Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine

Niu, Xuming; Yang, Chuanlei; Wang, Hechun; Wang, Yinyan

doi:10.1016/j.applthermaleng.2016.10.042

Cited by 121 publications

(56 citation statements)

References 26 publications

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“…ANNs are machine learning techniques that allow us to build computational representations of physical processes from measurable data. These models have the capacity to identify the complex relationships between input and output variables of a process [30]. Through generalisation, ANNs are able to produce responses to new inputs, not considered during the training process.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to represent a diesel–biodiesel engine

Lage

Hanriot

Zárate

2020

J Braz. Soc. Mech. Sci. Eng.

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In this work, six computational models based on artificial neural networks were developed to simulate an operating diesel engine fuelled with 8% biodiesel in order to predict performance and emissions of a diesel-biodiesel engine in a group generator. The ANN models were used to simulate a diesel-biodiesel engine that has four cylinders with a volume of 3.9 l, a compression ratio of 17:1, direct injection, and a rated power of 49 kW. The models were validated against experimental data for 10 kW, 20 kW, and 30 kW loads. The models were capable of accurately predicting the output power, thermal efficiency, and emissions of CO 2 , CO, NO, and NOx. Their comparison with experimental results showed a satisfactory agreement and the reliability of their predicted results for new operating conditions.

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Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to represent a diesel–biodiesel engine

Lage

Hanriot

Zárate

2020

J Braz. Soc. Mech. Sci. Eng.

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“…It can overcome the dimensionality disaster and guarantee that the local and global optimal solution are exactly the same. SVM minimizes the upper bound of the generalization error based on structural risk minimization (SRM) and considers the empirical risk minimization (ERM) simultaneously, having a better generalization performance than ANN [19][20][21][22]. Meanwhile, it has a smaller demand for samples [23,24].…”

Section: Introductionmentioning

confidence: 99%

A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

et al. 2018

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Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.

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“…Support vector machine (SVM) was first proposed by Vapnik [21], in accordance with the foundation of statistical theory and the principle of structural risk minimization. Among the existing AI technologies, SVM has been proved to have superior performance over previously developed methodologies, such as decision tree, artificial neural network and other conventional statistical models [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

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Accurate combustion state recognition of flame images not only plays an important role in social security, but also contributes to increasing thermal efficiency and product quality. To improve the accuracy of feature extraction and achieve the combustion state recognition, a novel method based on radial Chebyshev moment invariants (RCMIs) and an improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model is proposed. Firstly, the potential flame pixels and the potential flame contour are obtained in the pre-processing phase. Then, the rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Combing the region and contour moments, the RCMIs of pre-processed and edge images are calculated to construct multi-feature vectors. To enhance the recognition performance, an IFA-WSVM model is built, where the IFA is applied to search the best parameters of WSVM. Then, the IFA-WSVM model is used to recognize the combustion state. Finally, the result for case studies show that the proposed method is superior to methods based on HMIs and ZMIs, achieving the highest rate of 99.07% in real time. The IFA algorithm also outperforms other benchmark algorithms. Even for the images transformed by RTS and small size of training sets, the proposed method continues to exhibit the best performance.

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Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine

Cited by 121 publications

References 26 publications

Using artificial neural networks to represent a diesel–biodiesel engine

Using artificial neural networks to represent a diesel–biodiesel engine

A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

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