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学者姓名：蔡逢煌
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Abstract ：
Solving the problem of water level fluctuations in small hydropower station systems is challenging under traditional industrial control methods. This difficulty arises from the system’s high nonlinearity and the complexities involved in mechanism modeling. To address this, an improved neuro-fuzzy approach is proposed. In which, the multi-head attention mechanism based long short-term memory network is used to describe complex water level change patterns, and the fuzzy controller is introduced to dynamically adjust the control parameters to reduce water level fluctuation. Simulation-based on real hydropower station system data is carried out, and the superiority of the improved model under complex dynamic conditions is verified by comparing the prediction accuracy of different neural network methods and the effects of fuzzy controller and traditional PID control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Control nonlinearities Control nonlinearities Fuzzy neural networks Fuzzy neural networks Proportional control systems Proportional control systems Three term control systems Three term control systems Two term control systems Two term control systems
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| GB/T 7714 | Yang, Rongguo , Chai, Qinqin , Cai, Fenghuang et al. Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach [C] . 2025 : 469-478 . |
| MLA | Yang, Rongguo et al. "Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach" . (2025) : 469-478 . |
| APA | Yang, Rongguo , Chai, Qinqin , Cai, Fenghuang , Wang, Wu . Modeling and Control of Small Hydropower Stations Based on Neuro-Fuzzy Approach . (2025) : 469-478 . |
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To address the challenges of significant changes in the field of view and complex spatiotemporal information in unmanned aerial vehicle aerial image target detection, a model for small object detection in aerial photography based on low dimensional image feature fusion is presented grounded on the YOLOv5(you only look once version 5) architecture. Coordinate attention is introduced to improve the inverted residuals of MobileNetV3, thereby increasing the spatial dimension information of images while reducing parameters of the model. The YOLOv5 feature pyramid network structure is improved to incorporate feature images from shallow networks. The ability of the model to represent low-dimensional effective information of images is enhanced, and consequently the detection accuracy of the proposed model for small objects is improved. To reduce the impact of complex background in the image, the parameter-free average attention module is introduced to focus on both spatial attention and channel attention. VariFocal Loss is adopted to reduce the weight proportion of negative samples in the training process. Experiments on VisDrone dataset demonstrate the effectiveness of the proposed model. The detection accuracy is effectively improved while the model complexity is significantly reduced. © 2024 Science Press. All rights reserved.
Keyword ：
Aerial photography Aerial photography Antennas Antennas Complex networks Complex networks Image enhancement Image enhancement Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Cai, Fenghuang , Zhang, Jiaxiang , Huang, Jie . Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion [J]. | Pattern Recognition and Artificial Intelligence , 2024 , 37 (2) : 162-171 . |
| MLA | Cai, Fenghuang et al. "Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion" . | Pattern Recognition and Artificial Intelligence 37 . 2 (2024) : 162-171 . |
| APA | Cai, Fenghuang , Zhang, Jiaxiang , Huang, Jie . Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion . | Pattern Recognition and Artificial Intelligence , 2024 , 37 (2) , 162-171 . |
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Taking the cascaded H-bridge (CHB) inverter as the object of study, the structure of the inverter system is analyzed and the modulation strategy of the system is investigated. A control strategy based on a three-phase cascaded H-bridge topology is proposed for a PV grid-connected inverter system. The scheme adopts a carrier level phase-shift modulation strategy, which can realize the maximum power point tracking of the front-stage PV string, the control of the grid-connected current, and the grid-connection of the inverter. Finally, the inverter with the proposed strategy is simulated in MATLAB/SIMULINK environment, and the simulation results prove the correctness and feasibility of the control strategy.
Keyword ：
Cascade H-bridge Cascade H-bridge Inverter Inverter Modularization Modularization
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| GB/T 7714 | Liang, Xinzhao , Cai, Fenghuang . Study on the Control Strategy of Cascaded H-Bridge Photovoltaic Grid-Connected Inverters [J]. | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 3, ICWPT 2023 , 2024 , 1160 : 387-394 . |
| MLA | Liang, Xinzhao et al. "Study on the Control Strategy of Cascaded H-Bridge Photovoltaic Grid-Connected Inverters" . | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 3, ICWPT 2023 1160 (2024) : 387-394 . |
| APA | Liang, Xinzhao , Cai, Fenghuang . Study on the Control Strategy of Cascaded H-Bridge Photovoltaic Grid-Connected Inverters . | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 3, ICWPT 2023 , 2024 , 1160 , 387-394 . |
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Potential malicious attacks have been a significant security concern for network system applications. However, there are few studies on filtering for hybrid network attacks in switching systems. This paper considers a Kalman filtering problem for the switched systems that suffer from deception attacks and denial-of-service attacks. A new network transmission model for switching systems is established. Then, based on the minimum mean square error criterion, a Kalman filter with low conservatism is designed for the discrete-time switched system. The newly switched Kalman gain matrix is deduced including the random variation of the switching signal after being attacked by the network. Finally, the effectiveness of the proposed filter is verified by the numerical simulation. A switching system is considered to be a typical hybrid system. As it often works in a network environment, the switching signal is vulnerable to the network and thus to various cyber-attacks (e.g. spoofing attacks and denial-of-service attacks). Few studies have been conducted on the impact of switching signals in network transmission. To address this problem, this paper proposes a Kalman filter design method with low conservativeness, which describes network attacks and data loss by building a Kalman filter model associated with the switching signal in terms of satisfying Bernoulli random variables. For practitioners, the ability to recover data to a certain extent when the data transmission is affected by different network attacks will save a lot of costs and is important for improving the stability and performance of switching systems. Our future work will aim to improve the performance of different filtering algorithms under different cyber-attacks.
Keyword ：
deception attack deception attack denial-of-service attack denial-of-service attack Hybrid cyber-attack Hybrid cyber-attack Kalman filter Kalman filter switched system switched system
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| GB/T 7714 | Cai, Fenghuang , Liao, Shuying , Chen, Yucheng et al. Kalman Filter of Switching System Under Hybrid Cyber Attack [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2023 . |
| MLA | Cai, Fenghuang et al. "Kalman Filter of Switching System Under Hybrid Cyber Attack" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2023) . |
| APA | Cai, Fenghuang , Liao, Shuying , Chen, Yucheng , Wang, Wu . Kalman Filter of Switching System Under Hybrid Cyber Attack . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2023 . |
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To improve the efficiency of the dual active bridge (DAB) converter working in the low- and medium-power ranges, this article proposes a new triple-phase shift (TPS) modulation scheme based on frequency-domain fundamental harmonic analysis (FDFHA) with lower root mean square (rms) current and wider zero-voltage switching (ZVS) range. The DAB converter's Fourier decomposition expression under TPS is generated, and the FDFHA expression is created using fundamental harmonic analysis (FHA). With the rms current as the optimization target, the operating point with the smallest rms current is found by solving for it using the extended global optimum condition (EGOC) that applies to the FDFHA expression. Based on this, a control strategy with a simple controller structure is established, which has excellent dynamic performance. In addition, when comparing the proposed scheme to other optimized schemes, the results show that the proposed scheme can drastically lower the rms current in the low- and medium-power ranges and achieve a wider ZVS range, notably in the low-power range to enable all switches to achieve ZVS. Finally, an experimental prototype with a peak efficiency of 95.95% was built to verify the effectiveness of the proposed scheme.
Keyword ：
Dual active bridge (DAB) Dual active bridge (DAB) fundamental harmonic analysis (FHA) fundamental harmonic analysis (FHA) root mean square (rms) current root mean square (rms) current zero-voltage switching (ZVS) zero-voltage switching (ZVS)
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| GB/T 7714 | Xie, Hongbiao , Cai, Fenghuang , Jiang, Jiahui et al. A Fundamental Harmonic Analysis-Based Optimized Scheme for DAB Converters With Lower RMS Current and Wider ZVS Range [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2023 , 9 (3) : 4045-4058 . |
| MLA | Xie, Hongbiao et al. "A Fundamental Harmonic Analysis-Based Optimized Scheme for DAB Converters With Lower RMS Current and Wider ZVS Range" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 9 . 3 (2023) : 4045-4058 . |
| APA | Xie, Hongbiao , Cai, Fenghuang , Jiang, Jiahui , Li, Dongfang . A Fundamental Harmonic Analysis-Based Optimized Scheme for DAB Converters With Lower RMS Current and Wider ZVS Range . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2023 , 9 (3) , 4045-4058 . |
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A trajectory planning method based on Gaussian regression for Model Predictive Control is proposed to address the safety obstacle avoidance problem of tracked vehicle on unstructured roads. The proposed method takes into account the parameter uncertainty caused by different road conditions and slopes, with the goal of ensuring safe obstacle avoidance. In order to achieve safe obstacle avoidance in imprecise model situations, Gaussian regression process is used to learn the average model error caused by time-varying parameters, and variance is introduced in the design of control barrier function. The characteristic of this method is to increase constraints on the control barrier area to ensure safety. Simulation shows that the designed algorithm can achieve safe obstacle avoidance while reaching the target point. © 2023 IEEE.
Keyword ：
Gaussian distribution Gaussian distribution Model predictive control Model predictive control Predictive control systems Predictive control systems Regression analysis Regression analysis Tracked vehicles Tracked vehicles Trajectories Trajectories
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| GB/T 7714 | Zhang, Ke , Cai, Fenghuang , Huang, Jie . Learning Based Trajectory Planning and Safe Obstacle Avoidance for Tracked Vehicle under Imprecise Model [C] . 2023 : 8748-8753 . |
| MLA | Zhang, Ke et al. "Learning Based Trajectory Planning and Safe Obstacle Avoidance for Tracked Vehicle under Imprecise Model" . (2023) : 8748-8753 . |
| APA | Zhang, Ke , Cai, Fenghuang , Huang, Jie . Learning Based Trajectory Planning and Safe Obstacle Avoidance for Tracked Vehicle under Imprecise Model . (2023) : 8748-8753 . |
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The accuracy of defect detection is increased by the improved Faster R-CNN detection algorithm, which addresses the issue of an extended target scale span and complex characteristics causing missed detection in steel surface defect detection. The algorithm introduces path aggregation network (PANet), which uses bottom-up path aggregation to combine shallow details and deep semantic information to better capture features on various dimensions. It also adapts the reconstruction of the anchor box size information to filter out the optimal proposal box for more accurate localization. The backbone network of the original Faster R-CNN is replaced with an improved ResNet50, which has more powerful feature extraction skill and makes the model more flexible. On the NEU-DET dataset, the improved algorithm's detection performance is contrasted with that of other detection algorithms. According to the results of the investigation, the Faster R-CNN with additions has an average detection rate of 63.7 frames per second (FPS) and a mean average precision (mAP) of 80.2%, which is 8.7 percentage points higher than that of the Faster R-CNN. The conclusion is that the boosted Faster R-CNN can enhance both the precision and localization information of multi-scale faulty targets. © 2023 IEEE.
Keyword ：
Agglomeration Agglomeration K-means clustering K-means clustering Semantics Semantics Signal detection Signal detection Surface defects Surface defects
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| GB/T 7714 | Shen, Mingjie , Cai, Fenghuang , Xie, Jiaqi . A Multi-Scale Defect Detection for Steel Surface Based on Improved Faster R-CNN [C] . 2023 : 7448-7453 . |
| MLA | Shen, Mingjie et al. "A Multi-Scale Defect Detection for Steel Surface Based on Improved Faster R-CNN" . (2023) : 7448-7453 . |
| APA | Shen, Mingjie , Cai, Fenghuang , Xie, Jiaqi . A Multi-Scale Defect Detection for Steel Surface Based on Improved Faster R-CNN . (2023) : 7448-7453 . |
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To reduce the current stress in the whole operating area and improve the dynamic performance switching between different operating points of the dual-active-bridge (DAB) converter, a bidirectional power control (BPC-GCSO) scheme based on the global current stress optimization is proposed in this paper. The global current stress optimal solution (GCSO) set is first derived for the DAB converter, which enables the DAB converter to realize the minimum current stress at any operating point. Then, a virtual direct power control method is introduced based on the GCSO scheme and a bidirectional power control (BPC) strategy is designed. To validate the proposed scheme, a simulation experimental environment is built on the MATLAB/Simulink platform, and the simulation results prove the feasibility of the proposed method. © 2023 IEEE.
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| GB/T 7714 | Gong, Xingyang , Fu, Xiaofeng , Cai, Fenghuang et al. A Bidirectional Power Control Scheme Based on Global Current Stress Optimization for Dual Active Bridge DC-DC Converters [C] . 2023 : 865-870 . |
| MLA | Gong, Xingyang et al. "A Bidirectional Power Control Scheme Based on Global Current Stress Optimization for Dual Active Bridge DC-DC Converters" . (2023) : 865-870 . |
| APA | Gong, Xingyang , Fu, Xiaofeng , Cai, Fenghuang , Huang, Ruochen . A Bidirectional Power Control Scheme Based on Global Current Stress Optimization for Dual Active Bridge DC-DC Converters . (2023) : 865-870 . |
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The grid-connected inverter is a key device in the renewable energy power generation system and large-scale energy storage system, which the operational stability and reliability are the basis for the efficient and safe application of electrical energy. A real-time fault diagnosis method of a three-phase for grid-connected application combining a mixed logic dynamic (MLD) model and finitecontrol set model predictive control (FCS-MPC) is proposed. This paper not only realizes the open circuit fault diagnosis and location of the switching devices in the main power circuit, but also discusses the threshold issues and post-fault operations. The advantage of the proposed method is that it directly uses the control data and measurement signals of the controller without extra sensors and calculation, which will shorter the fault diagnosis time and occupy less calculation resource of the main processor. Simulation results illustrate the quickness of the fault identification and accurate position with robustness to the interference of the diagnosis method. Finally, the effectiveness of the diagnosis method was verified by a 1500 W experimental prototype in a laboratory.
Keyword ：
Fault diagnosis Fault diagnosis finite control set model predictive control finite control set model predictive control grid-connected inverter grid-connected inverter Impedance Impedance Information filters Information filters Inverters Inverters mixed logical dynamic model mixed logical dynamic model Power supplies Power supplies
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| GB/T 7714 | Lin, Qiongbin , Yu, Kai , Zeng, Xinglan et al. Online Fault Diagnosis Method for Grid-Connected Inverters Based on Finite-Set Mixed Logical Dynamical Model Prediction [J]. | IEEE TRANSACTIONS ON ENERGY CONVERSION , 2022 , 37 (3) : 1666-1676 . |
| MLA | Lin, Qiongbin et al. "Online Fault Diagnosis Method for Grid-Connected Inverters Based on Finite-Set Mixed Logical Dynamical Model Prediction" . | IEEE TRANSACTIONS ON ENERGY CONVERSION 37 . 3 (2022) : 1666-1676 . |
| APA | Lin, Qiongbin , Yu, Kai , Zeng, Xinglan , Zong, Yi , Su, Xianjin , Cai, Fenghuang et al. Online Fault Diagnosis Method for Grid-Connected Inverters Based on Finite-Set Mixed Logical Dynamical Model Prediction . | IEEE TRANSACTIONS ON ENERGY CONVERSION , 2022 , 37 (3) , 1666-1676 . |
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The state of charge (SOC) and the state of health (SOH) are two crucial parameters for monitoring the battery status because SOH determines whether the battery can continue to operate safely and stably, and the SOC determines the battery's endurance. This paper proposes an online synthesis method based on the response characteristics of load surges and an improved fuzzy cerebellar model neural network (IFCMNN) to co-estimate SOH and SOC. Firstly, multiple features are extracted from the voltage response signals. Then, the grey relational analysis is utilized to verify the rationality of the extracted features and to fuse key features for SOH and SOC estimation. Secondly, IFCMNN models aiming at estimating SOH and SOC are proposed to estimate the SOH and SOC simultaneously and quickly. Finally, experimental results on ten batteries with different aging levels show that the proposed method can achieve fast estimation of SOH and SOC at 1.64 % and 2 % resolution accuracy respectively, regardless of the temperature and in rush currents variations. In addition, the proposed model has higher estimation accuracy compared with other traditional methods. Thus, the proposed method has a high generalization ability.
Keyword ：
Improved fuzzy cerebellar model neural Improved fuzzy cerebellar model neural Lithium -ion battery Lithium -ion battery network network Response characteristics of load surges Response characteristics of load surges State of charge State of charge State of health State of health
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| GB/T 7714 | Lin, Qiongbin , Li, Huasen , Chai, Qinqin et al. Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges [J]. | JOURNAL OF ENERGY STORAGE , 2022 , 55 . |
| MLA | Lin, Qiongbin et al. "Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges" . | JOURNAL OF ENERGY STORAGE 55 (2022) . |
| APA | Lin, Qiongbin , Li, Huasen , Chai, Qinqin , Cai, Fenghuang , Zhan, Yin . Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges . | JOURNAL OF ENERGY STORAGE , 2022 , 55 . |
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