A grey-box machine learning based model of an electrochemical gas sensor

Aliramezani, Masoud; Norouzi, Armin; Koch, Charles Robert

doi:10.1016/j.snb.2020.128414

Cited by 33 publications

(8 citation statements)

References 40 publications

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“…Finally, there are Greybox approaches as well, which lie in between Blackbox and Whitebox models [ 41 , 52 ]. Examples of such approaches include Local Interpretable Model-agnostic Explanations (LIME) and Interpretable Mimic Learning [ 41 ].…”

Section: Current State-of-the-art Explainable Ai Methods and Approachesmentioning

confidence: 99%

Explainable AI: A Neurally-Inspired Decision Stack Framework

Khan

Nayebpour

et al. 2022

Biomimetics

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European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called “decision stacks” that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.

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Section: Current State-of-the-art Explainable Ai Methods and Approachesmentioning

confidence: 99%

Explainable AI: A Neurally-Inspired Decision Stack Framework

Khan

Nayebpour

et al. 2022

Biomimetics

View full text Add to dashboard Cite

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“…Therefore, by replacing the linear kernel with a nonlinear kernel, using so called kernel tricks, brings nonlinear pattern recognition at a reasonable computational cost. 65 Thus, the inner product of equation (14), u i T u j , is replaced by nonlinear kernel as K ( u i , u j ) . In this study the RBF (Radial basis function) kernel function is used as…”

Section: Methodsmentioning

confidence: 99%

Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine

Gordon

Norouzi

Blomeyer

et al. 2021

International Journal of Engine Research

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The internal combustion engine faces increasing societal and governmental pressure to improve both efficiency and engine out emissions. Currently, research has moved from traditional combustion methods to new highly efficient combustion strategies such as Homogeneous Charge Compression Ignition (HCCI). However, predicting the exact value of engine out emissions using conventional physics-based or data-driven models is still a challenge for engine researchers due to the complexity the of combustion and emission formation. Research has focused on using Artificial Neural Networks (ANN) for this problem but ANN’s require large training datasets for acceptable accuracy. This work addresses this problem by presenting the development of a simple model for predicting the steady-state emissions of a single cylinder HCCI engine which is created using an metaheuristic optimization based Support Vector Machine (SVM). The selection of input variables to the SVM model is explored using five different feature sets, considering up to seven engine inputs. The best results are achieved with a model combining linear and squared inputs as well as cross correlations and their squares totaling 26 features. In this case the model fit represented by R2 values were between 0.72 and 0.95. The best model fits were achieved for CO and CO2, while HC and NOx models have reduced model performance. Linear and non-linear SVM models were then compared to an ANN model. This comparison showed that SVM based models were more robust to changes in feature selection and better able to avoid local minimums compared to the ANN models leading to a more consistent model prediction when limited training data is available. The proposed machine learning based HCCI emission models and the feature selection approach provide insight into optimizing the model accuracy while minimizing the computational costs.

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“…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

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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.

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A grey-box machine learning based model of an electrochemical gas sensor

Cited by 33 publications

References 40 publications

Explainable AI: A Neurally-Inspired Decision Stack Framework

Explainable AI: A Neurally-Inspired Decision Stack Framework

Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine

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

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