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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) and International Conference on Information Visualization Theory and Applications (IVAPP)

作者： Kaur, Gurminder Karki, Bijaya B. Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

ISBN: (纸本)9789897583063

Visualization of multivariate data using parallel coordinates plot (PCP) becomes overwhelming as the number of dimensions/variables increases beyond one dozen or so. Here we propose bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering into the focus and context regions whose sizes can be adjusted to optimize the use of the available space. The focus area maps a few selected dimensions of interest, referred to as priority axes, at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way so as to retain useful information and provide the data continuity. The focus display can be further enhanced with various options, such as axes overlays, scatterplot, and nested juxtaposed PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports multi-level stacked view, each PCP level mapping a subset of the context axes. With flexible interactivity, BPCP can manage the priority axes and data rendering with respect to the corresponding dimensions to support exploratory visualization while providing useful context on the same visualization display.

关键词： parallel coordinates plot Multivariate Data High Dimensions Information Visualization Focus plus Context

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Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

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SN APPLIED SCIENCES 2021年第1期3卷 129页

作者： Hizarci, Berkan Umutlu, Rafet Can Kiral, Zeki Ozturk, Hasan Dokuz Eylul Univ Dept Mechatron Engn Grad Sch Nat & Appl Sci Izmir Turkey Dokuz Eylul Univ Dept Mech Engn Izmir Turkey

This study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincare plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

关键词： Condition monitoring Fault severity detection Poincare plot parallel coordinates plot Worm gearbox

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Impact of ambient air pollution exposure during pregnancy on adverse birth outcomes: generalized structural equation modeling approach

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BMC PUBLIC HEALTH 2023年第1期23卷 45-45页

作者： Mitku, Aweke A. Zewotir, Temesgen North, Delia Jeena, Prakash Asharam, Kareshma Muttoo, Sheena Tularam, Hasheel Naidoo, Rajen N. Univ KwaZulu Natal Coll Agr Engn & Sci Sch Math Stat & Comp Sci Durban South Africa Univ KwaZulu Natal Coll Hlth Sci Sch Nursing & Publ Hlth Discipline Occupat & Environm Hlth Durban South Africa Bahir Dar Univ Dept Stat Bahir Dar Ethiopia Univ KwaZulu Natal Coll Hlth Sci Sch Clin Med Discipline Paediat & Child Hlth Durban South Africa

Background Air pollution and several prenatal factors, such as socio-demographic, behavioural, physical activity and clinical factors influence adverse birth outcomes. The study aimed to investigate the impact of ambient air pollution exposure during pregnancy adjusting prenatal risk factors on adverse birth outcomes among pregnant women in MACE birth cohort. Methods Data for the study was obtained from the Mother and Child in the Environment (MACE) birth cohort study in Durban, South Africa from 2013 to 2017. Land use regression models were used to determine household level prenatal exposure to PM2.5, SO2 and NOx. Six hundred and fifty-six births of pregnant females were selected from public sector antenatal clinics in low socio-economic neighbourhoods. We employed a Generalised Structural Equation Model with a complementary log-log-link specification. Results After adjustment for potential prenatal factors, the results indicated that exposure to PM2.5 was found to have both significant direct and indirect effects on the risk of all adverse birth outcomes. Similarly, an increased level of maternal exposure to SO2 during pregnancy was associated with an increased probability of being small for gestational age. Moreover, preterm birth act a mediating role in the relationship of exposure to PM2.5, and SO2 with low birthweight and SGA. Conclusions Prenatal exposure to PM2.5 and SO2 pollution adversely affected birth outcomes after controlling for other prenatal risk factors. This suggests that local government officials have a responsibility for better control of air pollution and health care providers need to advise pregnant females about the risks of air pollution during pregnancy.

关键词： Birth cohort Complementary log-log-link Indirect effects parallel coordinates plot Prenatal exposure

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Enhancing parallel coordinates Visualization Using Genetic Algorithm with Smart Mutation

Enhancing Parallel Coordinates Visualization Using Genetic A...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Aldwib, Khiria Rahnamayan, Shahryar Ibrahim, Amin Ontario Tech Univ Nat Inspired Computat Intelligence NICI Lab Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (纸本)9781728185262

Visualization techniques have received a lot of attention regarding their potential to interpret and analyze the *** of the marked visualization methods is the parallel coordinates plot (PCP) utilized to high-dimensional datasets (more than three dimensions). Due to that, in visualizing large-scale datasets, the method suffers from high clutters produced from numerous intersection lines between neighboring axes, numbers of researchers have conducted techniques to boost PCPs. For instance, reducing the number of crossing lines by utilizing the re-ordering the neighboring axes in the PCP technique is a useful procedure to reduce the clutter. Motivated by this goal, the acquisition of the optimal coordinate's order can be classified as a combinatorial optimization problem. However, in high-dimensional datasets, the optimization algorithm may face difficulty to deal with this issue. In this paper, we propose a smart mutation operator to enhance the performance of Genetic Algorithm (GA) in finding the optimal order of PCP based on diminishing the numerous intersection lines. However, any other user-desired metric can be utilized as an objective function. To assess the introduced method, we conducted a Monte Carlo simulation and several experiments to find an optimal coordinates' order in PCP to visualize the datasets with various numbers of samples and dimensions. In the experimental results, utilizing the smart mutation represents an improvement in PCP visualization in terms of reducing the intersection lines between the neighboring coordinates compared to the original GA.

关键词： Visualization parallel coordinates plot Genetic Algorithm for Combinatorial Problems Monte Carlo

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Structure revealing techniques based on parallel coordinates plot

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VISUAL COMPUTER 2012年第6-8期28卷 541-551页

作者： Zhao, Xin Kaufman, Arie SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

parallel coordinates plot (PCP) is an excellent tool for multivariate visualization and analysis, but it may fail to reveal inherent structures for complex and large datasets. Therefore, polyline clustering and coordinate sorting are inevitable for the accurate data exploration and analysis. In this paper, we propose a suite of novel clustering and dimension sorting techniques in PCP, to reveal and highlight hidden trend and correlation information of polylines. Spectrum theory is first introduced to specifically design clustering and sorting techniques for a clear view of clusters in PCP. We also provide an efficient correlation based sorting technique to optimize the ordering of coordinates to reveal correlated relations, and show how our view-range metrics, generated based on the aggregation constraints, can be used to make a clear view for easy data perception and analysis. Experimental results generated using our framework visually represent meaningful structures to guide the user, and improve the efficiency of the analysis, especially for the complex and noisy data.

关键词： parallel coordinates plot Dimension sorting optimization Visual representation

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Visualizations for Communicating Intelligent Agent Generated Courses of Action 11th

Visualizations for Communicating Intelligent Agent Generated...

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11th International Conference on Virtual, Augmented and Mixed Reality (VAMR) Held as Part of the 21st International Conference on Human-Computer Interaction (HCII)

作者： Bartik, Jessica Ruff, Heath Calhoun, Gloria Behymer, Kyle Goodman, Tyler Frost, Elizabeth US Air Force Res Lab 711 HPW RHCI Dayton OH 45433 USA Infoscitex Dayton OH USA

ISBN: (纸本)9783030215651;9783030215644

Future human-autonomy teams will benefit from intelligent agents that can quickly deliberate across multiple parameters to generate candidate courses of action (COAs). This experiment evaluated the design of an interface to communicate agent-generated COAs to a human operator. Twelve participants completed 14 trials, each consisting of a series of tasks that required participants' selection of the best COA in terms of quality, speed, fuel, and detectability parameters. Trial score and speed of participants' selection were measured as a function of COA visualization (1, 4, or 8 COAs) as well as the type of agent. Supplemental trials in which participants could choose which visualization to employ for COA selection were also conducted. The data showed that presenting multiple COAs were better than a single COA. Differences between the 4 and 8 COA visualizations were not quite as definitive: selections were significantly faster with 4 COAs than 8, but participants' preferences were divided based upon agent comprehensiveness and individual strategy differences. The results also showed that the agent's reasoning process should be communicated more precisely besides just what parameters are being considered in generating COAs.

关键词： Human-autonomy teaming Intelligent agent parallel coordinates plot Plan comparison Visualization Human autonomy interaction

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Proposing a Pareto-VIKOR Ranking Method for Enhancing parallel coordinates Visualization 14

Proposing a Pareto-VIKOR Ranking Method for Enhancing Parall...

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14th International Conference on Computer Science and Education (ICCSE)

作者： Aldwib, Khiria Bidgoli, Azam Asilian Rahnamayan, Shahryar Ibrahim, Amin Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781728118468

Data visualization is an essential step in data science to get better interpretation to analyse data. The parallel coordinates plot (PCP) is a well-known method to visualize high-dimensional (D > 3) data without dimension reduction. In large-scale datasest, PCP may fail because of many clutters and crossing lines in the plot. The order of coordinates is one of the parameters in PCP which can affect on the performance of this method. Finding the best order can be considered as a multi-criteria comparison task based on different metrics such as minimizing the number of crossing lines between adjacent coordinates and the maximizing the pairwise correlation coefcient values. In order to improve the visualization of data using PCP, this paper presents a multi-metric Pareto-VIKOR ranking (PVRPCP), a new method which determines the best order of coordinates based on optimizing two or more metrics. The method consists of evaluating all possible coordinates permutations based on evaluation metrics and applying non-dominated sorting algorithm (NDS) to obtain the Pareto-front ranks (PF). The solutions on each Pareto front are then ranked by VIKOR, a multi-criteria decision making measure. In order to evaluate the effectiveness of the the proposed method in data visualization, we also designed several multi-dimensional benchmarks to represent the effect of ordering in PCP. In addition to author-created benchmarks, several multi-objective function benchmarks and real-world datasets are utilized to evaluate the proposed method. The experimental results show that the PVRPCP offers improved PCP visualization compared to the original order in terms of both utilized metrics.

关键词： Visualization parallel coordinates plot Pareto-front Non-dominated sorting Multi-criteria decision making VIKOR

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Accelerated Production Ramp-Up Utilising Clustering and Visualisation of Process Chain Interrelationships 9

Accelerated Production Ramp-Up Utilising Clustering and Visu...

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9th International Electric Drives Production Conference (EDPC)

作者： Meiners, Moritz Mayr, Andreas Lechler, Tobias Franke, Joerg Friedrich Alexander Univ Erlangen Nuremberg FAU Inst Factory Automat & Prod Syst FAPS Nurnberg Germany

ISBN: (纸本)9781728143194

With the increasing demand for electric cars, automobile manufacturers and suppliers are ramping up more and more plants for the production of electric traction motors. In the development of new plants, Industry 4.0 aspects are usually already considered, resulting in a large number of integrated sensors and thus a relatively high data availability. These process data in turn contain implicit knowledge that can be used to accelerate the ramp-up phase and to optimise the later series production. Especially in times characterised by frequent process adaptations and continuous learning, increasing knowledge can bring competitive advantages. This particularly applies to the automotive production of electric traction motors, which is currently confronted with ever changing product concepts, low production experience and uncertain quantities. Therefore, this paper presents a method for a simple, holistic visualisation of process chain interrelationships, which can already be used during series ramp-up and the associated limited data amount. By using suitable visualisation and clustering methods, process knowledge can be increased, ramp-up times be shortened and production be improved in the long run.

关键词： visualisation clustering visual analytics machine learning parallel coordinates plot process chain ramp-up

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DSPCP: A Data Scalable Approach for Identifying Relationships in parallel coordinates

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018年第3期24卷 1301-1315页

作者： Hoa Nguyen Rosen, Paul Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA

parallel coordinates plots (PCPs) are a well-studied technique for exploring multi-attribute datasets. In many situations, users find them a flexible method to analyze and interact with data. Unfortunately, using PCPs becomes challenging as the number of data items grows large or multiple trends within the data mix in the visualization. The resulting overdraw can obscure important features. A number of modifications to PCPs have been proposed, including using color, opacity, smooth curves, frequency, density, and animation to mitigate this problem. However, these modified PCPs tend to have their own limitations in the kinds of relationships they emphasize. We propose a new data scalable design for representing and exploring data relationships in PCPs. The approach exploits the point/ line duality property of PCPs and a local linear assumption of data to extract and represent relationship summarizations. This approach simultaneously shows relationships in the data and the consistency of those relationships. Our approach supports various visualization tasks, including mixed linear and nonlinear pattern identification, noise detection, and outlier detection, all in large data. We demonstrate these tasks on multiple synthetic and real-world datasets.

关键词： Correlation parallel coordinates plot large data visualization

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RBPCP:Visualization on Multi-set High-dimensional Data 2

RBPCP:Visualization on Multi-set High-dimensional Data

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Weiqiang Xie Yingmei Wei Hao Ma Xiaolei Du College of Information System and Management National University of Defense Technology

ISBN: (纸本)9781509036202

Due to the prevalence of Multi-set high-dimensional data in the era of big data,visualization and visual analysis of multiple sets of high-dimensional data are critical to the discovery of data *** coordinates plot（PCP） is mainly used for visual analysis of different attributes of the same *** classic method for visualization on multi-set high-dimensional data is to use a conventional PCP with superimposition design or multiple PCPs with juxtaposition ***,these methods can’t effectively detect *** propose a median-based rearrangement algorithm for bundled parallel coordinates *** on the algorithm,we present Rearranged Bundled parallel coordinates plot（RBPCP） which effectively improves the visual analysis capability of multi-set high-dimensional data and satisfy the aesthetic *** addition,the proposed RBPCP also utilize brushing,mode switch and dynamic axis scaling for the interactive analysis of set relations and hidden patterns.

关键词： parallel coordinates plot rearrangement algorithm high-dimensional data visualization

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