Results 81 to 90 of about 33,807 (240)

Outlier Detection of Photovoltaic Power Generation System Data Based on GMM-IF Algorithm

IEEE Access
The Gaussian Mixture Model (GMM) is widely used in anomaly detection due to its flexibility in handling complex data distributions and its soft classification mechanism.
Xin Li, Guoxin Du, Yuman Li, Zengqiang Ma, Lihu Liu, Minglong Jiang, Feiyu Liu, Tianming Mu   +7 more
doaj   +1 more source

A Novel Monitoring Strategy Combining the Advantages of NPE and GMM

IEEE Access, 2020
Semiconductor manufacturing process data usually have multimodel and multiphase characteristics which do not meet the application assumptions in neighborhood preserving embedding (NPE).
Cheng Zhang, Xunian Dai, Xiaofang Zheng, Yuan Li   +3 more
doaj   +1 more source

Machine Learning‐Driven Classification and Production Capacity Prediction of Tight Sandstone Reservoirs: A Case Study of the Taiyuan Formation, Ordos Basin

Energy Science &Engineering, EarlyView.
On the basis of core and log data, a Bayesian‐Optimized Random Forest model achieved 92.76% accuracy in classifying tight sandstone reservoirs. A gray relational analysis‐derived evaluation index shows > 80% consistency with actual gas zones. ABSTRACT Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs ...
Yin Yuan, Haijiao Fu, Detian Yan, Gang Wang, Shuguang Yang, Mengmeng Liu, Qingling Jiang, Xiaoming Wang, Wei Wang   +8 more
wiley   +1 more source

From shape to source: sedimentary charcoal morphology as a proxy for tropical burned biomass composition

Journal of Quaternary Science, EarlyView.
ABSTRACT Sedimentary charcoal elongation is increasingly being used in paleoecology to distinguish herbaceous from woody fuel in past fires. However, the relationship between charcoal morphotypes and plant types has never been formally tested in tropical environments, despite its potential to improve understanding of fire regimes and deforestation, and
Fiona Cornet, Vincent Montade, Stéphanie C. Bodin, Justine Descloitre, Maxime Réjou‐Méchain, Gaëlle Viennois, Julie C. Aleman, Chimène Assi‐Kaudjhis, William F. Kenney, Richard Oslisly, Monique G. Tossou, Laurent Bremond, Charly Favier   +12 more
wiley   +1 more source

Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization

Data in Brief, 2019
The data in this article provide details about MRI lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms.
Ju Qiao, Xuezhu Cai, Qian Xiao, Zhengxi Chen, Praveen Kulkarni, Craig Ferris, Sagar Kamarthi, Srinivas Sridhar   +7 more
doaj   +1 more source

A Self‐Supervised Machine Learning Approach for the Estimation of Open‐Circuit Voltage Degradation in Photovoltaic Systems

Progress in Photovoltaics: Research and Applications, EarlyView.
This study introduces a self‐supervised machine learning approach integrating physics‐based principles to estimate open‐circuit voltage (voc$$ {v}_{oc} $$) degradation in photovoltaic systems using SCADA data. By combining clustering and regression algorithms, our method detects performance deviations without labelled datasets.
Sandra Riaño, Jose‐Domingo Santos, Miguel Esteras, Amaia Abanda, Javier Del Ser   +4 more
wiley   +1 more source

On the merits of the Gaussian Mixture as a model for oriented edgel distributions [PDF]

, 2008
The aim of this report is to establish the credibility of the Gaussian Mixture Model (GMM) as a model for the distributions of oriented edgels of rigid and biological objects in noisy images.
Assheton, Philip, Hunter, Andrew
core  

Multi‐level fatigue reliability assessment of reinforced concrete railway bridges

Structural Concrete, EarlyView.
Abstract This paper presents a multi‐level reliability framework for assessing the fatigue life of reinforced concrete (RC) railway trough bridges subjected to cyclic loading. The framework incorporates increasing levels of analytical complexity and real‐world data in four steps. First, an analytical model applies S–N curves and the Palmgren–Miner rule
Silvia Sarmiento, Jaime Gonzalez‐Libreros, Chao Wang, Lennart Elfgren, Ola Enoksson, Tommy Höjsten, Lars‐Olof Andersson, Gabriel Sas   +7 more
wiley   +1 more source

Unsupervised Work Behavior Pattern Extraction Based on Hierarchical Probabilistic Model

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
In this study, we address the challenge of analyzing worker behaviors in high‐mix, low‐volume production environments, where traditional supervised learning methods struggle owing to the lack of labeled data and task variability among workers. To overcome these issues, we propose a novel hierarchical approach for unsupervised behavior pattern ...
Issei Saito, Tomoaki Nakamura, Toshiyuki Hatta, Wataru Fujita, Shintaro Watanabe, Shotaro Miwa   +5 more
wiley   +1 more source

Fish Movement Tracking Menggunakan Metode Gaussian Mixture Models (GMM) dan Kalman Filter

J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Indonesia Fish industries is one of the large in the world for market capital which covers for both natural growing and intensive culture. One part of the most challenging problem for intensive culture is related to counting when harvesting which been done by hand all this time.
Alim, Hafizhun, Muhammad, Eka Suryana, Mulyono   +2 more
openaire   +2 more sources