IDA flowchart. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

Ablation test results of QIMPN-PCA-DC model. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

QIMPN framework diagram. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

Multiple indicator test results. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

I/L type node quality entity type. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

Node coding process. by Jun Zhao (59250)

“…The optimization of anisotropic nodes significantly enhances the seismic performance of shear walls. …”

MGPC module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Comparative experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Pruning experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Parameter setting table. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

DTADH module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Ablation experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Multi scale detection. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFDPN module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection effect of different sizes. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Radar chart comparing indicators. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFD-YOLO structure. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection results of each category. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Data Sheet 1_Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent... by Peter Ruppersberg (22123543)

“…Notably, the majority of significant sources were not continuously active; however, when these sources switched “ON,” the spatial variability of AF cycle lengths in the respective atrium decreased by more than 50%, suggesting an entraining effect.…”