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A Novel ANN-PSO Method for Optimizing a Small-Signal Equivalent Model of a Dual-Field-Plate GaN HEMT.

Micromachines (Basel)

November 2024

Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

This study introduces a novel method that integrates artificial neural networks (ANNs) with the Particle Swarm Optimization (PSO) algorithm to enhance the efficiency and precision of parameter optimization for the small-signal equivalent model of dual-field-plate GaN HEMT devices. We initially train an ANN model to predict the S-parameters of the device, and subsequently utilize the PSO algorithm for parameter optimization. Comparative analysis with the NSGA2 and DE algorithms, based on convergence speed and accuracy, underscores the superiority of the PSO algorithm.

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Background: Renal impairment significantly affects morbidity and mortality rates of cirrhosis patients. Studies on glomerular filtration rate (eGFR) estimation did not include cirrhosis patients. These equations are erroneous and unreliable in cirrhosis due to sarcopenia.

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Background: High-quality assessment of prostate MRI is fundamental in both clinical practice and screening. There is a lack of national level data on variability in prostate volume measurement and PI-RADS assessment. Methods of quality assurance need to be developed.

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Article Synopsis
  • Differentiating between ventricular tachycardia (VT) and supraventricular wide tachycardia (SWCT) using 12-lead ECG is challenging and has traditionally depended on manual interpretation, which can be limited by the user's expertise.
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Addressing the global challenge of ensuring access to safe drinking water, especially in developing countries, demands cost-effective, eco-friendly, and readily available technologies. The persistence, toxicity, and bioaccumulation potential of organic pollutants arising from various human activities pose substantial hurdles. While high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is a widely utilized technique for identifying pollutants in water, the multitude of structures for a single elemental composition complicates structural identification.

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