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Interest in renewable, green, and sustainable energy has risen sharply in recent years. The use of marine turbines to extract kinetic energy from the tidal current is gaining popularity. CFD modeling is carried out to investigate ...
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Interest in renewable, green, and sustainable energy has risen sharply in recent years. The use of marine turbines to extract kinetic energy from the tidal current is gaining popularity. CFD modeling is carried out to investigate the surrounding flow behavior and thus develop effective marine turbine systems. However, visualizing the simulation results remains a challenging task for engineers. In this paper, we develop, explore and present customized visualization techniques in order to help engineers gain a fast overview and intuitive insight into the flow past the marine turbine. The system exploits multiple-coordinated information-assisted views of the CFD simulation data. Our application consists of a tabular histogram, velocity histogram, parallel coordinate plot, streamline plot and spatial views. Information-based streamline seeding is used to investigate the behavior of the flow deemed interesting to the engineer. Specialized, application-specific information based on swirling flow is derived and visualized in order to evaluate turbine blade design. To demonstrate the usage of our system, a selection of specialized case scenarios designed to answer the core questions brought out by engineers is described. We also report feedback on our system from CFD experts researching marine turbine simulations.
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Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data ...
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Visualization, a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources is increasing at a rapid pace. After a decades-equivalent long search process, we present a survey of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already many ranging from literature collections to collections of practitioner resources. Based on this, we develop a classification of visualization resource collections with a focus on the resource type, e.g. literature-based, web-based, developer focused and special topics. The result is an overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, developers, and researchers wishing to create their own advanced or novel visual designs. This paper is a response to students and others who frequently ask for visualization resources available to them.
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Abstract We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single...
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Abstract We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.
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Functional connectivity, a flourishing new area of research in human neuroscience, carries a substantial challenge for visualization: while the end points of connectivity are known, the precise path between them is not. Although a...
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Functional connectivity, a flourishing new area of research in human neuroscience, carries a substantial challenge for visualization: while the end points of connectivity are known, the precise path between them is not. Although a large body of work already exists on the visualization of anatomical connectivity, the functional counterpart lacks similar development. To optimize the clarity of whole-brain and complex connectivity patterns in three-dimensional brain space, we develop mean-shift edge bundling, which reveals the multitude of connections as derived from correlations in the brain activity of cortical regions.
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Abstract We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentionin...
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Abstract We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute‐based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.
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Modern simulation and imaging techniques are providing intricate blood-flow velocity data, the analysis of which can lead to new insights into how blood flow relates to the development of cardiovascular disease. Rapidly interpreti...
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Modern simulation and imaging techniques are providing intricate blood-flow velocity data, the analysis of which can lead to new insights into how blood flow relates to the development of cardiovascular disease. Rapidly interpreting this complex data requires novel comprehensive visual representations.
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As a result of the COVID-19 pandemic, the learning and evaluation processes have been moved to an online modality to keep social distance and reduce the spreading of the virus. The strategies implemented for assessment and proctor...
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As a result of the COVID-19 pandemic, the learning and evaluation processes have been moved to an online modality to keep social distance and reduce the spreading of the virus. The strategies implemented for assessment and proctoring in this online remote teaching and assessment emergency are no exception when proctoring test-takers. This problem is addressed from a practical context of study: the English Language Proficiency Tests of a University in southeast Mexico. Considering an iterative user-centered mixed methodology, a set of dashboards was designed, implemented and evaluated to visualize the information generated by test-takers during the administration process. An increase in the Usability of the dashboards is observed in all heuristic categories, with visual design being greater. The use of the mixed methodology and the constant user feedback during the process helped us to reduce development time compared with other works found in the literature. Moreover, it is possible to use the proposed dashboards in other application domains like medicine, or care facilities where user activity monitoring is needed to make informed decisions. categoryHuman-centered computing; Information visualization.
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We will review the discovery and characterization of gravitationally bound quantum states of neutrons. The lowest neutron quantum states in a gravitational potential were distinguished and characterized by a measurement of their s...
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We will review the discovery and characterization of gravitationally bound quantum states of neutrons. The lowest neutron quantum states in a gravitational potential were distinguished and characterized by a measurement of their spatial extent. The neutron transmission was observed through a slit with an absorbing top surface at a variable height. Second, a position-sensitive detector was used for a direct visualization of the wavefunction. An application is the search for new short-range interactions, which would alter the waveform of the quantum states.
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Robust automated vortex detection algorithms are needed to facilitate the exploration of large-scale turbulent fluid flow simulations. Unfortunately, robust non-local vortex detection algorithms are computationally intractable for...
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Robust automated vortex detection algorithms are needed to facilitate the exploration of large-scale turbulent fluid flow simulations. Unfortunately, robust non-local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non-local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms.We motivate the presence of an expert-in-the-loop using empirical results based on machine learning techniques. We employ adaptive boosting to combine a suite of widely used, local vortex detection algorithms, which we term weak classifiers, into a robust compound classifier. Fundamentally, the training phase of the algorithm, in which an expert manually labels small, spatially contiguous regions of the data, incorporates non-local information into the resulting compound classifier. We demonstrate the efficacy of our approach by applying the compound classifier to two data sets obtained from computational fluid dynamical simulations. Our results demonstrate that the compound classifier has a reduced misclassification rate relative to the component classifiers.
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This paper presents a tool for the visual analysis of fitness performance data, such as running speed and heart rate. The tool, called MOPET Analyzer, provides a set of interactive visualizations that allow the user to analyze the...
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This paper presents a tool for the visual analysis of fitness performance data, such as running speed and heart rate. The tool, called MOPET Analyzer, provides a set of interactive visualizations that allow the user to analyze the relations among the different parameters of a fitness session. We developed two versions of MOPET Analyzer: one for desktop systems and one for mobile devices. The desktop version allows for an analysis of sets of different fitness sessions, also providing details for each single fitness session. The mobile version provides very simple visualizations that can be useful on the field. MOPET Analyzer allows the user to monitor her fitness activity and to analyze her fitness progress, helping her to continuously improve the quality of her training.
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