Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target
Authors: Zitong Zhang, Yang Liu, Hao Sun
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of our method has been demonstrated through multiple sets of synthetic videos considering different nonlinear dynamics. Results from extensive experiments demonstrate the efficacy of the proposed method. |
| Researcher Affiliation | Academia | 1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper describes the methodology in prose and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to source code for the described methodology or state that it is publicly available in a repository or supplementary material. |
| Open Datasets | Yes | The datasets are derived from instances introduced in [Gilpin, 2021], where we utilize the following examples: Lorenz, Sprott E, Rayleigh Benard, Sprott F, Nose Hoover, Tsucs2 and Wang Sun. |
| Dataset Splits | No | The paper describes data generation and training processes, but it does not explicitly specify exact percentages or sample counts for training, validation, and test dataset splits. |
| Hardware Specification | Yes | All simulations in this study are conducted on an Intel Core i9-13900 CPU workstation with an NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch', 'MATLAB', and 'YOLO-v8' as software used but does not provide specific version numbers for any of them. |
| Experiment Setup | No | The paper describes the network training process and loss function but does not explicitly provide specific hyperparameter values or detailed system-level training settings. |