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Stealthy FDI Attacks Against Networked control systems Using Two Filters With an Arbitrary Gain

IEEE TRANSACTIONS ON CIRCUITS AND systemS II-EXPRESS BRIEFS 2022年第7期69卷 3219-3223页

作者： Guo, Haibin Sun, Jian Pang, Zhong-Hua Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China North China Univ Technol Key Lab Fieldbus Technol & Automat Beijing Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This brief investigates a stealthy false data injection (FDI) attack scheme against two communication channels to damage the performance of a networked control system. Compared with most existing works, a more practical and reasonable assumption that an attacker only needs to know the physical system knowledge is considered. By using two filters with an arbitrary gain that can be different from the filter gain of the system, a novel feedback channel attack scheme is proposed to ensure the attack process is stealthy, and the forward channel attack signal can be flexibly designed according to specific attack purposes. It is proved that the proposed attack scheme can render the closed-loop system unstable while bypassing a residual-based detector. Two cases of simulation results are provided to verify the theoretical analysis results.

关键词： Detectors Information filters Networked control systems Communication channels Circuits and systems Automation Sun Kalman filter networked control systems residual-based detector stealthy FDI attacks

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Development and Stiffness Optimization for a Flexible-Tail Robotic Fish

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 834-841页

作者： Lu, Ben Zhou, Chao Wang, Jian Fu, Yuzhuo Cheng, Long Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The integral flexible tail has the potential advantage of lifelike undulating motion. However, due to the complex manufacturing process and difficult modification of structural parameters, its application in robotic fish encounters many challenges. Combining rigid structure and flexible material, this letter proposes a passive flexible fish tail, which can achieve continuous movement and high swimming frequency with simple but effective structure. First, with the full consideration of bending deformation of the spring steels, a dynamic model is established. Next, a passive fitting method is particularly applied to imitate the traveling wave model of the carangiform fish. More importantly, a calculation model, which can be used to acquire the theoretical ranges of the stiffness of spring steels, is derived based on the bending model of the cantilever beam subjected to the concentrated force and moment. Finally, the extensive simulation and experiments validate the effectiveness of the proposed methods, and the designed robotic fish can achieve 0.77 m/s (i.e., 1.12 BL/s) at a swimming frequency of 2.5 Hz. The obtained results can provide a valuable sight for improving the swimming performance of the robotic fish.

关键词： Biomimetics biologically-inspired robots mechanism design

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HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2022年第11期23卷 21861-21872页

作者： Xi, Jinhao Zhu, Fenghua Ye, Peijun Lv, Yisheng Tang, Haina Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The imbalance of vehicle supply and demand is a common phenomenon that influences the efficiency of online ride-hailing systems greatly. To address this problem, a three-level hierarchical mixed deep reinforcement learning method (HMDRL) is proposed to reposition idle vehicles. A manager operates at the top level, where action-abstraction is conducted from the time dimension and is adaptive for spatially scalable and time-varying systems. Coordinators locate at the middle level and a parallel coordination mechanism that is independent of the decision order is designed to improve the efficiency of the repositioning. The bottom level is composed of executive workers to reposition vehicles with mixed states and the states contain spatiotemporal information of agents' neighbor areas. Two reward functions are designed for the manager and the coordinators, respectively, aiming to improve the training effect by avoiding sparse rewards. A simulator based on real orders is designed and HMDRL is compared with six methods. Experimental results demonstrate that HMDRL outperforms all the other methods. In three comparison experiments, the order response rate is increased by 0.62% to 8.29%, 1.5% to 7.78%, 1.18% to 4.75%, respectively.

关键词： Reinforcement learning Pricing Heuristic algorithms Supply and demand Surges Vehicle dynamics Vehicles Deep reinforcement learning online ride-hailing system hierarchical repositioning framework parallel coordination mechanism mixed state

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Dynamic Modeling and Performance Analysis for a Wire-Driven Elastic Robotic Fish

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 11174-11181页

作者： Liao, Xiaocun Zhou, Chao Zou, Qianqian Wang, Jian Lu, Ben Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The complex and continuous undulation of fishtail facilitates extraordinary underwater motion performance for natural fish. For the widely used Multi-Joint robotic fish, a lot of joints are used to simulate continuum fishtail, resulting in some challenges, e.g., the mechanism complexity, friction losses of adjacent joints, load disequilibrium and unsmooth servomotor output power. To overcome these intractable hurdles, motivated by natural fish, this letter proposes a wire-driven elastic robotic fish, which simulates fish muscle through multi-wire drive and adopts a fishlike spine design based on elastic component. Compared with the existing wire-driven robotic fish with discrete multiple-joints-spine and single-wire drive, our robotic fish not only has continuum fishtail, but also can swing with C-Shape and S-Shape owing to multi-wire coupling drive, and simulate the energy storage behavior of fish by elastic component. Further, a Lagrangian dynamic model that models a robotic fish with continuum fishtail and passive flexible joint is developed to explore the propulsive performance and validated by extensive experiments and simulations, and our robotic fish reaches the maximum swimming speed of 0.58 m/s, i.e., 1.04 BL/s. Finally, the superiority of the proposed drive mechanism in disposing of load disequilibrium and smoothing output power of servomotor, is analyzed and validated by the comparisons between a Multi-Joint robotic fish and our robotic fish.

关键词： Elastic component Lagrangian dynamics robotic fish wire-driven mode

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Sora-Based Parallel Vision for Smart Sensing of Intelligent Vehicles: From Foundation Models to Foundation Intelligence

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3123-3126页

作者： Yu, Hongkai Liu, Xinyu Tian, Yonglin Wang, Yutong Gou, Chao Wang, Fei-Yue Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA Cleveland State Univ Dept Comp Sci Cleveland OH 44115 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Sun Yat sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518063 Peoples R China

There are a large number of functional sensors installed on the modern intelligent vehicles. Many Artificial Intelligence based foundation models have been proposed for smart sensing to recognize the known object classes in the new but similar scenarios. However, it is still challenging for the foundation models of smart sensing to detect all the object classes in both seen and unseen scenarios. This letter aims at pushing the boundary of smart sensing research for intelligent vehicles. We first summarize the current widely-used foundation models and the foundation intelligence needed for smart sensing of intelligent vehicles. We then explain Sora-based Parallel Vision to boost the foundation models of smart sensing from basic intelligence (1.0) to enhanced intelligence (2.0) and final generalized intelligence (3.0). Several representative case studies are discussed to show the potential usages of Sora-based Parallel Vision, followed by its future research direction.

关键词： Intelligent vehicles smart sensing sora-based parallel vision

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BiFNet: Bidirectional Fusion Network for Road Segmentation

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 8617-8628页

作者： Li, Haoran Chen, Yaran Zhang, Qichao Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Artificial Intelligence Beijing 100049 Peoples R China

Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views (BEVs) of the LiDAR remains the space structure in the horizontal plane, this article proposes a bidirectional fusion network (BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) the dense space transformation (DST) module, which solves the mutual conversion between the camera image space and BEV space and 2) the context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features. This method has achieved competitive results on the KITTI dataset.

关键词： Roads Image segmentation Three-dimensional displays Cameras Laser radar Fuses Feature extraction Adaptive learning autonomous vehicles multisensor fusion road segmentation

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Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation

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MEDICAL IMAGE ANALYSIS 2025年 101卷 103442页

作者： Shi, Gen Lu, Hao Hui, Hui Tian, Jie Beihang Univ Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big DataBased Precis Med Minist Ind & Informat Technol China Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Natl Key Lab Kidney Dis Beijing 100853 Peoples R China

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance. In this study, we have constructed the largest preprocessed unlabeled TOF-MRA dataset to date, comprising 1510 subjects. Additionally, we provide manually annotated segmentation masks for 113 subjects based on existing external image datasets to facilitate evaluation. We propose a simple yet effective pretraining strategy utilizing the Frangi filter, known for its capability to enhance vessel-like structures, to optimize the use of the unlabeled data for 3D cerebrovascular segmentation. This involves a Frangi filter-based preprocessing workflow tailored for large-scale unlabeled datasets and a multi-task pretraining strategy to efficiently utilize the preprocessed data. This approach ensures maximal extraction of useful knowledge from the unlabeled data. The efficacy of the pretrained model is assessed across four cerebrovascular segmentation datasets, where it demonstrates superior performance, improving the clDice metric by approximately 2%-3% compared to the latest semi- and self-supervised methods. Additionally, ablation studies validate the generalizability and effectiveness of our pretraining method across various backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN.

关键词： Cerebrovascular segmentation Self-supervised learning Frangi filter

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Design and control of an Underactuated Finger Exoskeleton for Assisting Activities of Daily Living

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 2699-2709页

作者： Li, Houcheng Cheng, Long Sun, Ning Cao, Ran Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In this article, a novel underactuated finger exoskeleton is designed to assist grasping tasks for the elderly with weak muscle strength. In mechanical design, the human finger's phalanges and joints are considered as part of the kinematic chains to realize the human-robot kinematic compatibility. The proposed finger exoskeleton achieves the finger preshaping and grasps objects with generic shapes. The proposed exoskeleton is both actively and passively backdriveable. Moreover, the weight of the wearable part of the proposed exoskeleton is 127 g, and the overall weight is 476 g, which indicates the proposed exoskeleton is lightweight and portable. To improve the grasping performance, the multiobjective genetic algorithm is implemented to optimize contact forces, which is to maximize the sum of the forces exerted on the index finger phalanges by the exoskeleton and to minimize the difference between the contact forces. After optimization, the sum of contact forces is risen by 15%, and the difference between forces is decreased by 53%. Furthermore, the admittance control is applied to make the proposed finger exoskeleton more compliant in the preshaping phase, and the admittance control is also implemented to achieve the fingertip grasping force control in the grasping phase. Finally, experiments have been conducted to verify the range of motion, grasping forces, and feasibility of the proposed index finger exoskeleton. The effectiveness of the control algorithm has also been verified by experiments.

关键词： Admittance control assistance finger exoskeleton preshaping shape-adaptive grasping underactuation

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A Cross-Scale and Illumination Invariance-Based Model for Robust Object Detection in Traffic Surveillance Scenarios

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2023年第7期24卷 6989-6999页

作者： Lu, Yan-Feng Gao, Jing-Wen Yu, Qian Li, Yi Lv, Yi-Sheng Qiao, Hong Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China

Robust object detection methods in traffic surveillance scenarios often encounters challenges due to large-scale deformations and illumination variations in outdoor scenes. To enhance the tolerance of such methods against these variations, we design a cross-scale and illumination-invariant detection model (CSIM) based on the You Only Look Once (YOLO) architecture. A main cause of false detection in large-scale detection tasks is the inconsistency between various feature scales. To address this issue, we introduce an adaptive cross-scale feature fusion model to ensure the consistency of the constructed feature pyramid. To overcome the influence of uneven light, we build an illumination-invariant chromaticity space on the CSIM model, which is independent of the correlated color temperature. In addition, we adopt spatial attention modules, K-means clustering and the Mish activation function for further model optimization. The obtained experimental results show that the proposed CSIM produces excellent detection results for addressing the challenges derived from large-scale deformations and the illumination changes encountered during traffic surveillance. Compared with state-of-the-art object detection methods on public datasets, our proposed model has achieved competitive results in robust object detection tasks in traffic surveillance scenarios.

关键词： Feature extraction Object detection Lighting Traffic control Adaptation models Task analysis Robustness Traffic detection surveillance scenarios spatial feature fusion illumination invariance

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Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2022年第3期33卷 1228-1241页

作者： Zhu, Yuanheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The Nash equilibrium is an important concept in game theory. It describes the least exploitability of one player from any opponents. We combine game theory, dynamic programming, and recent deep reinforcement learning (DRL) techniques to online learn the Nash equilibrium policy for two-player zero-sum Markov games (TZMGs). The problem is first formulated as a Bellman minimax equation, and generalized policy iteration (GPI) provides a double-loop iterative way to find the equilibrium. Then, neural networks are introduced to approximate Q functions for large-scale problems. An online minimax Q network learning algorithm is proposed to train the network with observations. Experience replay, dueling network, and double Q-learning are applied to improve the learning process. The contributions are twofold: 1) DRL techniques are combined with GPI to find the TZMG Nash equilibrium for the first time and 2) the convergence of the online learning algorithm with a lookup table and experience replay is proven, whose proof is not only useful for TZMGs but also instructive for single-agent Markov decision problems. Experiments on different examples validate the effectiveness of the proposed algorithm on TZMG problems.

关键词： Games Nash equilibrium Mathematical model Markov processes Convergence Dynamic programming Training Deep reinforcement learning (DRL) generalized policy iteration (GPI) Markov game (MG) Nash equilibrium Q network zero sum

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