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arXiv 2022年

作者： Luo, Xiaoyu Fang, Chongrong He, Jianping Zhao, Chengcheng Paccagnan, Dario Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

Security issues have gathered growing interest within the control systems community, as physical components and communication networks are increasingly vulnerable to cyber attacks. In this context, recent literature has studied increasingly sophisticated false data injection attacks, with the aim to design mitigative measures that improve the systems’ security. Notably, data-driven attack strategies – whereby the system dynamics is oblivious to the adversary – have received increasing attention. However, many of the existing works on the topic rely on the implicit assumption of linear system dynamics, significantly limiting their scope. Contrary to that, in this work we design and analyze truly model-free false data injection attack that applies to general linear and nonlinear systems. More specifically, we aim at designing an injected signal that steers the output of the system toward a (maliciously chosen) trajectory. We do so by designing a zeroth-order feedback optimization policy and jointly use probing signals for real-time measurements. We then characterize the quality of the proposed model-free attack through its optimality gap, which is affected by the dimensions of the attack signal, the number of iterations performed, and the convergence rate of the system. Finally, we extend the proposed attack scheme to the systems with internal noise. Extensive simulations show the effectiveness of the proposed attack scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Networked control systems

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RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

arXiv

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arXiv 2023年

作者： Liu, Jiuming Wang, Guangming Liu, Zhe Jiang, Chaokang Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China China University of Mining and Technology China ETH Zürich Switzerland Microsoft United States

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://***/IRMVLab/RegFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic System

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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International Conference on control, Decision and information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency information technology

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AI-driven target screening and microfluidic sonication-assembled oral delivery of fucoxanthin-loaded probiotic vesicles for targeted alleviation of diet-induced obesity

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Chemical Engineering Journal 2025年 518卷

作者： Wang, Kuiyou Li, Xueqian Xu, Xiaoyu Chang, Jingru Sun, Mingyang Huang, Kexin Huang, Shasha Jiang, Hongyu Wang, Huihui Tan, Mingqian Su, Wentao State Key Laboratory of Marine Food Processing and Safety Control National Engineering Research Center of Seafood Dalian Polytechnic University Liaoning Dalian116034 China Dalian Key Laboratory for Precision Nutrition Academy of Food Interdisciplinary Science School of Food Science and Technology Dalian Polytechnic University Liaoning Dalian116034 China Collaborative Innovation Center of Seafood Deep Processing Dalian Polytechnic University Liaoning Dalian116034 China School of Mechanical Engineering and Automation Dalian Polytechnic University Liaoning Dalian116034 China School of Information Science and Engineering Dalian Polytechnic University Liaoning Dalian116034 China

Obesity is associated with numerous diseases, and its increasing prevalence poses serious public health challenges. Current pharmacological treatments, including appetite suppressants and fat absorption inhibitors, exhibit limited efficacy and adverse side effects. This study developed a Lactobacillus-derived vesicle (LCEV) system using microfluidic-assisted ultrasound technology to encapsulate fucoxanthin (Fx). In parallel, AI-based target screening was employed to evaluate its role in regulating lipid metabolism both in vitro and in vivo. Through simulation modeling and experimental validation, the microfluidic ultrasound technology generated a stable, uniformly distributed acoustic pressure field. It also optimized energy input and minimized localized high-pressure damage to vesicle structures, thereby enhancing encapsulation efficiency. In vitro, Fx-loaded LCEV (LCEV@Fx), assembled with microfluidic ultrasound, inhibited free fatty acid-induced lipid accumulation in liver cells, reduced oxidative stress, and suppressed adipocyte differentiation and lipid synthesis in 3T3-L1 preadipocytes. In vivo, vesicle encapsulation prolonged retention in the gastrointestinal tract, enhanced absorption across the serosal layer, and increased liver accumulation. AI-driven target screening and molecular docking analysis identified AMPKα1 as a key target for Fx in regulating lipid metabolism, providing a theoretical basis for its mechanism of action. Further experiments confirmed that LCEV@Fx reduced white adipose tissue accumulation and improved hepatic steatosis in HFD-induced mice. It promoted fatty acid oxidation through the AMPK/SIRT1 pathway while downregulating the expression of SREBP1 and FAS to inhibit lipid synthesis. Overall, this study, through AI-driven target screening and verification, clarified the mechanism of action of LCEV@Fx, providing new insights for its application in metabolic diseases, particularly demonstrating significant therapeutic potential in regul

关键词： Nutrition

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

arXiv

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arXiv 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China the Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China the Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. © 2025, CC BY.

关键词： Mixed-integer linear programming

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Event-Triggered Optimal Tracking control for Multiplayer Non-Zero-Sum Games of Nonlinear Systems via Concurrent Learning

Event-Triggered Optimal Tracking Control for Multiplayer Non...

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Data Driven control and Learning Systems (DDCLS)

作者： Yi Qin Lijie Wang College of Information Science and Technology Bohai University Jinzhou P. R. China School of Automation Institute of Complexity Science Qingdao University Qingdao P. R. China Shandong Key Laboratory of Industrial Control Technology Qingdao P. R. China

This paper develops an event-triggered optimal tracking control algorithm to handle the problem of the N-player non-zero-sum (NZS) games for nonlinear systems with infinite horizon discount cost. A decay term is introduced to accelerate the convergence rate of the value function containing multiple control inputs. To lighten the correspondence burden, a state-dependent triggering condition is designed to ensure that the lower bound of the minimal triggering time interval is positive. Moreover, the single critic neural network is applied such that the computational complexity is greatly reduced. Unlike existing optimization results that require the continuous excitation condition, the concurrent learning technique is introduced into the update rate of neural network weights, thereby removing the limitation of additional noise excitation. Eventually, all signals of the closed-loop system are guaranteed to be uniformly ultimately bounded via the Lyapunov stability theory. Meanwhile, simulation results are presented to verify the feasibility of the proposed scheme.

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Trajectory Selection Optimization for Behavior-based Aerial-Ground Robots Heterogeneous System : A Predictive Approach

Trajectory Selection Optimization for Behavior-based Aerial-...

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Chinese automation Congress (CAC)

作者： Jianfei Chen Yutao Chen Zihang Liu Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China Fuzhou No.1 High School Fuzhou China

ISBN: (纸本)9781665426480

To address the problem of unpredictable trajectory caused by sensing limitation of UGV, in this paper, a heterogeneous multi-agent behavioral control architecture with predictive function is proposed, in which UAV intervenes in the decision-making of UGV. The basic task functions of UGV and UAV are defined respectively and the intervention signal of UAV is connected to the task function of UGV in null-space-based control framework to solve the problem of multi-task conflict. The UAV platform adopts the nonlinear model predictive control to predict the trajectory according to the environment data collected in advance and the current state of the UGV. The prediction results are input into the decision model to produce the final intervention signal and sent to the UGV platform. The UGV adjusts the task strategy in time to ensure the selection of the optimal trajectory for the UGV. Finally, the simulation results show the effectiveness of the proposed cooperative control method.

关键词： Simulation Robot kinematics Decision making Predictive models Robot sensing systems Trajectory Sensors

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GOGICP: A Real-time Gaussian Octree-based GICP Method for Faster Point Cloud Registration

GOGICP: A Real-time Gaussian Octree-based GICP Method for Fa...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Zhitao Yu Wei Yuan Hengwang Zhao Hanyang Zhuang 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 Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Point cloud registration algorithms are crucial for robot localization based on prior maps. Classical methods often struggle to achieve both high accuracy and real-time performance simultaneously, as these two goals can conflict. This limitation impedes the application of point cloud registration in high-speed scenarios, such as the LiDAR localization of fast-moving robots, where both precision and real-time capabilities are essential. This paper proposes a real-time Gaussian Octree-based Generalized Iterative Closest Point (GICP) method, which leverages the censoring properties of the Gaussian Octree to expedite the nearest neighbor search and incorporates an incremental calculation of the Gaussian distribution to hasten the tree-building process. Experimental results show that the proposed method can achieve a 61.8% improvement in speed over GICP while maintaining accuracy comparable to that of GICP. The average registration accuracy is up to 5 cm, with an angular error of less than 0.2°, and the average registration time for a single frame is 77 ms, which satisfies the demands of practical application scenarios.

关键词： Point cloud compression Location awareness Accuracy Laser radar Octrees Semantics Gaussian distribution

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High information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

arXiv

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

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