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第34届中国过程控制会议

作者： Shunjian MA Yuanyuan ZOU Shaoyuan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Heating,Ventilation and Air Conditioning(HVAC) for multi-zone buildings includes centralized plants and remote equipment to jointly regulate indoor air temperature of every zone to satisfy difference *** to the limited capacity of the total supply air in centralized equipment,two conflicts are raised when controlling indoor air temperature for all *** is between meeting diverse demands to meet user's preferences and limited global resources,and another conflict is between distributed regulation facilities and centralized air supply *** this paper,a distributed model predictive control(DMPC) with global resource constrained is proposed for multi-zone HVAC ***,a centralized MPC with energy savings and thermal comforts is formulated to balance the demands of each zone and the limited resource ***,this centralized MPC is decomposed into subsystems by dual *** Lagrange function is integrated in the resulting optimization to deal with global constraints,so that a fully distributed solvable DMPC can be solved by a distributed primal-dual *** effectiveness of the proposed method is proved by simulation examples.

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MOSFormer: A Transformer-based Multi-Modal Fusion Network for Moving Object Segmentation

MOSFormer: A Transformer-based Multi-Modal Fusion Network fo...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zike Cheng Hengwang Zhao Qiyuan Shen Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene diversity. Meanwhile, the prevalent methods are projection-based, struggling with the challenge of blurred boundaries. To tackle the dataset issue, we introduce a nuScenes-based MOS dataset, which provides richer scenes and more dynamic instances. To alleviate the boundary blur-ring issue and further improve accuracy and generalizability, we propose a dual-branch multimodal fusion MOS network, MOSFormer. The Transformer structure is incorporated to extract spatio-temporal information better, while image semantic information is utilized to refine the boundaries of moving objects. Finally, experiments on two datasets show that our method achieves state-of-the-art performance, and a mapping experiment with our method confirms its effectiveness in downstream tasks such as mapping and localization.

关键词： Location awareness Three-dimensional displays Autonomous systems Semantics Object segmentation Transformers Real-time systems Data mining Faces Intelligent robots

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Data-Driven Distributionally Robust Electricity-Hydrogen Trading Considering The Carbon Intensity of Hydrogen Production

Data-Driven Distributionally Robust Electricity-Hydrogen Tra...

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Asian control Conference

作者： Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper proposes a data-driven distributionally robust local electricity-hydrogen market framework, where Hydrogen-based Micro-Grids (HMGs) supply hydrogen to heterogeneous Hydrogen Users (HUs) including hydrogen refueling stations and industrial plants. In this framework, the coordination between HMGs and HUs is cast as a multi-leader multi-follower Stackelberg game. Specifically, HMGs determine an integrated hydrogen-carbon price and carry out electricity trading through a non-cooperative game. Meanwhile, HUs act as followers adjusting hydrogen purchase strategies. Furthermore, the self-dispatching of HMGs and HUs are modeled as distributionally robust optimization problems considering source-load and hydrogen demand uncertainties, respectively. To hedge against these uncertainties, a novel Bayesian nonparametric hybrid ambiguity set is constructed based on local Wasserstein balls and moment information. Finally, a distributed algorithm is developed to solve the market clearing problem. Comparative studies validate that the proposed framework outperforms existing methods, demonstrating a total income improvement of 12.3% and a carbon emission reduction of 11.6%.

关键词： Uncertainty Hydrogen Carbon dioxide Pricing Games Production Power markets

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Non-Repetitive: A Promising LiDAR Scanning Pattern

Non-Repetitive: A Promising LiDAR Scanning Pattern

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Angchen Xie Yeqiang Qian Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR is an essential sensor for intelligent vehicles. Recently, LiDARs used in vehicles produced by different companies have significant differences in their scanning patterns. Some vehicles use mechanical and solid-state (repetitive) LiDARs, while others use prism-based (non-repetitive) LiDARs. The scanning pattern of a LiDAR has a profound impact on its scanning performance. To investigate the influence of LiDAR scanning patterns, we created the "Repetitive-or-not" dataset, which is collected simultaneously by LiDARs with both repetitive and non-repetitive scanning patterns in the CARLA simulation environment. Using this dataset, we conducted a comprehensive statistical analysis of the scanning ability of repetitive and non-repetitive LiDARs. Furthermore, we looked into the effects of these two LiDAR scanning patterns on the performance of various 3D object detection algorithms. Finally, we explored the domain gap in the point cloud data produced by repetitive and non-repetitive LiDARs. Through an in-depth investigation of the "Repetitive-or-not" dataset, we have discovered that non-repetitive LiDAR shows great promise. This conclusion is primarily supported by its superior object scanning capabilities.

关键词： Point cloud compression Laser radar Three-dimensional displays Statistical analysis Intelligent vehicles Object detection Companies Robot sensing systems Intelligent robots

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons under DoS Attacks 13

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Han, Qiaoni Cheng, Guowen Tang, Zhenning Liu, Chang School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China

ISBN: (纸本)9798350315196

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security. © 2023 IEEE.

关键词： Fuzzy control

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Self-Supervised Depth Estimation Based on the Consistency of Synthetic-real image Prediction 8

Self-Supervised Depth Estimation Based on the Consistency of...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Tong, Wei Gao, Yubing Wu, Edmond Q. Zhu, Li-Min School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu210014 China Ministry of Education Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

ISBN: (纸本)9798350300178

Learning-based multi-view stereo aims to restore the real scene from multiple images with overlapping areas. The mainstream self-supervised MVS method trains the model based on the assumption that spatial points from different perspectives share the same color information. To further suppress the interference from specular reflection and illumination noise, this work proposes a self-supervised MVS network based on the consistency of synthetic-real image prediction. The network first applies the coarse-to-fine manner to gradually refine the depth map, and the source images are projected to the reference view to generate the synthesized reference image. Then the synthesized image with real source images are re-input into the network to form a cycled network, and the consistency constraint of the prediction results of the two periods is introduced to improve the color anti-interference of the self- supervised MVS model. The comprehensive experiments on the public dataset show that the proposed work can further improve the reconstruction performance of the benchmark model, which verifies the effectiveness of the proposed work. © 2023 IEEE.

关键词： Forecasting

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Uncertainty-Aware Reactive control for Signal Temporal Logic with Unknown Obstacles

Uncertainty-Aware Reactive Control for Signal Temporal Logic...

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Asian control Conference

作者： Chen Chen Shaoyuan Li Xiang Yin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing the Ministry of Education of China Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we investigate the challenge of con-trol synthesis for Signal Temporal Logic (STL) specifications within workspaces harboring unknown obstacles. We consider a sensing-based control setting, where robots are equipped with sensors with a limited sensing range to acquire obstacle locations with prior information. The objective is to synthesize a control strategy that maximize the robustness degree of the STL tasks while simultaneously minimizing the probability of collisions with obstacles during its operation. To address this, we present a receding horizon reactive control framework, tackling the problem by solving an uncertainty-aware optimal planning problem at each time instant. A key aspect of our approach is the avoidance of explicit computation of collision probability. Instead, we transform the uncertainty-aware synthesis problem into a non-probabilistic optimization problem, and the results of which still provide probabilistic guarantees. Simulation results are provided to illustrate the effectiveness of our approach.

关键词： Measurement Simulation Transforms Probability Robot sensing systems Probabilistic logic Robustness

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Wang, Ziwei Wu, Dongrui The Key Laboratory of the Ministry of Education for 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 Shenzhen China

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness. © 2024, CC BY.

关键词： Adversarial machine learning

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Boltzmann Robust Soliton Distribution for Rateless Codes 24

Boltzmann Robust Soliton Distribution for Rateless Codes

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24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Tang, Jinhui Fu, Yusun Qiao, Yue Yin, Junpeng Huang, Haobo Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350363760

The decoding success rate may be worse when classical degree distributions are used in short-packet transmission of rateless codes, especially in industrial communication scenarios. A novel Boltzmann degree distribution is proposed for Luby Transform (LT) codes. The Boltzmann degree distribution has a high decoding success rate for short-packet transmission but a lower decoding success rate in long-packet transmission. To balance the performance of rateless codes for short and long packet transmission, the Boltzmann degree distribution and the robust soliton distribution (RSD) are combined with a new joint degree distribution, called Boltzmann Robust Soliton Distribution (BRSD). Simulation results show that compared with RSD and Poisson robust soliton distribution (PRSD), the proposed joint distribution can achieve a higher decoding success rate and reduce the average degree, especially in short-packet transmission of industrial communication. © 2024 IEEE.

关键词： Decoding

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Energy Management Strategies for PEMFC-Lithium Battery Hybrid Power Systems Based on Pt Degradation

Energy Management Strategies for PEMFC-Lithium Battery Hybri...

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第42届中国控制会议

作者： Ziang Guo Beijia Li Linghong Zeng Jun Fu Chuang Sheng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the platinum(Pt) catalyst during *** this study,we have established a one-dimensional model to investigate the effects of different voltage conditions on Pt *** potential is found to be the most significant factor affecting the *** exposure to high potentials will significantly reduce the PEMFC's lifespan,and frequent voltage changes will exacerbate the *** mitigate this issue,we have designed three energy management strategies with different limitations and simulated their performance in the operating scenario of a fuel-cell hybrid electric vehicle(FCHEV).The results demonstrate that strategies restricting high potential occurrences and voltage variations can effectively alleviate Pt degradation.

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