Zobrazeno 1 - 10
of 1 015
pro vyhledávání: '"TANG Jingjing"'
Publikováno v:
Renmin Zhujiang, Vol 42 (2021)
Affected by the monsoon climate,there are abrupt changes,non-smoothing and variation in monthly runoff.However,good accuracy cannot be realized if the monthly runoff is simulated and predicted directly by the gray self-memory model.This paper propose
Externí odkaz:
https://doaj.org/article/d7ba2fbba6534bf0b0d307c4a521c358
This paper presents a comprehensive study of automatic performer identification in expressive piano performances using convolutional neural networks (CNNs) and expressive features. Our work addresses the challenging multi-class classification task of
Externí odkaz:
http://arxiv.org/abs/2310.00699
Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pai
Externí odkaz:
http://arxiv.org/abs/2307.09670
Capturing intricate and subtle variations in human expressiveness in music performance using computational approaches is challenging. In this paper, we propose a novel approach for reconstructing human expressiveness in piano performance with a multi
Externí odkaz:
http://arxiv.org/abs/2306.06040
Publikováno v:
In Journal of Water Process Engineering January 2025 69
Autor:
Zhang, Sijing, Yang, Juan, Yang, Lei, Yang, Tingting, Liu, Yingkang, Zhou, Liuxi, Xu, Zhenglong, Zhou, Xiangyang, Tang, Jingjing
Publikováno v:
In Applied Catalysis B: Environment and Energy 15 December 2024 359
Autor:
Zhang, Yaguang ⁎, Zou, Jianxun, Chang, Ruirui, Min, Zixuan, Dong, Shuping, Tang, Zhijia, Li, Xuandu, Tang, Jingjing, Yang, Juan, Zhou, Xiangyang
Publikováno v:
In Chemical Engineering Journal 1 November 2024 499
Publikováno v:
In Neural Networks March 2025 183
Publikováno v:
In Separation and Purification Technology 1 March 2025 355 Part A
Traffic flow forecasting is essential and challenging to intelligent city management and public safety. Recent studies have shown the potential of convolution-free Transformer approach to extract the dynamic dependencies among complex influencing fac
Externí odkaz:
http://arxiv.org/abs/2111.03459