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Related Concept Videos

Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Absolute and Local Extreme Values01:22

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The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
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Machines: Problem Solving II01:30

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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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Machines: Problem Solving I01:22

Machines: Problem Solving I

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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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.
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Hypercomplex extreme learning machine with its application in multispectral palmprint recognition.

Longbin Lu1, Xinman Zhang1, Xuebin Xu2

  • 1MOE Key Lab for Intelligent Networks and Network Security, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

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Summary
This summary is machine-generated.

This study introduces the hypercomplex extreme learning machine (HELM) for multispectral palmprint recognition. HELM effectively integrates multisource information, achieving competitive performance in biometric identification tasks.

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Area of Science:

  • Machine Learning
  • Biometrics
  • Computer Vision

Background:

  • Extreme Learning Machines (ELMs) are efficient for single-hidden layer feedforward neural networks (SLFNs).
  • ELMs offer rapid training, good generalization, and simple implementation.
  • Integrating multisource information is crucial for enhancing recognition accuracy.

Purpose of the Study:

  • To extend Extreme Learning Machine theory to hypercomplex spaces.
  • To develop a novel Hypercomplex Extreme Learning Machine (HELM) model.
  • To apply HELM for multispectral palmprint recognition, leveraging multispectral image data.

Main Methods:

  • Constructing a hypercomplex space using images from different spectral bands.
  • Implementing the HELM algorithm for feature extraction and classification.
  • Evaluating HELM performance on established multispectral palmprint databases.

Main Results:

  • The HELM scheme demonstrated competitive recognition accuracy.
  • Hypercomplex representation effectively utilized multisource spectral information.
  • Experimental results validated the efficacy of the proposed HELM approach.

Conclusions:

  • HELM provides an effective framework for incorporating multisource information in neural networks.
  • The proposed method shows significant potential for advanced biometric recognition systems.
  • The study contributes a novel approach to hypercomplex machine learning applications.