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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

541
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
541
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

745
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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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

244
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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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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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Related Experiment Video

Updated: Jan 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.8K

A hybrid machine learning-enhanced MCDM model for transport safety engineering.

Xingjian Zhang1, Haowen Chen2, Jingxuan Chen3

  • 1Courant Institute of Mathematical Sciences, New York University, New York, NY, 10012, USA.

Scientific Reports
|October 20, 2025
PubMed
Summary

This study introduces a novel DCRITIC-WASPAS-K-means model for multi-criteria decision-making in transport safety. It enhances reliability and reduces computation time for better policy decisions.

Keywords:
Decision reliabilityGraph-based techniqueK-meansMachine learningOASTransport safety engineering

Related Experiment Videos

Last Updated: Jan 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.8K

Area of Science:

  • Engineering
  • Computer Science
  • Operations Research

Background:

  • Multi-criteria decision-making (MCDM) is crucial for transport safety engineering.
  • Traditional methods face challenges with data uncertainty and computational efficiency.

Purpose of the Study:

  • To propose a hybrid machine learning-enhanced MCDM model for robust decision recommendations.
  • To improve the reliability and efficiency of decision-making processes in transport safety.

Main Methods:

  • Integration of Distance Correlation-based Criteria Importance through Intercriteria Correlation (DCRITIC) for criteria weighting.
  • Application of Weighted Aggregated Sum Product Assessment (WASPAS) for decision aggregation.
  • Utilization of a graph-based machine learning technique to enhance K-means clustering for robust centroid selection, creating the DCRITIC-WASPAS-K-means model.

Main Results:

  • The DCRITIC-WASPAS-K-means model demonstrated improved robustness and reliability in decision outcomes.
  • The machine learning integration reduced uncertainty associated with initial centroid selection in K-means clustering, decreasing iterations and runtime.
  • A case study in the Organization of American States (OAS) region validated the model's practical utility and superior performance.

Conclusions:

  • The proposed model offers a reliable, scalable, and data-driven tool for strategic planning and resource allocation in transport safety.
  • It enhances the credibility of policy interventions by providing consistent decision outputs and effective communication of policy implications.
  • Public administrators, policymakers, and government agencies can leverage this framework for improved decision-making in uncertain environments.