بدائل البحث:
required optimization » guided optimization (توسيع البحث), resource optimization (توسيع البحث), feature optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
sample design » sampling design (توسيع البحث)
final sample » fecal samples (توسيع البحث), total sample (توسيع البحث)
required optimization » guided optimization (توسيع البحث), resource optimization (توسيع البحث), feature optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
sample design » sampling design (توسيع البحث)
final sample » fecal samples (توسيع البحث), total sample (توسيع البحث)
-
1
-
2
-
3
-
4
-
5
-
6
Multi objective optimization design process.
منشور في 2024"…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …"
-
7
-
8
-
9
Optimal Latin square sampling distribution.
منشور في 2024"…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …"
-
10
The flowchart of Algorithm 2.
منشور في 2024"…To solve this optimization model, a multi-level optimization algorithm is designed. …"
-
11
Proposed Algorithm.
منشور في 2025"…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. …"
-
12
-
13
-
14
PANet network design.
منشور في 2025"…Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. …"
-
15
Comparisons between ADAM and NADAM optimizers.
منشور في 2025"…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. …"
-
16
BiFPN network design.
منشور في 2025"…Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. …"
-
17
Design variables and range of values.
منشور في 2024"…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …"
-
18
Feasibility diagram of design points.
منشور في 2024"…Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth (<i>H</i>), mass flow (<i>Q</i>), and inlet and outlet diameter (<i>d</i>), combined with a genetic algorithm for multi-objective analysis. …"
-
19
-
20