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相关概念视频

Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

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Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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Machines01:19

Machines

270
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.
A free-body diagram of the...
270
Classification of Signals01:30

Classification of Signals

456
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
456
Upsampling01:22

Upsampling

232
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
232
Deconvolution01:20

Deconvolution

159
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
159

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Updated: Jun 29, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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用深度学习识别手工织面料.

Lipi B Mahanta1, Deva Raj Mahanta2, Taibur Rahman2

  • 1Mathematical and Computational Sciences Division, Institute of Advanced Study in Science & Technology (IASST) (An Autonomous R&D Institute Under Department of Science & Technology), Vigyan Path, Paschim Boragaon, P.O. Garchuk, Guwahati, Assam, 781035, India. lbmahanta@iasst.gov.in.

Scientific reports
|April 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种人工智能工具,可以从假冒中识别真实的印度手工织物"gamucha"毛巾. 一个新的深度学习模型的性能优于现有的模型,为保护织遗产提供了高效和准确的检测.

关键词:
人工智能的人工智能是人工智能.自动识别自动化识别深度学习是一种深度学习.手工织物织物是手工织物的一种.强力织机面料的织物是什么织织机类型的织物织机

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科学领域:

  • 织科学 织科学
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 印度的手工织物行业对于文化遗产和工匠的生计至关重要.
  • 假冒的强力织布产品威胁到真正的手工织布产品的真实性和市场.
  • 区分出真正的手工织织品,比如

研究的目的:

  • 开发一种人工智能驱动的工具,用于自动检测真实的手工织物产品.
  • 为了区分真正的手工织品.

主要方法:

  • 在17484个手工织物和电力织物毛巾图像上训练了六个先前存在的深度学习架构 (VGG16,VGG19,ResNet50,InceptionV3,InceptionResNetV2,DenseNet201).
  • 为同一个图像数据集开发和训练了一种新的深度学习模型.
  • 基于验证准确性,损失和计算效率来评估模型性能.

主要成果:

  • 这种新的深度学习模型表现出比预先训练的模型更高的性能.
  • 拟议的模型实现了更高的验证准确性和更低的验证损失.
  • 预先训练的模型在对未见的数据进行概括时遇到了困难,并提出了计算挑战.

结论:

  • 一个新的AI模型为验证手工织物产品提供了高效和准确的解决方案.
  • 开发的方法表明了在织品认证中的可扩展性和更广泛应用的潜力.
  • 这种计算机辅助的方法是保护手工织物遗产免受模仿的突破性方法.