Distinguishing the Representative Marshes in China Based on Artificial Intelligence

2017 ◽  
Vol 33 (4) ◽  
pp. 471-476
Author(s):  
Yongming Chen ◽  
Ping Lin
Keyword(s):  
Artificial Intelligence ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Marsh Habitat ◽  
Principal Component Analysis Algorithm ◽  
True Color

Abstract. A new means to distinguish the habitat spectacles of the representative marshes in China based on artificial intelligence is presented in this article. Three typical instances including Yancheng mudflat marsh, Zoige plateau marsh, and Dongzhai Harbor mangrove forest marsh were investigated. Firstly, the RGB true-color pictures of the marsh habitat spectacles were resized to the appropriate sizes and switched to gray intensity pictures. Secondly, the GIST descriptors were evaluated for encoding the marsh habitat spectacles at both a basic level and a superordinate level. Thirdly, the principal component analysis algorithm was performed to extract the principal components from the encoded features. Finally, the multi-class support vector machine (MSVM) algorithm was used to discriminate the marsh habitat spectacles using the principal components. The recognition percisions for the training and test set reached 72.5% and 70.6%, respectively. It was accounted that the proposed methods could be applied to distinguishing the representative marsh habitat spectacles in China. Keywords: Classification, GIST descriptiors, Marsh Habitat spectacle, Multi-class support vector machine, Principal component analysis.

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.

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