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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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Machines: Problem Solving I01:22

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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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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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Simplified Synchronous Machine Model

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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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Automatic Segmentation of Plants and Weeds in Wide-Band Multispectral Imaging (WMI).

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Development of a Multispectral Image Database in Visible-Near-Infrared for Demosaicking and Machine Learning

Vahid Mohammadi1, Sovi Guillaume Sodjinou1, Pierre Gouton1

  • 1ImViA Laboratory, UFR Sciences et Techniques, Université Bourgogne Europe, 21000 Dijon, France.

Journal of Imaging
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

Researchers created a free multispectral image database featuring diverse plants and weeds. This resource supports advancements in demosaicking, segmentation, and deep learning for crop/weed discrimination tasks.

Keywords:
annotated image databasedeep learningdemosaickingmulti-spectral filter arraysegmentation

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Image Processing

Background:

  • Multispectral (MS) imaging is increasingly utilized across various research domains.
  • A significant challenge is the scarcity of accessible multispectral image databases due to the recent development and limited availability of MS cameras.
  • The creation of comprehensive MS image databases is essential for advancing research in this field.

Purpose of the Study:

  • To establish a freely accessible multispectral image database to address the limitations in current data availability.
  • To provide high-resolution MS images of plants and weeds, including annotations and masks, to facilitate research.
  • To support the development and evaluation of algorithms for MS image analysis, such as demosaicking, segmentation, and crop/weed discrimination.

Main Methods:

  • Utilized two high-end MS cameras (visible and near-infrared) based on filter array technology from the PImRob platform at the University of Burgundy.
  • Acquired and curated a dataset of high-resolution MS images.
  • Provided both original raw and demosaicked images, alongside annotated images and segmentation masks.

Main Results:

  • Developed and released a freely accessible multispectral image database.
  • The database contains diverse MS images of plants and weeds with detailed annotations and masks.
  • Included both raw and processed (demosaicked) image data.

Conclusions:

  • The established MS image database serves as a valuable resource for the scientific community.
  • It is particularly beneficial for research focusing on demosaicking techniques, segmentation algorithms, and deep learning applications in agriculture.
  • The database aims to accelerate progress in automated crop and weed identification using multispectral imaging.