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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.1K
Decision Making01:20

Decision Making

879
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
879
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Reason and Intuition01:37

Reason and Intuition

7.4K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

384
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...
384
Design Example: Automobile Ignition System01:14

Design Example: Automobile Ignition System

521
The automobile's ignition system plays a vital role by ensuring the timely ignition of the fuel-air mixture in each cylinder. This ignition is facilitated by a spark plug, which is composed of two electrodes separated by an air gap. A spark forms across this air gap when a substantial voltage is generated between the electrodes, leading to the ignition of the fuel.
One can generate a large voltage using a car battery of 12 volts with the help of inductors. Inductors are known for opposing...
521

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Updated: Jan 13, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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考虑个性化要求的智能汽车生物驾驶选择:多个标准模型和决策方法.

Liangliang Shi1, Shaolin Zhang2,3, Tao Han2,3

  • 1State Key Laboratory of Intelligent Vehicle Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China.

Biomimetics (Basel, Switzerland)
|October 28, 2025
PubMed
概括

本研究引入了一种新的决策模型,用于选择智能车辆驾驶,考虑驾驶员的舒适性,安全性和娱乐需求. 该方法有效地解决了汽车驾驶设计中复杂的多属性选择挑战.

关键词:
在决策过程中做出决定.衡量的方法是.智能汽车 生物驾驶 选择 选择 选择球形模糊集是一个球形模糊集.

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相关实验视频

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科学领域:

  • 汽车工程 汽车工程
  • 决策科学 决策科学 决策科学
  • 人与计算机的交互

背景情况:

  • 汽车行业正在迅速整合智能和生物技术,增加对先进智能汽车驾驶的需求.
  • 智能驾驶个性化存在挑战,原因是用户需求多样化和不完整的多属性评估方法.
  • 现有的选择过程阻碍了复杂和以用户为中心的智能汽车生物驾驶的开发.

研究的目的:

  • 开发智能汽车生物驾驶的多标准决策模型.
  • 为满足司机和乘客的个性化需求,包括舒适,安全和精神娱乐.
  • 为选择智能车辆驾驶引入一种新的决策方法.

主要方法:

  • 构建一个包含驾驶员和乘客个性化需求的多标准模型.
  • 整合测量与消除和选择表达现实 (ELECTRE) 方法进行决策.
  • 利用球形模糊集 (SFS) 来准确地解释决策矩阵中的数据.

主要成果:

  • 开发了一种新的决策方法,将和ELECTRE方法结合在球体模糊集框架内.
  • 拟议的方法通过实践应用来验证,包括对三种智能汽车驾驶类型的评估.
  • 敏感性分析证实了决策方法的稳定性和有效性.

结论:

  • 开发的决策方法为选择智能汽车驾驶提供了强有力的工具.
  • 这项研究为旨在加强智能汽车驾驶开发的设计师提供了宝贵的见解.
  • 该研究成功地解决了个性化智能汽车驾驶多属性选择的复杂性.