Zobrazeno 1 - 10
of 630
pro vyhledávání: '"Zhang Xiyuan"'
Autor:
Dong Jian, Qin Lan, Han Shilong, Wang Zhanpeng, Chen Junyue, Liu Xiaochen, Xu Jiaxin, Zhang Xiyuan
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In this paper, the HSV color space model is used to collect the color characteristics of modern interior design and classify them into three types: hue (H), saturation (S), and luminance (V). At the same time, it combines with the theory of color psy
Externí odkaz:
https://doaj.org/article/6127af4b96c74b169b2723c722bce2d9
Autor:
Zhang, Xiyuan, Teng, Diyan, Chowdhury, Ranak Roy, Li, Shuheng, Hong, Dezhi, Gupta, Rajesh K., Shang, Jingbo
Motion time series collected from mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR due to their low-power, alw
Externí odkaz:
http://arxiv.org/abs/2410.19818
Protocol reverse engineering (PRE) aims to infer the specification of network protocols when the source code is not available. Specifically, field inference is one crucial step in PRE to infer the field formats and semantics. To perform field inferen
Externí odkaz:
http://arxiv.org/abs/2409.01994
Autor:
He, Ye, Li, Xingchen, Xu, Zijun, Qi, Ming, Wang, Congcong, Wang, Chenwei, Lu, Hai, Nie, Xiaojun, Fan, Ruirui, Jing, Hantao, Song, Weiming, Wang, Keqi, Liu, Kai, Liu, Peilian, Li, Hui, Li, Zaiyi, Fu, Chenxi, Zhang, Xiyuan, Kang, Xiaoshen, Li, Zhan, Lu, Weiguo, Xiao, Suyu, Shi, Xin
A high precision beam monitor system based on silicon carbide PIN sensor is designed for China Spallation Neutron Source 1.6 GeV proton beam to monitor the proton beam fluence.The concept design of the beam monitor system is finished together with fr
Externí odkaz:
http://arxiv.org/abs/2403.09244
Autor:
Ansari, Abdul Fatir, Stella, Lorenzo, Turkmen, Caner, Zhang, Xiyuan, Mercado, Pedro, Shen, Huibin, Shchur, Oleksandr, Rangapuram, Syama Sundar, Arango, Sebastian Pineda, Kapoor, Shubham, Zschiegner, Jasper, Maddix, Danielle C., Wang, Hao, Mahoney, Michael W., Torkkola, Kari, Wilson, Andrew Gordon, Bohlke-Schneider, Michael, Wang, Yuyang
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model a
Externí odkaz:
http://arxiv.org/abs/2403.07815
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domain
Externí odkaz:
http://arxiv.org/abs/2402.01801
Autor:
Zhang, Xiyuan, Fu, Xiaohan, Teng, Diyan, Dong, Chengyu, Vijayakumar, Keerthivasan, Zhang, Jiayun, Chowdhury, Ranak Roy, Han, Junsheng, Hong, Dezhi, Kulkarni, Rashmi, Shang, Jingbo, Gupta, Rajesh
Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approache
Externí odkaz:
http://arxiv.org/abs/2311.06968
Autor:
Wang, Keqi, Yang, Tao, Fu, Chenxi, Gong, Li, Jiang, Songting, Kang, Xiaoshen, Li, Zaiyi, Shi, Hangrui ShiXin, Song, Weimin, Wang, Congcong, Xiao, Suyu, Xu, Zijun, Zhang, Xiyuan
Silicon-based fast time detectors have been widely used in high energy physics, nuclear physics, space exploration and other fields in recent years. However, silicon detectors often require complex low-temperature systems when operating in irradiatio
Externí odkaz:
http://arxiv.org/abs/2306.09576
Time-series data augmentation mitigates the issue of insufficient training data for deep learning models. Yet, existing augmentation methods are mainly designed for classification, where class labels can be preserved even if augmentation alters the t
Externí odkaz:
http://arxiv.org/abs/2303.14254
Autor:
Yu, Xiaofan, Cherkasova, Ludmila, Vardhan, Harsh, Zhao, Quanling, Ekaireb, Emily, Zhang, Xiyuan, Mazumdar, Arya, Rosing, Tajana
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies
Externí odkaz:
http://arxiv.org/abs/2301.06646