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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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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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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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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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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.
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Machine-learning reprogrammable metasurface imager.

Lianlin Li1, Hengxin Ruan2, Che Liu3

  • 1State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University, 100871, Beijing, China. lianlin.li@pku.edu.cn.

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This study introduces a real-time digital-metasurface imager that overcomes limitations of conventional microwave imaging. The novel imager enables rapid, in-situ sensing and high-accuracy image recognition for dynamic scenes.

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Area of Science:

  • Applied Physics
  • Metamaterials
  • Machine Learning

Background:

  • Conventional microwave imagers face challenges with slow data acquisition and complex post-processing, limiting their use in dynamic environments.
  • Existing technologies are often ineffective for complex in-situ sensing and real-time monitoring applications.

Purpose of the Study:

  • To develop and demonstrate a real-time digital-metasurface imager capable of in-situ training and machine-learning optimized measurements.
  • To enable fast data acquisition, processing, and high-accuracy image recognition in dynamically varying scenes.

Main Methods:

  • Experimental implementation of a real-time digital-metasurface imager.
  • In-situ training of the imager to generate machine-learning optimized radiation patterns.
  • Electronic reprogramming for real-time access to optimized measurement modes and data handling.

Main Results:

  • Demonstrated real-time data acquisition and processing capabilities.
  • Achieved high-accuracy in-situ image coding and recognition for diverse datasets, including handwritten digits and through-wall gestures.
  • Showcased the ability to store and transfer full-resolution raw data in dynamic scenes.

Conclusions:

  • The developed electronically controlled metasurface imager offers a significant advancement over conventional microwave imaging systems.
  • This technology enables new possibilities for intelligent surveillance, rapid data handling, and multi-frequency imaging.
  • The real-time, reprogrammable nature of the imager facilitates versatile applications in various sensing and monitoring scenarios.