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相关概念视频

Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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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...
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Introduction and Methods of Leveling01:26

Introduction and Methods of Leveling

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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Classification of Signals01:30

Classification of Signals

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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...
310
Levels of Use of a GIS01:29

Levels of Use of a GIS

15
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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相关实验视频

一个具有两级分辨率的神经网络,可用于高速公路交通预测的增强解释性.

Semin Kwak1,2, Danya Li3,4, Nikolas Geroliminis5

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA. seminkwak@gmail.com.

Scientific reports
|December 31, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了双层分辨率神经网络 (TwoResNet),用于改进高速公路交通预测. 通过捕捉区域和地方空间相关性,TwoResNet提高了长期预测的准确性和可解释性.

关键词:
自由路传感器网络的自由路传感器网络.几何深度学习的几何深度学习多变量时间序列预测.交通预测,交通预测.两个级别的分辨率神经网络的神经网络.

相关实验视频

科学领域:

  • 人工智能的人工智能
  • 运输工程 运输工程
  • 网络科学 网络科学

背景情况:

  • 深度学习模型,特别是图形神经网络 (GCN),对于交通预测很普遍,因为它们能够建模复杂的时空数据.
  • 传统的GCN难以捕捉长距离的空间依赖性,限制了长期交通预测的准确性.
  • 准确的交通预测对于高效的交通管理和城市规划至关重要.

研究的目的:

  • 开发一种新的深度学习模型,克服传统GCN在长期交通预测方面的局限性.
  • 提高交通预测模型的可解释性,特别是在处理杂或不完整的传感器数据时.
  • 改进本地和远程交通信息的整合,以实现更可靠的预测.

主要方法:

  • 提出了双层分辨神经网络 (TwoResNet),这是一个用于流量预测的新型架构.
  • TwoResNet采用两块结构:第一个块用于大型区域流量模式,第二个基于GCN的块用于细粒度的局部空间相关性.
  • 该模型将区域预测集成到本地相关性分析中,使交通动态的更全面的理解成为可能.

主要成果:

  • 与传统方法相比,拟议的TwoResNet模型在长期交通预测方面显示出更高的准确性.
  • 两级分辨率结构有效地捕捉了交通网络中的区域和地方空间相关性.
  • 该模型表现出增强的可解释性,在数据不完善的情况下证明是有益的.

结论:

  • TwoResNet在基于深度学习的交通预测方面取得了重大进展,特别是对于高速公路网络.
  • 该架构能够以多个空间分辨率处理信息,从而提高预测性能和模型可解释性.
  • 这种方法提供了一种更直观,更有效的方法来整合各种交通数据,以改善运输系统管理.