Zobrazeno 1 - 10
of 892
pro vyhledávání: '"Li Chenghao"'
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
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
We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA). It simplifies the attention operation by replacing it wit
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
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent research suggests that this replication may stem from the insufficient
Externí odkaz:
http://arxiv.org/abs/2309.07254
Autor:
Li, Chenghao, Zhang, Chaoning
The success of Vision Transformer (ViT) has been widely reported on a wide range of image recognition tasks. ViT can learn global dependencies superior to CNN, yet CNN's inherent locality can substitute for expensive training resources. Recently, the
Externí odkaz:
http://arxiv.org/abs/2309.05375