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相关概念视频

Distributed Loads01:19

Distributed Loads

538
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
538
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

646
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
646
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

694
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
694
Eccentric Loading01:16

Eccentric Loading

382
Eccentric loading is a crucial concept in the study of structural engineering and mechanics, particularly when analyzing the stability and stress distribution in columns. Unlike centric loading, where the force is applied along the centroidal axis, causing uniform compression, eccentric loading occurs when a force is applied off-center. This off-center application introduces not only direct compressive stress but also bending stress, significantly influencing the column's behavior under...
382
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

335
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
335
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

386
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
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基于LSTM的重型车辆虚拟负载传感器

Abdurrahman İşbitirici1,2, Laura Giarré2, Wen Xu3

  • 1Department of Electrical, Electronic and Information Engineering, University of Bologna, 40126 Bologna, Italy.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

一个长短期记忆 (LSTM) 网络使用传感器数据估计重型车辆的质量. 这种虚拟负载传感器的准确性与商业传感器相美.

关键词:
长期短期记忆 长期短期记忆质量估计量估计量经常性的神经网络.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 汽车工程 汽车工程

背景情况:

  • 准确估计重型车辆质量对于物流和安全至关重要.
  • 传统的负载传感器可能昂贵且安装复杂.
  • 开发虚拟传感技术提供了一个具有成本效益的替代方案.

研究的目的:

  • 设计和评估一个虚拟负载传感器来估计重型车辆的质量.
  • 使用长期短期记忆 (LSTM) 循环神经网络进行质量估计.
  • 与商业传感器对比,评估拟议的基于LSTM的方法的准确性.

主要方法:

  • 开发了一个双层的长短期存储器 (LSTM) 网络.
  • 该LSTM网络使用车辆速度,加速度,发动机转速,扭矩和踏板位置作为输入.
  • 该模型使用来自卡车制造商模拟环境的数据进行训练,并使用全球协调的轻型车辆测试周期 (WLTC) 数据进行测试.

主要成果:

  • 基于LSTM的虚拟负载传感器准确估计了重型车辆的质量.
  • 该估计的准确性与商业负载传感器相美.
  • 该系统在4的负载质量范围内表现出可靠的性能.

结论:

  • 一个长短期内存 (LSTM) 网络可以有效地作为重型车辆的虚拟负载传感器.
  • 拟议的方法为车辆质量估计提供了一个有希望的,准确的,潜在的更经济的方法.
  • 这项技术对智能运输系统和车队管理具有重大影响.