Hyundai motor company (20240104272). FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE simplified abstract
Contents
- 1 FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE
Organization Name
Inventor(s)
Hae Young Jeon of Seongnam-si (KR)
Seong Keun Park of Asan-si (KR)
FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104272 titled 'FREE MOTION HEADFORM IMPACT PERFORMANCE PREDICTION DEVICE AND A METHOD USING ARTIFICIAL INTELLIGENCE
Simplified Explanation
The patent application describes a free motion headform (FMH) impact performance prediction device that utilizes artificial intelligence to predict impact performance.
- Data processing processor extracts a pre-processed test target image and generates a pre-processed test target distance value.
- Machine learning processor concatenates the image and distance value to predict impact performance using a neural network.
- Output processor outputs the value learned by the machine learning processor.
Potential Applications
This technology can be applied in the automotive industry for crash test simulations, helmet design for sports, and safety equipment development.
Problems Solved
This technology helps in predicting impact performance accurately, reducing the need for physical testing, and speeding up the product development process.
Benefits
The benefits of this technology include improved safety standards, cost savings in product development, and faster time to market for new products.
Potential Commercial Applications
The potential commercial applications of this technology include automotive safety testing companies, sports equipment manufacturers, and industrial safety gear producers.
Possible Prior Art
One possible prior art could be impact testing machines that rely on physical testing rather than AI prediction for impact performance.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications may include the need for extensive training data for accurate predictions and potential biases in the AI algorithms.
How does this technology compare to traditional impact testing methods in terms of accuracy and efficiency?
This technology offers the potential for higher accuracy and efficiency compared to traditional impact testing methods by leveraging AI algorithms for predictive analysis.
Original Abstract Submitted
a free motion headform (fmh) impact performance prediction device using artificial intelligence includes a data processing processor configured to generate an image by extracting a pre-processed test target image, generated by pre-processing test target design data, using a pre-trained model and generate a pre-processed test target distance value by pre-processing the test target design data. the fmh input performance prediction device also includes a machine learning processor configured to concatenate the image generated by extraction on the basis of the pre-trained model and the pre-processed test target distance value and to predict impact performance using a neural network in which parameters are updated by learning based on an image obtained by pre-processing existing design data and existing impact amount data corresponding to the existing design data. the fmh input performance prediction device further includes an output processor configured to output a value learned by the machine learning processor.
