17953950. APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF simplified abstract (Hyundai Motor Company)

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APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF

Organization Name

Hyundai Motor Company

Inventor(s)

Sung Tae Choi of Gwangmyeong (KR)

APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 17953950 titled 'APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF

Simplified Explanation

The present disclosure describes an apparatus and method for predicting the performance of a power window. This is achieved through the use of a deep learning model that is trained to predict the performance of the power window based on various factors such as the slide resistance of the glass run, the stroke distance of the door glass, the weight of the door glass, the torque of the motor, and the durability of the power window.

  • The apparatus includes a memory storage that stores the deep learning model and any trained updates.
  • A controller is responsible for training the deep learning model using the mentioned factors to predict the performance of the power window.
  • The system can then use the trained deep learning model to predict the performance of a specific power window.

Potential Applications

  • Automotive industry: This technology can be used in the design and manufacturing of power windows for vehicles, allowing for better prediction of their performance.
  • Quality control: The deep learning model can be used to assess the performance of power windows during production, ensuring that they meet the required standards.

Problems Solved

  • Lack of accurate prediction: This technology solves the problem of not being able to accurately predict the performance of a power window based on various factors.
  • Inefficient quality control: By using the deep learning model, this technology improves the efficiency of quality control processes by automating the assessment of power window performance.

Benefits

  • Improved design: The ability to predict power window performance allows for better design decisions, resulting in more efficient and reliable power windows.
  • Cost savings: By identifying potential issues in power window performance early on, this technology helps to reduce costs associated with repairs and replacements.
  • Enhanced customer satisfaction: Power windows that perform optimally lead to improved customer satisfaction with the overall vehicle experience.


Original Abstract Submitted

The present disclosure relates to an apparatus for predicting performance of a power window and a method thereof. The apparatus for predicting performance of a power window may include a memory storage that stores a deep learning model and trained updates thereto and a controller that trains the deep learning model to predict the performance of the power window using a slide resistance of a glass run, a stroke distance of a door glass, a weight of the door glass, a torque of a motor, and a durability of the power window. The system may then predict performance of a target power window based on the deep learning model on which training has been performed.

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