17953950. APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF simplified abstract (Kia Corporation)

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APPARATUS FOR PREDICTING PERFORMANCE OF POWER WINDOW AND METHOD THEREOF

Organization Name

Kia Corporation

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. The apparatus includes a memory storage that stores a deep learning model and trained updates, and a controller that trains the deep learning model using various factors such as slide resistance of a glass run, stroke distance of a door glass, weight of the door glass, torque of a motor, and durability of the power window. The system can then use this trained model to predict the performance of a target power window.

  • The apparatus uses a deep learning model to predict the performance of a power window.
  • Factors such as slide resistance, stroke distance, weight, torque, and durability are considered in training the model.
  • The trained model can be used to predict the performance of a target power window.

Potential Applications

This technology can be applied in various industries and sectors, including:

  • Automotive industry: The apparatus can be used to predict the performance of power windows in vehicles, allowing manufacturers to optimize design and functionality.
  • Building industry: The technology can be utilized in the design and construction of windows in buildings, ensuring better performance and durability.
  • Consumer electronics: The apparatus can be integrated into devices with power windows, such as smartphones or tablets, to enhance user experience and reliability.

Problems Solved

The apparatus and method described in the patent application address several problems related to power windows, including:

  • Lack of accurate prediction: By using a deep learning model trained on various factors, the system can provide more accurate predictions of power window performance.
  • Design optimization: The technology allows for better design optimization by considering factors such as slide resistance, stroke distance, weight, torque, and durability.
  • Improved reliability: By predicting the performance of power windows, potential issues and failures can be identified and addressed before they occur, improving overall reliability.

Benefits

The use of this technology offers several benefits, including:

  • Enhanced user experience: By predicting power window performance, users can expect smoother operation and improved reliability.
  • Cost savings: Identifying potential issues and failures in advance can help reduce maintenance and repair costs.
  • Design optimization: The ability to predict performance allows for better design optimization, resulting in more efficient and durable power windows.


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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