Results 31 to 40 of about 98,857 (165)

Abrupt user load change detection based on multiple features and LOF algorithm

Zhejiang dianli, 2023
The sudden load changes impact power grids by frequency and power oscillations. In order to distinguish the complex and massive abnormal user load data， this paper proposes a method combining multiple features and LOF （local outlier factor） algorithm ...
ZENG Jing, LOU Bing, LYU Na, DENG Jun, WANG Guanming   +4 more
doaj   +1 more source

A geometrical analysis of global stability in trained feedback networks [PDF]

, 2019
Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantial progress in designing effective learning algorithms has been achieved in
Mastrogiuseppe, Francesca, Ostojic, Srdjan   +1 more
core   +2 more sources

Enhanced Connectivity Validity Measure Based on Outlier Detection for Multi-Objective Metaheuristic Data Clustering Algorithms

Applied Computational Intelligence and Soft Computing, 2022
Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems.
Hossam M. J. Mustafa, Masri Ayob
doaj   +1 more source

Model-free detection of unique events in time series

Scientific Reports, 2022
Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown.
Zsigmond Benkő, Tamás Bábel, Zoltán Somogyvári   +2 more
doaj   +1 more source

A Novel GPR-Based Prediction Model for Strip Crown in Hot Rolling by Using the Improved Local Outlier Factor

IEEE Access, 2021
In the hot rolling process, the prediction of strip crown is the key factor to improve the flatness quality of the strip. However, the traditional prediction method can only provide prediction values, but does not quantitatively evaluate the prediction ...
Yan Wu, Xu Li, Feng Luan, Yaodong He
doaj   +1 more source

Probabilistic Anomaly Detection in Natural Gas Time Series Data [PDF]

, 2016
This paper introduces a probabilistic approach to anomaly detection, specifically in natural gas time series data. In the natural gas field, there are various types of anomalies, each of which is induced by a range of causes and sources.
Akouemo Kengmo Kenfack, Hermine Nathalie, Povinelli, Richard J.   +1 more
core   +2 more sources

Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

Remote Sensing, 2022
In hyperspectral remote sensing, the clustering technique is an important issue of concern. Affinity propagation is a widely used clustering algorithm.
Haimiao Ge, Liguo Wang, Haizhu Pan, Yuexia Zhu, Xiaoyu Zhao, Moqi Liu   +5 more
doaj   +1 more source

Robust regression for large-scale neuroimaging studies [PDF]

, 2015
Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts.
,, Banaschewski, Tobias, Barker, Gareth J, Bokde, Arun L W, Brühl, Rüdiger, Butzek, Brigitte, Conrod, Patricia, Da Mota, Benoit, Flor, Herta, Fritsch, Virgile, Frouin, Vincent, Garavan, Hugh, Lemaitre, Hervé, Loth, Eva, Mann, Karl, Nees, Frauke, Paus, Tomas, Poline, Jean-Baptiste, Schad, Daniel J, Schümann, Gunter, Thirion, Bertrand, Varoquaux, Gaël   +21 more
core   +1 more source

Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model

Sensors, 2016
In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving ...
Changhong Fu, Ran Duan, Dogan Kircali, Erdal Kayacan   +3 more
doaj   +1 more source

Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor

2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), 2022
Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial and physical effects.
Johannesen, Nils Jakob, Kolhe, Mohan Lal, Goodwin, Morten   +2 more
openaire   +2 more sources