Machine Learning-based Diesel Engine-Out NOx Reduction Using a plug-in PD-type Iterative Learning Control

Norouzi, Armin; Gordon, David C.; Aliramezani, Masoud; Koch, Charles Robert

doi:10.1109/ccta41146.2020.9206277

Cited by 21 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] [58], [59], [60], [66], [67], [79], [102], [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

mentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

View full text Add to dashboard Cite

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

show abstract

mentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

View full text Add to dashboard Cite

show abstract

“…[119] 6 Solutions Health 24 [1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] Solutions in Analytics and Artificial 13 [52], [3], [58], [59], [60], [66], [67], [79], [102], Intelligence [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Shukla¹

2022

IJSREM

View full text Add to dashboard Cite

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

show abstract

“…In SVM, a convex quadratic programming is solved to find a correlation between input-output or classification. A least-squares version of SVM to solve a set of linear equations to lower the computation cost for the constrained optimization programming is described in [114,115,117,118].…”

Section: And Mpc Integration In Icesmentioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

Self Cite

View full text Add to dashboard Cite

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

show abstract

Machine Learning-based Diesel Engine-Out NOx Reduction Using a plug-in PD-type Iterative Learning Control

Cited by 21 publications

References 21 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Contact Info

Product

Resources

About