  
Technology Abuse

This study focuses on the understanding of the conceptualization of the global phenomenon of cyberbullying among university students in Bangladesh. The emerging themes of this study investigate and explore the concepts of university students using their social-ecological perspective. The study's aim is to learn about the antecedents, contexts, and conditions that influence the phenomenon, and the consequences of the victims through focus group discussions (FGD). Using the thematic coding data analysis, the study findings will contribute to having an in-depth idea about the perceptions of university students. This timely needed research work will provide the South Asian point of view where a handful study was undertaken in comparison to the Global North. The novelty of this study consists to explore young people's technology abuse, which can lead to cyberbullying, in addition to finding methods to deal with cyberbullying issues if they arise. This study is intended to assist all parties including young people, parents, teachers, and other social-ecological stakeholders.

Sign in / Sign up

Export Citation Format

Share Document