Algorithm detection performance. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

The system model of UAV-enabled RSMA network. by Nguyen Hong Nhu (22127327)

Model structure of the Small_obj algorithm. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

Ablation study on the improved YOLOv5 algorithm. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Comparison of different algorithms for worm tracking. All methods were executed on identical hardware (as detailed in Section 2.2) and evaluated on the same dataset using an image... by Xiaoke Liu (11388323)

Federated averaging algorithm pseudocode. by Hadi Mohammadian KhalafAnsar (22783784)

Mosaic dataset augmentation. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

Three types of loss curves. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

The structure of the Res2Block module. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

The structure of the BiFPN. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

Randomly adding Gaussian white noise. by Kangning Li (6069629)

“…The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.…”

Distributed and explainable GHSOM for anomaly detection in sensor networks by Paolo Mignone (9760832)

Users data points. by Alon Sela (12231559)

Subjects:

Separation between political (orange) and disasters (blue) cascades. by Alon Sela (12231559)

Subjects:

Separation of political and natural events by word-to-tweet bipartite graphs. by Alon Sela (12231559)

Subjects:

Bipartite network data points. by Alon Sela (12231559)

Subjects:

Distribution of hashtags on a log-log scale for 6 political (left column) vs. 6 disasters (right column) cascades. by Alon Sela (12231559)

Subjects:

Data sets used for bipartite graphs network analysis. by Alon Sela (12231559)

Subjects:

Additional slopes images (complementary to Figs 2 and 3 for hashtags of Egypt and Iran politics. by Alon Sela (12231559)

Subjects: