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5th International Conference on Computer Vision, image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Jia, Tianwang Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While previous studies have addressed one or two of these issues, simultaneous accommodation of all three challenges remains challenging and unexplored. This paper fills this gap, by proposing an Aligned and Augmented Adversarial Ensemble (A3E) algorithm and integrating it into three privacy protection scenarios (centralized source-free transfer, federated source-free transfer, and source data perturbation), achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on three public EEG datasets demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness in all three privacy-preserving scenarios, even outperforming state-of-the-art transfer learning approaches that do not consider privacy protection at all. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs. © 2024, CC BY.

关键词： Transfer learning

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Chinese Summary Generation Algorithm Based on Contrastive Attention Mechanism 4

Chinese Summary Generation Algorithm Based on Contrastive At...

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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Guo, Liupeng Xiao, Yannan Wen, Yayuan Wen, Peizhi Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China Guangxi Normal University School of Electronic and Information Engineering School of Integrated Circuits Guilin China

ISBN: (纸本)9798350306323

Generative summarization based on the seq2seq model has gained widespread adoption due to the advancements and efficacy of the sequence-to-sequence framework. However, these models encounter challenges in handling out-of-vocabulary words and may produce summaries with factual inaccuracies. This paper addresses these issues by proposing a summarization generation model, MBC, which is built upon the Transformer as the baseline model. Additionally, it incorporates the BERT pre-trained model, pointer generation network, contrastive attention mechanism, and coverage loss. The model employs a pointer generation network to augment its vocabulary coverage capabilities by generating new terms. To enhance the representation of key information from the source text, a contrastive attention mechanism is employed during summary generation. In addition, by introducing coverage loss, the model can reduce repetitive content in the summary. To assess the model's performance, this paper employs ROUGE values as evaluation metrics and conducts comparative experiments with other models using the LCSTS dataset. The experimental results demonstrate that, in comparison with other models, this model is more adept at attending to salient information within the source text and generating summaries of greater accuracy. © 2023 IEEE.

关键词： BERT pre-training model contrastive attention mechanism coverage loss pointer generation network seq2seq model

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Model Predictive control Based on Amplitude Decaying Aggregation Strategy for Hydrogen Gas Turbine

Model Predictive Control Based on Amplitude Decaying Aggrega...

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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform

arXiv

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arXiv 2024年

作者： Zhu, Huizhi Xu, Wenxia Huang, Jian The Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for message-passing, providing high expressiveness with a small parameter count, making it suitable for practical deployment. Additionally, we introduce the GPU-friendly Newton-Raphson method, an efficient parallelized optimization method executed on the GPU to refine DisGNet’s output poses, achieving ultra-high-precision pose. This novel two-stage approach delivers ultra-high precision output while meeting real-time requirements. Our results indicate that on our dataset, DisGNet can achieves error accuracys below 1mm and 1deg at 79.8% and 98.2%, respectively. As executed on a GPU, our two-stage method can ensure the requirement for real-time computation. Codes are released at https://***/FLAMEZZ5201/DisGNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Newton-Raphson method

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MP-MAT: A 3D-AND-INSTANCE-AWARE HUMAN MATTING AND EDITING FRAMEWORK WITH MULTIPLANE REPRESENTATION

arXiv

引用

arXiv 2025年

作者： Jiao, Siyi Zeng, Wenzheng Li, Yerong Zhang, Huayu Gao, Changxin Sang, Nong Shou, Mike Zheng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Show Lab National University of Singapore Singapore

Human instance matting aims to estimate an alpha matte for each human instance in an image, which is challenging as it easily fails in complex cases requiring disentangling mingled pixels belonging to multiple instances along hairy and thin boundary structures. In this work, we address this by introducing MP-Mat, a novel 3D-and-instance-aware matting framework with multiplane representation, where the multiplane concept is designed from two different perspectives: scene geometry level and instance level. Specifically, we first build feature-level multiplane representations to split the scene into multiple planes based on depth differences. This approach makes the scene representation 3D-aware, and can serve as an effective clue for splitting instances in different 3D positions, thereby improving interpretability and boundary handling ability especially in occlusion areas. Then, we introduce another multiplane representation that splits the scene in an instance-level perspective, and represents each instance with both matte and color. We also treat background as a special instance, which is often overlooked by existing methods. Such an instance-level representation facilitates both foreground and background content awareness, and is useful for other down-stream tasks like image editing. Once built, the representation can be reused to realize controllable instance-level image editing with high efficiency. Extensive experiments validate the clear advantage of MP-Mat in matting task. We also demonstrate its superiority in image editing tasks, an area under-explored by existing matting-focused methods, where our approach under zero-shot inference even outperforms trained specialized image editing techniques by large margins. Code is open-sourced at https://***/JiaoSiyi/***. Copyright © 2025, The Authors. All rights reserved.

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Passivity-Based Robust control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

引用

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1440-1450页

作者： Yue Zhao Ze Chen Chunjie Zhou Yu-Chu Tian Yuanqing Qin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science Queensland University of TechnologyBrisbane QLD 4001Australia

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control *** quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion ***,we design an energy controller to dynamically adjust the system energy changes caused by unknown *** designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping ***,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack *** addition,this secure control method is simple to implement because it avoids complicated mathematical *** effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

关键词： Cyber-physical systems energy controller energy conversion false data injection attacks L2 disturbance attenuation technology

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Architecture Design of Equipment Warehouse Scheduling System Based on Software Definition 5th

Architecture Design of Equipment Warehouse Scheduling System...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Zhang, Xue Ting Pan, Yan Peng Yang, Li Jie Xue, Chen Chen Zhang, Fu Quan China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819908479

The technology of software-defined changing all areas of life, but the equipment storage system in the field of application is still in its infancy. At present, the traditional equipment warehousing scheduling system is difficult to meet the new needs of rapid scheduling and flexible maintenance of large quantities of equipment. Based on the development and application characteristics of software definition technology, this paper integrates the traditional equipment storage scheduling system and presents the research on the architecture design technology of equipment storage scheduling system under software definition technology. This paper clarifies the key application technologies of the system, realizes the design of new open system architecture based on software-defined technology and the optimization design of system center service platform architecture, and improves the architecture design ability of automation, configuration ability, and easy maintenance of the existing equipment storage scheduling system. It solves the new needs of rapid deployment, intelligent upgrade, flexible configuration, and convenient service of large quantities of equipment warehouse scheduling system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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