Main parameters of steering system. حسب Honglei Pang (22693724)

الموضوعات:

HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. حسب Honglei Pang (22693724)

الموضوعات:

Co-simulation architecture. حسب Honglei Pang (22693724)

الموضوعات:

Overall framework diagram of the study. حسب Honglei Pang (22693724)

الموضوعات:

Braking system model. حسب Honglei Pang (22693724)

الموضوعات:

Vehicle parameters. حسب Honglei Pang (22693724)

الموضوعات:

HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. حسب Honglei Pang (22693724)

الموضوعات:

Comparison of average reward values in various simulation scenarios. حسب Honglei Pang (22693724)

الموضوعات:

HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. حسب Honglei Pang (22693724)

الموضوعات:

The GOR4 and SOPMA algorithm findings. حسب Ahmad Abdullah Mahdeen (19704401)

Wav2DDK: An automated DDK estimation algorithm (Kadambi et al., 2023) حسب Prad Kadambi (16680635)

"…The clinical utility of the algorithm was demonstrated on a corpus of 7,919 assessments collected longitudinally from 26 healthy controls and 82 ALS speakers. …"

The training results of WA-YOLO and YOLOv8 on the custom blasting site dataset are presented, where (a) shows the mAP_0.5 comparison, and (b) illustrates the box loss tra... حسب LinNa Li (5665885)

RMSE results. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the Kherson Area Visual Assessment. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Work flow chart. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Experimental data. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the PY area visual assessment. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Reliability of the optimized frequency parameters and sFC matrix edges in the high-dimensional cases as well as of the GoF. حسب Kevin J. Wischnewski (21354521)

Comparison of the improved LRM and RM results. حسب YueSheng Jiang (19267984)

Result charts for different window sizes. حسب YueSheng Jiang (19267984)