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Related Concept Videos

State Space Representation01:27

State Space Representation

324
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
324
State Space to Transfer Function01:21

State Space to Transfer Function

346
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
346
Transfer Function to State Space01:23

Transfer Function to State Space

450
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
450
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

169
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
169
Block Diagram Reduction01:22

Block Diagram Reduction

321
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
321
Social Loafing01:37

Social Loafing

36.7K
Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
36.7K

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Related Experiment Video

Updated: Oct 11, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
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A novel state space reduction algorithm for team formation in social networks.

Muhammad Zubair Rehman1, Kamal Z Zamli2, Mubarak Almutairi3

  • 1Faculty of Computing and Information Technology, Sohar University, Sohar, Sultanate of Oman.

Plos One
|December 2, 2021
PubMed
Summary

This study introduces SSR-TF, a novel team formation algorithm that uses communication cost and graph reduction to build cost-effective research teams. It efficiently identifies experts for collaboration, enhancing team communication and expertise.

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Area of Science:

  • Social Network Analysis
  • Computer Science
  • Information Retrieval

Background:

  • Traditional team formation algorithms struggle with large datasets.
  • Expert collaboration networks are crucial for cost-effective research teams.
  • Geolocation and dataset size pose challenges for existing methods.

Purpose of the Study:

  • To present a novel team formation (TF) algorithm, SSR-TF.
  • To address scalability issues in expert team formation for large datasets.
  • To improve the cost-effectiveness and communication within research teams.

Main Methods:

  • Developed the SSR-TF algorithm based on communication cost and graph reduction metrics.
  • Communication cost assesses the potential for researcher collaboration.
  • Graph reduction scales data to relevant skills and experts for real-time extraction.

Main Results:

  • SSR-TF effectively builds cost-effective teams with highly suitable experts.
  • The algorithm demonstrates improved team communication and expertise levels.
  • Tested on five diverse datasets (UMP, DBLP, ACM, IMDB, Bibsonomy).

Conclusions:

  • SSR-TF offers a scalable and effective approach to expert team formation.
  • The algorithm enhances collaboration by optimizing expert selection.
  • SSR-TF provides a foundation for future advancements in team formation strategies.