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A data-driven multidimensional visualization technique for process fault detection and diagnosis

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 154卷 122-136页

作者： Gajjar, Shriram Palazoglu, Ahmet Univ Calif Davis Dept Chem Engn & Mat Sci Davis CA 95616 USA

A multidimensional visualization technique is described for fault detection and diagnosis of a multivariate process by principal component analysis (PCA) of historical data. The technique uses a parallel coordinate system to visualize data that allows for monitoring of abnormal process events that lead to process faults, enabling the visualization of multiple principal components effectively and facilitating the study of how each principal component varies with respect to time. The principal component and residual space control limits are established for fault detection and the Random Forests machine learning tool is adopted for fault diagnosis. The key features of the methodology are demonstrated through a study of the benchmark Tennessee Eastman process. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Fault detection Fault diagnosis Random Forests Principal component analysis (PCA) Multivariate statistical process monitoring multidimensional visualization Tennessee Eastman process

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The perspective-based observational tunnels method: A new method of multidimensional data visualization

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INFORMATION visualization 2017年第4期16卷 346-360页

作者： Jamroz, Dariusz AGH Univ Sci & Technol Dept Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

The article describes a new unique method of multidimensional data visualization. It has been developed as modified observational tunnels method, which was previously known and used many times. The modification consists in supplementing the observational tunnels method used for visualization of multidimensional data with the concept of perspective. In this way, the orientation and navigation in multidimensional space are largely facilitated. The differences in effects of observational tunnels method and perspective-based observational tunnels method have been presented. The effectiveness of the new visualization method has been compared with selected four well-known methods of multidimensional data visualization: parallel coordinates, orthogonal projection, principal component analysis, and multidimensional scaling. The research revealed that the perspective-based observational tunnels method sometimes makes it possible to obtain information about significant features of analyzed data even when other methods selected for comparative studies are not able to show it. This article includes a presentation of the views of 5-dimensional data obtained from the print recognition process, which allowed the author to state that the features chosen for the development of spatial features are, in this case, sufficient for the correct recognition process. The previously published ranking presenting seven different methods of multidimensional data visualization was supplemented with the perspective-based observational tunnels method. This ranking was conducted using 7-dimensional data describing different types of coal. Thus, it was shown that, in this case, the presented method constitutes the efficient tool among other qualitative visualization analysis methods.

关键词： multidimensional visualization observational tunnels method principal component analysis multidimensional scaling parallel coordinates multidimensional perspective pattern recognition

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multidimensional virtual reality-MVR method: a new method of visualization of multidimensional worlds

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VISUAL COMPUTER 2020年第4期36卷 733-742页

作者： Jamroz, Dariusz AGH Univ Sci & Technol Dept Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

The paper presents a new, original method of multidimensional worlds' visualization. It allows to present views of any dimension objects out of which it is possible to construct even the most complicated multidimensional virtual world on a computer screen. Due to this, it is possible to observe multidimensional worlds modeled in this way, analyze mutual relations between multidimensional objects, move between them and, most importantly, verify whether human brain is able to adapt to the perception of more than three-dimensional space. This paper presents example interior views of four-dimensional and five-dimensional labyrinths. It also presents results of the research performed on 97 IT students at the AGH University of Science and Technology. Students in total made 357 attempts to leave virtual four-dimensional and five-dimensional labyrinths, each having three difficulty levels. The method presented in this paper is sufficiently general to allow observation of objects in an n-dimensional space for any n >= 3. Simultaneously, it is the natural extension of our reality perception because using this method for n=3 we obtain views known to us from our human experience from the three-dimensional space.

关键词： multidimensional virtual reality MVR method multidimensional world multidimensional labyrinth multidimensional visualization Perception

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Improving visualization Design for Effective Multi-Objective Decision Making

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第10期28卷 3405-3416页

作者： Dy, Bianchi Ibrahim, Nazim Poorthuis, Ate Joyce, Sam Singapore Univ Technol & Design SUTD Singapore 487372 Singapore Katholieke Univ Leuven B-3000 Leuven Belgium

Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex 'solution spaces', but there is limited research on its ability to support multi-objective decisions. In this article, we explore the effects of chart complexity and data volume on decision quality in multi-objective scenarios with complex trade-offs. We look at the impact of four common multidimensional chart types (scatter plot matrices, parallel coordinates plots, heat maps, radar charts), the number of options and dimensions and participant chart usage experience on decision time and accuracy when selecting the 'optimal option'. As objectively evaluating the quality of multi-objective decisions and the trade-offs involved is challenging, we employ rank- and score-based accuracy metrics. While heat maps demonstrate a time advantage, our findings show no strong performance benefit for one chart type over another for accuracy. We find mixed evidence for the impact of chart complexity on performance, with our results suggesting the existence of a 'ceiling' in the number of dimensions considered by participants. This points to a potential limit to data complexity that is useful for decision making. Lastly, participants who use charts frequently performed better, suggesting that users can potentially be trained to effectively use complex visualizations in their decision-making.

关键词： Data visualization Task analysis Decision making Tools Radar Heating systems Radar scattering Multi-objective decision-making multidimensional visualization parallel coordinates scatter plot matrix heat map radar chart evaluation

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Feature selection based on star coordinates plots associated with eigenvalue problems

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VISUAL COMPUTER 2021年第2期37卷 203-216页

作者： Sanchez, Alberto Raya, Laura Mohedano-Munoz, Miguel A. Rubio-Sanchez, Manuel Univ Rey Juan Carlos Madrid Spain Res Ctr Computat Simulat Madrid Spain U Tad Madrid Spain

Feature selection consists of choosing a smaller number of variables to work with when analyzing high-dimensional data sets. Recently, several visualization tools, techniques, and feature relevance measures have been developed in order to help users carry out the feature selection. Some of these approaches are based on radial axes methods, where analysts perform backward feature elimination by discarding features that have a low impact on the visualizations. Similarly, in this paper, we propose a new feature relevance measure for star coordinates plots associated with the class of linear dimensionality reduction mappings defined through the solutions of eigenvalue problems, such as linear discriminant analysis or principal component analysis. We show that the approach leads to enhanced feature subsets for class separation or variance maximization in the plots for numerous data sets of the UCI repository. Lastly, in practice, the tool allows analysts to decide which features to discard by examining their relevance and by taking into account previous domain knowledge.

关键词： Feature selection Eigenvalue problems Linear projections multidimensional visualization Star coordinates Principal component analysis Linear discriminant analysis

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Visual dimension analysis based on dimension subdivision (vol 24, pg 117, 2021)

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JOURNAL OF visualization 2021年第5期24卷 1113-1113页

作者： Zhang, Yi Yu, Chenxi Wang, Ruoqi Liu, Xunhan Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

visualization of multidimensional data has always been a research hotspot. Dimensional analysis is an efficient way to solve multidimensional problems. The current dimensional analysis methods mostly consider that all dimension correlations are at the same granularity, but actually the correlation between dimensions may be multi-scale. Multi-scale dimensions can also reflect the multi-scale data association mode, which is of certain value for analyzing the hidden information of multidimensional data. In this paper, we propose a method of dimension subdivision to resolve the multi-scale correlations between dimensions. To explore the multi-scale complex relationship between dimensions, we subdivide the original dimensions into finer sub-dimensions and build a graph-based data structure of the correlations to partition strongly relevant and irrelevant dimensions. We also proposed D-div, a visual dimension analysis system to support our method. In D-div, we provide visualization and interaction techniques to explore subdivided dimensions. Via case studies with two datasets, we demonstrate the effectiveness of our method of dimension subdivision.

关键词： multidimensional data Dimensional analysis Correlation analysis multidimensional visualization

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multidimensional Unified Processing Systems for Industrial Machines and its Controlling Mechanism

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 9179-9195页

作者： Nath, Sayantan Mala, C. Natl Inst Technol Tiruchirappalli Trichy India

In this paper, a continuous and unified process for controlling various industrial machineries in different sites and factories is described in a single and linear connected fashion. The coined technique is fundamentally designed on the multi-dimensional structural model of command interface for machines to be controlled in a single operation center in all industrial units collectively. The basic controlling method of the continuous and integrated command process for greater pieces of machinery with multiple units, campuses, and different sections of factories under the same regulating company is very much difficult to manage and operate effectively. The regular controlling system of machinery is mainly designed with a closed operating control built locally. But with the help of the proposed technique, all the machines could be controlled under a single lining framework from any distance. It could be observed that the proposed method has confined the structural outcomes of the controlling process of the machinery in industrial machinery effectively.

关键词： multidimensional visualization Intelligent controlling Unified processing Industrial mechanism Inter-connectivity

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Application of multidimensional Data visualization in Creation of Pattern Recognition Systems 3

Application of Multidimensional Data Visualization in Creati...

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3rd International Conference on Man-Machine Interactions (ICMMI)

作者： Jamroz, Dariusz AGH Univ Sci & Technol PL-30059 Krakow Poland

ISBN: (纸本)9783319023090

The paper presents the application of multidimensional data visualization to obtain the views of 5-dimensional space of features created by recognition of printed characters. On the basis of these views it was stated that the features chosen to construction of features space are sufficient to correct recognition process. This is the significant help by constructing the recognition systems because the correct selection of objects properties on the basis of which the recognition should occur is one of the hardest stages.

关键词： multidimensional visualization pattern recognition

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The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

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NEURAL COMPUTING & APPLICATIONS 2020年第18期32卷 15221-15235页

作者： Jamroz, Dariusz AGH Univ Sci & Technol Dept Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

The qualitative analysis of multidimensional data using their visualization allows to observe some characteristics of data in a way which is the most natural for a human, through the sense of sight. Thanks to such an approach, some characteristics of the analyzed data are simply visible. This allows to avoid using often complex algorithms allowing to examine specific data properties. visualization of multidimensional data consists in using the representation transforming a multidimensional space into a two-dimensional space representing a computer screen. The important information which can be obtained in this way is the possibility to separate points belonging to different classes in the multidimensional space. Such information can be directly obtained if images of points belonging to different classes occupy other areas of the picture presenting these data. The paper presents the effectiveness of the qualitative analysis of multidimensional data conducted in this way through their visualization with the application of Kohonen maps and autoassociative neural networks. The obtained results were compared with results obtained using the perspective-based observational tunnels method, PCA, multidimensional scaling and relevance maps. Effectiveness tests of the above methods were performed using real seven-dimensional data describing coal samples in terms of their susceptibility to fluidal gasification. The methods' effectiveness was compared using the criterion for the readability of the multidimensional visualization results, introduced in earlier papers.

关键词： multidimensional visualization multidimensional data analysis Data mining Self-organized neural network Autoassociative neural network Kohonen maps

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The examination of the effect of the criterion for neural network's learning on the effectiveness of the qualitative analysis of multidimensional data

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第8期62卷 3263-3289页

作者： Jamroz, Dariusz AGH Univ Sci & Technol Dept Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

A variety of multidimensional visualization methods are applied for the qualitative analysis of multidimensional data. One of the multidimensional data visualization methods is a method using autoassociative neural networks. In order to perform visualizations of n-dimensional data, such a network has n inputs, n outputs and one of the interlayers consisting of two outputs whose values represent coordinates of the analyzed sample's image on the screen. Such a criterion for the network's learning consists in that the same value as the one at the ith input appears at each ith output. If the network is trained in this way, the whole information from n inputs was compressed to two outputs of the interlayer and then decompressed to n network outputs. The paper shows the application of different learning criteria can be more beneficial from the point of view of the results' readability. Overall analysis was conducted on seven-dimensional real data representing three coal classes, five-dimensional data representing printed characters, 216-dimensional data representing hand-written digits and, additionally, in order to illustrate additional explanations using artificially generated seven-dimensional data. Readability of results of the qualitative analysis of these data was compared using the multidimensional visualization utilizing neural networks for different learning criteria. Also, the obtained results of applying all analyzed criteria on 20 randomly selected sets of multidimensional data obtained from one of the publicly available repositories are presented.

关键词： multidimensional data analysis Data mining multidimensional visualization Self-organized neural network Autoassociative neural network

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