scholarly journals A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

2021 ◽  
Author(s):  
Riccardo Capelli ◽  
Andrea Gardin ◽  
Charly Empereur-mot ◽  
Giovanni Doni ◽  
Giovanni M. Pavan
Keyword(s):  
Dimensionality Reduction ◽  
Force Fields ◽  
Data Driven ◽  
Reduction Approach

scholarly journals Spectrus: A Dimensionality Reduction Approach for Identifying Dynamical Domains in Protein Complexes from Limited Structural Datasets

2016 ◽  
Vol 110 (3) ◽  
pp. 54a
Author(s):  
Luca Ponzoni ◽  
Guido Polles ◽  
Vincenzo Carnevale ◽  
Cristian Micheletti
Keyword(s):  
Dimensionality Reduction ◽  
Protein Complexes ◽  
Reduction Approach

Dimensionality reduction approach to multivariate prediction

2001 ◽  
Vol 29 (2) ◽  
pp. 191-200 ◽  
Author(s):  
Giovanni M. Merola ◽  
Bovas Abraham
Keyword(s):  
Dimensionality Reduction ◽  
Multivariate Prediction ◽  
Reduction Approach

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

2022 ◽  
pp. 146808742110707
Author(s):  
Aran Mohammad ◽  
Reza Rezaei ◽  
Christopher Hayduk ◽  
Thaddaeus Delebinski ◽  
Saeid Shahpouri ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Emission Models ◽  
Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

scholarly journals SPECTRUS: A Dimensionality Reduction Approach for Identifying Dynamical Domains in Protein Complexes from Limited Structural Datasets

Structure ◽  
2015 ◽  
Vol 23 (8) ◽  
pp. 1516-1525 ◽  
Author(s):  
Luca Ponzoni ◽  
Guido Polles ◽  
Vincenzo Carnevale ◽  
Cristian Micheletti
Keyword(s):  
Dimensionality Reduction ◽  
Protein Complexes ◽  
Reduction Approach
Sign in / Sign up

Export Citation Format

Share Document