Results 81 to 90 of about 5,452,269 (307)

Electrical Conductivities of Conductors, Semiconductors, and Their Mixtures at Elevated Temperatures

Advanced Engineering Materials, EarlyView.
This article presents a comprehensive review of temperature‐dependent electrical conductivity data for multiple material classes at elevated temperatures, highlighting a persistent conductivity gap between metals and semiconductors in the range of 102$\left(10\right)^{2}$– 107$\left(10\right)^{7}$ S/m. Metal–ceramic irregular metamaterials are proposed
Valentina Torres Nieto, Marcia A. Cooper
wiley   +1 more source

Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization

Jisuanji kexue yu tansuo, 2020
Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
doaj   +1 more source

Future Frame Prediction for Anomaly Detection - A New Baseline [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger
Wen Liu, Weixin Luo, Dongze Lian, Shenghua Gao   +3 more
semanticscholar   +1 more source

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

Advanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour, Prithwish Tarafder, Anton Wiberg, Huanyu Li, Johan Moverare, Patrick Lambrix   +5 more
wiley   +1 more source

Surface anomaly detection on island-based PV panels using edge neural networks

Zhejiang dianli
Surface anomaly detection on photovoltaic （PV） panels is crucial for their operation and maintenance， especially in island environments where challenges such as small anomaly sizes and minimal color differences are prevalent. Due to the poor accuracy and
ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya
doaj   +1 more source

Toward Generalist Anomaly Detection via In-Context Residual Learning with Few-Shot Sample Prompts [PDF]

Computer Vision and Pattern Recognition
This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target ...
Jiawen Zhu, Guansong Pang
semanticscholar   +1 more source

Composites of Shellac and Silver Nanowires as Flexible, Biobased, and Corrosion‐Resistant Transparent Conductive Electrodes

Advanced Functional Materials, EarlyView.
Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein, Tiago Carneiro Gomes, Eliza Ng, Simon Rondeau‐Gagné, Tricia Breen Carmichael   +4 more
wiley   +1 more source

UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection

Information
Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity.
Jianmei Zhong, Yanzhi Song
doaj   +1 more source

Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model

International Journal of Distributed Sensor Networks, 2016
As WSNs gain popularity, they are becoming more and more necessary for traffic anomaly detection. Because worms, attacks, intrusions, and other kinds of malicious behaviors can be recognized by traffic analysis and anomaly detection, WSN traffic anomaly ...
Qin Yu, Jibin Lyu, Lirui Jiang, Longjiang Li   +3 more
doaj   +1 more source

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]

PLoS ONE, 2016
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
doaj   +1 more source