Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Zhang, Yin; Xu, Ling; Zhang, Yikuan

doi:10.1177/00202940221110164

Cited by 4 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although neural networks offer strong nonlinear learning abilities with high accuracy, but they may face challenges like local minimum convergence and overfitting. SVM require careful kernel function selection, influencing predictive accuracy (Xiao et al, 2014;Gao and Su, 2020;Ren, 2021;Zhang et al, 2022). With the continuous improvement in data availability and computing power in recent years, deep learning has become a crucial component of time series prediction models.…”

Section: Introductionmentioning

confidence: 99%

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Tian,

Gao,

Tian

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

Under the influence of climate change and human activities, the intensification of salinity intrusion in the Modaomen (MDM) estuary poses a significant threat to the water supply security of the Greater Bay Area of Guangdong, Hong Kong, and Macao. Based on the daily exceedance time data from six stations in the MDM waterway for the years 2016-2020, this study conducted Empirical Orthogonal Function (EOF) and decision tree analyses with runoff, maximum tidal range, and wind. It investigated the variation characteristics and key factors influencing salinity intrusion. Additionally, Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) were employed to predict the severity of salinity intrusion. The results indicated that: (1) the first mode (PC1) obtained from EOF decomposition explained 89% of the variation in daily chlorine exceedance time, effectively reflecting the temporal changes in salinity intrusion; (2) the largest contributor to salinity intrusion was runoff (40%), followed by maximum tidal range, wind speed, and wind direction, contributing 25%, 20%, and 15%, respectively. Salinity intrusion lagged behind runoff by 1-day, tidal range by 3 days, and wind by 2 days; North Pacific Index (NPI) has the strongest positive correlation with saltwater intrusion among the 9 atmospheric circulation factors. (3) LSTM achieved the highest accuracy with an R2 of 0.89 for a horizon of 1 day. For horizons of 2 days and 3 days, CNN exhibited the highest accuracy with R2 values of 0.73 and 0.68, respectively. This study provides theoretical support for basin scheduling and salinity intrusion prediction and serves as a reference for ensuring water supply security in coastal areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Tian,

Gao,

Tian

et al. 2024

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

Pedestrian detection based on channel feature fusion and enhanced semantic segmentation

Zong,

Xu,

et al. 2023

Appl Intell

View full text Add to dashboard Cite

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

Tang,

Tao,

Qiu

et al. 2024

Measurement and Control

View full text Add to dashboard Cite

5MW wind turbine gearbox high-speed bearing temperature rise failure is one of the important factors affecting the stable operation of the wind turbine, accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine. In this paper, a combined modelling wind turbine gearbox high-speed bearing temperature rise prediction method based on Bayesian-LightGBM and improved PSO-SVM is proposed with a 5 MW wind turbine as the research object. Firstly, the initial dimensionality reduction of SCADA data is performed by sparse random projection matrix, which reduces the redundant data. Secondly, feature selection is performed on the remaining data using Bayesian-LightGBM to identify 13 key input feature parameters. Then, the hyperparameters of the PSO algorithm are optimised using Bayesian algorithm and further, the optimised PSO algorithm is applied to identify the SVM parameters. Finally, a simulation experiment platform is established based on MATLAB to verify the temperature rise of high-speed bearings in gearboxes of wind turbines by example calculation and comparative analysis. The results show that the model established in this paper is effective, the prediction results are accurate and the performance is stable, and then compared with the algorithms such as PSO-SVM, SVM, BP, etc, the coefficient of determination of the algorithm is greater than 0.994 in both the training set and the test set, and the average decision percentage error is around 1.88% in both.

show abstract

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Cited by 4 publications

References 28 publications

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Pedestrian detection based on channel feature fusion and enhanced semantic segmentation

Temperature rise of high-speed bearing in gearbox of 5 MW wind turbine based on Bayesian-LightGBM and improved PSO-SVM troubleshooting

Contact Info

Product

Resources

About