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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

98
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
98
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

140
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
140
Levels of Use of a GIS01:29

Levels of Use of a GIS

45
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...
45
State Space to Transfer Function01:21

State Space to Transfer Function

174
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
174
Three-Winding Transformers01:19

Three-Winding Transformers

206
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
206
Types Of Transformers01:16

Types Of Transformers

949
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
949

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相关实验视频

Updated: Jun 8, 2025

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一个空间时间预测变压器网络,用于3级自动驾驶汽车决策.

Hongbo Gao, Xiao Zheng, Qingchao Liu

    IEEE transactions on neural networks and learning systems
    |November 6, 2024
    PubMed
    概括

    这项研究通过准确预测接管时间 (TOT) 来增强自动驾驶汽车的决策能力. 改进的TOT预测可以为3级自动驾驶汽车带来更安全,更舒适的驾驶体验.

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    科学领域:

    • 自主驾驶系统 自主驾驶系统
    • 决策算法 决策算法
    • 人与机器的互动 人与机器的互动

    背景情况:

    • 现有关于3级自动驾驶汽车 (L3-AV) 的研究缺乏对接管时间 (TOT) 机制的深入分析.
    • 目前的L3-AV研究忽略了对TOT预测至关重要的特征的时空变化.
    • 在L3-AV的下游轨迹规划中,TOT没有得到充分的考虑.

    研究的目的:

    • 调查接管时间 (TOT) 对3级自动驾驶汽车 (L3-AV) 决策的影响.
    • 为准确的TOT预测和轨迹预测开发先进的模型.
    • 为了提高L3-AV决策过程的安全性和舒适性.

    主要方法:

    • 为TOT预测提出了一个指数级光滑变压器 (ETSformer) 模型.
    • 采用时空预测变压器 (ST-Preformer) 进行周围车辆轨迹预测.
    • 整合TOT预测和轨迹评估到L3-AV决策框架中.

    主要成果:

    • ETSformer模型解释了超过83%的TOT分布特征.
    • 实现了对TOT预测的绝对百分比误差的0.7%降低.
    • 决策框架使安全和舒适的最佳决策成为可能.

    结论:

    • 准确的TOT预测对于L3-AV安全性和决策稳定性至关重要.
    • 了解TOT的影响可以改善自动驾驶的安全性和决策技巧.
    • 拟议的模型为更强大,更可靠的L3-AV系统提供了途径.