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第三十八届中国控制会议

作者： Jianpei Hu Hao Sun Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

Fuel quantity actuator can directly control fuel injection quantity of the diesel engine, so it is the main component of the electronic distribution pump. In this paper, a position tracking problem for fuel quantity actuator system under the influence of disturbances and system uncertainties is studied via a high-order sliding mode observer(HOSMO) based nonsingular terminal sliding mode control(NTSMC) approach. The proposed finite-time NTSMC method integrates the lumped disturbance estimation information estimated by the HOSMO into the designed control law, and realizes the finite time tracking performance under the influence of disturbances and uncertainties. The simulation results show the effectiveness of the proposed method.

关键词： Lumped Disturbances Fuel Quantity Actuator Non-singular Terminal Sliding Mode control(NTSMC) High-order Sliding Mode Observer(HOSMO)

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Driver action recognition based on attention mechanism 6

Driver action recognition based on attention mechanism

引用

6th International Conference on systems and Informatics, ICSAI 2019

作者： Wang, Wenhao Lu, Xiaobo Zhang, Pengguo Xie, Huibin Zeng, Wenbin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Zhejiang Uniview Technologies Co. Ltd Hangzhou310018 China

ISBN: (纸本)9781728152561

According to the world health organization, millions of people are killed by traffic accidents worldwide every year, and more than 80 percent of accidents are caused by unsafe driving. This paper studies driver behavior recognition, aiming to standardize driver's driving behavior and reduce the probability of traffic accidents. However, the inter-class variance of drivers' different actions is small, making it difficult to identify. To improve fine-grained identification, an attention module is designed to be inserted into convolutional neural network, which consists of two parallel parts: channel level attention and space level attention. The introduction of attention mechanism can focus the weight of the network on the meaningful pixels and channels, promote the expression of effective features, and suppress the interference of noise. The experiments show that the recognition accuracy is improved after applying attention mechanism. The visualization results show that the introduction of attention mechanism can make the network focus on the prominent areas of the feature map. © 2019 IEEE.

关键词： Behavioral research

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Identity Recognition Based on Face Image 4

Identity Recognition Based on Face Image

引用

2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Qin, Chen Lu, Xiaobo Zhang, Pengguo Xie, Huibin Zeng, Wenbin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Zhejiang Uniview Technologies Co. Ltd Zhejiang310018 China

This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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Optimizing PV-battery energy storage for netzero emission traction power system through artificial rabbit optimization method

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Journal of Energy Storage 2025年 126卷

作者： Li, Zhijian Hu, Yihua Ollerenshaw, Richard Li, Xinian He, Zengxiang Esfahani, Mohammad Nasr School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom Department of Engineering King's College London LondonWC2R 2LS United Kingdom Siemens Rail Electrification Siemens Mobility York United Kingdom School of Information and Electronics Engineering Shandong Technology and Business University Yantai264005 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

The transition from traditional fossil fuels to renewable energy is becoming increasingly feasible and is essential for addressing energy and environmental challenges. In transportation electrification, the variability of power demand and the intermittent nature of renewable energy generation present challenges to achieving high renewable energy utilization. Battery energy storage systems (BESS) integrated to renewable resources offer a viable solution to these intermittency issues, though their costs require careful optimization. This paper explores the cost implications of using on-site renewable energy to power a railway traction system. It proposes a photovoltaic (PV) energy storage strategy optimized with the Artificial Rabbit Algorithm (ARO). By applying railway load and regional PV data, we optimize the capacity of BESS and the rated power of the PV site to meet railway electricity demands while minimizing investment and operational costs. The study shows that the capacity of BESS and the size of the PV installation depend on different self-sufficiency ratio (SSR) requirements, leading to an optimized configuration for highly renewable systems. Additionally, the study highlights that considering battery aging is crucial for reducing costs in traction power systems using renewable energy and remains economically viable under time-of-use pricing policies. © 2025 Elsevier Ltd

关键词： Battery storage

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Fire Detection based on Convolutional Neural Networks with Channel Attention

Fire Detection based on Convolutional Neural Networks with C...

引用

Chinese automation Congress (CAC)

作者： Xiaobo Zhang Kun Qian Kaihe Jing Jianwei Yang Hai Yu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education of China Future Science & Technology Park Beijing China Global Energy Interconnection Research Institute State Grid Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.

关键词： Fires Semantics Feature extraction Annotations Image color analysis Deep learning Cameras

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A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence 12

A Driver Fatigue Recognition Algorithm Based on Spatio-Tempo...

引用

12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhang, Chen Lu, Xiaobo Huang, Zhiliang Xia, Shudong Fu, Changqing School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Beijing PKU TransInfo Technology Co. Ltd Beijing100193 China

ISBN: (纸本)9781728148526

Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark detection and head pose estimation network, fatigue recognition network. Experiments show that the algorithm has the advantages of small volume, high speed and high accuracy. © 2019 IEEE.

关键词： Face recognition

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Deep-learning based Optimal PMU Placement and Fault Classification for Power System

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Expert systems with Applications 2025年

作者： Xin Lei Zhen Li Huaiguang Jiang Samson S. Yu Yu Chen Bin Liu Peng Shi School of Future Technology South China University of Technology Panyu District Guangdong China School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria 3216 Australia College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China School of Electrical and Electronic Engineering University of Adelaide 5005 SA Australia

Phasor measurement units (PMUs) are vital for power grid monitoring, yet their high cost restricts widespread adoption. PMU measurement data is also crucial for fault analysis in power systems. However, existing research seldom explores the interplay between optimal PMU placement (OPP) and fault analysis, impeding advancements in grid economy and security. This study introduces a perception-driven, deep learning-based optimization approach that integrates OPP, multi-task learning, and fault data augmentation. First, deep reinforcement learning optimizes PMU placement, balancing cost-effectiveness with observability requirements. Next, multi-task learning, enhanced by Bayesian optimization, improves fault classification efficiency using PMU data. Finally, pre-trained models paired with k -means clustering augment fault data, boosting classification accuracy. Extensive simulations across four IEEE standard test systems validate the proposed method’s effectiveness.

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Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking

Visual Pencil: Design of Portable Human-Computer Interaction...

引用

IEEE International Conference on Mechatronics and automation

作者： Ru Tong Tianzhu Wang Junzhi Yu State Key Lab Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China College of Engineering Peking University Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers.

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

引用

arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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