A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

Wang, Bo; Wang, Liandong; Tang, Xianzhi; Yang, Shujun

doi:10.1155/2019/7543496

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…In the study of methane dispersion and the behavior of air flow, Jundika et al [13] used CFD simulations for various conditions that may arise in a mine tunnel. Wang and Tang [14] described this problem for the whole mine system, Shuai et al [15] for tunneling and for mining face was depicted by Qi et al [16], Guobao et al [17] and Tao et al [18].…”

Section: State Of the Artmentioning

confidence: 99%

Possibilities of 3D laser scanning data utilization for numerical analysis of airflow in mining excavations

Wróblewski,

Trybała,

Banasiewicz

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Underground mining of deposits is strongly dependent on several key processes enabling its safe operation. One of these processes is mine ventilation aimed at supplying an appropriate amount of air with a specific chemical composition, diluting harmful gases generated in technological processes and emitted from the rock mass, and ensuring appropriate climatic conditions at workplaces. For all that mine ventilation has strategic importance and is responsible for 30-40 % of overall mine operation costs. A very important task is to conduct calculations and simulations to determine the airflow in ventilation networks, both existing and planned in the future, along with the progress of the mine operation. In the era of the development of modern technologies, new perspectives for changing the current approach to the design and optimization of ventilation networks are available. Their utilization may contribute to accuracy improvement, which can drive safety increases and process optimization. In this article, the authors evaluate the possibilities of using laser scanning data, collected with LiDAR / Terrestrial Laser Scanner, to generate 3D models of underground structures geometry, and its further utilization to conduct numerical simulations of airflow in mining excavations with Computational Fluid Dynamics. The performed analysis shows that the proposed approach may be a useful instrument for analyzing local airflow phenomena, particularly in complex ventilation networks, where disturbances in air velocity vectors are caused by sudden changes in geometry.

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Section: State Of the Artmentioning

confidence: 99%

Possibilities of 3D laser scanning data utilization for numerical analysis of airflow in mining excavations

Wróblewski,

Trybała,

Banasiewicz

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…In this paper, the porcine acoustic signal is firstly decomposed. EMD can decompose the signals into IMFs from high to low frequency self-adaptively [18,19], which is based on the decomposition principle that any signal is composed of IMFs [20]. e IMF must satisfy two conditions: (1) e number of extreme points is equal to the number of zero-crossings.…”

Section: Porcine Acoustic Signal Decomposition Based On Eemdmentioning

confidence: 99%

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Zhang

Tian

Banerjee

et al. 2019

Mathematical Problems in Engineering

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Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

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“…It was concluded that GMM is more sensitive to the change in vehicle speed and has better clustering performance. After the decomposition and feature extraction of brake pedal displacement signal, Wang et al 30 used fuzzy C-means (FCM) to cluster the signal feature vectors for braking intention identification. This method, to some extent, solves the problem of low accuracy of traditional identification methods in distinguishing slight braking from normal braking intention, but it requires a higher quality of the pedal signal.…”

Section: Introductionmentioning

confidence: 99%

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

Zhao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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The braking intention is of great significance to the realization of driver assistant features, the improvement of braking safety, and the maximization of energy recovery efficiency for electric vehicles. With the aim of accurate identification of braking intention, an identification model based on Gated Recurrent Unit (GRU) Network with Attention mechanism is proposed in this paper. Based on numerous vehicle braking test data, braking process analysis, characteristic parameters selection, identification model training, and verification are carried out. Through the difference analysis based on the Kruskal-Wallis test and the importance evaluation based on random forest, combined with the real-time requirements of practical application, the appropriate characteristic parameters are selected as the model input. The attention mechanism is introduced into the proposed model, which can improve identification accuracy by capturing valuable feature information. The comparative verification results show that the Attention-GRU model performs better than the other three comparison models, and its identification accuracy is 96.7%, of which the accuracy of slight braking, normal braking, and emergency braking are 96.3%, 95.8%, and 100% respectively. The identified braking intention can provide an effective basis for the establishment of vehicle control strategies.

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A Braking Intention Identification Method Based on Data Mining for Electric Vehicles

Cited by 7 publications

References 23 publications

Possibilities of 3D laser scanning data utilization for numerical analysis of airflow in mining excavations

Possibilities of 3D laser scanning data utilization for numerical analysis of airflow in mining excavations

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

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