检索结果-内蒙古大学图书馆

Interactive Occlusion-Free System for Accessible Volume Exploration

ieee ACCESS 2023年 11卷 86544-86560页

作者： Sun, Jing-Ru Josephine, Nathania Wang, Chao Wang, Ko-Chih Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei 11677 Taiwan

When exploring a 3D medical dataset, data occlusion creates difficulties for users to understand the dataset in detail since many internal structures are occluded. Techniques such as transfer function design, isosurface extraction, etc., have been proposed in the past to address the occlusion problem and assist expert users in exploring volumetric data. However, these techniques may not be feasible for some non-expert users' scenarios, such as science museum visitors exploring a brain CT image data volume on a large touch screen or high school students learning the human body structure on a tablet because they have no need to conduct long-term data exploration and rigorous scientific analysis. To address the non-expert users' need for an easy-to-use data exploration tool, we propose an interactive data exploration system for non-expert users to interact with the dataset with a short learning time and simple inputs. Non-expert users can remove the obstructing material to reveal the occluded structures by simply clicking on the obstructing material. Our system also preserves the context for non-expert users to understand the dataset after the obstructing material is removed easily. Additionally, for data providers such as the engineers in a museum, our system provides a semi-automatic workflow to set up a dataset into our system for non-expert users. In this work, we conduct two user studies to evaluate our system's usability.

关键词： ~3D data visualization and exploration human-computer interaction occlusion management

来源：评论

学校读者我要写书评

暂无评论

Roslingifier: Semi-Automated Storytelling for Animated Scatterplots

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2980-2995页

作者： Shin, Minjeong Kim, Joohee Han, Yunha Xie, Lexing Whitelaw, Mitchell Kwon, Bum Chul Ko, Sungahn Elmqvist, Niklas Australian Natl Univ Canberra ACT 0200 Australia Ulsan Natl Inst Sci & Technol Ulsan 689798 South Korea IBM Res Cambridge MA 02142 USA Univ Maryland Maryland MD 20742 USA

We present Roslingifier, a data-driven storytelling method for animated scatterplots. Like its namesake, Hans Rosling (1948-2017), a professor of public health and a spellbinding public speaker, Roslingifier turns a sequence of entities changing over time-such as countries and continents with their demographic data-into an engaging narrative elling the story of the data. This data-driven storytelling method with an in-person presenter is a new genre of storytelling technique and has never been studied before. In this article, we aim to define a design space for this new genre-data presentation-and provide a semi-automated authoring tool for helping presenters create quality presentations. From an in-depth analysis of video clips of presentations using interactive visualizations, we derive three specific techniques to achieve this: natural language narratives, visual effects that highlight events, and temporal branching that changes playback time of the animation. Our implementation of the Roslingifier method is capable of identifying and clustering significant movements, automatically generating visual highlighting and a narrative for playback, and enabling the user to customize. From two user studies, we show that Roslingifier allows users to effectively create engaging data stories and the system features help both presenters and viewers find diverse insights.

关键词： data visualization visualization Annotations Visual effects Streaming media Organizations Natural languages data-driven storytelling narrative visualization hans rosling gapminder trendalyzer

来源：评论

学校读者我要写书评

暂无评论

Continual Learning of Image Classes With Language Guidance From a Vision-Language Model

引用

ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 13152-13163页

作者： Zhang, Wentao Huang, Yujun Zhang, Weizhuo Zhang, Tong Lao, Qicheng Yu, Yue Zheng, Wei-Shi Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

Current deep learning models often catastrophically forget the knowledge of old classes when continually learning new ones. State-of-the-art approaches to continual learning of image classes often require retaining a small subset of old data to partly alleviate the catastrophic forgetting issue, and their performance would be degraded sharply when no old data can be stored due to privacy or safety concerns. In this study, inspired by human learning of visual knowledge with the effective help of language, we propose a novel continual learning framework based on a pre-trained vision-language model (VLM) without retaining any old data. Rich prior knowledge of each new image class is effectively encoded by the frozen text encoder of the VLM, which is then used to guide the learning of new image classes. The output space of the frozen text encoder is unchanged over the whole process of continual learning, through which image representations of different classes become comparable during model inference even when the image classes are learned at different times. Extensive empirical evaluations on multiple image classification datasets under various settings confirm the superior performance of our method over existing ones. The source code is available at https://***/Fatflower/CIL_LG_VLM/.

关键词： visualization Continuing education Adaptation models data models Task analysis Knowledge engineering Semantics Continual learning vision-language model language guidance

来源：评论

学校读者我要写书评

暂无评论

Deep Hierarchical Super Resolution for Scientific data

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第12期29卷 5483-5495页

作者： Wurster, Skylar W. Guo, Hanqi Shen, Han-Wei Peterka, Tom Xu, Jiayi Ohio State Univ Dept Comp Sci & Engn Columbus OH 43201 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA

We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform gridwith minimal seam artifacts on octree node boundaries. Our method uses existing state-of-the-art SR models and adds flexibility to upscale input data with varying levels of detail across the domain, instead of only uniform grid data that are supported in previous *** key is to use a hierarchy of SR NNs, each trained to perform 2x SR between two levels of detail, with a hierarchical SR algorithm that minimizes seam artifacts by starting from the coarsest level of detail and working *** show that our hierarchical approach outperforms baseline interpolation and hierarchical upscaling methods, and demonstrate the usefulness of our proposed approach across three use cases including data reduction using hierarchical downsampling+SR instead of uniform downsampling+SR, computation savings for hierarchical finite-time Lyapunov exponent field calculation, and super-resolving low-resolution simulation results for a high-resolution approximation visualization.

关键词： Spatial resolution Rendering (computer graphics) Octrees data models Computational modeling Interpolation Isosurfaces Deep learning super-resolution hierarchical data

来源：评论

学校读者我要写书评

暂无评论

Digital Twin Platform Based on Virtual Sensing for Real-time Monitoring of Mechanical Features in Weaving Motion

引用

ieee Sensors Journal 2025年第11期25卷 20406-20414页

作者： Zhang, Pengfei Zhang, Jinrui Pan, Ruru Wang, Lei Zhou, Jian Zhang, Ning Xiang, Jun Jiangnan University College of Textile Science and Engineering Jiangsu214122 China Beijing University of Civil Engineering and Architecture School of Electromechanical and Vehicle Engineering Beijing100044 China

Intelligent monitoring of the weaving workshop, as a key link in the upgrading and transformation of the textile industry, directly affects the production quality and yield of textiles. One of the limitations to efficiently monitoring the weaving workshop is the accuracy and the number of sensors. In this paper, a digital twin model of a weaving workshop was constructed using virtual sensing technology. The virtual weaving workshop, driven by real workshop data, simulated the fabric production process and enabled visual monitoring of the weaving process. According to the motion coordination of the weaving equipment, the simulation of the weaving process was further simplified to meet the timeliness requirements of the actual production. For yarns and fabric, the complex variational operations in mechanics simulations were replaced using a linear elastic model and neural networks. The experimental results demonstrate that the total time, including data acquisition, virtual computation, and visualization, is less than 100 ms. The average accuracy of real-time virtual sensing for yarns and fabrics was 99.8% and 85.1%, respectively. In practice, the optimized digital twin model improves the transfer efficiency of sensing data in web services by 2 times. © 2025 ieee.

关键词： Textile industry

来源：评论

学校读者我要写书评

暂无评论

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第7期29卷 3312-3326页

作者： Eckelt, Klaus Hinterreiter, Andreas Adelberger, Patrick Walchshofer, Conny Dhanoa, Vaishali Humer, Christina Heckmann, Moritz Steinparz, Christian Streit, Marc Johannes Kepler Univ Linz A-4040 Linz Austria Pro2Future GmbH A-4040 Linz Austria

In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups are accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

关键词： visualization Task analysis Layout data visualization Space exploration Visual analytics Trajectory Dimensionality reduction projection visual analytics layout enrichment aggregation comparison

来源：评论

学校读者我要写书评

暂无评论

A Novel Big data Algorithm of data visualization and Generation Adversarial Network Fusion for International Trade 1

A Novel Big Data Algorithm of Data Visualization and Generat...

引用

1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sun, Feng Yao, Wenge Guangdong University of Science and Technology College of Finance and Economics Dongguan China

ISBN: (纸本)9798350352931

When using the traditional method to evaluate the international trade capacity, there is a problem of poor evaluation accuracy. To this end, the international trade big data algorithm of data visualization and generation confrontation network fusion is proposed. The fuzzy evaluation theory is used to establish the evaluation factor set of data visualization and generation confrontation network fusion, calculate the weight of each factor, and obtain the ratio of the weight of each factor. The international trade big data algorithm is used to study the fusion of data visualization and generating adversarial network, and combined with the gray theory to calculate the evaluation rate of data visualization and generated adversarial network fusion, to complete the research of data visualization and generated adversarial network fusion. According to the simulation experiment results, the proposed method can effectively improve the effectiveness and reliability of data visualization and generation adversarial network (GAN) fusion capability evaluation. © 2024 ieee.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

引用

ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 548-558页

作者： Neuroth, Tyson Rieth, Martin Aditya, Konduri Lee, Myoungkyu Chen, Jacqueline H. Ma, Kwan-Liu University of California Davis United States Sandia National Laboratories United States India Institute of Science India University of Alabama United States

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features. © 2022 ieee.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Automated Audio data Augmentation Network Using Bi-Level Optimization for Sound Event Localization and Detection

引用

ieee SIGNAL PROCESSING LETTERS 2024年 31卷 2770-2774页

作者： Zhang, Wenjie Yu, Peng Yin, Jun Jiang, Xiaoheng Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Minist Educ Engn Res Ctr Intelligent Swarm Syst Zhengzhou 450001 Peoples R China Natl Supercomp Ctr Zhengzhou 450001 Peoples R China

In sound event localization and detection (SELD), traditional methods often treat localization and detection algorithms separately from data augmentation. During the model training process, the strategy for data augmentation is typically implemented in a non-learnable manner. Existing audio data augmentation strategies struggle to find optimal parameter solutions for data augmentation that can be effectively applied to SELD systems. To address this challenge, we introduce an innovative network-based strategy, termed the Automated Audio data Augmentation (AADA) network. This strategy employs bi-level optimization to synergistically integrate audio data augmentation techniques with SELD tasks. In the AADA network, the lower-level SELD task serves as a constraint for the higher-level data augmentation process. The audio data augmentation parameters are adaptively optimized by utilizing the transfer of intermediate feature information from the SELD tasks, thus obtaining optimal parameters for these tasks. Evaluation of our approach on the Sony-TAU Realistic Spatial Soundscapes 2023 dataset achieves a SELD score of 0.4801, significantly surpassing the performance metrics of all traditional data augmentation strategies for SELD.

关键词： data augmentation Optimization data models Feature extraction Location awareness Training Adaptation models visualization Time-frequency analysis Spatial databases Sound event localization and detection audio data augmentation strategies bi-level optimization

来源：评论

学校读者我要写书评

暂无评论

***: Compressing Linear Set Diagrams

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2875-2887页

作者： Wallinger, Markus Dobler, Alexander Noellenburg, Martin TU Wien Algorithms & Complex Grp A-1040 Vienna Austria

Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present ***, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of *** to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.

关键词： Task analysis visualization data visualization Image color analysis Heuristic algorithms Taxonomy Sparse matrices Computational experiment linear diagrams set visualization user evaluation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：