Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Jianpeng Ding"'
Autor:
Jianpeng Ding, Menghua Deng
Publikováno v:
Water Supply, Vol 22, Iss 9, Pp 7272-7280 (2022)
Water, energy, food, and ecology are essential for human survival and development. The Yangtze River Delta is one of the most important regions for China's sustainable development. It is of great significance to study the coupling coordinated develop
Externí odkaz:
https://doaj.org/article/eeb4e8f0bff6402f9d33443ed7ab6a32
Publikováno v:
Water Supply, Vol 22, Iss 6, Pp 5947-5956 (2022)
In this study, we propose an optimization-simulation approach to investigate the impact of yield uncertainty on the farmer's decisions for planting high water consumption crops. In addition, the influence of the subsidy programs provided by the gover
Externí odkaz:
https://doaj.org/article/4113fa07dae44dacbb2e414f52977d42
Publikováno v:
Production and Operations Management. 32:1187-1204
Autor:
Jianpeng Ding, Youming Lei
Publikováno v:
Journal of Vibration and Control. 29:1461-1471
We propose a new method to enhance stochastic resonance based on reinforcement learning , which does not require a priori knowledge of the underlying dynamics. The reward function of the reinforcement learning algorithm is determined by introducing a
Autor:
Jianpeng Ding, Youming Lei
Publikováno v:
Physica D: Nonlinear Phenomena. 451:133767
Publikováno v:
International Transactions in Operational Research. 29:760-782
Autor:
Jianpeng Ding, Youming Lei
Publikováno v:
Journal of Vibration & Control; Apr2023, Vol. 29 Issue 7/8, p1461-1471, 11p
Publikováno v:
Journal of Cleaner Production. 362:132335
Publikováno v:
SSRN Electronic Journal.
We study a stochastic inventory risk pooling problem, in which the objective is to minimize the risk that the remaining inventory and the unsatisfied demand exceed the pre-specified acceptable levels. We use the robustness optimization framework to m
Autor:
Jianpeng Ding
Publikováno v:
Journal of Physics: Conference Series. 2171:012030
In traditional deep Convolutional Neural Network (CNN) based person re-identification (Re-ID) methods, there must be thousands of training samples with annotated information under the same data distribution, in other words, the data used to train the