SVR approach for predicting vehicle velocity for comfortable ride while crossing speed humps

Darwiche, Mohamad; Mokhiamar, Ossama

doi:10.1016/j.aej.2021.11.045

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…In this paper, we hope to use the data of known molecular descriptors for fitting, find a function that fits the relationship between molecular descriptors and molecular activity sequences, and expect to get a result with the least fitting error, so as to use this function for prediction. This can be achieved by a support vector machine (SVM) model [57], which mainly maps inputs to a high-dimensional feature space via nonlinear mapping (kernel function), and then constructs an optimal classification hyperplane in this space. For the existing molecular data sample D, the optimization problem expression corresponding to its SVR is as follows:…”

Section: Support Vector Machine Regression (Svr)mentioning

confidence: 99%

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

Du,

Xu,

Huang

et al. 2023

Preprint

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Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs.

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Section: Support Vector Machine Regression (Svr)mentioning

confidence: 99%

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

Du,

Xu,

Huang

et al. 2023

Preprint

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“…There are several difficulties associated with fuzzy logic control, including high dependence on human knowledge and skill; fuzzy rules need to be updated over time, and there is no set method for creating fuzzy controllers [19]. On the other hand, robust nonlinear control techniques such as H-infinity loop shaping, sliding mode control, and so on [23][24][25][26] can be applied.…”

Section: Introductionmentioning

confidence: 99%

Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles

Chamas,

Amine,

Gazo Hanna

et al. 2023

Applied Sciences

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The use of delivery drones is currently hindered by the inability of transported objects to maintain a steady position, which can result from roll-, pitch-, and heave-induced vibrations. This paper proposes a novel parallel manipulator for stabilizing the platform of unmanned aerial vehicles. The proposed mechanism builds upon an existing study of a 3-SRR/SRU parallel stabilizing mechanism by incorporating the dynamical properties of the system into the control model. The resultant control technique is then applied to both the 3-RRS and 3-SRR mechanisms, and a comparative study is conducted to identify the most reliable stabilizer for regulating the platform’s orientation. The results demonstrate that the 3-SRR mechanism exhibits superior robustness and stability characteristics compared to the other two mechanisms. Additionally, the 3-SRR mechanism is controlled using artificial neural networks, which significantly improves the accuracy and stability of the system. Overall, this research presents a novel and effective solution for stabilizing the platform of unmanned aerial vehicles, with significant implications for the development of delivery drone technology.

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“…New energy vehicles, marked by their emphasis on environmental sustainability and energy conservation, confront distinctive challenges [2]. Notably, the absence of the masking effect associated with traditional engine noise accentuates the prominence of other sources of noise, including road noise, motor noise, and fan noise [3,4]. Among these, road noise emerges as the predominant contributor to the comprehensive acoustic profile of a vehicle, commanding heightened attention.…”

Section: Introductionmentioning

confidence: 99%

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Jia,

Zhou,

Huang

et al. 2023

Electronics

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In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state.

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SVR approach for predicting vehicle velocity for comfortable ride while crossing speed humps

Cited by 8 publications

References 31 publications

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest

Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

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