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Related Concept Videos

Understanding Deception01:14

Understanding Deception

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Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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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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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Updated: Jan 12, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

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Published on: January 19, 2019

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Constraint-Based Fuzzy Adaptive Security Formation Control for Nonlinear Multiagent Systems Against Deception

Huaguang Zhang, Lei Wan, Jiayue Sun

    IEEE Transactions on Cybernetics
    |November 4, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study presents an adaptive security formation control for nonlinear multiagent systems (MASs) against unknown deception attacks. The fuzzy adaptive control scheme ensures stable formation and collision-free patterns despite malicious attacks.

    Related Experiment Videos

    Last Updated: Jan 12, 2026

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.8K

    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) are crucial for coordinated tasks.
    • Formation control stability relies on secure communication.
    • Unknown deception attacks threaten system integrity.

    Purpose of the Study:

    • Develop an adaptive security formation control for nonlinear MASs.
    • Address unknown deception attacks and asymmetric constraints.
    • Achieve collision-free formation patterns with guaranteed stability.

    Main Methods:

    • State observer for estimating states under FDI attacks.
    • Nonlinear state-dependent function for asymmetric constraints.
    • Fuzzy logic systems (FLSs) and coordinate transformation for adaptive control with attack compensation.

    Main Results:

    • Formation tracking error is uniformly ultimately bounded.
    • State constraints of the MAS are always maintained.
    • Collision-free desired formation patterns are achieved.

    Conclusions:

    • The proposed fuzzy adaptive formation control effectively handles unknown deception attacks.
    • The method ensures robust and stable operation of MASs.
    • Simulation results validate the control scheme's effectiveness.