RMSE versus architectural parameters. حسب Zhe Hu (787283)

"…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…"

Kalman process. حسب Zhe Hu (787283)

"…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…"

Attention mechanism. حسب Zhe Hu (787283)

"…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…"

Shape error measurement results statistics. حسب Zhe Hu (787283)

"…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…"

Data. حسب Hamid Reza Eisvand (19872297)

"…<div><p>Water scarcity is a global challenge with profound implications, particularly for agriculture, where it undermines crop production by diminishing yields and heightening vulnerability to environmental stresses. …"

Experiment 3 –trial E. حسب Ottavia Benedicenti (9100619)

Experiment 2, trial D. حسب Ottavia Benedicenti (9100619)

Box plot of queens and males in the young colony (left) and old colony (right) A-B compares the queens and males distribution in the young and old colonies, respectively. حسب Moïse Pierre Exélis (21578515)

Minimal test data set حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

OSNet network structure. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

YOLOv8 overall framework. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

The performance of YOFGD model on MOT16. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

Network structure of OSA. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

The performance of S-YOFEO model on MOT17. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

Five multi-target tracking evaluation indexes. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

Algorithm flowchart of OFEO. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

Partial tracking results of MOT17 dataset. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

Improved detection layer. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

The performance of S-YOFEO model on MOT16. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"

The matching process of EIOU. حسب Wenshun Sheng (21485393)

"…OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. …"