Zobrazeno 1 - 10
of 711
pro vyhledávání: '"Li Jindong"'
Publikováno v:
Guan'gai paishui xuebao, Vol 41, Iss 4, Pp 127-134 (2022)
【Objective】 The purpose of this paper is to improve the hydraulic performance of the pump device in the reconstructed project of Siyang Second Station and clarify its hydraulic performance. 【Method】 Taking the vertical axial-flow pump device
Externí odkaz:
https://doaj.org/article/55cbfe2b00da49eb88befa73ee4bec71
Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model's performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most ex
Externí odkaz:
http://arxiv.org/abs/2410.15811
Remote sensing image change detection (RSCD) is crucial for monitoring dynamic surface changes, with applications ranging from environmental monitoring to disaster assessment. While traditional CNN-based methods have improved detection accuracy, they
Externí odkaz:
http://arxiv.org/abs/2410.11580
Autor:
Wang, Qi, Li, Jindong, Wang, Shiqi, Xing, Qianli, Niu, Runliang, Kong, He, Li, Rui, Long, Guodong, Chang, Yi, Zhang, Chengqi
Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well a
Externí odkaz:
http://arxiv.org/abs/2410.19744
Systolic architectures are widely embraced by neural network accelerators for their superior performance in highly parallelized computation. The DSP48E2s serve as dedicated arithmetic blocks in Xilinx Ultrascale series FPGAs and constitute a fundamen
Externí odkaz:
http://arxiv.org/abs/2409.03508
Spiking Neural Networks (SNNs), with their brain-inspired structure using discrete spikes instead of continuous activations, are gaining attention for their potential of efficient processing on neuromorphic chips. While current SNN hardware accelerat
Externí odkaz:
http://arxiv.org/abs/2408.15578
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mainly consider pairwise relationships between first-order neighbors, whi
Externí odkaz:
http://arxiv.org/abs/2407.02057
Unsupervised graph-level anomaly detection (UGAD) has attracted increasing interest due to its widespread application. In recent studies, knowledge distillation-based methods have been widely used in unsupervised anomaly detection to improve model ef
Externí odkaz:
http://arxiv.org/abs/2407.00383
Publikováno v:
Waike lilun yu shijian, Vol 25, Iss 01, Pp 69-73 (2020)
Objective To investigate the clinical characteristics and laparoscopic strategy of female inguinal hernia with cyst of round ligament of uterus. Methods The clinical data of 63 female patients of inguinal hernia with cyst of round ligament of uterus
Externí odkaz:
https://doaj.org/article/50ae38971e1c4c769ddbf7c75df99681
Unsupervised graph-level anomaly detection (UGAD) has received remarkable performance in various critical disciplines, such as chemistry analysis and bioinformatics. Existing UGAD paradigms often adopt data augmentation techniques to construct multip
Externí odkaz:
http://arxiv.org/abs/2405.02359