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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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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 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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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

Feixiang Zhao1, Yongxiang Liu2, Kai Huo3

  • 1College of Electronic Science, National University of Defense Technology, Changsha 410073, China. zhaofeixiang14@nudt.edu.cn.

Sensors (Basel, Switzerland)
|January 11, 2018
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Summary

This study introduces a new radar target recognition method using stacked autoencoders (SAE) and extreme learning machines (ELM). The combined approach enhances accuracy and real-time performance, particularly with limited training data for high-resolution range profiles (HRRP).

Keywords:
deep learningextreme learning machinehigh-resolution range profileradar target recognitionstacked autoencoder

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Area of Science:

  • Radar signal processing
  • Machine learning for pattern recognition
  • Artificial intelligence in defense applications

Background:

  • High-resolution range profile (HRRP) target recognition is crucial for radar systems.
  • Existing methods face challenges in balancing learning speed, generalization, and utilizing target class information.
  • Stacked Autoencoders (SAE) offer deep feature learning but can struggle with generalization and speed.
  • Extreme Learning Machines (ELM) provide fast learning but require more nodes and may not fully leverage class information.

Purpose of the Study:

  • To develop a novel radar target recognition method combining SAE and a regularized ELM.
  • To leverage the feature extraction capabilities of SAE and the efficiency of ELM.
  • To improve target recognition accuracy and real-time performance, especially in data-scarce scenarios.

Main Methods:

  • A stacked autoencoder (SAE) is employed for multi-level feature extraction from HRRP data.
  • A regularized extreme learning machine (ELM) incorporating target class information is proposed.
  • The SAE and regularized ELM are combined to synergistically enhance target recognition.

Main Results:

  • The proposed combined SAE-ELM method demonstrates effective radar target recognition.
  • Experimental results confirm good performance in both real-time processing and accuracy.
  • The method shows particular effectiveness when dealing with a limited number of training samples.

Conclusions:

  • The novel SAE-ELM method effectively addresses limitations of individual techniques for HRRP target recognition.
  • The approach achieves a favorable balance between learning speed and generalization performance.
  • This method offers a promising solution for radar target recognition, especially in scenarios with limited data availability.