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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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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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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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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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Open-environment machine learning.

Zhi-Hua Zhou1

  • 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.

National Science Review
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning faces challenges in open environments where data changes over time. This research explores techniques for handling evolving data streams, new classes, and changing features in machine learning models.

Keywords:
artificial intelligencemachine learningopen MLopen-environment machine learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional machine learning assumes stable environments, limiting its application in real-world dynamic scenarios.
  • The increasing prevalence of big data and time-accumulated datasets necessitates adaptive machine learning approaches.
  • Open-environment machine learning addresses tasks where key factors change over time, posing significant challenges.

Purpose of the Study:

  • To introduce advances in open-environment machine learning techniques.
  • To address the challenges of adapting machine learning models to dynamic and evolving data streams.
  • To discuss theoretical issues related to machine learning in open environments.

Main Methods:

  • Focus on techniques for emerging new classes.
  • Exploration of methods for decremental/incremental feature adaptation.
  • Investigation of strategies for handling changing data distributions and varied learning objectives.

Main Results:

  • Advances in handling evolving data streams and dynamic environments.
  • Development of techniques for adapting to new classes and changing features.
  • Discussion of theoretical underpinnings for open-environment machine learning.

Conclusions:

  • Open-environment machine learning is crucial for practical big data tasks.
  • Adaptive techniques are essential for handling data streams with changing characteristics.
  • Further research is needed to address the theoretical challenges in dynamic machine learning scenarios.