Chaotic time series from KIMI stock data. by Mohammadmahdi Taheri (21722285)

“…In the third stage, the fuzzy goal programming (FGP) method is applied, incorporating the prediction errors from the previous stage. The model is optimized in GAMS software, considering each Index’s objectives in a fuzzy context, with the results presented separately for different objectives. …”

Configuration of training parameters. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

The principle of surface compression. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

Accuracy comparison results. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

Schematic diagram of YOLOv5ds structure. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

Schematic diagram of YOLOv5ds-RC structure. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

Cropped image block diagram. by Fangzhe Chang (20896737)

“…These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. …”

NIR Imaging System Specification. by Gareth Gallagher (20874910)

“…Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). …”

Perfusion model schematic. by Gareth Gallagher (20874910)

“…Here, to close this technical gap, we present our development of a colonoscope-compatible flexible imaging probe for NIR-ICG visualization combined with a full field of view machine learning (ML) algorithm for fluorescence quantification and perfusion pattern cross-correlation (including first in human testing). …”

Node feature vector of the Karate network. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Parameter Settings for LFR Networks. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Community label of the node. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Analysis and comparison of q parameter results. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

The process of OSFCM. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Details of Real-World Network Datasets. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Network structure dataset. by Ailian Wang (5537663)

“…We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. …”

Dataset. by Xuanyi Zhao (5896112)

“…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …”

SCUNet structured flowchart. by Xuanyi Zhao (5896112)

“…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …”

SCUNet Network structure diagram. by Xuanyi Zhao (5896112)

“…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …”

Overall system framework. by Xuanyi Zhao (5896112)

“…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …”