Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Global maximum GPP from 2001-2018 by Xu (7493744)

“…The GPPmax estimates from this study and the changes in their trends were highly correlated with GPPmax estimates from the vegetation photosynthesis model, with R2 > 0.70 for most vegetation types. The GPPmax significantly increased in western North America, northern Europe, and eastern China, but decreased in tropical regions. …”

Highly Sensitive and Selective Electrochemical Sensor via Cu-BTC/Au@Cu-BTC Modified Screen-Printed Electrode for the Detection of Chemical Agents by Xiaosen Li (6263651)

“…Chemical agents present significant threat to international peace, security, and human health due to their potential toxicity. …”

<b>Differences in White Matter Microstructure in Children With Type 1 Diabetes Persist During Longitudinal Follow up: Relation to Dysglycemia</b> by Nelly Mauras (8103110)

“…</p><p dir="ltr">We observed in 182 children (121 type 1 diabetes, vs. 61 controls) who had testing at Time 4 that FA increased, and RD, AD, MD decreased significantly in both groups, with no differences between groups for FA, RD and MD over time. …”

Generated spline library. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Correlation coefficient matrix. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Actual measurement of shape errors. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus learning rate. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus training parameters. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Assembly process of machine recognition form. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Process of steel truss incremental launching. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

CGAN and AutoML stacking device. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Comprehensive prediction process of shape errors. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Shape error manual calculation process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Sliding window process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Original record form of error matrix. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Form for machine recognition. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

RMSE versus architectural parameters. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”

Kalman process. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …”