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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54280 条记录，以下是3931-3940 订阅

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VIS 2024 conference Committee

VIS 2024 Conference Committee

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International conference on Visualisation, VIS

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Research on the Innovative Strategy of Teaching Advertising Design Based on Data visualization 10

Research on the Innovative Strategy of Teaching Advertising ...

引用

10th International conference on Orange Technology, ICOT 2022

作者： Jiang, Qian School of Art Design and Media Sanda University Shanghai China

ISBN: (纸本)9781665454193

In terms of properties, advertising includes marketing and communication. Therefore, in addition to wonderful creativity, a good marketing and communication method is essential to a good advertising. At present, most of the advertising design courses in Chinese universities are design-oriented and cut off from marketing, which makes the works only have creative concepts but weak practical application values. Based on data visualization, the purpose of this study is to explore the innovative ideas and methods of teaching advertising design in the new media environment from three aspects of course concept, method and practical effect. Comparing the curriculum of advanced art colleges in the world and combining the characteristics of local art education, the paper also tries to conclude what changes will happen to the role of designers under the influence of digital technology and what the advertising will be in the future. © 2022 ieee.

关键词： Data visualization

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Research on Key Technologies of 3D Real Scene visualization Based on Multi-source Observation Data Fusion 3

Research on Key Technologies of 3D Real Scene Visualization ...

引用

3rd International conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Jing, Qiang Miao, Hongzhi Yang, Xiaowei Ming, Huifang Yin, Hui Hong Kong Zhuhai Macao Bridge Authority Zhuhai China 722 Research Institute of China Shipbuilding Corporation Wuhan China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

Based on the big data of multi-source observation of the underwater environment of the Hong Kong-Zhuhai-Macao Bridge,this study constructed an underwater three-dimensional(3D) scene with multi-source data fusion,realized the visual presentation and management of the underwater environment of large structures,and satisfied the requirements of visual operation and safety detection and protection of large *** this paper,the key technologies related to multisource spatio-temporal big data fusion and 3D real scene visualization were studied,a multi-storage distributed storage framework for hybrid spatio-temporal big data is constructed,a hierarchical organization model for spatio-temporal big data is proposed,and multi-source observation data fusion of large underwater structures is ***,3D scenes are rendered based on technologies such as WebGL,onshore/undersea terrain simulation,and gridding to realize 3D real-world *** construction of a 3D real scene visualization model based on multi-source spatiotemporal big data fusion can quickly realize the deformation monitoring and spatiotemporal deduction of the Hong Kong-Zhuhai-Macao *** on the fused 3D scene,the evolution analysis of underwater terrain and stratum can be conducted,which can effectively support the monitoring and management of the underwater environment of large structures. © 2022 ieee.

关键词： visualization

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ResNet50 Utilization for Bag Classification: A CNN Model visualization Approach in Deep Learning

ResNet50 Utilization for Bag Classification: A CNN Model Vis...

引用

information Technology, Electronics and Intelligent Communication Systems (ICITEICS), ieee International conference on

作者： Muskan Singla Kanwarpartap Singh Gill Rahul Chauhan Hemant Singh Pokhariya Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The research work revolves around integrating user-centric interactive platforms and mobile applications with algorithms designed for bag classification. It is imperative to develop user-friendly interfaces that allow users to engage with and comprehend the model's classifications, fostering confidence. In this study, a lightweight CNN model known as ResNet50 is employed to categorize bag images within a dataset, with the primary goal being to leverage ResNet50's efficiency for swift and accurate bag classification. Beyond the enhancement of bag classification methods, ongoing research in these domains seeks to improve the reliability and adaptability of deep learning systems. Future investigations may concentrate on exploring ResNet50's generalization capabilities in bag categorization. Innovative strategies such as domain adaptation, transfer learning, or an augmentation of data could be explored to ensure the model's robust performance across diverse datasets. The deep learning model undergoes training using an extensive collection of bag images featuring diverse textures, colors, and shapes. In addition to showcasing ResNet50's classification prowess, the study employs visualization techniques to reveal the inner workings of the CNN. The characteristics and patterns discerned by the algorithm during the bag categorization process are highlighted through data visualization tools. The research underscores how ResNet50 adeptly extracts nuanced information from bag images, thereby facilitating precise and reliable classification through the visualization of the CNN model. Overall, this study underscores the potential of ResNet50 by showing 98% precision as a pragmatic and versatile deep learning model, offering implications for item recognition and classification across various domains.

关键词： Deep learning Adaptation models Analytical models visualization Computational modeling Transfer learning Data visualization

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Towards Consistent Visual-Inertial Navigation for Unmanned Aerial Vehicles using Depth information 35

Towards Consistent Visual-Inertial Navigation for Unmanned A...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Gomaa, Mahmoud A. K. De Silva, Oscar Jayasiri, Awantha Mann, George K., I Memorial Univ Intelligent Syst Lab St John NB Canada CNR Flight Res Lab Ottawa ON Canada

ISBN: (数字)9781665484329

ISBN: (纸本)9781665484329

This paper proposes a new filtering-based depth enhanced visual inertial navigation system (DE-VINS) for quadrotor unmanned aerial vehicles (UAV). This filter addresses the drifting and degraded performance of a class of conventional monocular VINS filters at hovering conditions. A theoretical nonlinear observability analysis is performed to verify the filter design. The performance of the proposed DE-VINS is numerically evaluated using a Matlab simulator and then compared against the conventional VINS filter proposed in literature. The results show improved performance of the DE-VINS in terms of estimation accuracy and consistency at zero-velocity flight conditions.

关键词： visualization Monte Carlo methods Three-dimensional displays Estimation Position measurement information filters Autonomous aerial vehicles

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ICOSNIKOM 2022 - 2022 ieee International conference of Computer Science and information Technology: Boundary Free: Preparing Indonesia for Metaverse Society

ICOSNIKOM 2022 - 2022 IEEE International Conference of Compu...

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4th ieee International conference of Computer Science and information Technology, ICOSNIKOM 2022

ISBN: (纸本)9798350399073

The proceedings contain 64 papers. The topics discussed include: application of backpropagation and linear regression prediction methods on land deformation results using the DInSAR method (Case Study: Dolok Sanggul, North Sumatra);web browser extension development of structured query language injection vulnerability detection using long short term memory algorithm;design and build a visitor data collection system using 2 authentication factors;automated nutrition Doser for hydroponic system based on IoT;user experience analysis of DiTenun website-based application and mobile-based application using user persona and user experience questionnaire (UEQ);blockchain applications in product recall management: a study on the automotive industry;mapping and visualization of artificial intelligence for digital economy research: a bibliometric approach;a review text-based recommendation system in text mining;software complexity analysis of integrated accounting information systems for cooperatives using the use case point (UCP) method;and framework of optimization model for traceability in a sequential scheduling production planning for meat product.

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LIVE: A Local Interpretable model-agnostic visualizations and Explanations 10

LIVE: A Local Interpretable model-agnostic Visualizations an...

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10th ieee International conference on Healthcare Informatics (ieee ICHI)

作者： Shi, Peichang Gangopadhyay, Aryya Yu, Ping Booz Allen Hamilton Rockville MD 20852 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA

ISBN: (数字)9781665468459

ISBN: (纸本)9781665468459

Deep learning has been successfully applied to various types of classification and prediction tasks including images, text, and tabular data, however interpretability of deep learning models still remains challenges, which becomes a limiting factor for further *** of current interpretable models are limited to how to interpret the deep learning models. In this paper, we propose a simple but effective novel Local Interpretable model-agnostic visualizations and Explanations (LIVE) algorithm through combining decision tree and deep learning models. The proposed LIVE algorithm is validated through 4 different data sets with different types of deep learning models including convolutional neural network (CNN) and long shortterm memory models (LSTM). Our experiment results show that 1) LIVE algorithm could generally consistently help improve the deep learning models by 0.02 0.03 in AUC;2) Our LIVE algorithm is a weakly supervised localization tool for region of interest identification;3) Our LIVE algorithm could be applied to different deep learning models and different types of data.

关键词： Deep learning Interpretability visualization Decision tree

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Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging 21

Cappella: Establishing Multi-User Augmented Reality Sessions...

引用

21st ACM/ieee International conference on information Processing in Sensor Networks (IPSN)

作者： Miller, John Soltanaghai, Elahe Duvall, Raewyn Chen, Jeff Bhat, Vikram Pereira, Nuno Rowe, Anthony Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665496247

Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella(1), an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across multiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter.

关键词： visualization Three-dimensional displays Buildings Collaboration Distance measurement Real-time systems Peer-to-peer computing

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Big Data Intelligence Empowered Specialized Disciplines Development Pattern Recognition in Power Industry Universities 5th

Big Data Intelligence Empowered Specialized Disciplines Deve...

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5th International conference on Big Data and Security (ICBDS)

作者： Zhang, Yan Liu, PengCheng Xu, Hao Ge, Linlin Zhu, Zhentao Nanjing Inst Technol Sch Econ & Management Nanjing 211167 Peoples R China Nanjing Inst Technol Sch Elect Engn Nanjing 211167 Peoples R China Nanjing Univ Sch Informat Management Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9789819743865;9789819743872

Universities are concentrating on building industry characteristics to strengthen and expand their influence in the information age. Big data on intellectual output is a key representation of discipline construction. We created an algorithm to identify development features of disciplines, including temporal trend, research hotspots, and mutation characteristics. Using CNKI as data source, with the aid of scientific knowledge graph and social network analysis, 10786 core journal thesis published between 2012 and 2021 demonstrated the co-occurrence, keyword development trajectory, and mutation word development path. According to data mining, high-level intellectual output that is relevant to industry increased in quantity and proportion, and intimacy also improved. High levels of interdisciplinary interaction, a variety of disciplinary innovations, and in-depth disciplinary culture formation should characterize the pattern of disciplinary development. This study is an attempt of specialized disciplines development pattern recognition by big data intelligence, and the recognition algorithms can be used for feature recognition in multidisciplinary fields.

关键词： Big data mining and visualization Big Data Intelligence Social network construction Pattern Recognition Specialized Disciplines in Power Industry Universities

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Visualizing Fault Induced Traveling Waves In Medium Voltage Systems

Visualizing Fault Induced Traveling Waves In Medium Voltage ...

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2022 ieee IAS Petroleum and Chemical Industry Technical conference, PCIC 2022

作者： Prabakar, Kumaraguru Lunacek, Monte Velaga, Yaswanth Nag Singh, Akanksha National Renewable Energy Laboratory 15013 Denver West Pkwy GoldenCO80401 United States

ISBN: (纸本)9781665497169

Traveling waves are induced in power systems during most transient events in the grid. These waves travel close to the speed of light in overhead lines and 50% to 60% the speed of light in underground cables. Even though traveling wave-based protection schemes for transmission systems are available commercially, traveling waves in medium voltage distribution networks are still in research space. Compared to transmission system, medium voltage distribution systems contain more reflections and refractions. Thus, visualization is challenging and is critical in locating faults in distribution network. To address this visualization challenge, this paper presents an open-source tool to visualize the traveling waves using Bewley lattice approach. The developed visualization tool will be useful for the protection engineers to detect and triangulate fault locations in the medium voltage systems and isolate the faults. © 2022 ieee.

关键词： visualization

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