Sampling points during the 6th respiration. by Feng Gao (3548)

Action potential of sample points in model 1. by Hang Zhao (143592)

Action potential of sample points in model 2. by Hang Zhao (143592)

Action potential of sample points in model 0. by Hang Zhao (143592)

The structure of the sample point generator in Dysample. by Mengqi Yuan (4852555)

N₂O mass fraction at 9 sampling points. by Feng Gao (3548)

Test results of composite sample distribution. by Shaoxia Mou (16510383)

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Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Spatial multivariate optimization for a sampling redesign with a reduced sample size of soil chemical properties by Tamara Cantú Maltauro (15199725)

Failure Samples and Fitting Results. by Dongyan Wang (195482)

Grid independence test for: (a) Temperature, (b) Velocity W, (c) Velocity U, and (d) Sampling points in the X-Z plane. by Feng Gao (3548)

Statistics of GI data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

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Statistics of HEWL data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

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Graphical illustration of sample size estimation for the study protocol. by Müge Yalçin (16486563)

<i>T</i>_<i>ca</i> and schemes distribution of the sample point. by Weijian Li (762796)

Description of data sets. by Shaoxia Mou (16510383)

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Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Comparison of mining accuracy. by Shaoxia Mou (16510383)

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Influence of parameter setting on mining performance. by Shaoxia Mou (16510383)

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Comparison of clustering effects. by Shaoxia Mou (16510383)

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