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Composite structures, 2021
In this work, we design and test a parameter optimization method by Python script to meet the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on the selection of parameters according to the mass and Poisson’s ratio of the ...
Liang Wang, Hai‐Tao Liu
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In this work, we design and test a parameter optimization method by Python script to meet the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on the selection of parameters according to the mass and Poisson’s ratio of the ...
Liang Wang, Hai‐Tao Liu
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An automated breast cancer diagnosis using feature selection and parameter optimization in ANN
Computers & electrical engineering, 2021Detecting and treating breast cancer at earlier stages is highly proved to improve the survival rate of breast cancer patients as breast cancer is considered a major cause of death worldwide. Classical methods for diagnosing breast cancer depend on human
Punitha Stephan +2 more
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Optimizing Parameters for Smoke Evacuation
Dermatologic Surgery, 2021BACKGROUND Current literature lacks recommendations regarding the ideal organization of the smoke evacuation system to minimize inhalation of surgical smoke. OBJECTIVE This study determines optimal parameters of the smoke evacuation system with respect to the surgical field.
Jusleen, Ahluwalia +3 more
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On Optimization of Machining Parameters
The Fourth International Conference on Control and Automation 2003 ICCA Final Program and Book of Abstracts ICCA-03, 2003In this paper, an optimization model based on minimum production cost for multi-pass face-milling operations with single-tool applications is presented. Limits on cutting speed, feed rate, depth of cut as well as tool life, surface roughness, cutting force, cutting power are constraints of the model.
null Libao An, null Mingyuan Chen
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Optimization of LDA parameters
2020 28th Signal Processing and Communications Applications Conference (SIU), 2020The aim of topic modeling is to automatically discover topics in large collections of documents. Although it is used in many different fields, the questions of how to eliminate topic instability and how to optimize model parameters are not fully answered yet.
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Proximal Parameter Distribution Optimization
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021Encouraging the agent to explore has become a hot topic in the field of reinforcement learning (RL). The popular approaches to engage in exploration are mainly by injecting noise into neural network (NN) parameters or by augmenting additional intrinsic motivation term.
Xuesong Wang 0001 +2 more
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WCDMA handover parameters optimization
2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577), 2004WCDMA soft handover is assisted by mobile station's measurements of the monitored and active set cells' quality. The rules for measurement reporting are based on thresholds whose computation depends on many parameters. The setting of parameters greatly affects handover, performance and downlink capacity consumption.
Vieri Vanghi, Christophe Chevallier
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, 2020
Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting.
Yeming Dai, Pei Zhao
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Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting.
Yeming Dai, Pei Zhao
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Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction
Applied Soft Computing, 2020Nonlinear Grey Bernoulli Model (NGBM(1,1)) and its derivative model utilize the specific power exponent function to manifest the nonlinear characteristics of the energy consumption data pattern.
Qinzi Xiao +4 more
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International Conference on Software Engineering, 2020
Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows ...
Ke Li +4 more
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Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows ...
Ke Li +4 more
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