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Yimu Ji - IEEE Xplore Author Profile

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Author details

Yimu Ji

Also published under: Yi-mu Ji, Yi-Mu Ji

Publications

32

Citations

140

Publications by Year

20152025

Co-Authors:

Zhongjie BaQiang BiHonggen BianWeiqing CaoXiangyu Cao

Show All Co-Authors (104)

Yimu Ji

Also published under: Yi-mu Ji, Yi-Mu Ji

Affiliation

School of Computer Science and Technology

Nanjing University of Posts and Telecommunications

Nanjing, China

Nanjing University of Posts and Telecommunications

Institue of High Performance Computing and Bigdata

Nanjing, China

Publication Topics

Convolutional Neural Network,
Discriminative Features,
Infrared Imaging,
Local Features,
Long Short-term Memory,
Visible Images,
Attention Mechanism,
Feature Information,
Feature Maps,
Feature Representation,
Global Features,
Long-range Dependencies

Biography

Yimu Ji received the Ph.D. degree in computer science from the Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, China, in 2006. He is currently a Professor with NJUPT. His research interests include intelligent driving, computer vision, and Big Data processing.(Based on document published on 16 December 2022).

Publications

32

Citations

140

Publications by Year

20152025

Co-Authors:

Zhongjie Ba
Qiang Bi
Honggen Bian
Weiqing Cao
Xiangyu Cao

Show All Co-Authors (104)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (2)

Journals (2)

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Dongjun Lee(4)
ChangU Kim(3)
Bukun Son(2)
Jihyun Moon(2)
Minhyeong Lee(2)
Department of Mechanical Engineering, IAMD and IOER, Seoul National University, Seoul, South Korea(1)
HD Hyundai XiteSolution, Gyeonggi-do, South Korea(1)
Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea(1)
Doosan Infracoore, Seoul, South Koera(1)
Department of Mechanical Engineering, IAMD and IER, Seoul National University, Seoul, South Korea(1)
IEEE Robotics and Automation Letters(2)
2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)(1)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(1)
IEEE(4)
Media(3)
Goyangi, Korea (South)(1)
Las Vegas, NV, USA(1)
Inertial Measurement Unit(3)
Data-driven Models(2)
Inertial Frame(2)
Multilayer Perceptron(2)
Neural Network(2)

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Real-Time Excavation Trajectory Modulation for Slip and Rollover Prevention

ChangU Kim;Bukun Son;Minhyeong Lee;Hyelim Choi;Seokhyun Hong;Minsung Kang;Jihyun Moon;Dongmok Kim;Dongjun Lee

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

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We propose a novel real-time excavation trajectory modulation framework on a slope for an autonomous excavator with a low-level digital kinematic control as common for hydraulic industrial excavators. Excavation on a slope is challenging because of a higher risk of slips and rollovers. To deal with this, we propose a real-time excavation trajectory modulation framework based on slope tangential/no...Show More

Real-Time Excavation Trajectory Modulation for Slip and Rollover Prevention

ChangU Kim;Bukun Son;Minhyeong Lee;Hyelim Choi;Seokhyun Hong;Minsung Kang;Jihyun Moon;Dongmok Kim;Dongjun Lee

IEEE Robotics and Automation Letters

Year: 2025 | Volume: 10, Issue: 4 | Journal Article |

Precision Motion Control of Robotized Industrial Hydraulic Excavators via Data-Driven Model Inversion

Minhyeong Lee;Hyelim Choi;ChangU Kim;Jihyun Moon;Dongmok Kim;Dongjun Lee

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

Cited by: Papers (25)

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This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing a single neural network to approximate the whole excavator dynamics, including input delays and dead-zones, we construct a physics-inspired data-driven model with a modular structure. The data-driven model is then inverted in a modular fa...Show More

Precision Motion Control of Robotized Industrial Hydraulic Excavators via Data-Driven Model Inversion

Minhyeong Lee;Hyelim Choi;ChangU Kim;Jihyun Moon;Dongmok Kim;Dongjun Lee

IEEE Robotics and Automation Letters

Year: 2022 | Volume: 7, Issue: 2 | Journal Article |

Expert-Emulating Excavation Trajectory Planning for Autonomous Robotic Industrial Excavator

Bukun Son;ChangU Kim;Changmuk Kim;Dongjun Lee

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

Cited by: Papers (20)

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We propose a novel excavation (i.e., digging) trajectory planning framework for industrial autonomous robotic excavators, which emulates the strategies of human expert operators to optimize the excavation of (complex/unmodellable) soils while also upholding robustness and safety in practice. First, we encode the trajectory with dynamic movement primitives (DMP), which is known to robustly preserve...Show More

Expert-Emulating Excavation Trajectory Planning for Autonomous Robotic Industrial Excavator

Bukun Son;ChangU Kim;Changmuk Kim;Dongjun Lee

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2020 | Conference Paper |

2-D cooperative localization with omni-directional mobile robots

Changu Kim;Hyunsoo Yang;Dongho Kang;Dongjun Lee

2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Year: 2015 | Conference Paper |

Cited by: Papers (4)

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In this paper, we propose cooperative localization scheme based feedback control for the multiple omni-directional mobile robots. Using the proposed cooperative localization scheme, localization capability of each mobile robot is enhanced, although some of the robots loss its absolute position measurement. Position error is bounded based on relative measurements which is unbounded for dead reckoni...Show More

2-D cooperative localization with omni-directional mobile robots

Changu Kim;Hyunsoo Yang;Dongho Kang;Dongjun Lee

2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Year: 2015 | Conference Paper |

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