惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
WordPress大学
WordPress大学
T
Tailwind CSS Blog
V
Visual Studio Blog
月光博客
月光博客
Hugging Face - Blog
Hugging Face - Blog
小众软件
小众软件
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - Franky
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Last Week in AI
Last Week in AI
阮一峰的网络日志
阮一峰的网络日志
量子位
有赞技术团队
有赞技术团队
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
Jina AI
Jina AI
雷峰网
雷峰网
博客园 - 【当耐特】
博客园 - 叶小钗
美团技术团队
宝玉的分享
宝玉的分享
IT之家
IT之家

cs.DB updates on arXiv.org

Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
Visualization of Real-time Displacement Time History supe...
M. Aguero, D. Doyle, D. Mascarenas, F. Moreu · 2021-10-17 · via cs.DB updates on arXiv.org

Wireless Smart Sensors (WSS) process field data and inform structural engineers and owners about the infrastructure health and safety. In bridge engineering, inspectors make decisions using objective data from each bridge. They decide about repairs and replacements and prioritize the maintenance of certain structure elements on the basis of changes in displacements under loads. However, access to displacement information in the field and in real-time remains a challenge. Displacement data provided by WSS in the field undergoes additional processing and is seen at a different location by an inspector and a sensor specialist. When the data is shared and streamed to the field inspector, there is a inter-dependence between inspectors, sensor specialists, and infrastructure owners, which limits the actionability of the data related to the bridge condition. If inspectors were able to see structural displacements in real-time at the locations of interest, they could conduct additional observations, which would create a new, information-based, decision-making reality in the field. This paper develops a new, human-centered interface that provides inspectors with real-time access to actionable structural data (real-time displacements under loads) during inspection and monitoring enhanced by Augmented Reality (AR). It summarizes the development and validation of the new human-infrastructure interface and evaluates its efficiency through laboratory experiments. The experiments demonstrate that the interface accurately estimates dynamic displacements in comparison with the laser. Using this new AR interface tool, inspectors can observe and compare displacement data, share it across space and time, and visualize displacements in time history.