Xiaoying Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Xiaoying Yang has a total of 39 co-authors. Here are the top co-authors by number of shared publications:
- Tianxiang Cui
University of Nottingham Ningbo China
6 shared publications - Fuhua Jia
University of Nottingham Ningbo China
3 shared publications - Yujian Ye
Southeast University
3 shared publications - Ruibin Bai
University of Nottingham Ningbo China
2 shared publications - Adam Rushworth
Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
1 shared publication - Boon Giin Lee
University of Nottingham Ningbo China, China
1 shared publication
Latest Publications
- Open Access
- 12 September 2026
- Published by Elsevier in Computers & Industrial Engineering
- Vol. 222
Hierarchical deep reinforcement learning for multi-timescale Robotic Mobile Fulfillment System optimization
- 12 September 2026
- Published by Elsevier in Computers & Industrial Engineering
- Vol. 222
Open AccessHierarchical deep reinforcement learning for multi-timescale Robotic Mobile Fulfillment System optimization
- Open Access
- 23 July 2025
- Published by Elsevier in Energy and AI
- Vol. 21
Toward profitable energy futures trading strategies using reinforcement learning incorporating disagreement and connectedness methods enabled by large language models
- 23 July 2025
- Published by Elsevier in Energy and AI
- Vol. 21
Open AccessToward profitable energy futures trading strategies using reinforcement learning incorporating disagreement and connectedness methods enabled by large language models
- Open Access
- 6 August 2024
- Published by Elsevier in Expert Systems with Applications
- Vol. 257
Mobile robot sequential decision making using a deep reinforcement learning hyper-heuristic approach
- 6 August 2024
- Published by Elsevier in Expert Systems with Applications
- Vol. 257
Open AccessMobile robot sequential decision making using a deep reinforcement learning hyper-heuristic approach
- Top Cited Papers
- 4 January 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Informatics
- Vol. 20 (4) , 6345-6355
Multiagent Deep Reinforcement Learning for Electric Vehicle Fast Charging Station Pricing Game in Electricity-Transportation Nexus
Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), constitutes one of the key enablers toward decarbonization. Coordination of EV charging routes and demand through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled transportation network (TN) and power distribution network (PDN). In this work, we model the noncooperative pricing game of self-interested FCSs, taking into account the complex interactions between the EV users and the coupled operation of TN and PND. The uncertainties stemming from the EV users' cost elasticity and their travel energy requirements are encapsulated in the modeling of the TN, while the power flows in the PDN are coordinated considering the penetration of renewable energy sources. A modified multiagent proximal policy optimization method is developed to solve the pricing game. It employs an attention mechanism to selectively incorporate agents' representative information for estimating the Q-values. As such, it not only mitigates the nonstationary effect without exploding the input of the centralized critic but also safeguards the business confidentiality of FCSs. Moreover, a sequential updating scheme is used to ensure policy monotonic improvement and a Bayesian inference technique is adopted to enhance the robustness of the pricing strategy. Case studies on a large-scale test CTPN system reveal that the proposed method facilitates sufficient competition among FCSs, which is able to drive down the average charging prices for EV users. It also smooths out the spatial distribution of EV charging demands, which reduces the traffic congestions in the TN while enhancing the wind absorption and cost efficiency of the PDN.
- 4 January 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Informatics
- Vol. 20 (4) , 6345-6355
Top Cited PapersMultiagent Deep Reinforcement Learning for Electric Vehicle Fast Charging Station Pricing Game in Electricity-Transportation Nexus
Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), constitutes one of the key enablers toward decarbonization. Coordination of EV charging routes and demand through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled transportation network (TN) and power distribution network (PDN). In this work, we model the noncooperative pricing game of self-interested FCSs, taking into account the complex interactions between the EV users and the coupled operation of TN and PND. The uncertainties stemming from the EV users' cost elasticity and their travel energy requirements are encapsulated in the modeling of the TN, while the power flows in the PDN are coordinated considering the penetration of renewable energy sources. A modified multiagent proximal policy optimization method is developed to solve the pricing game. It employs an attention mechanism to selectively incorporate agents' representative information for estimating the Q-values. As such, it not only mitigates the nonstationary effect without exploding the input of the centralized critic but also safeguards the business confidentiality of FCSs. Moreover, a sequential updating scheme is used to ensure policy monotonic improvement and a Bayesian inference technique is adopted to enhance the robustness of the pricing strategy. Case studies on a large-scale test CTPN system reveal that the proposed method facilitates sufficient competition among FCSs, which is able to drive down the average charging prices for EV users. It also smooths out the spatial distribution of EV charging demands, which reduces the traffic congestions in the TN while enhancing the wind absorption and cost efficiency of the PDN.
- Open Access
- 2 November 2023
- Published by Elsevier in Technological Forecasting and Social Change
- Vol. 198
Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach
- 2 November 2023
- Published by Elsevier in Technological Forecasting and Social Change
- Vol. 198
Open AccessMulti-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach
- Open Access
ScatterHough: Automatic Lane Detection from Noisy LiDAR Data
Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, neighbor voting and neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors’ voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.Open AccessScatterHough: Automatic Lane Detection from Noisy LiDAR Data
Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, neighbor voting and neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors’ voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods. - 1 August 2015
- Published by American Scientific Publishers in Journal of Nanoscience and Nanotechnology
- Vol. 15 (8) , 6078-6081
Enhanced Electrochemical Oxidation of p-Nitrophenol Using Single-Walled Carbon Nanotubes/Silver Nanowires Hybrids Modified Electrodes
The electrochemical oxidation of p-nitrophenol (p-Np) has been studied on glassy carbon electrode modified with the single-walled carbon nanotubes/silver nanowires hybrids (SWNTs-Ag) by using cyclic and differential pulse voltammetry. p-Np is irreversibly oxidized at +0.88 V (vs. the Ag/AgCl) in PBS solutions of pH 7.4. The modified electrodes display the detection sensitivity of 0.0212 μA/μM with an unusually wide linear response of 5–1700 μM (R 2 = 0.998) and the detection limit of 1 μM. The current response of SWNTs-Ag modified electrode to p-Np is better than that of SWNTs or Ag nanowires modified electrode under the same concentration. Combining the adsorption ability of SWNTs and the conductivity of SWNTs and Ag nanowires, the detection performance of SWNTs-Ag modified electrode to p-Np was greatly improved.
- 1 August 2015
- Published by American Scientific Publishers in Journal of Nanoscience and Nanotechnology
- Vol. 15 (8) , 6078-6081
Enhanced Electrochemical Oxidation of p-Nitrophenol Using Single-Walled Carbon Nanotubes/Silver Nanowires Hybrids Modified Electrodes
The electrochemical oxidation of p-nitrophenol (p-Np) has been studied on glassy carbon electrode modified with the single-walled carbon nanotubes/silver nanowires hybrids (SWNTs-Ag) by using cyclic and differential pulse voltammetry. p-Np is irreversibly oxidized at +0.88 V (vs. the Ag/AgCl) in PBS solutions of pH 7.4. The modified electrodes display the detection sensitivity of 0.0212 μA/μM with an unusually wide linear response of 5–1700 μM (R 2 = 0.998) and the detection limit of 1 μM. The current response of SWNTs-Ag modified electrode to p-Np is better than that of SWNTs or Ag nanowires modified electrode under the same concentration. Combining the adsorption ability of SWNTs and the conductivity of SWNTs and Ag nanowires, the detection performance of SWNTs-Ag modified electrode to p-Np was greatly improved.
- Open Access
- 18 November 2013
- Published by Mathematical Sciences Publishers in Journal of Mechanics of Materials and Structures
- Vol. 8 (5-7) , 341-357
Predicting the effective stiffness of cellular and composite materials with self-similar hierarchical microstructures
- 18 November 2013
- Published by Mathematical Sciences Publishers in Journal of Mechanics of Materials and Structures
- Vol. 8 (5-7) , 341-357
Open AccessPredicting the effective stiffness of cellular and composite materials with self-similar hierarchical microstructures