Zobrazeno 1 - 10
of 941
pro vyhledávání: '"Li Chenghao"'
Autor:
FU Hongjun, ZHU Shaoxuan, WANG Buhua, XIE Yan, XIONG Haoqing, TANG Xiaojun, DU Xiaoyong, LI Chenghao, LI Xiaomeng
Publikováno v:
发电技术, Vol 45, Iss 2, Pp 353-362 (2024)
With the expansion of power grid scale and the increase of power components, the maintenance methods of power system become more and more complex. It is difficult to evaluate the low-frequency oscillation risk of power grid under massive maintenance
Externí odkaz:
https://doaj.org/article/07bf7d3b612a42ff8e4efa61c000dfe8
Publikováno v:
电力工程技术, Vol 41, Iss 2, Pp 10-19 (2022)
Subsequent commutation failure of the high voltage direct current (HVDC) transmission system has a seriously negative impact on the stable operation of the AC-DC hybrid power grid. To reduce the probability of subsequent commutation failure,an adapti
Externí odkaz:
https://doaj.org/article/5cda9a3e3d234ff69a37e8d336971c1f
Diffusion models have demonstrated significant potential in image generation. However, their ability to replicate training data presents a privacy risk, particularly when the training data includes confidential information. Existing mitigation strate
Externí odkaz:
http://arxiv.org/abs/2412.01118
Autor:
Li, Chenghao, Chong, Nak Young
Grasping a diverse range of novel objects in dense clutter poses a great challenge to robotic automation mainly due to the occlusion problem. In this work, we propose the Pyramid-Monozone Synergistic Grasping Policy (PMSGP) that enables robots to eff
Externí odkaz:
http://arxiv.org/abs/2409.06959
This paper presents an efficient deep reinforcement learning (DRL) framework for online 3D bin packing (3D-BPP). The 3D-BPP is an NP-hard problem significant in logistics, warehousing, and transportation, involving the optimal arrangement of objects
Externí odkaz:
http://arxiv.org/abs/2408.09694
In industrial anomaly detection, model efficiency and mobile-friendliness become the primary concerns in real-world applications. Simultaneously, the impressive generalization capabilities of Segment Anything (SAM) have garnered broad academic attent
Externí odkaz:
http://arxiv.org/abs/2402.19145
Unrestricted Error-Type Codebook Generation for Error Correction Code in DNA Storage Inspired by NLP
Recently, DNA storage has surfaced as a promising alternative for data storage, presenting notable benefits in terms of storage capacity, cost-effectiveness in maintenance, and the capability for parallel replication. Mathematically, the DNA storage
Externí odkaz:
http://arxiv.org/abs/2401.15915
Autor:
Li, Chenghao, Zhang, Chaoning, Zeng, Boheng, Lu, Yi, Shi, Pengbo, Chen, Qingzi, Liu, Jirui, Zhu, Lingyun, Yang, Yang, Shen, Heng Tao
Attention mechanisms have significantly advanced visual models by capturing global context effectively. However, their reliance on large-scale datasets and substantial computational resources poses challenges in data-scarce and resource-constrained s
Externí odkaz:
http://arxiv.org/abs/2401.05738
Publikováno v:
J. Phys. Chem. B 2024, 128, 34, 8103-8115
Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalise to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of
Externí odkaz:
http://arxiv.org/abs/2312.14847
Autor:
Li, Chenghao, Wu, Yifei, Shen, Wenbo, Zhao, Zichen, Chang, Rui, Liu, Chengwei, Liu, Yang, Ren, Kui
Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to
Externí odkaz:
http://arxiv.org/abs/2310.17186