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Fusion-Perception-to-Action Transformer: Enhancing Robotic Manipulation With 3-D Visual Fusion Attention and Proprioception

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ieee TRANSACTIONS ON ROBOTICS 2025年 41卷 1553-1567页

作者： Liu, Yangjun Liu, Sheng Chen, Binghan Yang, Zhi-Xin Xu, Sheng Univ Macau Ctr Artificial Intelligence & Robot Dept Electromech Engn State Key Lab Internet Things Smart City Macau 999078 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen 518055 Peoples R China

Most prior robot learning methods focus on image-based observations, limiting their capability in 3-D robotic manipulation. Voxel representation naturally delivers rich spatial features but remains underutilized. Specifically, current voxel-based methods struggle with fine-grained tasks, since precise actions are not fully achievable. However, humans can accomplish these tasks well using vision and proprioception. Inspired by this, this article proposed a novel Fusion-Perception-to-Action Transformer (FP2AT) with cross-layer feature aggregation to handle fine-grained manipulation in 3-D space. In particular, a multiscale 3-D visual fusion attention mechanism is devised to draw attention to local regions of interest and maintain awareness of global scenes, thereby boosting the capabilities of visual perception and action planning. Meanwhile, a 3-D visual mutual attention mechanism is designed and it can also enhance spatial perception. Besides, we further explore the potential of FP2AT by developing its coarse-to-fine version, which progressively refines the action space for more precise predictions. In addition, a proprioceptive encoder is developed to mimic the perception of body movements and contact, elevating the effectiveness of the FP2AT. Furthermore, a new metric, the average number of key actions (ANKA), is introduced to evaluate efficiency and planning capability. In various simulated and real-robot examples, our methods significantly outperform state-of-the-art 3-D-vision-based methods in success rate and ANKA metrics.

关键词： Three-dimensional displays Robots visualization Transformers Robot learning Feature extraction Planning data mining Collision avoidance Cameras 3-D manipulation imitation learning task and motion planning transformer

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MetaGlyph: Automatic Generation of Metaphoric Glyph-based visualization

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 331-341页

作者： Ying, Lu Shu, Xinhuan Deng, Dazhen Yang, Yuchen Tang, Tan Yu, Lingyun Wu, Yingcai Zhejiang University State Key Lab of CAD&CG Hangzhou China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong School of Art and Archaeology Zhejiang University Hangzhou China Xi'an Jiaotong-Liverpool University Department of Computing Suzhou China

Glyph-based visualization achieves an impressive graphic design when associated with comprehensive visual metaphors, which help audiences effectively grasp the conveyed information through revealing data semantics. However, creating such metaphoric glyph-based visualization (MGV) is not an easy task, as it requires not only a deep understanding of data but also professional design skills. This paper proposes MetaGlyph, an automatic system for generating MGVs from a spreadsheet. To develop MetaGlyph, we first conduct a qualitative analysis to understand the design of current MGVs from the perspectives of metaphor embodiment and glyph design. Based on the results, we introduce a novel framework for generating MGVs by metaphoric image selection and an MGV construction. Specifically, MetaGlyph automatically selects metaphors with corresponding images from online resources based on the input data semantics. We then integrate a Monte Carlo tree search algorithm that explores the design of an MGV by associating visual elements with data dimensions given the data importance, semantic relevance, and glyph non-overlap. The system also provides editing feedback that allows users to customize the MGVs according to their design preferences. We demonstrate the use of MetaGlyph through a set of examples, one usage scenario, and validate its effectiveness through a series of expert interviews. © 2022 ieee.

关键词： visualization

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Discrete Wavelet Transform-Based Detection Transformer for Battery Weld Defect Detection

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Kang Liao, Limin Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Foshan Lianchuang Grad Sch Engn Foshan 528000 Peoples R China

There are three main challenges in applying target inspection methods to the detection of weld defects between the top cover and casing of a battery: 1) weld defects are difficult to visualize and label;2) the limited amount of sample data constrains the efficacy of the deep learning model;and 3) the depth sequence information at the weld seam of the battery case is rich in high-frequency features, which helps judge defects. However, existing feature extraction methods fail to consider the high-frequency features of the battery weld. To address these issues, we propose solutions to the problems separately: First, the Keyence Laser Scanner is used to capture the weld long depth sequence, and the optional feature interval visualization (OFIV) method is used to visualize the defects and further annotate the data. Second, the random slicing augmentation (RSA) method is proposed to augment the dataset. Third, the real-time discrete wavelet transform-based detection transformer (RT-DWTR) is employed to capture high-frequency features with the objective of adapting deep sequence data and enhancing detection performance. The experimental results indicate that the RSA method significantly enhances the performance of the detection network, while the RT-DWTR shows improvements in mAP50 by 0.6%, mAP50:90 by 3.4%, mAPs by 6.1%, and F1 score by 1.6% compared to the benchmark network while maintaining a similar number of model parameters.

关键词： Feature extraction Welding Defect detection data visualization Batteries Transformers Discrete wavelet transforms data mining Labeling data models Battery weld deep learning defect detection depth sequence laser scanner power battery RT-DETR transformer

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Presentation of Strategic Information in the Agriculture Domain at the Agriculture Office of Garut Regency using data visualization

Presentation of Strategic Information in the Agriculture Dom...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Shidik, Muhammad Jafar Soeryanto Soegoto, Eddy Warlina, Lia Dharmayanti, Dian Universitas Komputer Indonesia Department of Informatics Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Management Bandung Indonesia Universitas Komputer Indonesia Department of Urban & Regional Planning Bandung Indonesia

ISBN: (纸本)9798350317862

The purpose of this research is to produce the right visualization to help the Food Crops Sector obtain the information needed more quickly. The form of information presentation uses tables in Microsoft Excel, resulting in the field of food crops taking longer to get information. So that it hampers the understanding of information and policymaking. The method used in this research is data visualization. Based on research on data visualization, it is stated that effective data presentation is by transforming data into visual form. This research produces a visualization design and is implemented in the form of a prototype. The next stage is to test the results of the prototype using the usability testing method for food crop staff. Based on the test results, it can be concluded that the prototype of food crop data visualization can help the food crop field understand the information needed more quickly. © 2023 ieee.

关键词： data visualization

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dAn-oNo: Learning Environment for data Journalists Teaching data Analytics Principles 1

dAn-oNo: Learning Environment for Data Journalists Teaching ...

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1st ieee VIS Workshop on visualization Education, Literacy, and Activities (EduVis) part of the ieee VIS Conference

作者： Stoiber, Christina Emrich, Stefan Radkohl, Sonja Goldgruber, Eva Aigner, Wolfgang St Polten Univ Appl Sci St Polten Austria FH Joanneum Univ Appl Sci Graz Austria Landsiedl Popper OG & Datengeschichten EU Drahtwarenhandlung Veliki Boristof Austria

ISBN: (纸本)9798350330304

Several journalistic abilities are required to derive narratives from data, including the skill to discover and construct compelling stories (data storytelling), employ data-driven techniques to research and analyze information (data literacy), utilize visualization methods effectively (visualization literacy), and approach data with a combination of creativity and critical thinking. Despite their expertise in journalism, journalists often encounter challenges in comprehending and utilizing novel visual representations or understanding data analysis methods. The main objective of the dAn-oNo learning environment is to guide journalists through the data analytics process by removing coding hurdles and allowing them to experiment with and understand the code. The learning environment utilizes a Jupyter notebook with Markdown sections. It incorporates a step-by-step approach, covering various stages of the data analytics workflow, such as data importing, inspection, statistical analysis, and in-depth analysis. The learning environment also includes an automated profiler that translates warnings and information into human-understandable insights. The design and implementation of the dAn-oNo learning environment were informed by a literature review, user research, interviews with Austrian data journalists, a phase of exploring different technical possibilities for the learning environment, and rapid prototyping. We also report on the results of a preliminary study with students. The prototype is accessible here: https://***/stemrich/SEVA_DA-Onboarding-Tool

关键词： data analytics jupyter notebook learning data-journalism

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Interactive visualization for Smart Power Grid Efficiency and Outage Exploration

Interactive Visualization for Smart Power Grid Efficiency an...

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2023 ieee International Conference on Big data, Bigdata 2023

作者： Han, Dongyun Cho, Isaac Utah State University Computer Science Utah United States

ISBN: (纸本)9798350324457

This paper introduces an interactive visualization interface prototype tailored for electrical power grid system planners and analysts to facilitate the exploration of historical outage data and gain actionable insights. With the advent of smart sensors, data generation has exponentially increased. As the amount of data has significantly grown, so has the need to effectively monitor it. Our prototype addresses this challenge by seamlessly integrating heterogeneous datasets such as GIS, AMI, and OMS. Our interface is designed to empower system planners and analysts in assessing the performance of distribution systems, focusing on circuit efficiency and outage events. This paper describes the functionalities of the visualization interface and presents its example usage scenarios. © 2023 ieee.

关键词： interactive interface interactive visualization outage data power grid

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Research on Fast Analysis Model of Power Big data Integrating 3D data visualization Scene 3

Research on Fast Analysis Model of Power Big Data Integratin...

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2023 ieee 3rd International Conference on data science and Computer Application, ICDSCA 2023

作者： Qingrui, Lin Bixing, Lin Shuze, Gao Jiangping, Yang Chaowang, Wu State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fujian Fuzhou350000 China

ISBN: (纸本)9798350341546

After determining large-scale power data and spatial scenarios, a new analysis model is constructed for 3D visualization scenarios. In this model, big data mining and 3D visualization technology are organically integrated, information fusion and fuzzy tomographic analysis method are adopted to realize efficient mining of power big data, and 3D rendering algorithm based on attribute reduction is used to improve the real-time performance of power 3D scene rendering. The visual feature reconstruction technology is used to realize the visual reconstruction of the 3D scene of power big data in the distribution network. The experiment shows that the data visualization management method can realize the efficient mining of power big data and real-time rendering of 3D model. © 2023 ieee.

关键词： 3D Scene Rendering Information Fusion Power data Three-Dimensional data Visual Scene

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The Quest for : Embedded visualization for Augmenting Basketball Game Viewing Experiences

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 962-971页

作者： Lin, Tica Chen, Zhutian Yang, Yalong Chiappalupi, Daniele Beyer, Johanna Pfister, Hanspeter John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science ETH Zürich Switzerland Harvard University United States

Sports game data is becoming increasingly complex, often consisting of multivariate data such as player performance stats, historical team records, and athletes' positional tracking information. While numerous visual analytics systems have been developed for sports analysts to derive insights, few tools target fans to improve their understanding and engagement of sports data during live games. By presenting extra data in the actual game views, embedded visualization has the potential to enhance fans' game-viewing experience. However, little is known about how to design such kinds of visualizations embedded into live games. In this work, we present a user-centered design study of developing interactive embedded visualizations for basketball fans to improve their live game-watching experiences. We first conducted a formative study to characterize basketball fans' in-game analysis behaviors and tasks. Based on our findings, we propose a design framework to inform the design of embedded visualizations based on specific data-seeking contexts. Following the design framework, we present five novel embedded visualization designs targeting five representative contexts identified by the fans, including shooting, offense, defense, player evaluation, and team comparison. We then developed Omnioculars, an interactive basketball game-viewing prototype that features the proposed embedded visualizations for fans' in-game data analysis. We evaluated Omnioculars in a simulated basketball game with basketball fans. The study results suggest that our design supports personalized in-game data analysis and enhances game understanding and engagement. © 2022 ieee.

关键词： data visualization

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Incorporation of Human Knowledge into data Embeddings to Improve Pattern Significance and Interpretability

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 723-733页

作者： Li, Jie Zhou, Chun-Qi College of Intelligence and Computing Tianjin University China

Embedding is a common technique for analyzing multi-dimensional data. However, the embedding projection cannot always form significant and interpretable visual structures that foreshadow underlying data patterns. We propose an approach that incorporates human knowledge into data embeddings to improve pattern significance and interpretability. The core idea is (1) externalizing tacit human knowledge as explicit sample labels and (2) adding a classification loss in the embedding network to encode samples' classes. The approach pulls samples of the same class with similar data features closer in the projection, leading to more compact (significant) and class-consistent (interpretable) visual structures. We give an embedding network with a customized classification loss to implement the idea and integrate the network into a visualization system to form a workflow that supports flexible class creation and pattern exploration. Patterns found on open datasets in case studies, subjects' performance in a user study, and quantitative experiment results illustrate the general usability and effectiveness of the approach. © 2022 ieee.

关键词： Embeddings

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An Analytics Framework for Analyzing Social Network News

An Analytics Framework for Analyzing Social Network News

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Sun, Fu-Shing Guo, Miao Ball State Univ Comp Sci Dept Muncie IN 47306 USA Univ Connecticut Dept Digital Media & Design Storrs CT USA

ISBN: (纸本)9798350361513;9798350372304

We present an analytics framework for analyzing news posts available on Instagram. There are four major components in this framework including data collection, data processing, data analytics, and data visualization. The data collection module is responsible for collecting the news posts from the Instagram website. data cleaning, filtering, and parsing unstructured data are done in the data processing module. The data analytics module computes the desired analytics in various domains, and finally the data visualization module displays meaningful findings to the users. We also demonstrate how the framework works with real-world online news samples in this paper.

关键词： social network news data analytics data engineering data visualization

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