Zobrazeno 1 - 10
of 179
pro vyhledávání: '"TANG Lv"'
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 146-152 (2022)
Combined with the monitoring data of the new distribution network Feeder Terminal Unit (FTU), an innovative three-phase real-time line loss calculation method based on the feeder segment rather than the entire feeder as the basic calculation unit is
Externí odkaz:
https://doaj.org/article/bb8757cdb6e5485394c00cdcf822f416
Autor:
Tang Lv, Juan Li, Lanyu Zhou, Tao Zhou, Hugh W. Pritchard, Chaoxiang Ren, Jiang Chen, Jie Yan, Jin Pei
Publikováno v:
Plants, Vol 13, Iss 5, p 659 (2024)
Seed storage underpins global agriculture and the seed trade and revealing the mechanisms of seed aging is essential for enhancing seed longevity management. Safflower is a multipurpose oil crop, rich in unsaturated fatty acids that are at high risk
Externí odkaz:
https://doaj.org/article/cacea5ab44674127b0116a46f44d4dab
For decades, video compression technology has been a prominent research area. Traditional hybrid video compression framework and end-to-end frameworks continue to explore various intra- and inter-frame reference and prediction strategies based on dis
Externí odkaz:
http://arxiv.org/abs/2410.01654
The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object segmentation model, quickly garnered widespread attention and significantly influenced the academic community. To extend its application to video, Meta further devel
Externí odkaz:
http://arxiv.org/abs/2407.21596
Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. However, the Transformer's quadratic complexity poses challenges for handli
Externí odkaz:
http://arxiv.org/abs/2405.14480
In the evolving landscape of computer vision, foundation models have emerged as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks. Among these, the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmen
Externí odkaz:
http://arxiv.org/abs/2405.00256
In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilities of Multimodal Large Language Models (MLLMs). Recognizing the inher
Externí odkaz:
http://arxiv.org/abs/2311.11273
The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing. This paper delves into the exploration o
Externí odkaz:
http://arxiv.org/abs/2310.10912
Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent advancements in deep learning models, these models still rely on train
Externí odkaz:
http://arxiv.org/abs/2309.05499
Publikováno v:
E3S Web of Conferences, Vol 236, p 02014 (2021)
Demand response adjusts demand through market signals such as price to promote grid reliability. By changing the demand for electricity, the demand response can realize the friendly interaction of sourcenetwork-load, promote the absorption of new ene
Externí odkaz:
https://doaj.org/article/6b12feced7a24f9ebf9e1de81b0dc3ad