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SSR-TVD: Spatial Super-Resolution for time-varying data Analysis and visualization

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第6期28卷 2445-2456页

作者： Han, Jun Wang, Chaoli Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

We present SSR-TVD, a novel deep learning framework that produces coherent spatial super-resolution (SSR) of time-varying data (TVD) using adversarial learning. In scientific visualization, SSR-TVD is the first work that applies the generative adversarial network (GAN) to generate high-resolution volumes for three-dimensional time-varying data sets. The design of SSR-TVD includes a generator and two discriminators (spatial and temporal discriminators). The generator takes a low-resolution volume as input and outputs a synthesized high-resolution volume. To capture spatial and temporal coherence in the volume sequence, the two discriminators take the synthesized high-resolution volume(s) as input and produce a score indicating the realness of the volume(s). Our method can work in the in situ visualization setting by downscaling volumetric data from selected time steps as the simulation runs and upscaling downsampled volumes to their original resolution during postprocessing. To demonstrate the effectiveness of SSR-TVD, we show quantitative and qualitative results with several time-varying data sets of different characteristics and compare our method against volume upscaling using bicubic interpolation and a solution solely based on CNN.

关键词： time-varying data visualization deep learning super-resolution generative adversarial network

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TSR-TVD: Temporal Super-Resolution for time-varying data Analysis and visualization

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2020年第1期26卷 205-215页

作者： Han, Jun Wang, Chaoli Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

We present TSR-TVD, a novel deep learning framework that generates temporal super-resolution (TSR) of time-varying data (TVD) using adversarial learning. TSR-TVD is the first work that applies the recurrent generative network (RGN), a combination of the recurrent neural network (RNN) and generative adversarial network (GAN), to generate temporal high-resolution volume sequences from low-resolution ones. The design of TSR-TVD includes a generator and a discriminator. The generator takes a pair of volumes as input and outputs the synthesized intermediate volume sequence through forward and backward predictions. The discriminator takes the synthesized intermediate volumes as input and produces a score indicating the realness of the volumes. Our method handles multivariate data as well where the trained network from one variable is applied to generate TSR for another variable. To demonstrate the effectiveness of TSR-TVD, we show quantitative and qualitative results with several time-varying multivariate data sets and compare our method against standard linear interpolation and solutions solely based on RNN or CNN.

关键词： time-varying data visualization super-resolution deep learning recurrent generative network

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time-varying data visualization Using Clustered Heatmap and Dual Scatterplots 20

Time-Varying Data Visualization Using Clustered Heatmap and ...

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20th International Conference Information Visualisation (IV)

作者： Kumatani, Satsuki Itoh, Takayuki Motohashi, Yousuke Umezu, Keisuke Takatsuka, Masahiro Ochanomizu Univ Tokyo Japan NEC Corp Ltd Tokyo Japan Univ Sydney Sydney NSW 2006 Australia

ISBN: (纸本)9781467389426

Heatmap is one of the effective representations for time-varying data visualization. It may require large display spaces when an input dataset contains large number of data items or time steps. We may often want mechanisms to interactively filter non-important data items or time steps, so that we can form appropriate sizes of heatmaps and focus on important data items or time steps. This paper presents a heatmap-based time-varying data visualization technique featuring an interactive mechanism to display meaningful data items and time steps. This technique firstly calculates distances between arbitrary pairs of data items, and constructs a dendrogram consisting the data items. It then generates clusters of the data items and displays the data items belonging to the specified sizes of clusters in the heatmap, so that we can focus on groups of similar or correlated data items. It applies a similar mechanism to a set of time steps so that we can remove outlier time steps from the heatmap. Our implementation features two scatterplots, which represent distribution of data items and time steps respectively, and slider widgets to interactively adjust the thresholds of the clustering process. We can intuitively understand how clusters of data items or time steps are constructed, by looking at the scatterplots while operating the sliders.

关键词： time-varying data visualization Heatmap Scatterplot Clustering

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PCSAGAN: a physics-constrained generative network based on self-attention for high-fidelity flow field reconstruction

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JOURNAL OF visualization 2024年第4期27卷 661-676页

作者： Shen, Liming Deng, Liang Wang, Yueqing Zhang, Jian Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Peoples R China China Aerodynam Res & Dev Ctr State Key Lab Aerodynam Mianyang Peoples R China

We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volumes for two-dimensional time-varying datasets. PCSAGAN consists of two discriminators and one generator, capable of preserving temporal coherence and spatial characteristics. It avoids the heavy computation of multilayer convolution and obtains spatial flow evolution information by using the self-attention mechanism. A soft-constraint loss function is further employed to effectively utilize the underlying physical properties. Moreover, the dataset is generated from real-world simulations rather than artificial synthesis, which ensures generalizability to practical applications. We compare PCSAGAN against volume upscaling methods using BI, ESPCN and SSR-TVD, and demonstrate its effectiveness with several time-varying flow fields through quantitative and qualitative evaluations. Meanwhile, a re-analysis framework is introduced to enable potential later visualization and exploratory analysis of the reconstructed flow field data.

关键词： time-varying data visualization Deep learning Super-resolution Generative adversarial network

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A unified framework for exploring time-varying volumetric data based on block correspondence

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Visual Informatics 2019年第4期3卷 157-165页

作者： Kecheng Lu Chaoli Wang Keqin Wu Minglun Gong Yunhai Wang School of Computer Science and Technology Shandong UniversityShandong ProvinceChina Department of Computer Science and Electrical Engineering University of MarylandBaltimore CountyUSA National Oceanic and Atmospheric Administration WashingtonD.C.HIUSA School of Computer Science University of GuelphGuelphOntarioCanada

Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific *** great advances have been made over the years,existing solutions typically focus on only one or two aspects of data analysis and visualization.A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still *** this goal,we present a novel approach for time-varying data visualization that encompasses keyframe identification,feature extraction and tracking under a single,unified *** the heart of our approach lies in the GPU-accelerated BlockMatch method,a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D *** on the results of dense correspondence,we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity ***,in conjunction with the graph cut algorithm,this framework enables us to perform fine-grained feature extraction and *** tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.

关键词： time-varying data visualization Block correspondence Feature extraction and tracking

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Mining Graphs for Understanding time-varying Volumetric data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2016年第1期22卷 965-974页

作者： Gu, Yi Wang, Chaoli Peterka, Tom Jacob, Robert Kim, Seung Hyun Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.

关键词： time-varying data visualization graph simplification community detection visual recommendation

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A visualization Tool for Building Energy Management System 19

A Visualization Tool for Building Energy Management System

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19th International Conference on Information Visualisation IV 2015

作者： Itoh, Takayuki Kawano, Masato Kutsuna, Shuji Watanabe, Takeshi Ochanomizu Univ Tokyo Japan NTT Facil Tokyo Japan

ISBN: (纸本)9781467375689

Many public offices and companies manage their energy consumption by Building Energy Management System (BEMS). It is not an easy task to determine whether the past energy consumption was really necessary or just wasted. visualization for energy consumption is useful to understand the situations of energy consumption to determine their necessity. This paper presents a visualization tool for energy consumption with BEMS. The tool firstly divides the daily variation of the energy usage and environmental measurements (e.g. temperature and humidity) into the meaningful number of patterns. It displays "long-term polyline chart" to represent the frequency of the daily pattern so that users can easily focus on particular dates at particular places. It also displays "one-day polyline chart" to represent the daily variation of the recorded values of the particular dates and places specified by users' click operations. The paper introduces the examples of visualization to demonstrate the effectiveness of the presented tool, with a real dataset of business office building.

关键词： Building management time-varying data visualization support vector machine

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visualization and analysis of 3D time-varying simulations with time lines

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2013年第5期24卷 402-418页

作者： Yu, Li Lu, Aidong Chen, Wei UNC Charlotte Dept Comp Sci Charlotte NC 28262 USA Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China

This paper presents a time line visualization approach, which allows users to study temporal relationships through encoding their interested data properties to time lines with different shapes and locations. Specifically, our approach extracts key data features as virtual words and uses them to encode various data properties. The distributions of virtual words across time are further applied to study various temporal relationships by generating time lines, which renders sampled time steps as points and temporal sequence as a line. Our approach consists of the three following components. First, we select feature points and collect feature descriptors to build a space of data properties, where virtual words are extracted as representative vectors. Second, the virtual words are applied to characterize feature points and their distribution statistics are used to measure temporal relationships. Third, we demonstrate several methods to visualize time lines flexibly for different data visualization and analysis purposes. We present several case studies to visualize time lines for different data visualization and analysis purposes. Our time line visualization can be used for both summarization and exploration of overall temporal relationships. We demonstrate with examples that time lines can serve as effective exploration, comparison, and visualization tools to study time-varying datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： time line Virtual words Feature description time-varying data visualization

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TransGraph: Hierarchical Exploration of Transition Relationships in time-varying Volumetric data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2015-2024页

作者： Gu, Yi Wang, Chaoli Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA

A fundamental challenge for time-varying volume data analysis and visualization is the lack of capability to observe and track data change or evolution in an occlusion-free, controllable, and adaptive fashion. In this paper, we propose to organize a time-varying data set into a hierarchy of states. By deriving transition probabilities among states, we construct a global map that captures the essential transition relationships in the time-varying data. We introduce the TransGraph, a graph-based representation to visualize hierarchical state transition relationships. The TransGraph not only provides a visual mapping that abstracts data evolution over time in different levels of detail, but also serves as a navigation tool that guides data exploration and tracking. The user interacts with the TransGraph and makes connection to the volumetric data through brushing and linking. A set of intuitive queries is provided to enable knowledge extraction from time-varying data. We test our approach with time-varying data sets of different characteristics and the results show that the TransGraph can effectively augment our ability in understanding time-varying data.

关键词： time-varying data visualization hierarchical representation states transition relationship user interface

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Parallel vectors criteria for unsteady flow vortices

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2008年第3期14卷 615-626页

作者： Fuchs, Raphael Peikert, Ronald Hauser, Helwig Sadlo, Filip Muigg, Philipp Vienna Univ Technol Inst Comp Graph & Algorithms A-1040 Vienna Austria ETH Inst Computat Sci CH-8092 Zurich Switzerland Univ Bergen Dept Informat N-5020 Bergen Norway TechGate Vienna VRVis Res Ctr A-1220 Vienna Austria

Feature-based flow visualization is naturally dependent on feature extraction. To extract flow features, often higher order properties of the flow data are used such as the Jacobian or curvature properties, implicitly describing the flow features in terms of their inherent flow characteristics ( for example, collinear flow and vorticity vectors). In this paper, we present recent research that leads to the ( not really surprising) conclusion that feature extraction algorithms need to be extended to a time-dependent analysis framework ( in terms of time derivatives) when dealing with unsteady flow data. Accordingly, we present two extensions of the parallel-vectors-based vortex extraction criteria to the time-dependent domain and show the improvements of feature-based flow visualization in comparison to the steady versions of this extraction algorithm both in the context of a high-resolution data set, that is, a simulation specifically designed to evaluate our new approach and for a real-world data set from a concrete application.

关键词： vortex feature detection time-varying data visualization

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