Yiling Fan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yiling Fan has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Pengfei Xu
School of Intelligence and Information Engineering, Tangshan University, Tangshan 063000, China
2 shared publications - Wanwei Tang
Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China
2 shared publications - Chao Wang
Intelligence and Information Engineering College, Tangshan University, Tangshan 063000, China
1 shared publication - Chengbo Wang
Department of Electrical and Electronic Engineering Faculty of Science and Engineering, University of Nottingham Ningbo China Ningbo Zhejiang China
1 shared publication - Jianyu Wang
School of Intelligence and Information Engineering, Tangshan University, Tangshan 063000, China
1 shared publication - Jichang Zhang
Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
1 shared publication
Latest Publications
- Open Access
- 18 November 2025
- Published by MDPI AG in Magnetochemistry
- Vol. 11 (11) , 100
Three-Dimensional Look-Locker Method for Free-Breathing T1 Mapping of Oxygen-Enhanced Pulmonary Magnetic Resonance Imaging
Oxygen-enhanced magnetic resonance imaging (OE-MRI) enables non-invasive assessment of lung function by measuring longitudinal relaxation time () changes induced by alternating inhalation of room air and pure oxygen. In this study, the pulmonary and its reduction after breathing pure oxygen were quantified by using the free-breathing three-dimensional (3D) Look-Locker technique based on a stack-of-stars acquisition scheme. This method applied a continuous acquisition model to collect signals during both room-air and pure oxygen conditions without the need for breath-holding or respiratory gating. Comparative evaluations were conducted between the proposed 3D Look-Locker method and the conventional two-dimensional (2D) Look-Locker approach, using both phantom and in vivo experiments. The results demonstrate that the 3D technique yields more pronounced and reproducible reductions between air and oxygen conditions compared to the 2D method. Additionally, the of the average respiratory phase obtained by the 3D approach was compared with the at end-expiration and end-inspiration measured by the 2D approach. A consistent decline in across respiratory phases was demonstrated, from end-expiration to end-inspiration, as well as the average respiratory phase under free-breathing. These findings suggest that the proposed OE-MRI measurement based on the 3D Look-Locker method provides a robust and clinically feasible approach for quantitative lung imaging.- 18 November 2025
- Published by MDPI AG in Magnetochemistry
- Vol. 11 (11) , 100
Open AccessThree-Dimensional Look-Locker Method for Free-Breathing T1 Mapping of Oxygen-Enhanced Pulmonary Magnetic Resonance Imaging
Oxygen-enhanced magnetic resonance imaging (OE-MRI) enables non-invasive assessment of lung function by measuring longitudinal relaxation time () changes induced by alternating inhalation of room air and pure oxygen. In this study, the pulmonary and its reduction after breathing pure oxygen were quantified by using the free-breathing three-dimensional (3D) Look-Locker technique based on a stack-of-stars acquisition scheme. This method applied a continuous acquisition model to collect signals during both room-air and pure oxygen conditions without the need for breath-holding or respiratory gating. Comparative evaluations were conducted between the proposed 3D Look-Locker method and the conventional two-dimensional (2D) Look-Locker approach, using both phantom and in vivo experiments. The results demonstrate that the 3D technique yields more pronounced and reproducible reductions between air and oxygen conditions compared to the 2D method. Additionally, the of the average respiratory phase obtained by the 3D approach was compared with the at end-expiration and end-inspiration measured by the 2D approach. A consistent decline in across respiratory phases was demonstrated, from end-expiration to end-inspiration, as well as the average respiratory phase under free-breathing. These findings suggest that the proposed OE-MRI measurement based on the 3D Look-Locker method provides a robust and clinically feasible approach for quantitative lung imaging. - Open Access
- 15 November 2024
- Published by MDPI AG in Electronics
- Vol. 13 (22) , 4496
A Triangular Structure Constraint for Pedestrian Positioning with Inertial Sensors Mounted on Foot and Shank
To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI).
- 15 November 2024
- Published by MDPI AG in Electronics
- Vol. 13 (22) , 4496
Open AccessA Triangular Structure Constraint for Pedestrian Positioning with Inertial Sensors Mounted on Foot and Shank
To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI).
- Open Access
Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation
Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.
Open AccessUsing Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation
Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.