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Collaborative Operation Between Power Network and Hydrogen Fueling Stations With Peer-to-Peer Energy Trading

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第1期9卷 1521-1540页

作者： Li, Yuanzheng Yu, Chaofan Liu, Yun Ni, Zhixian Ge, Leijiao Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Belt & Rd Joint Lab Measurement & Control T Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

In recent years, hydrogen fueling electric vehicles (H2EVs) have developed rapidly and drawn much attention. Indeed, the prevalence of H2EVs may introduce a notable interaction between the power network (PN) and the transportation network (TN) via the hydrogen fueling station (HFS). Therefore, it is worth investigating the efficient interaction of PN and HFSs under the constraints of coupled power flow in the PN and traffic flow in the TN. To this end, this article establishes the operation model of HFSs coupled power-traffic system, in order to investigate their impact relationship. Then, a collaborative operation framework with peer-to-peer (P2P) energy trading is proposed to promote the interactive efficiency between the PN and the HFS, considering both energy and reserve. On this basis, the P2P energy trading strategy with dynamic pricing and arbitrage prevention mechanism is developed to further reduce the total operation cost and ensure the fairness of the transaction. Case studies on the HFS-coupled power-traffic system verify the effectiveness of our proposed operation model and the collaborative framework considering P2P energy trading, which results in a lower operation cost, compared with the independent operation and collaborative operation without P2P energy trading, respectively.

关键词： Collaborative operation framework coupled power-traffic system hydrogen fueling electric vehicle (H2EV) hydrogen fueling station (HFS) peer-to-peer (P2P) energy trading

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

arXiv

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arXiv 2025年

作者： Liu, Dingkun Li, Siyang Wang, Ziwei Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Transfer learning

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MVCNet: Multi-View Contrastive Network for Motor imagery Classification

arXiv

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arXiv 2025年

作者： Wang, Ziwei Li, Siyang Chen, Xiaoqing Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to its intuitive mechanism. However, most existing models rely on single-stream architectures and overlook the multi-view nature of EEG signals, leading to limited performance and generalization. We propose a multi-view contrastive network (MVCNet), a dual-branch architecture that parallelly integrates CNN and Transformer models to capture both local spatial-temporal features and global temporal dependencies. To enhance the informativeness of training data, MVCNet incorporates a unified augmentation pipeline across time, frequency, and spatial domains. Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both branches. Final representations are fused and jointly optimized by contrastive and classification losses. Experiments on five public MI datasets across three scenarios demonstrate that MVCNet consistently outperforms seven state-of-the-art MI decoding networks, highlighting its effectiveness and generalization ability. MVCNet provides a robust solution for MI decoding by integrating multi-view information and dual-branch modeling, contributing to the development of more reliable BCI systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Incremental learning of object detection with output merging of compact expert detectors 4

Incremental learning of object detection with output merging...

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4th International Conference on intelligent Autonomous Systems (ICoIAS)

作者： Zhang, Nan Sun, Zhigang Zhang, Kai Xiao, Li Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781665441957

Object detection has been widely applied and has good performance in recent years. However, it has a practical problem that we have to retrain the network by all images if we want to add a new task. It will require much memory to store images of all tasks and time to retrain the network. If we only use the data set of the new task to fine-tune the original model, it will perform well on the new task but catastrophic forgetting of previous tasks. This paper presents a method to address this issue and achieves incremental learning of object detection when only the current task data set is available each time. The core of our proposed solution is to train an expert detector for each task and merge the output of expert detectors by the merging module. Unlike previous works, our method has no limitation on the number of tasks added and will not cause any loss of mAP. We also use some tricks such as adding pre-classifiers, pruning networks and combining the convolutional layer and batch normalization layer to optimize the model. We conduct extensive experiments on the PASCAL VOC dataset and compare the results with the previous methods, along with a detailed empirical analysis of our approach.

关键词： incremental learning object detection network slimming merging pre-classifier

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Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair

arXiv

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arXiv 2023年

作者： Luo, Xianrui Peng, Juewen Zhao, Weiyue Xian, Ke Lu, Hao Cao, Zhiguo The Key Laboratory of Image Processing and Intelligent Control Ministry of Education The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The S-Lab for Advanced Intelligence Nanyang Technological University Singapore

All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultra-wide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Finite-time sliding-mode control for semi-Markov systems with delayed impulses

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Braininspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

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Online Koopman Operator Learning to Identify Cross-Coupling Effect of Piezoelectric Tube Scanners in Atomic Force Microscopes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第2期18卷 1111-1120页

作者： Huang, Xiang Zhou, Wei Li, Xiuting Zhu, LiMin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430070 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Key Lab Networked Mfg & Enterprise Infor Shanghai Peoples R China

As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challenging mission to establish a niche model for the nonlinear cross-coupling dynamics of the PTS. As an emergent pure data-driven learning approach, the Koopman operator sheds some light on the PTS modeling methodology, which is thus adopted in this article to approximate the nonlinear cross-coupling dynamics of PTSs in an infinite-dimensional space. Moreover, an online high-order extended dynamic mode decomposition algorithm is proposed for the finite-dimensional approximation of the Koopman operator online. The merit of the present model lies in updating the identified cross coupling upon the arrival of new data in an incremental way. Finally, experiments are conducted to approximate the X-Y axes cross coupling of PTSs of an NTMDT Prima AFM, which verifies the effectiveness and superiority of the proposed modeling algorithm. This article is expected to pave the way from the Koopman operator learning theory to real applications in dynamics modeling of abundant nanoscale measurement systems.

关键词： Hysteresis Heuristic algorithms Couplings Nanoscale devices Dynamics Approximation algorithms Force Atomic force microscope (AFM) cross coupling modeling nonlinearity piezoelectric tube scanner (PTS)

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Joint Optimal Transport With Convex Regularization for Robust image Classification

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1553-1564页

作者： Qian, Jianjun Wong, Wai Keung Zhang, Hengmin Xie, Jin Yang, Jian Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Peoples R China Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 200092 Peoples R China

The critical step of learning the robust regression model from high-dimensional visual data is how to characterize the error term. The existing methods mainly employ the nuclear norm to describe the error term, which are robust against structure noises (e.g., illumination changes and occlusions). Although the nuclear norm can describe the structure property of the error term, global distribution information is ignored in most of these methods. It is known that optimal transport (OT) is a robust distribution metric scheme due to that it can handle correspondences between different elements in the two distributions. Leveraging this property, this article presents a novel robust regression scheme by integrating OT with convex regularization. The OT-based regression with L-2 norm regularization (OTR) is first proposed to perform image classification. The alternating direction method of multipliers is developed to handle the model. To further address the occlusion problem in image classification, the extended OTR (EOTR) model is then presented by integrating the nuclear norm error term with an OTR model. In addition, we apply the alternating direction method of multipliers with Gaussian back substitution to solve EOTR and also provide the complexity and convergence analysis of our algorithms. Experiments were conducted on five benchmark datasets, including illumination changes and various occlusions. The experimental results demonstrate the performance of our robust regression model on biometric image classification against several state-of-the-art regression-based classification methods.

关键词： image classification nuclear norm optimal transport (OT) robust regression

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Coarse- and Fine-Grained Fusion Hierarchical Network for Hole Filling in View Synthesis

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IEEE TRANSACTIONS ON image processing 2024年 33卷 322-337页

作者： Wang, Guangcheng Jiang, Kui Gu, Ke Liu, Hongyan Liu, Hantao Zhang, Wenjun Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing Key Lab Computat Intelligence & Intelligen Fac Informat TechnolEngn Res Ctr Intelligent Perc Beijing 100124 Peoples R China Beijing Univ Technol Beijing Artificial Intelligence Inst Beijing 100124 Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA Wales Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China

Depth image-based rendering (DIBR) techniques play an essential role in free-viewpoint videos (FVVs), which generate the virtual views from a reference 2D texture video and its associated depth information. However, the background regions occluded by the foreground in the reference view will be exposed in the synthesized view, resulting in obvious irregular holes in the synthesized view. To this end, this paper proposes a novel coarse and fine-grained fusion hierarchical network (CFFHNet) for hole filling, which fills the irregular holes produced by view synthesis using the spatial contextual correlations between the visible and hole regions. CFFHNet adopts recurrent calculation to learn the spatial contextual correlation, while the hierarchical structure and attention mechanism are introduced to guide the fine-grained fusion of cross-scale contextual features. To promote texture generation while maintaining fidelity, we equip CFFHNet with a two-stage framework involving an inference sub-network to generate the coarse synthetic result and a refinement sub-network for refinement. Meanwhile, to make the learned hole-filling model better adaptable and robust to the "foreground penetration" distortion, we trained CFFHNet by generating a batch of training samples by adding irregular holes to the foreground and background connection regions of high-quality images. Extensive experiments show the superiority of our CFFHNet over the current state-of-the-art DIBR methods. The source code will be available at https://***/wgc-vsfm/view-synthesis-CFFHNet.

关键词： Depth image-based rendering hole filling coarse and fine-grained fusion hierarchical network two-stage framework

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Gait Phase Recognition Based on Air-pressure Mechanomyogram and Sensor Fusion 6

Gait Phase Recognition Based on Air-pressure Mechanomyogram ...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wang, Enkai Huang, Jian Li, Yuge Cui, Yuqi Li, Xiaolong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9780738133645

Gait phase recognition can improve the control of lower limb exoskeleton robot and promote human-machine collaboration. One of the current mainstream methods is to use electromyogram (EMG) to recognize gait phase, but the EMG signal has shortcomings such as weak signal, difficult to wear, easy to be affected by noise and sweat. Therefore, we designed a novel air-pressure mechanomyograph (PMMG) sensor, and further made a wearable PMMG-based sensing system composed of PMMG-based thighrings and inertial measurement units (IMUs). In order to improve the performance of gait phase recognition, we used five popular machine learning algorithms to fuse the data from PMMG-based thighrings and IMUs. We recruited three experimental subjects and constructed two datasets for different walking conditions: constant speed walking and variable speed walking. Experimental results show that the proposed PMMG sensor is effective, and the gait phase recognition accuracy reached 96.25% by using only the PMMG-based thighrings. In addition, we found that the performance of multi-modal sensor fusion is better than that of single-modal sensor fusion through three comparative experiments. Among the five machine learning algorithms, the SVM fusion model got the highest average accuracy of 98.82%.

关键词： Legged locomotion Support vector machines Machine learning algorithms Mechatronics Measurement units Multimodal sensors Sensor fusion

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