Displacement cloud maps. by Maogang Tian (21485116)

Subjects:

Thematic clouds for lectures. by Ignacio García-Espona (21395335)

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

HDECO: A method for Decreasing energy and cost by using virtual machine migration by considering hybrid parameters by Arash GhorbanniaDelavar (22563696)

“…<h2>Summary</h2><p dir="ltr">This research introduces <b>HDECO</b> (Hybrid Decreasing Energy and Cost Optimization) — a method designed to reduce both energy consumption and execution cost in cloud datacenters through intelligent virtual machine migration. …”

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

Subjects:

Thematic cloud for posters [2013-23]. by Ignacio García-Espona (21395335)

Schematic diagram of time stamp synchronization between LIDAR and camera in a multi-sensor system. by Hongliang Zou (20707270)

Subjects:

Linear regression. by Myriam Aïche (22290591)

Positioning errors of different algorithms on the a multi-modal and multi-scenario dataset for ground robots dataset. by Hongliang Zou (20707270)

Subjects:

Specific parameters of the test sequence in the a multi-modal and multi-scenario dataset for ground robots dataset. by Hongliang Zou (20707270)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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)

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

Subjects:

Results of dose linearity. by Masakazu Tsujimoto (22339504)

Subjects:

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. …”