TIME SERIES FOR FAULT DETECTION IN AN INDUSTRIAL PILOT PLANT

2012 ◽  
Vol 26 (25) ◽  
pp. 1246003
Author(s):  
ANTONIO MORÁN ◽  
JUAN J. FUERTES ◽  
SERAFÍN ALONSO ◽  
CARLOS DEL CANTO ◽  
MANUEL DOMÍNGUEZ
Keyword(s):  
Time Series ◽  
Dimensional Space ◽  
Industrial Processes ◽  
Self Organizing Map ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Analysis Of Time Series

Forecasting the evolution of industrial processes can be useful to discover faults. Several techniques based on analysis of time series are used to forecast the evolution of certain critical variables; however, the amount of variables makes difficult the analysis. In this way, the use of dimensionality reduction techniques such as the SOM (Self-Organizing Map) allows us to work with less data to determine the evolution of the process. SOM is a data mining technique widely used for supervision and monitoring. Since the SOM is projects data from a high dimensional space into a 2-D, the SOM reduces the number of variables. Thus, time series with the variables of the low dimensional projection can be created to make easier the prediction of future values in order to detect faults.

scholarly journals A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2377 ◽  
Author(s):  
Michał Król ◽  
Magdalena Ewa Król
Keyword(s):  
Eye Movement ◽  
Spatial Information ◽  
Dimensional Space ◽  
Pairwise Comparison ◽  
Experimental Manipulation ◽  
Movement Data ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Human Faces

Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features.

scholarly journals Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

2021 ◽  
Author(s):  
Lars Kegel ◽  
Claudio Hartmann ◽  
Maik Thiele ◽  
Wolfgang Lehner
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Dimensional Space ◽  
Symbolic Aggregate Approximation ◽  
Current State ◽  
Optimal Representation ◽  
Symbolic Approximation ◽  
Low Dimensional ◽  
Deterministic Behavior ◽  
Support Time

AbstractProcessing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

scholarly journals Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

2020 ◽  
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Ali Salari ◽  
Alan C. Evans ◽  
Tristan Glatard
Keyword(s):  
Monte Carlo ◽  
Data Augmentation ◽  
Learning Models ◽  
Reduction Techniques ◽  
Numerical Noise ◽  
Numerical Instabilities ◽  
Dimensionality Reduction Techniques ◽  
Improved Performance ◽  
Low Dimensional ◽  
Machine Learning Models

AbstractMachine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.

scholarly journals Using Affiliation Rules-based Data Mining Technique in Referral System

2020 ◽  
pp. 3084-3092
Author(s):  
Tola John Odule ◽  
Ademola Olusola Adesina ◽  
Adebisi Khadijat-Kubrat Abdullah ◽  
Peter Ibikunle Ogunyinka
Keyword(s):  
Architectural Design ◽  
Dimensional Space ◽  
Motion Pictures ◽  
General Purpose ◽  
Two Dimensional ◽  
Data Mining Technique ◽  
Domain Specific ◽  
Mining Technique ◽  
Business Applications ◽  
Internet Business

Referral techniques are normally employed in internet business applications. Existing frameworks prescribe things to a particular client according to client inclinations and former high evaluations. Quite a number of methods, such as cooperative filtering and content-based methodologies, dominate the architectural design of referral frameworks. Many referral schemes are domain-specific and cannot be deployed in a general-purpose setting. This study proposes a two-dimensional (User × Item)-space multimode referral scheme, having an enormous client base but few articles on offer. Additionally, the design of the referral scheme is anchored on the  and  articles, as expressed by a particular client, and is a combination of affiliation rules mining and the content-based method. The experiments used the dataset of MovieLens, consisting of 100,000 motion pictures appraisals on a size of 1-5, from 943 clients on 1,682 motion pictures. It utilised a five-overlap cross appraisal on a (User × Item)-rating matrix with 12 articles evaluated by a minimum of 320 clients. A total of 16 rules were generated for both  and  articles, at 35% minimum support and 80% confidence for the  articles and 50% similitude for the . Experimental results showed that the anticipated appraisals in denary give a better rating than other measures of exactness. In conclusion, the proposed algorithm works well and fits on two dimensional -space with articles that are significantly fewer than users, thus making it applicable and effective in a variety of uses and scenarios as a general-purpose utility.

AN EFFECTIVE DATA MINING TECHNIQUE FOR RECONSTRUCTING GENE REGULATORY NETWORKS FROM TIME SERIES EXPRESSION DATA

2007 ◽  
Vol 05 (03) ◽  
pp. 651-668 ◽  
Author(s):  
PATRICK C. H. MA ◽  
KEITH C. C. CHAN
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Statistical Verification ◽  
Series Expression ◽  
Gene Regulatory

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.

scholarly journals Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Shingchern D. You ◽  
Ming-Jen Hung
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Temporal Data ◽  
Reduction Techniques ◽  
Temporal Features ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Motion Picture Expert Group

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

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