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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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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

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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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Simplified Synchronous Machine Model01:30

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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 in breast MRI.

Beatriu Reig1, Laura Heacock2, Krzysztof J Geras2

  • 1The Department of Radiology, New York University School of Medicine, New York, New York, USA.

Journal of Magnetic Resonance Imaging : JMRI
|July 6, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning enhance breast MRI analysis by improving segmentation and integrating diverse data. These techniques promise advancements in texture analysis, radiomics, and radiogenomics for better patient outcomes.

Keywords:
MRartificial intelligencebreastdeep learningmachine learningradiomics

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Data Science

Background:

  • Machine learning (ML) significantly advances medical imaging data extraction and analysis.
  • Breast MRI applications are rapidly expanding due to improved 3D segmentation capabilities.
  • Integration of radiologist interpretation (BI-RADS), multiparametric imaging, and genetic data is becoming feasible.

Purpose of the Study:

  • To provide a comprehensive overview of ML and deep learning (DL) techniques in breast MRI.
  • To cover supervised and unsupervised methods, anatomic and lesion segmentation.
  • To explore current limitations and future applications in texture analysis, radiomics, and radiogenomics.

Main Methods:

  • Review of supervised and unsupervised machine learning algorithms applied to breast MRI.
  • Discussion of 3D breast and lesion segmentation techniques.
  • Analysis of feature extraction methods for rapid dataset analysis.

Main Results:

  • ML and DL enable accurate segmentation of breast anatomy and lesions.
  • These techniques facilitate the integration of diverse data sources for comprehensive analysis.
  • Advances in feature extraction support large-scale, multi-institutional data analysis.

Conclusions:

  • ML and DL are transforming breast MRI analysis, offering powerful tools for interpretation and data integration.
  • Future applications in texture analysis, radiomics, and radiogenomics hold significant promise for advancing breast cancer research and clinical practice.
  • Continued development is crucial for overcoming current limitations and maximizing the potential of AI in breast MRI.