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相关概念视频

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.2K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
544
Observational Learning01:12

Observational Learning

1.5K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

Updated: May 2, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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YOLO-MS:重新思考多尺度表示学习,实现实时对象检测.

Yuming Chen, Xinbin Yuan, Jiabao Wang

    IEEE transactions on pattern analysis and machine intelligence
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    PubMed
    概括
    此摘要是机器生成的。

    介绍YOLO-MS,一个高效的对象检测模型,增强多尺度特征表示. YOLO-MS的性能优于YOLO-v8和RTMDet等最先进的探测器,使用更少的资源提供更好的性能.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 实时对象检测对于许多应用程序至关重要.
    • 现有的模型在有效地表示多尺度特征方面面临挑战.

    研究的目的:

    • 开发一个高效,高性能的物体探测器,YOLO-MS.
    • 在实时物体检测器中增强多尺度特征表示.

    主要方法:

    • 研究了多分支特征和不同内核大小对物体检测性能的影响.
    • 开发了一种用于增强多尺度特征表示的新策略.
    • 从零开始对MS COCO数据集进行训练的YOLO-MS.

    主要成果:

    • YOLO-MS的性能优于包括YOLO-v7,RTMDet和YOLO-v8.8在内的最先进的实时物体探测器.
    • 在MS COCO上,YOLO-MS-XS实现了超过42%的AP,在类似模型尺寸下超过RTMDet2%.
    • 通过YOLO-MS模块,YOLOv8-N的AP,APl和AP在降低参数和MAC的基础上得到了显著的改进.

    结论:

    • 在实时物体检测方面,YOLO-MS提供了显著的进步.
    • 拟议的方法有效地增强了多尺度特征表示.
    • 可以将YOLO-MS集成为一个插电模块,以提高其他YOLO模型的性能.