Zobrazeno 1 - 10
of 164
pro vyhledávání: '"PENG Bowen"'
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 41, Iss 2, Pp 117-127 (2024)
Highly homogeneous magnetic fields in magnetic resonance spectrometers are an important guarantee for improving the quality of spectra for chemical structure analysis and kinetic information. The active shimming process using a shimming power supply
Externí odkaz:
https://doaj.org/article/67968866e0894d21b8634fb902b0035b
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 40, Iss 03, Pp 332-340 (2023)
Temperature drift is an important factor affecting the measurement accuracy of desktop NMR spectrometers, and adding a field-locking coil to the probe to achieve field-frequency interlocking is a common means of suppressing temperature drift. In this
Externí odkaz:
https://doaj.org/article/0969e18e06bd4f33bf2e5e5c5c16a217
Transfer-based targeted adversarial attacks against black-box deep neural networks (DNNs) have been proven to be significantly more challenging than untargeted ones. The impressive transferability of current SOTA, the generative methods, comes at the
Externí odkaz:
http://arxiv.org/abs/2407.15683
Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with
Externí odkaz:
http://arxiv.org/abs/2401.17038
Network binarization exhibits great potential for deployment on resource-constrained devices due to its low computational cost. Despite the critical importance, the security of binarized neural networks (BNNs) is rarely investigated. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2312.13575
Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another
Externí odkaz:
http://arxiv.org/abs/2309.00071
The deep neural networks (DNNs) have freed the synthetic aperture radar automatic target recognition (SAR ATR) from expertise-based feature designing and demonstrated superiority over conventional solutions. There has been shown the unique deficiency
Externí odkaz:
http://arxiv.org/abs/2304.01747
Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN infer
Externí odkaz:
http://arxiv.org/abs/2209.04779
Autor:
Peng, Bowen, Yang, Dongmei, Li, Ziyao, Yuan, Haoyu, Wu, Pengcheng, Huang, Kexin, Sun, Kenan, Zhu, Junfang, Wu, Keliang, Liu, Zhiyong
Publikováno v:
In Journal of Industrial and Engineering Chemistry 25 August 2024 136:341-348
Publikováno v:
In Journal of Magnetic Resonance October 2023 355