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Computing just what you need: Online data analysis and reduction at extreme scales
A growing disparity between supercomputer computation speeds and I/O rates makes it
increasingly infeasible for applications to save all results for offline analysis. Instead …
increasingly infeasible for applications to save all results for offline analysis. Instead …
Streaming anomaly detection using randomized matrix sketching
Data is continuously being generated from sources such as machines, network traffic,
application logs, etc. Timely and accurate detection of anomalies in massive data streams …
application logs, etc. Timely and accurate detection of anomalies in massive data streams …
Quantum assimilation-based data augmentation for state of health prediction of lithium-ion batteries with peculiar degradation paths
H Gao, K Lin, Y Cui, Y Chen - Applied Soft Computing, 2022 - Elsevier
Lithium-ion batteries with more rapid capacity loss or “peculiar degradation paths” are
usually hard to completely avoid in production given complex electrochemical systems and …
usually hard to completely avoid in production given complex electrochemical systems and …
Quantum assimilation-based state-of-health assessment and remaining useful life estimation for electronic systems
Y Cui, J Shi, Z Wang - IEEE Transactions on Industrial …, 2015 - ieeexplore.ieee.org
State-of-health (SOH) assessment and remaining useful life (RUL) estimation are among the
key issues in prognostics and health management (PHM) for electronic systems. Unlike …
key issues in prognostics and health management (PHM) for electronic systems. Unlike …
Using Dirichlet marked Hawkes processes for insider threat detection
Malicious insiders cause significant loss to organizations. Due to an extremely small number
of malicious activities from insiders, insider threat is hard to detect. In this article, we present …
of malicious activities from insiders, insider threat is hard to detect. In this article, we present …
Physics-based anomaly detection defined on manifold space
Current popular anomaly detection algorithms are capable of detecting global anomalies but
often fail to distinguish local anomalies from normal instances. Inspired by contemporary …
often fail to distinguish local anomalies from normal instances. Inspired by contemporary …
Diverse power iteration embeddings and its applications
Spectral Embedding is one of the most effective dimension reduction algorithms in data
mining. However, its computation complexity has to be mitigated in order to apply it for real …
mining. However, its computation complexity has to be mitigated in order to apply it for real …
Diverse power iteration embeddings: Theory and practice
Manifold learning, especially spectral embedding, is known as one of the most effective
learning approaches on high dimensional data, but for real-world applications it raises a …
learning approaches on high dimensional data, but for real-world applications it raises a …
[PDF][PDF] A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection.
Purpose: The main aim of this study is to build a robust novel approach that is able to detect
outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to …
outliers in the datasets accurately. To serve this purpose, a novel approach is introduced to …
[کتاب][B] Dynamic Fraud Detection via Sequential Modeling
P Zheng - 2020 - search.proquest.com
The impacts of information revolution are omnipresent from life to work. The web services
have significantly changed our living styles in daily life, such as Facebook for …
have significantly changed our living styles in daily life, such as Facebook for …