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相关概念视频

Space Trusses: Problem Solving01:29

Space Trusses: Problem Solving

579
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. Due to its adaptability and capacity to withstand complex loads, the space truss is widely used in various construction projects.
Consider a tripod consisting of a tetrahedral space truss with a ball-and-socket joint at C. Suppose the height and lengths of the horizontal and vertical...
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Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

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When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal...
1.3K
Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

549
Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
549
Mesh Analysis01:20

Mesh Analysis

600
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
600
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

831
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
831
Method of Joints: Problem Solving I01:30

Method of Joints: Problem Solving I

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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint. Consider a truss structure with two forces of 20 N and 10 N acting at joints C and D, respectively. The method of joints can be used to determine the forces FCB, FDC,...
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Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
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自动Spikformer:Spikformer架构的搜索结构.

Kaiwei Che1,2, Zhaokun Zhou1,2, Jun Niu1

  • 1School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, Guangdong, China.

Frontiers in neuroscience
|August 7, 2024
PubMed
概括

自动Spikformer通过搜索变压器架构和SNN参数来优化尖端神经网络 (SNN),降低能源消耗,同时保持性能. 这种方法提高了高级深度学习应用程序的尖端变压器效率.

关键词:
进化算法 (EA) 是一种进化算法.网络架构搜索 (NAS)尖端神经网络 (SNN) 是一个神经网络.变压器的变压器是一个变压器.变压器架构的搜索架构

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科学领域:

  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.

背景情况:

  • 尖端神经网络 (SNN) 整合了高级深度学习的自我注意机制.
  • 虽然Spikformer表现有前途,但由于冗余的架构,可能会受到高能耗的影响.
  • 优化SNN以提高能源效率对于其实际应用至关重要.

研究的目的:

  • 开发一个高效的架构搜索方法,用于Spiking变压器.
  • 在不影响性能的情况下降低尖端神经网络的能源消耗.
  • 探索更广泛的搜索空间,包括架构和内部SNN参数.

主要方法:

  • 提出了自动Spikformer,一个一拍子的Spiking变压器架构搜索方法.
  • 使用了重量纠,进化搜索和离散尖峰参数搜索 (DSPS).
  • 引入了一种新的健身功能,以实现能量和精度的最佳巴列托平衡.

主要成果:

  • 自动Spikformer子网实现了与 Spikformer 相比的性能,具有继承权重.
  • 与原来的Spikformer相比,拟议的方法显著降低了能源消耗.
  • 自动Spikformer在效率和参数数量方面超过了Spikformer和其他模型 (CNN,ViT).

结论:

  • 自动Spikformer有效地优化尖端变压器架构,以提高能源效率.
  • 该方法展示了一个可行的方法来平衡SNNs中的精度和能耗.
  • 这项研究有助于开发更具生物可信性和高效的深度学习模型.