Quantitative results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Main module structure. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Effective connectivity analysis. by Akke Mats Houben (21572855)

Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory by Drippy Stone (21218843)

Subjects:

<b>Force-Position-Speed Planning and Roughness rediction for Robotic Polishing</b> by Ma Haohao (19780875)

“…The improved dung beetle optimization algorithm, back propagation neural network, finite element analysis and response surface method provide a strong guarantee for the selection of robotic polishing process parameters. …”

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process by Alexis Boulin (20659921)

“…A data-driven selection method for the tuning parameter is also proposed. …”

Data Sheet 1_Extraction of exact symbolic stationary probability formulas for Markov chains with finite space with application to production lines. Part I: description of methodolo... by Konstantinos S. Boulas (21709982)

“…</p>Results<p>A general algorithm that commences with the Markov chain transition matrix as an input element and forms the state transition diagram. …”

Video 1_Extraction of exact symbolic stationary probability formulas for Markov chains with finite space with application to production lines. Part I: description of methodology.mp... by Konstantinos S. Boulas (21709982)

“…</p>Results<p>A general algorithm that commences with the Markov chain transition matrix as an input element and forms the state transition diagram. …”

Multi-Task Learning for Gaussian Graphical Regressions with High Dimensional Covariates by Jingfei Zhang (580874)

“…We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. …”

Practical implementation of an End-to-end methodology for SPC of 3-D part geometry: A case study by Yulin An (833223)

“…The approach is based on monitoring the spectrum of the Laplace–Beltrami (LB) operator of each scanned part estimated using finite element methods (FEM). The spectrum of the LB operator is an intrinsic summary of the geometry of a part, independent of the ambient space. …”