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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Flame Photometry: Overview01:02

Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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多目标分数级粒子群集优化算法,用于处理多波长高 pyrometer 的数据.

Mei Liang, Yongsheng Wang, Changhui Wang

    Optics express
    |October 20, 2023
    PubMed
    概括

    这项研究引入了两个增强的粒子群优化,以准确确定高温目标的真实温度,其发射率不明. 这些算法改善了多目标优化,以实现精确的温度逆转.

    科学领域:

    • * 热力学和热传递
    • * 计算智能和优化算法

    背景情况:

    • * 对于高温目标来说,准确的真实温度反转是具有挑战性的,原因是未知的辐射率.
    • *现有的方法往往在多目标优化和局部优化方面扎.

    研究的目的:

    • * 开发新的算法,准确地实现高温目标的真实温度反转,其发射率未知.
    • * 增强多目标优化能力,以提高反转精度.

    主要方法:

    • *将多光谱真实温度反转转化为多目标最小优化问题.
    • * 开发了两个改进的分数级粒子群集优化 (IFOPSO):高阶非线性时间变化的惯性重量 (Hntiw) IFOPSO和全球本地最佳值 (Glbest) IFOPSO.
    • * Hntiw IFOPSO使用非线性函数用于惯性重量以逃避局部最佳值; Glbest IFOPSO增强粒子更新以获得更好的优化.

    主要成果:

    • * Hntiw IFOPSO和Glbest IFOPSO都在解决真实温度反转的多目标优化问题方面表现出有效性.
    • *使用光谱发射率模型和真实世界火箭尾火焰数据的模拟验证了拟议的算法的准确性.
    • * 算法显示出更好的处理未知发射率的能力,并实现精确的温度反转.

    结论:

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    • * 拟议的Hntiw IFOPSO和Glbest IFOPSO算法为未知发射率的高温目标的真实温度反转提供了强大的解决方案.
    • *这些先进的优化技术显著提高了在具有挑战性的环境中温度测量的准确性和可靠性.