Displacement cloud maps. by Maogang Tian (21485116)

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The linearly decreasing convergence factor a. by Junhao Wei (6816803)

Displacement cloud map (geology). by Hui Cheng (195456)

Linear regression. by Myriam Aïche (22290591)

CNN Model Architecture. by Mudhafar Jalil Jassim Ghrabat (22177655)

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Flowchart of Proposed SMO_CNN. by Mudhafar Jalil Jassim Ghrabat (22177655)

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CNN Model Layers Summary. by Mudhafar Jalil Jassim Ghrabat (22177655)

Subjects:

Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

Point cloud fusion instance effect. by Hongliang Zou (20707270)

“…<div><p>The study proposes a multi-sensor localization and real-timeble mapping method based on the fusion of 3D LiDAR point clouds and visual-inertial data, which addresses the issue of decreased localization accuracy and mapping in complex environments that affect the autonomous navigation of robot dogs. …”

Why does task performance decrease with burst length? by Swathi Anil (17382903)

Linear contact model. by Qingwen Li (1510687)

Linear regression analysis. by Young Kyu Lee (14076058)

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

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Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 1. by Hongliang Zou (20707270)

Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 6. by Hongliang Zou (20707270)

Positioning trajectories and pose truths of different simultaneous localization and mapping algorithms under Sequence 5. by Hongliang Zou (20707270)

Performance Results of Proposed SMO_CNN Model. by Mudhafar Jalil Jassim Ghrabat (22177655)

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Results of dose linearity. by Masakazu Tsujimoto (22339504)

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Multiple linear regression analysis of trust change predictors. by Alain Abi-Rizk (8549358)

CNN model. by Longfei Gao (698900)

“…According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. …”