检索结果-内蒙古大学图书馆

Large-Scale Oceanic Dynamic Field visualization Based on WebGL

ieee ACCESS 2023年 11卷 82816-82829页

作者： Fan, Donglin Liang, Tianlong He, Hongchang Guo, Mengyuan Wang, Menghui Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541006 Peoples R China Guilin Univ Technol Ecol Spatiotemporal Big Data Percept Serv Lab Guilin 541006 Peoples R China Guilin Univ Technol Coll Arts Guilin 541006 Peoples R China

The dynamic visualization of ocean dynamics data is essential for effectively presenting oceanic information data. However, with the increasing spatial resolution of ocean remote sensing data, performing global-scale visualization in a single pass has become challenging. Directly rendering ocean data with high spatial resolution can result in problems such as increased communication time, blocked resource loading, and page lag. To address this issue, this paper proposes a common ocean data standard based on the characteristics of massive ocean element data, constructs an efficient three-level LOD model for ocean dynamic field rendering, and achieves particle rendering of ocean dynamic data by converting ocean data into image slices to complete the rendering of high-volume ocean data on the Web side. Compared with traditional methods, the proposed method significantly improves rendering performance. The average window construction time (FT) is reduced to 51.77ms, enhancing the overall rendering speed by approximately 64%. Meanwhile, the average frames per second (FPS) increase to 57.45fps, augmenting rendering stability and smoothness by around 18%. The peak memory consumption of the highest resolution data used in this paper is about 90MB, which is only 1/4 of the original. The proposed method effectively compensates for the disadvantage of slow rendering of large-scale ocean data visualization in some systems, enabling fast rendering of dynamic ocean data on the Web.

关键词： Oceanic engineering and marine technology oceanography application software

来源：评论

学校读者我要写书评

暂无评论

Tac-Trainer: A Visual Analytics System for IoT-based Racket Sports Training

引用

ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 951-961页

作者： Wang, Jiachen Ma, Ji Hu, Kangping Zhou, Zheng Zhang, Hui Xie, Xiao Wu, Yingcai Zhejiang University State Key Lab of CAD&CG China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China Zhejiang University Department of Sports Science China

Conventional racket sports training highly relies on coaches' knowledge and experience, leading to biases in the guidance. To solve this problem, smart wearable devices based on Internet of Things technology (IoT) have been extensively investigated to support data-driven training. Considerable studies introduced methods to extract valuable information from the sensor data collected by IoT devices. However, the information cannot provide actionable insights for coaches due to the large data volume and high data dimensions. We proposed an IoT + VA framework, Tac-Trainer, to integrate the sensor data, the information, and coaches' knowledge to facilitate racket sports training. Tac-Trainer consists of four components: device configuration, data interpretation, training optimization, and result visualization. These components collect trainees' kinematic data through IoT devices, transform the data into attributes and indicators, generate training suggestions, and provide an interactive visualization interface for exploration, respectively. We further discuss new research opportunities and challenges inspired by our work from two perspectives, VA for IoT and IoT for VA. © 2022 ieee.

关键词： Sports

来源：评论

学校读者我要写书评

暂无评论

Local Latent Representation Based on Geometric Convolution for Particle data Feature Exploration

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第7期29卷 3354-3367页

作者： Li, Haoyu Shen, Han-Wei Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

Feature related particle data analysis plays an important role in many scientific applications such as fluid simulations, cosmology simulations and molecular dynamics. Compared to conventional methods that use hand-crafted feature descriptors, some recent studies focus on transforming the data into a new latent space, where features are easier to be identified, compared and extracted. However, it is challenging to transform particle data into latent representations, since the convolution neural networks used in prior studies require the data presented in regular grids. In this article, we adopt Geometric Convolution, a neural network building block designed for 3D point clouds, to create latent representations for scientific particle data. These latent representations capture both the particle positions and their physical attributes in the local neighborhood so that features can be extracted by clustering in the latent space, and tracked by applying tracking algorithms such as mean-shift. We validate the extracted features and tracking results from our approach using datasets from three applications and show that they are comparable to the methods that define hand-crafted features for each specific dataset.

关键词： Feature extraction Neural networks Point cloud compression data visualization Convolution Three-dimensional displays Kernel data transformation particle data feature extraction and tracking deep learning

来源：评论

学校读者我要写书评

暂无评论

Big data visualization System Based on Target data Query

Big Data Visualization System Based on Target Data Query

引用

2023 International Conference on Electronics and Devices, Computational science, ICEDCS 2023

作者： Wu, Xiaoling He, Wenhai Chen, Qilong Qiu, Minghui Guangzhou College of Commerce School of Information Technology and Engineering Guangdong Guangzhou511363 China

ISBN: (纸本)9798350343038

This paper aims to explore a Big data visualization system, which applies Big data technology to actual business to meet user needs. Firstly, based on the user's query request, determine whether the corresponding business data needs to be generated according to the request;Secondly, generate corresponding business data based on the user's query operation;Finally, based on the characteristics of business data, the results of the system will be presented so that users can better understand and analyze business processes. By querying to obtain target business data, we can determine the best visualization processing methods and use these methods to improve the visualization effect of the data. In this way, we can effectively solve the problem of poor data and information visualization effect in current technology. © 2023 ieee.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

ieee VAST Challenge 2021 Winner: Visual Analytics for Spatial-Temporal Situation Awareness

引用

ieee COMPUTER GRAPHICS AND APPLICATIONS 2023年第5期43卷 12-22页

作者： Shen, Siqi Lai, Xingui Chen, Siming Wang, Qinghong Gao, Junting Fudan Univ Shanghai 200437 Peoples R China Data Star Observ Beijing 100088 Peoples R China

ieee VAST Challenge 2021 provides fruitful data to test the visual analytics capability of participants. We summarize our work in this article. Trajectory data and consumption data contain a lot of information, such as consumption patterns, behavior characteristics, and so on. The information can provide favorable clues for law enforcement departments to crack a case about missing employees. We designed a visual analytics system called Sundial for spatio-temporal situation awareness with multidata fusion. It contains three views, that is, consumption view, temporal behavior view, and spatial-temporal map. With the system, analysts can effectively identify the consumption and behavior patterns of employees, and detect the suspicious activities and informal or formal relationships. Through case analysis, we illustrated how to use the system and obtain effective information.

关键词： Trajectory Visual analytics Behavioral sciences data visualization Credit cards Task analysis Automobiles

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Potential of SDGSAT-1 Glimmer Imagery for Urban Road Detection

引用

ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 785-794页

作者： Wang, Yu Huang, Hailan Wu, Bin Sun Yat Sen Univ Sch Geospatial Engn & Sci Zhuhai 519082 Peoples R China

Sustainable Development science Satellite 1 (SDGSAT-1), an innovative satellite engineered by the Chinese Academy of sciences, focuses on achieving sustainable development goals (SDGs) by using high-resolution low-light imaging for urban planning and environmental monitoring. Although its potential to provide substantial information relevant to various SDG indicators has been demonstrated across multiple domains, a comprehensive analysis of the potential of SDGSAT-1 glimmer imagery for urban road detection has not yet conducted. To address this gap, we aimed to evaluate the applicability of SDGSAT-1 glimmer imagery in road detection across five cities with well-developed road networks in China, namely Chengdu, Guangzhou, Hangzhou, Shanghai, and Wuhan. Watershed segmentation and optimal thresholding algorithms were applied to extract the road network data of these five cities. We observed that the large connecting roads, including highways (Motorway), urban expressways (Trunk), urban main roads (Primary), urban secondary roads (Secondary), and urban tertiary roads (Tertiary), were visible in the SDGSAT-1 glimmer data. Watershed segmentation and optimal thresholding algorithms demonstrated a remarkable precision of approximately 80% for road extraction across all cities. Approximately, 64% of urban roads were detectable within the SDGSAT-1 glimmer imager data. Our study suggests that the SDGSAT-1 glimmer data have great potential for extracting urban roads.

关键词： Roads Watersheds Urban areas data mining Image segmentation Transportation Sustainable development Economics visualization Thresholding (Imaging) Glimmer data nighttime light (NTL) sustainable development science satellite 1 (SDGSAT-1) urban roads watershed segmentation

来源：评论

学校读者我要写书评

暂无评论

Ambient Temperature Estimation of Mountain Freeways Based on Roadside Camera Images

引用

ieee SENSORS JOURNAL 2024年第22期24卷 38453-38465页

作者： Sun, Zhu Jin, Yin-Li Zhang, Yu-Jie Xu, Wen-Peng Li, Li Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China CCCC First Highway Consultants Co Ltd ITS Branch Xian 710075 Peoples R China

Accurate estimation of the ambient temperature of mountain freeways enables freeway management agencies to provide weather-related information to drivers. This article proposed an image-based data-driven method, namely the visual temperature estimation network (VTENet), to estimate freeway ambient temperature based on images captured by roadside cameras. The VTENet had a convolutional neural network (CNN) architecture to extract temperature-related image features, and two extra networks to capture space-time information on data collection and time-series image features. The VTENet was trained and tested based on a self-established dataset collected at a mountain freeway. The results showed that the VTENet can estimate freeway ambient temperature with high accuracy. The model gives a more accurate temperature estimation with data collected from 10 to 11 A.M. and 2 to 3 P.M. than other periods. It also performed better using four-day or five-day sequence images than other data inputs.

关键词： Traffic control Kernel Estimation Feature extraction Cameras Temperature sensors visualization Temperature distribution Accuracy data mining Convolutional neural network (CNN) freeway temperature image feature long short-term memory (LSTM) roadside camera

来源：评论

学校读者我要写书评

暂无评论

FlowNL: Asking the Flow data in Natural Languages

引用

ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 1200-1210页

作者： Huang, Jieying Xi, Yang Hu, Junnan Tao, Jun School of Computer Science and Engineering Sun Yat-sen University China National Supercomputer Center Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation. © 2022 ieee.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Mix-Spectrum for Generalization in Visual Reinforcement Learning

引用

ieee ACCESS 2025年 13卷 7939-7950页

作者： Lee, Jeong Woon Hwang, Hyoseok Kyung Hee Univ Dept Software Convergence Yongin 17104 Gyeonggi South Korea

Visual Reinforcement Learning (RL) trains agents on policies using images showing the potential for real-world applications. However, the limited diversity in the training environment often results in overfitting with agents underperforming in unseen environments. To address this issue, image augmentation is utilized in visual RL to increase data diversity, but the effectiveness is limited due to the potential to alter the semantic information of the image. Therefore, we introduce Mix-Spectrum, a straightforward yet highly effective frequency-based augmentation method that maintains the semantic consistency of data and enhances the agent's focus on semantic information. The proposed method combines two existing methods: mixing amplitudes of original and reference images, and Random Convolution. Through this synergistic combination of established methods, our approach not only maintains the advantages of each method but also introduces a novel characteristic that enhances performance. Furthermore, the proposed method stands out for adaptability when integrated with any visual RL algorithm, whether off-policy or on-policy. Through extensive experiments on the DMControl Generalization Benchmark (DMControl-GB) and Procgen, our method demonstrates superior performance compared to existing frequency-based, normalization-based, and image augmentation methods in zero-shot generalization. In DMControl-GB, our method improved by 35.5% over the baseline and 15.2% over the second-best. In Procgen, it achieved 15.2% and 10.1% improvements, respectively.

关键词： visualization Reinforcement learning Convolution Semantics Benchmark testing Image augmentation Training Image color analysis Games Frequency diversity Deep reinforcement learning data augmentation fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

ChartNavigator: An Interactive Pattern Identification and Annotation Framework for Charts

引用

ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第2期35卷 1258-1269页

作者： Zhang, Tianye Feng, Haozhe Chen, Wei Chen, Zexian Zheng, Wenting Luo, Xiaonan Huang, Wenqi Tung, Anthony Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China Zhejiang Univ China Southern Power Grid Joint Res Hangzhou 310058 Peoples R China Guilin Univ Elect Technol Guilin 541214 Peoples R China China Southern Power Grid Digital Grid Res Inst Guangzhou 510670 Peoples R China Natl Univ Singapore Dept Comp Sci Singapore 117417 Singapore

Patterns in charts refer to interesting visual features or forms. Identifying patterns not only helps analysts understand the 'shape' of the data but also supports better and faster decision-making. Existing solutions for identifying patterns in charts require a large number of labeled data instances, making it intractable without user supervision. In this paper, we propose ChartNavigator, an interactive pattern identification and annotation framework for unlabeled visualization charts. ChartNavigator leverages a novel chart-sensitive deep factor model to map patterns into a low-dimensional factor representation space, and facilitates rich analysis with the derived representations. We design and implement a visual interface to support efficient identification and annotation of potential patterns in charts. Evaluations with multiple datasets show that our approach outperforms the baseline models in identifying and annotating patterns.

关键词： visualization data models Annotations data visualization Solid modeling Inference algorithms Estimation Pattern identification chart variational autoencoder user interaction visual analysis

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：