Search alternatives:
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
process optimization » model optimization (Expand Search)
ips derived » ipsc derived (Expand Search), its derived (Expand Search), hipsc derived (Expand Search)
primary aim » primary care (Expand Search), primary data (Expand Search)
aim process » a process (Expand Search), acp process (Expand Search), ii process (Expand Search)
binary ips » binary pairs (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
process optimization » model optimization (Expand Search)
ips derived » ipsc derived (Expand Search), its derived (Expand Search), hipsc derived (Expand Search)
primary aim » primary care (Expand Search), primary data (Expand Search)
aim process » a process (Expand Search), acp process (Expand Search), ii process (Expand Search)
binary ips » binary pairs (Expand Search)
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1
Process fault of Tennessee Eastman process.
Published 2024“…This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. …”
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2
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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3
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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4
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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5
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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6
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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7
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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8
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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9
Quantitative analysis of ACSA for TEP process.
Published 2024“…This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. …”
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10
ACSA pseudo code for proposed control process.
Published 2024“…This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. …”
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11
Study design.
Published 2024“…At the delivery stage, all patients will receive both a Providence-type brace optimized by the semi-automatic algorithm leveraging a patient-specific FEM (Test) and a conventional Providence-type brace (Control), both designed using CAD/CAM methods. …”
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12
Methodology for minimum nitrogen compounds removal efficiencies estimation and wastewater treatment systems pre-selection: a watershed approach
Published 2019“…A water quality model and the Genetic Algorithm Metaheuristic were associated in order to solve the optimization problem. …”
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13
Fig 9 -
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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14
Predictive performance indicators.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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15
Fig 8 -
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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16
GBO procedure.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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17
LEO pseudocode.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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18
Boxplots in EV tests.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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19
GBO parameters for HEV.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”
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20
GBO parameters for HEV.
Published 2023“…The GBO minimizes a cost with the aim of selecting the optimal candidate for updating the SOH through a memory-fading forgetting factor. …”