Results 141 to 150 of about 4,738,087 (364)

Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection [PDF]

arXiv, 2020
Anomaly detection in time series has been widely researched and has important practical applications. In recent years, anomaly detection algorithms are mostly based on deep-learning generative models and use the reconstruction error to detect anomalies.
arxiv  

Internal Temperature Evolution Metrology and Analytics in Li‐Ion Cells

Advanced Functional Materials, EarlyView.
This study investigates the non‐linear evolution of internal temperatures across diverse operating conditions, highlighting the disparities between internal and external measurements and the resulting thermal asymmetries. The coupled thermo‐electrochemical modeling framework provides a comprehensive analysis of various heat generation modes, examining ...
Anuththara S. J. Alujjage, Bairav S. Vishnugopi, Avijit Karmakar, David P. Magee, Yevgen Barsukov, Partha P. Mukherjee   +5 more
wiley   +1 more source

Universal Superconductivity in FeTe and All‐Iron‐Based Ferromagnetic Superconductor Heterostructures

Advanced Functional Materials, EarlyView.
The first all‐iron‐based ferromagnetic superconductor heterostructures with high‐temperature superconductivity and strong ferromagnetism aredemonstrated. From this, it is discovered that FeTe becomes universallysuperconducting with a minute level of cationic impurities through doping ordiffusion from neighboring layers, suggesting its ground state can ...
Hee Taek Yi, Xiong Yao, Deepti Jain, Ying‐Ting Chan, An‐Hsi Chen, Matthew Brahlek, Kim Kisslinger, Kai Du, Myung‐Geun Han, Yimei Zhu, Weida Wu, Sang‐Wook Cheong, Seongshik Oh   +12 more
wiley   +1 more source

Improved Hyperspectral Anomaly Detection Algorithm with Double Layer Collaborative Structure [PDF]

Hangkong bingqi
In the field of hyperspectral target detection, algorithms based on collaborative representation have shown excellent performance. However, the pollution of background dictionaries by anomaly has always been a problem, which affects the detection ...
Li Huan, Zhao Jiahao, Liu Guanghan, Shi Jinhui, Song Jiangluqi, Zhou Huixin
doaj   +1 more source

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data [PDF]

arXiv
Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that most unlabeled data are normal, and train anomaly detectors by minimizing the anomaly scores for the unlabeled ...
arxiv  

Detection of renal anomalies by abdominal palpation in newborn infants. [PDF]

, 1976
M Perlman, John A. Williams
openalex   +1 more source

Electroplating of Wear‐ and Corrosion‐Resistant CrCoNi Medium‐Entropy Alloys beyond Hard Chromium Coatings

Advanced Functional Materials, EarlyView.
The electroplating of a CrCoNi medium‐entropy alloy is achieved using a mixture of an ionic liquid and an aqueous solution containing metal salts. The CrCoNi medium‐entropy alloy thin film exhibits high wear and corrosion resistance superior to conventional hard chromium coatings. Abstract High‐entropy alloys (HEAs) and medium‐entropy alloys (MEAs) are
Yuki Murakami, Shuhei Yoshida, Kosuke Ishii, Naoya Nishi, Akiyuki Ryoki, Nobuhiro Tsuji, Kuniaki Murase, Kazuhiro Fukami   +7 more
wiley   +1 more source

Research on traffic representation in network anomaly detection

Tongxin xuebao
Aiming to address the problem of information loss in traffic representation for network anomaly detection, the impact of feature information dimension of different traffic representation on anomaly detection performance was analyzed from the perspective ...
SUN Jianwen, ZHANG Bin, CHANG Heyu
doaj  

Possibility for Proactive Anomaly Detection [PDF]

arXiv
Time-series anomaly detection, which detects errors and failures in a workflow, is one of the most important topics in real-world applications. The purpose of time-series anomaly detection is to reduce potential damages or losses. However, existing anomaly detection models detect anomalies through the error between the model output and the ground truth
arxiv  

ErMn6Sn6: A Promising Kagome Antiferromagnetic Candidate for Room‐Temperature Nernst Effect‐Based Thermoelectrics

Advanced Functional Materials, EarlyView.
This work investigates the Nernst effect in the Kagome magnet ErMn6Sn6 which exhibits both topological and anomalous Nernst effects with the anomalous Nernst coefficient reaching 1.71 µV K⁻¹ at 300 K. This value surpasses that of most canted antiferromagnetic materials, making ErMn6Sn6 a promising candidate for advancing thermoelectric devices based on
Olajumoke Oluwatobiloba Emmanuel, Shuvankar Gupta, Xianglin Ke   +2 more
wiley   +1 more source