检索结果-内蒙古大学图书馆

Immersive Analytics as a Support Medium for data-Driven Monitoring in Hydropower

ieee Transactions on visualization and Computer Graphics 2025年第5期31卷 3536-3546页

作者： Medeiros, Marina Lima Kaufmann, Hannes Schmidt, Johanna VRVis GmbH TU Wien Austria TU Wien Austria

Hydropower turbines are large-scale equipment essential to sustainable energy supply chains, and engineers have few opportunities to examine their internal structure. Our Immersive Analytics (IA) application is part of a research project that combines and compares simulated water turbine flows and sensor-measured data, looking for data-driven predictions of the lifetime of the mechanical parts of hydroelectric power plants. Our prototype combines spatial and abstract data in an immersive environment in which the user can navigate through a full-scale model of a water turbine, view simulated water flows of three different energy supply conditions, and visualize and interact with sensor-collected data situated at the reference position of the sensors in the actual turbine. In this paper, we detail our design process, which resulted from consultations with domain experts and a literature review, give an overview of our prototype, and present its evaluation, resulting from semi-structured interviews with experts and qualitative thematic analysis. Our findings confirm the current literature that IA applications add value to the presentation and analysis of situated data, as they show that we advance in the design directions for IA applications for domain experts that combine abstract and spatial data, with conclusions on how to avoid skepticism from such professionals. © 1995-2012 ieee.

关键词： Hydroelectric power plants

来源：评论

学校读者我要写书评

暂无评论

Subjective and Objective analysis of Indian Social Media Video Quality

引用

ieee TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 140-153页

作者： Mishra, Sandeep Jha, Mukul Bovik, Alan C. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA ShareChat Inc Bengaluru 560103 India

We conducted a large-scale subjective study of the perceptual quality of User-Generated Mobile Video Content on a set of mobile-originated videos obtained from ShareChat, a social media platform widely used across India. The content viewed by volunteer human subjects under controlled laboratory conditions has the benefit of culturally diversifying the existing corpus of User-Generated Content (UGC) video quality datasets. There is a great need for large and diverse UGC-VQA datasets, given the explosive global growth of the visual internet and social media platforms. This is particularly true in regard to videos obtained by smartphones, especially in rapidly emerging economies like India. ShareChat provides a safe and cultural community oriented space for users to generate and share content in their preferred Indian languages and dialects. Our subjective quality study, which is based on this data, supplies much needed cultural, visual, and language diversification to the overall shareable corpus of video quality data. We expect that this new data resource will also allow for the development of systems that can predict the perceived visual quality of Indian social media videos, and in this context, control scaling and compression protocols for streaming, provide better user recommendations, and guide content analysis and processing. We demonstrate the value of the new data resource by conducting a study of leading No-Reference Video Quality Assessment (NR-VQA) models on it, including a simple new model, called MoEVA, which deploys a mixture of experts to predict video quality. Both the new LIVE-ShareChat database and sample source code for MoEVA are being made freely available to the research community at https://***/sandeep-sm/LIVE-SC.

关键词： databases Quality assessment Video recording Predictive models Feature extraction Social networking (online) Cultural differences Smart phones data models visualization No-reference video quality assessment user-generated culturally-oriented mobile video quality database

来源：评论

学校读者我要写书评

暂无评论

IVESA - Visual analysis of Time-Stamped Event Sequences

引用

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第4期31卷 2235-2256页

作者： Bernard, Jurgen Barth, Clara-Maria Cuba, Eduard Meier, Andrea Peiris, Yasara Shneiderman, Ben Univ Zurich CH-8006 Zurich Switzerland Univ Zurich Digital Soc Initiat Zurich DSI CH-8001 Zurich Switzerland Univ Zurich Digital Soc Initiat Zurich DSI CH-8006 Zurich Switzerland Univ Maryland College Pk MD 20742 USA

Time-stamped event sequences (TSEQs) are time-oriented data without value information, shifting the focus of users to the exploration of temporal event occurrences. TSEQs exist in application domains, such as sleeping behavior, earthquake aftershocks, and stock market crashes. Domain experts face four challenges, for which they could use interactive and visual data analysis methods. First, TSEQs can be large with respect to both the number of sequences and events, often leading to millions of events. Second, domain experts need validated metrics and features to identify interesting patterns. Third, after identifying interesting patterns, domain experts contextualize the patterns to foster sensemaking. Finally, domain experts seek to reduce data complexity by data simplification and machine learning support. We present IVESA, a visual analytics approach for TSEQs. It supports the analysis of TSEQs at the granularities of sequences and events, supported with metrics and feature analysis tools. IVESA has multiple linked views that support overview, sort+filter, comparison, details-on-demand, and metadata relation-seeking tasks, as well as data simplification through feature analysis, interactive clustering, filtering, and motif detection and simplification. We evaluated IVESA with three case studies and a user study with six domain experts working with six different datasets and applications. Results demonstrate the usability and generalizability of IVESA across applications and cases that had up to 1,000,000 events.

关键词： Time series analysis Task analysis Filtering Metadata Measurement Electronic mail Feature extraction Time-stamped event sequences time-oriented data visual analytics data-first design study iterative design visual interfaces user evaluation

来源：评论

学校读者我要写书评

暂无评论

Empowering IoT-Based Autonomous Driving via Federated Instruction Tuning With Feature Diversity

引用

ieee INTERNET OF THINGS JOURNAL 2025年第6期12卷 6095-6108页

作者： Chen, Jiao He, Jiayi Chen, Fangfang Lv, Zuohong Tang, Jianhua Jia, Yunjian South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

Integrating large language models (LLMs) with the Internet of Things (IoT) offer great potential for enhancing vehicle personalization and adaptability in autonomous driving (AD), particularly in open-world scenarios. However, the increasing scarcity of high-quality public data poses a challenge, which could hinder the progress of LLMs in AD. To address this, we propose a novel approach, federated instruction tuning (FIT), that leverages federated learning (FL) to enable collaborative training of a shared model across multiple data owners without sharing raw data, thereby preserving privacy and mitigating data scarcity. Complementing FIT, we introduce a feature diversity (FD) strategy that enriches visual and textual diversity and significantly expands AD data by generating new instruction-following data across key dimensions, such as time, weather, and occlusion. Extensive experiments using LLaMA-Adapter as the base model and four FL methods validate the effectiveness of the FIT framework and the FD strategy. Our analysis also compares LLMs ranging from 1.1 to 7B parameters, with results evaluated using GPT score, demonstrating the potential of FIT in AD. Our findings suggest that FIT and FD can support intelligent network operation and optimization in IoT, benefiting both the AD and artificial intelligence (AI) industries.

关键词： data models Internet of Things Training Autonomous vehicles Tuning Decision making Adaptation models visualization Sensors data privacy Autonomous driving (AD) federated learning (FL) instruction tuning Internet of Things (IoT) large language models (LLMs)

来源：评论

学校读者我要写书评

暂无评论

Explainable Video Topics for Content Taxonomy: A Multimodal Retrieval Approach to Industry-Compliant Contextual Advertising

引用

ieee ACCESS 2025年 13卷 30597-30612页

作者： de Silva, Waruna Fernando, Anil Univ Strathclyde Dept Comp & Informat Sci Glasgow G1 1XQ Scotland

Owing to the increased video content consumption in recent years, the need for advanced contextual advertising methods that leverage increasing user engagement and relevance on advertisement-based video-on-demand platforms has increased. Traditional behavior-based advertisement targeting is waning, particularly owing to the recent strict privacy policies that favor user consent and privacy. This study proposes an innovative approach for integrating advanced natural language processing with multimodal analysis for video contextual advertising. To this end, transformer-based architectures, specifically BERTopic, computer vision techniques, and large language models were used to extract sets of topics from visual and textual video data automatically and systematically. The proposed framework decodes the taxonomy of content efficiently through videos in different levels of noise and languages. Empirical analysis of the YouTube-8M dataset shows the potential for the approach to change the paradigm in video advertising. Built to be scalable and easily adaptable, this solution can handle multifarious and complex user-generated content well, suited for a wide range of applications across various media platforms.

关键词： Advertising Semantics visualization Taxonomy Analytical models Feature extraction Context modeling Streaming media Media Image color analysis Natural language processing video contextual advertisements multimodal fusion topic modeling BERTopic contextual taxonomy standards multi-label classification

来源：评论

学校读者我要写书评

暂无评论

Research on the Automatic Generation and Display System of Multi-Dimensional Intelligent visualization of GIS Landscape data

Research on the Automatic Generation and Display System of M...

引用

2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Yi, Anqi Art Center College of Design Hubei University of Technology Wuhan China

ISBN: (纸本)9798331504205

This paper aims to build an efficient multi-dimensional visualization platform for GIS landscape data. Through this system, large-scale, multi-type landscape data in the geographic information system can be automatically processed and displayed in multiple dimensions. In the design, this paper adopts an optimized data processing algorithm to ensure the efficiency of data extraction, conversion and integration. Then, multi-dimensional visualization views of different landscape features are automatically generated to support accurate analysis and display of multiple dimensions such as geography, ecology, and climate. The model simulation part is based on a large amount of measured data, and multiple rounds of tests and evaluations have been carried out. The results show that the system is superior to traditional display systems in data display accuracy, real-time performance, and response speed, especially in the fusion display of multi-dimensional data. data analysis shows that the system can quickly identify and dynamically display different geographical landscape features, providing an effective tool for landscape analysis in geographic information systems. © 2025 ieee.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

Survey on 3D Reconstruction Techniques: large-Scale Urban City Reconstruction and Requirements

引用

ieee Transactions on visualization and Computer Graphics 2025年第5期31卷 2268-2276页

作者： Christodoulides, Andreas Tam, Gary K.L. Clarke, James Smith, Richard Horgan, Jon Micallef, Nicholas Morley, Jeremy Villamizar, Nelly Walton, Sean Swansea University United Kingdom Ordnance Survey United Kingdom

3D representations of large-scale and urban scenes are crucial across various industries, including autonomous driving, urban planning, natural resource supervision and many more. large-scale industrial reconstructions are inherently complex and multifaceted. Many existing surveys primarily focus on academic progressions and often neglect the intricate and diverse needs of industry. This survey aims to bridge this gap by providing a comprehensive analysis of 3D reconstruction methods, with a focus on industrial requirements such as scalability and integration of human interaction. Our approach involves utilizing Affinity Diagramming to systematically analyze qualitative data gathered from industrial partners. This methodology enables us to gain deep insights into how recent literature addresses these specific industrial needs. The survey encompasses various aspects, including input and reconstruction modalities, architectural models, datasets, evaluation metrics, and the incorporation of prior knowledge. We further discuss practical implications derived from our analysis, highlighting key considerations for future advancements in 3D reconstruction methods tailored for large-scale applications. © 2025 ieee.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

Computer Vision for Clinical Gait analysis: A Gait Abnormality Video dataset

引用

ieee ACCESS 2025年 13卷 45321-45339页

作者： Ranjan, Rahm Ahmedt-Aristizabal, David Armin, Mohammad Ali Kim, Juno Univ New South Wales Sch Optometry & Vis Sci Sydney NSW 2033 Australia CSIRO Imaging & Comp Vis Grp Data61 Canberra ACT 2601 Australia

Clinical gait analysis (CGA) using computer vision is an emerging field in artificial intelligence facing great challenges in obtaining accessible and annotated real-world data and clear task objectives. This paper lays the groundwork for advancing CGA, presenting vision-based methods and datasets tailored for gait analysis. We introduce the Gait Abnormality in Video dataset (GAVD) in response to a review of over 150 existing gait-related datasets, designed to address the critical need for a large, clinically annotated gait dataset. GAVD contains 1,874 video sequences, capturing normal, abnormal, and pathological gait patterns. It features RGB data, clinically annotated from publicly available online sources, and encompassing over 400 subjects who have undergone clinical-grade visual screening. The dataset spans diverse environments, such as hospital clinics and uncontrolled urban settings. We validated GAVD through action recognition models, achieving abnormality detection rates of 94% and 92% with Temporal Segment Networks (TSN) and SlowFast networks, respectively. We further demonstrate GAVD's clinical utility by testing on the Clinical Abnormality Simulated dataset (CASD) achieving abnormality detection accuracy of 73% and 87%, respectively, with TSN and SlowFast networks. To facilitate further research, we provide a GitHub repository GAVD with URL links and clinically relevant annotation for over 450 videos featuring a variety of gait patterns and unique subjects.

关键词： Gait recognition Computer vision Reviews Biomedical monitoring Monitoring Accuracy Legged locomotion visualization Pathology Motion capture Clinical gait dataset abnormal gait detection vision-based action recognition gait pathology classification annotated gait videos

来源：评论

学校读者我要写书评

暂无评论

Advancing Sequential Manga Colorization for AR Through data Synthesis

引用

ieee ACCESS 2025年 13卷 7526-7537页

作者： Golyadkin, Maksim Saraev, Sergey Makarov, Ilya Artificial Intelligence Res Inst AIRI Moscow 105064 Russia HSE Univ Int Lab Intelligent Syst & Struct Anal Moscow 101000 Russia ITMO Univ AI Talent Hub St Petersburg 191101 Russia ISP RAS Res Ctr Trusted Artificial Intelligence Moscow 109004 Russia

Manga colorization in augmented reality (AR) environments presents unique challenges, particularly when colorizing manga pages captured in photos under various real-world conditions. Testing models in AR settings for manga colorization has been a significant challenge, primarily because of the absence of suitable datasets tailored for this task. To address this, we propose a benchmark for evaluating existing colorization models. We first collected a relatively small dataset of manga book photos taken in settings suitable for AR applications. Then, we developed a method that leverages a pretrained diffusion model to generate synthetic photos from scans of manga pages. Using large datasets of manga scans, we created an extensive synthetic dataset. Combining both real and synthetic data, we established a comprehensive benchmark for manga colorization in AR scenarios. We tested existing models for natural image and manga colorization on this benchmark. As a result, our evaluation showed that current models are not well-suited for AR-based colorization tasks, indicating a need for further improvement.

关键词： Diffusion models Adaptation models Image color analysis Synthetic data data models visualization Training Image synthesis Benchmark testing Translation Manga colorization augmented reality dataset benchmark

来源：评论

学校读者我要写书评

暂无评论

LLMER: Crafting Interactive Extended Reality Worlds with JSON data Generated by large Language Models

引用

ieee Transactions on visualization and Computer Graphics 2025年第5期31卷 2715-2724页

作者： Chen, Jiangong Wu, Xiaoyi Lan, Tian Li, Bin Pennsylvania State University Department of Electrical Engineering United States George Washington University Department of Electrical and Computer Engineering United States

The integration of large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 1995-2012 ieee.

关键词： Coding errors

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：