Application of Machine Learning Models to the Analysis of Skid Resistance Data

Koné, Aboubakar; Es-Sabar, Ahmed; Do, Minh-Tan

doi:10.3390/lubricants11080328

Cited by 4 publications

(3 citation statements)

References 46 publications

(54 reference statements)

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“…In the context of flower recognition, traditional machine learning methods like support vector machines (SVM) and K-nearest neighbors (KNN) are commonly employed. SVM effectively finds the optimal separating hyperplane for different classes, while KNN, a straightforward yet potent algorithm, classifies objects based on the majority vote of their neighbors [5].…”

Section: Flower Recognition Methods Based On Manual Featuresmentioning

confidence: 99%

Research on a Flower Recognition Method Based on Masked Autoencoders

Li,

Lv,

Ding

et al. 2024

Horticulturae

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Accurate and efficient flower identification holds significant importance not only for the general public—who may use this information for educational, recreational, or conservation purposes—but also for professionals in fields such as botany, agriculture, and environmental science, where precise flower recognition can assist in biodiversity assessments, crop management, and ecological monitoring. In this study, we propose a novel flower recognition method utilizing a masked autoencoder, which leverages the power of self-supervised learning to enhance the model’s feature extraction capabilities, resulting in improved classification performance with an accuracy of 99.6% on the Oxford 102 Flowers dataset. Consequently, we have developed a large-scale masked autoencoder pre-training model specifically tailored for flower identification. This approach allows the model to learn robust and discriminative features from a vast amount of unlabeled flower images, thereby enhancing its generalization ability for flower classification tasks. Our method has been applied successfully to flower target detection, achieving a Mean Average Precision (mAP) of 71.3%. This result underscores the versatility and effectiveness of our approach across various flower-related tasks, including both detection and recognition. Simultaneously, we have developed a straightforward, user-friendly flower recognition and classification software application, which offers convenient and reliable references for flower education, teaching, dataset annotation, and other uses.

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Section: Flower Recognition Methods Based On Manual Featuresmentioning

confidence: 99%

Research on a Flower Recognition Method Based on Masked Autoencoders

Li,

Lv,

Ding

et al. 2024

Horticulturae

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show abstract

“…Currently, QSPR models have been widely used in fields of medicinal chemistry, toxicology, biology, and materials, etc. [6][7][8][9][10][11][12]. The QSPR model, created using strong machine learning algorithms and integrated modeling software, can identify and optimize the experimental direction, reducing needless experimental measurement, shortening the experimental time, and lowering experiment expenses [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Antioxidant Properties and Molecular Design of Lubricant Antioxidants Based on QSPR Model

Liu,

Zhang,

et al. 2023

Lubricants

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Two quantitative structure–property relationship (QSPR) models of hindered phenolic antioxidants in lubricating oils were established to help guide the molecular structure design of antioxidants. Firstly, stepwise regression (SWR) was used to filter out essential molecular descriptors without autocorrelation, including electronic, topological, spatial, and structural descriptors, and multiple linear regression (MLR) was used to construct QSPR models based on the screened variables. The two models are statistically sound, with R2 values of 0.942 and 0.941, respectively. The models’ reliability was verified by the frontier molecular orbital energy gaps of the antioxidants. A hindered phenolic additive was designed based on the models. Its antioxidant property is calculated to be 20.9% and 11.0% higher than that of typical commercial antioxidants methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate and 2,2′-methylenebis(6-tert-butyl-4-methylphenol), respectively. The structure–property relationship of hindered phenolic antioxidants in lubricating oil obtained by computer-assisted analysis can not only predict the antioxidant properties of existing hindered phenolic additives but also provide theoretical basis and data support for the design or modification of lubricating oil additives with higher antioxidant properties.

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“…Various characteristic parameters of the pavement texture and factors affecting the anti-skid performance of the surface are taken as input, and the specific road friction coefficient is provided as the output. There are also machine-learning anti-skid performance evaluation models based on classification tasks [27]. However, the number of data sets for such models are not large, which limits the model's ability to evaluate anti-skid performance.…”

Section: Introductionmentioning

confidence: 99%

Incep-FrictionNet-Based Pavement Texture Friction Level Classification Prediction Method

Xu,

Lin,

Wang

et al. 2023

Lubricants

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Pavement skid resistance is crucial for driving safety, and pavement texture significantly impacts skid resistance performance. To realize the application of pavement texture data in assessing pavement skid resistance performance, this paper proposes a convolutional neural network model based on the InceptionV4 module to predict the pavement friction level from the pavement texture dataset. The surface texture data of indoor test-rutted slabs were collected using a portable laser scanner. The surface friction coefficient of rutted slabs was measured using a pendulum tribometer. After data pre-processing, a total of nine types of texture data that are in the range of 0.4 to 0.8 skid resistance levels are selected at an interval of 0.05 for training, validation, and testing of the network model. The same dataset and training parameters were also used to train a conventional convolutional network model for comparison. The results showed that the proposed network model achieved 97.89% classification accuracy on the test set, which was 11.94 percentage points higher than the comparison model. This demonstrates that the proposed model in this paper can evaluate pavement friction levels by non-contact scanning of textures and has higher evaluation accuracy.

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Application of Machine Learning Models to the Analysis of Skid Resistance Data

Cited by 4 publications

References 46 publications

Research on a Flower Recognition Method Based on Masked Autoencoders

Research on a Flower Recognition Method Based on Masked Autoencoders

Evaluation of Antioxidant Properties and Molecular Design of Lubricant Antioxidants Based on QSPR Model

Incep-FrictionNet-Based Pavement Texture Friction Level Classification Prediction Method

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