Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Yan Jiangpeng"'
Publikováno v:
High Temperature Materials and Processes, Vol 39, Iss 1, Pp 457-465 (2020)
The cylindrical samples of TC4 titanium alloy prepared by spark plasma sintering (SPS) were compressed with hot deformation of 70% on the thermosimulation machine of Gleeble-1500. The temperature of the processes ranged from 850°C to 1,050°C, and t
Externí odkaz:
https://doaj.org/article/b0ea8571e5d14ec8a3fa2a1fa13d7932
Publikováno v:
High Temperature Materials and Processes, Vol 39, Iss 1, Pp 328-339 (2020)
The TC4 titanium alloy powder test piece was prepared by spark plasma sintering. The multi-pass hot deformation of the TC4 titanium alloy was tested by using the Gleeble-1500 experiment machine. Measurement of relative density, X-ray diffraction, opt
Externí odkaz:
https://doaj.org/article/82b9325a1fed40fb973be683db7f9993
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used a
Externí odkaz:
http://arxiv.org/abs/2312.08631
Autor:
He, Chunming, Li, Kai, Xu, Guoxia, Yan, Jiangpeng, Tang, Longxiang, Zhang, Yulun, Li, Xiu, Wang, Yaowei
Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to
Externí odkaz:
http://arxiv.org/abs/2307.07829
Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can no
Externí odkaz:
http://arxiv.org/abs/2304.04660
Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-leve
Externí odkaz:
http://arxiv.org/abs/2211.14491
Autor:
Yu, ChengHui, Tang, MingKang, Yang, ShengGe, Wang, MingQing, Xu, Zhe, Yan, JiangPeng, Chen, HanMo, Yang, Yu, Zeng, Xiao-Jun, Li, Xiu
Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair an
Externí odkaz:
http://arxiv.org/abs/2207.07303
Recently, deep neural networks have greatly advanced undersampled Magnetic Resonance Image (MRI) reconstruction, wherein most studies follow the one-anatomy-one-network fashion, i.e., each expert network is trained and evaluated for a specific anatom
Externí odkaz:
http://arxiv.org/abs/2206.07364
Autor:
Ou, Yimin, Yang, Rui, Ma, Lufan, Liu, Yong, Yan, Jiangpeng, Xu, Shang, Wang, Chengjie, Li, Xiu
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple dupl
Externí odkaz:
http://arxiv.org/abs/2205.12646
Publikováno v:
In Materials Science & Engineering A September 2024 911