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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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相关实验视频

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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从用户错误报告中推断测试模型,使用多目标搜索.

Giovani Guizzo1, Francesco Califano1, Federica Sarro1

  • 1University College London, London, UK.

Empirical software engineering
|June 23, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种多目标的进化方法,从错误报告中自动生成有限状态机器 (FSM). NSGA-II在软件测试的错误检测和模型生成方面表现出卓越的性能.

关键词:
模型推理推理模型推理.多目标优化多目标优化基于搜索的软件工程是基于搜索的.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 正式方法 正式方法

背景情况:

  • 软件测试人员使用错误报告来识别异常的软件行为.
  • 自动捕获不正确的软件行为对于高效的测试至关重要.
  • 有限状态机器 (FSM) 可以用于测试目的来表示软件行为.

研究的目的:

  • 提出一个多目标的进化方法,从自然语言错误报告中自动生成FSM.
  • 为了使测试人员能够使用生成的FSM练习报告的错误,并发现新的错误.
  • 评估不同多目标进化算法 (MOEA) 对此任务的有效性.

主要方法:

  • 使用多目标进化算法 (MOEA) 引导FSM生成.
  • 同时最小化三个目标:模型大小,过度概括和过少概括.
  • 评估了使用NSGA-II,NSGA-III和MOEA/D的10个现实世界软件程序的方法,与KLFA进行了对比.

主要成果:

  • 基线工具KLFA由于过度概括而不切实际.
  • NSGA-II显著优于NSGA-III和MOEA/D,在90%的测试程序中检测到更多的错误.
  • 仅使用两个目标就导致了不可实现或低于最佳的解决方案,而使用三个目标则导致了不可实现或低于最佳的解决方案.

结论:

  • 拟议的MOEA方法有效地从软件测试的错误报告中生成FSM.
  • 对于这项任务,NSGA-II是最有效的MOEA,可以平衡模型大小,准确性和覆盖范围.
  • 采用所有三个目标 (大小,过度泛化,不足泛化) 会导致多样化,优化FSM,避免局部优化和改进测试生成.