Results 81 to 90 of about 34,052 (223)

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

The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Quantification of metabolite concentrations using MRS requires tissue‐dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS‐derived concentration ...
Jessica Archibald, Kay Chioma Igwe, Antonia Kaiser, Karl Landheer, Jaimie Lee, John L. K. Kramer, Aaron T. Gudmundson, Helge J. Zöllner, Georg Oeltzschner, Candace C. Fleischer, Niklaus Zölch, Jamie Near, Mark Mikkelsen   +12 more
wiley   +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

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

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

One-Pass Sparsified Gaussian Mixtures

, 2019
We present a one-pass sparsified Gaussian mixture model (SGMM). Given $N$ data points in $P$ dimensions, $X$, the model fits $K$ Gaussian distributions to $X$ and (softly) classifies each point to these clusters.
Becker, Stephen, Kightley, Eric
core  

Evolutionary Dynamic Multiobjective Optimisation Assisted by Inverse Regression Tree Predictor

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Dynamic multiobjective optimisation problems (DMOPs) are optimisation problems with multiple conflicting objectives that can change over time. Most dynamic multiobjective optimisation evolutionary algorithms (DMOEAs) attempt to estimate Pareto‐optimal sets (PS) directly in the decision space.
Kai Gao, Lihong Xu
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

Harmonic mean density fusion in distributed tracking: Performance and comparison

IET Radar, Sonar &Navigation, EarlyView.
In distributed tracking, the harmonic mean density (HMD) fusion can deal with issues, such as non‐independent estimates, inflated covariance and fusion of Gaussian mixtures. This article provides some important results on HMD and shares its resemblance with the state‐of‐the‐art inverse covariance intersection (ICI). Abstract A distributed sensor fusion
Nikhil Sharma, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan   +2 more
wiley   +1 more source

Speech Emotion Recognition System Using Gaussian Mixture Model and Improvement proposed via Boosted GMM

IRA-International Journal of Technology & Engineering (ISSN 2455-4480), 2017
<p>Speech emotion recognition is an important issue which affects the human machine interaction. Automatic recognition of human emotion in speech aims at recognizing the underlying emotional state of a speaker from the speech signal. Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e.
Pavitra Patel, A. A. Chaudhari, M. A. Pund, D. H. Deshmukh   +3 more
openaire   +2 more sources