Results 71 to 80 of about 34,052 (223)

FLARE-GMM: an automatic aerosol typing model based on Mie–Raman–fluorescence lidar measurements with LILAS [PDF]

Atmospheric Measurement Techniques
This study presents the development of an automated aerosol typing model utilizing Mie–Raman–fluorescence lidar data collected by LILAS (Lille Lidar for Atmospheric Study), located on the ATOLL (ATmospheric Observations at LiLLe) platform in Lille ...
R. Miri, O. Pujol, Q. Hu, P. Goloub, I. Veselovskii, I. Veselovskii, T. Podvin, F. Ducos   +7 more
doaj   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring

Accepted at ECCV ...
Emanuele Santellani, Martin Zach, Christian Sormann, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer   +5 more
openaire   +2 more sources

Efficient Implementation Of Gmm Based Speaker Verification Using Sorted Gaussian Mixture Model

, 2006
Publication in the conference proceedings of EUSIPCO, Florence, Italy ...
Sadegh Mohammadi, Hamid, Saeidi, Rahim
openaire   +1 more source

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +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

An Electromyographic‐Based Control Using Gaussian Mixture Model on an Upper‐Limb Cable‐Driven Rehabilitation Robot

Advanced Intelligent Systems
Electromyographic (EMG)‐based admittance control by arm force can provide continuous motion control in robot‐assisted rehabilitation. Natural and complex physical human–robot interactions utilizing intelligent EMG‐based interfaces require a computational
Jianlin Zheng, Zhijun Li, Ting Xu, Rong Song   +3 more
doaj   +1 more source

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

Affective Music Information Retrieval [PDF]

, 2015
Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR ...
Wang, Hsin-Min, Wang, Ju-Chiang, Yang, Yi-Hsuan   +2 more
core  

Automated FRET Analysis for Enhanced Characterization of Protein–Protein Interactions

Microscopy Research and Technique, EarlyView.
A visual summary of the SONLab FRET Tool, displaying the entire workflow—from imaging to the plotting of quantified data. The results provided in this article were obtained using this automated pipeline. ABSTRACT Förster Resonance Energy Transfer (FRET) analysis is a powerful technique for studying protein–protein interactions; however, manual methods ...
Ahmet Zübeyir Nursoy, Orkun Cevheroğlu, Çağdaş Devrim Son   +2 more
wiley   +1 more source