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For the Inventor, By the Inventor

Pohang-si, South Korea

Bongnam Kang

USPTO Granted Patents = 11 

 

Average Co-Inventor Count = 3.5

ph-index = 3

Forward Citations = 36(Granted Patents)


Location History:

  • Seoul, KR (2015)
  • Pohang-si, KR (2021 - 2022)

Company Filing History:

goldMedal9 out of 162 
 
Stradvision, Inc.
 patents
silverMedal1 out of 1 
 
Stradavision, Inc.
 patent
bronzeMedal1 out of 54,688 
 
Intel Corporation
 patents

Years Active: 2015-2022

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11 patents (USPTO):Explore Patents
1. 11254331 - Learning method and learning device for updating object detector, based on deep learning, of autonomous vehicle to adapt the object detector to driving circumstance, and updating method and updating device using the same (2022-02-22),
2. 11250298 - Methods for training and testing perception network by using images obtained from multiple imaging devices having diverse specifications and learning device and testing device using the same (2022-02-15),
3. 11157813 - Method and device for on-vehicle active learning to be used for training perception network of autonomous vehicle (2021-10-26),
4. 11113574 - Methods for performing self-supervised learning of deep-learning based detection network by using deep Q-network and devices using the same (2021-09-07),
5. 11080544 - Method and device for calibrating pitch of camera on vehicle and method and device for continual learning of vanishing point estimation model to be used for calibrating the pitch (2021-08-03),
6. 10970645 - Method for explainable active learning, to be used for object detector, by using Bayesian dual autoencoder and active learning device using the same (2021-04-06),
7. 10970633 - Method for optimizing on-device neural network model by using sub-kernel searching module and device using the same (2021-04-06),
8. 10970598 - Learning method and learning device for training an object detection network by using attention maps and testing method and testing device using the same (2021-04-06),
9. 10963792 - Method for training deep learning network based on artificial intelligence and learning device using the same (2021-03-30),
10. 10922788 - Method for performing continual learning on classifier in client capable of classifying images by using continual learning server and continual learning server using the same (2021-02-16),
11. 9208375 - Face recognition mechanism (2015-12-08),

Title: Innovations of Bongnam Kang in Autonomous Vehicle Technology

Introduction

Bongnam Kang is a prominent inventor based in Pohang-si, South Korea. He has made significant contributions to the field of autonomous vehicle technology, holding a total of 11 patents. His work focuses on enhancing object detection systems through advanced learning methods and devices.

Latest Patents

Kang's latest patents include a learning method and learning device for updating object detectors based on deep learning. This innovation aims to adapt the object detector of an autonomous vehicle to various driving circumstances. The method involves inputting training images into a circumstance-specific object detector, applying convolution to generate feature maps, and utilizing a ranking network to produce segmentation maps and rank scores. Additionally, he has developed methods for training and testing perception networks using images from multiple imaging devices, enhancing the overall performance of autonomous systems.

Career Highlights

Throughout his career, Bongnam Kang has worked with notable companies in the technology sector, including Stradvision, Inc. His experience in these organizations has allowed him to refine his skills and contribute to cutting-edge innovations in the field of autonomous vehicles.

Collaborations

Kang has collaborated with talented individuals such as Hongmo Je and Wooju Ryu, further enriching his work and expanding the impact of his inventions.

Conclusion

Bongnam Kang's innovative approaches to object detection and perception networks are paving the way for advancements in autonomous vehicle technology. His contributions are vital in enhancing the safety and efficiency of self-driving systems.

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