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Jan B. Preibisch - IEEE Xplore Author Profile
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Jan B. Preibisch

Also published under: Jan Birger Preibisch, J. B. Preibisch

Publications
15
Citations
97
Publications by Year
20132017
Co-Authors:
Show All Co-Authors (19)
Image of author Jan B. Preibisch

Jan B. Preibisch

Also published under: Jan Birger Preibisch, J. B. Preibisch

Affiliation

Institute of Electromagnetic Theory
Hamburg University of Technology
Hamburg, Germany

Publication Topics

Biography

Jan B. Preibisch (S’12) received the B.Sc. and M.Sc. (Hons.) degrees in electrical engineering from the Hamburg University of Technology (TUHH), Hamburg, Germany, in 2010 and 2013, respectively, and the master’s degree in technology management from the Northern Institute of Technology Management, Hamburg, in 2013. He is currently pursuing the Ph.D. degree with the Institute of Electromagnetic Theory, TUHH.,His current research interests include effective uncertainty quantification in signal and power integrity.,Mr. Preibisch received the DesignCon Best Paper Award in 2017.(Based on document published on 28 September 2017).
Publications
15
Citations
97
Publications by Year
20132017
Co-Authors:
Show All Co-Authors (19)
Author's Published Works

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Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios

Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi

Journal of Intelligent and Connected Vehicles
Year: 2025 | Volume: 8, Issue: 1 | Journal Article |
Lane changing is common in driving. Thus, the possibility of traffic accidents occurring during lane changes is high given the complexity of this process. One of the primary objectives of intelligent driving is to increase a vehicle's behavior, making it more similar to that of a real driver. This study proposes a decision-making framework based on deep reinforcement learning (DRL) in a lane-chang...Show More

Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios

Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi

Year: 2025 | Volume: 8, Issue: 1 | Journal Article |

Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar

Yiren Wang;Zelun Wu;Wenfang Lu;Shujie Yu;Shihui Li;Lingsheng Meng;Xupu Geng;Xiao-Hai Yan

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Remote sensing study of the carbon cycle in coastal marine systems using machine learning methods has received significant attention recently. The partial pressure of carbon dioxide (CO2) in seawater ( {p} CO2w) is a crucial parameter for quantifying the air–sea carbon dioxide exchange. However, previous studies did not consider the effect of sea surface roughness (SSR) on {p} CO2w caused by ...Show More

Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar

Yiren Wang;Zelun Wu;Wenfang Lu;Shujie Yu;Shihui Li;Lingsheng Meng;Xupu Geng;Xiao-Hai Yan

Year: 2024 | Volume: 62 | Journal Article |

O2SNet: Unsupervised Ship Instance Segmentation from Optical to SAR Images Via Domain Adaptation

Yiren Wang;Chunbo Zhu;Pengmei Xu

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Considering insufficient data and difficulty of labeling in Synthetic Aperture Radar (SAR) images, we propose a method for unsupervised SAR ship instance segmentation based on domain adaptation. Compared with optical images, unsupervised learning in SAR images faces the difficulties of insufficient data and lacking detail features. The proposed method O2SNet contains both sample and model adaptati...Show More

O2SNet: Unsupervised Ship Instance Segmentation from Optical to SAR Images Via Domain Adaptation

Yiren Wang;Chunbo Zhu;Pengmei Xu

Year: 2022 | Conference Paper |

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