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Robust Monitor for Industrial IoT Condition Prediction

IEEE INTERNET OF THINGS JOURNAL 2023年第10期10卷 8618-8629页

作者： Zhang, Xingwei Tian, Hu Zheng, Xiaolong Zeng, Daniel Dajun 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 100190 Peoples R China

The robustness of machine learning (ML) models has gained much attention along with their wide application on various safety-required Industrial Internet of Things (IIoT) paradigms. Researchers found that some specific attacks added on sensor measurements can maliciously disturb IIoT monitors that are designed using ML architectures. The Traditional detection methods could judge whether the measurements are attacked to prevent the failure of monitors. Unfortunately, recent works argue that the commonly used detection methods could be circumvented through adaptive attacks that could acquire the mechanism of detectors;they could not truly enhance the robustness of ML models. Instead, general robust mechanisms should be performed to authentically enhance the robustness of models against any potential attacks with specific restrictions. On the basis of the above argument, we design a robust condition monitor for predicting the fault condition of IIoT systems using the adversarial training technique called robust temporal convolutional network (RTCN). The model is designed to be formally robust to attacks with restricted magnitude. The temporal convolutional network (TCN) is employed to design the base structure of the monitor. TCN can capture temporal information from sensors to enhance the feature extraction performance of models. We also present a novel false data injection (FDI) attack-generating method that utilizes the conception of adversarial perturbations to disturb well-trained monitors. The experimental results verify the efficiency of feature extraction performance of our model from IIoT systems. Furthermore, adversarial training mechanism through a min-max manner could effectively improve the reliability of ML-based IIoT monitors against strong FDI attacks.

关键词： Perturbation methods Monitoring Industrial Internet of Things Training Predictive models Robustness Temperature sensors Adversarial perturbation adversarial training Industrial Internet of Things (IIoT) machine learning (ML) temporal convolutional network (TCN)

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A Tremor Suppression and Noise Removal Algorithm for Microscopic Robot-Assisted Cataract Surgery

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第5期28卷 2941-2952页

作者： Bian, Gui-Bin Wang, Jie Fu, Pan Wei, Bing-Ting Li, Ming-Jun Li, Zhen Beijing Informat Sci & Technol Univ Sch Automat Beijing 100101 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Ophthalmic surgery consists of a series of precise manipulations performed using a surgical microscope. A surgical robot with a master-slave paradigm is commonly adopted to improve the precision and guidance intelligence for ophthalmic surgery;however, the surgeon's hand tremor, misoperation, inherent characteristics of the robot, and environmental interference may cause tremors of the end-effector of the surgical manipulator, thus increasing the risk of soft tissue damage and poor prognosis for the patient. A symmetrical threshold noise reduction combiner (STIC) is proposed to suppress hand tremors and reduce environmental noise interference. Additionally, the inverse kinematic model of the eight-degrees-of-freedom surgical robot for cataract surgery is developed, with the constraints of continuous circular capsulorhexis manipulation. Simulation results show that the signal-to-noise ratio of the STIC is increased from 35.3907 to 57.0158 dB, whereas the maximum, average, standard deviation, and root mean square of the output of the STIC are decreased by 64.79%, 77.24%, 74.50%, and 75.86%, respectively. Three sets of in vitro experimental results show that these four indicators of the STIC performance are reduced by 59.73%, 76.57%, 57.23%, and 65.02%, respectively. These results validate the feasibility and effectiveness of the proposed method on hand tremor suppression and inherent high-frequency disturbance elimination, thereby enhancing the accuracy of the microsurgical operation.

关键词： Cataract capsulorhexis constraint in vitro experiment microscopic surgical robot noise removal teleoperation tremor suppression

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Scenarios Engineering for Trustworthy AI: Domain Adaptation Approach for Reidentification With Synthetic Data

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第11期54卷 6901-6910页

作者： Li, Xuan Wang, Xiao Deng, Fang Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Peng Cheng Lab Dept Strateg & Intersecting Frontiers Shenzhen 518000 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Reidentification (Re-ID) is a crucial computer vision application with a variety of potential uses in many maritime scenarios, including search, rescue, and surveillance. However, the development of advanced boat reidentification (Boat Re-ID) algorithms necessitates the availability of large-scale Re-ID datasets for model training and evaluation. Inspired by scenarios engineering, this study proposes a new framework for automatically generating a realistic synthetic dataset for boat Re-ID investigation. The synthetic dataset contains 107 boat models and various visual conditions in 36 real backgrounds. The use of synthetic datasets enables the learning-based Re-ID algorithm's performance to be quantitatively verificated under varying imaging conditions. Nonetheless, our experiments prove that synthetic datasets are inadequate to handle real-world challenges. Therefore, we present a domain adaptation approach that integrates both real and synthetic data to create trustworthy models. This approach employs a multistep training strategy, gradient reversal layer and novel loss functions to preserve the features from two distribution dataset domains. The results of the experiments demonstrate that 1) synthetic datasets can be employed to train boat Re-ID algorithms and quantitatively test the performance of these algorithms under diverse imaging conditions and 2) our approach utilizes the attributes of the two data domains (real and synthetic) to achieve exceptional performance in real-world applications.

关键词： Boats Synthetic data Training labeling Deep learning Feature extraction Data models Boat reidentification (Boat Re-ID) domain adaptation (DA) scenarios engineering (SE) synthetic dataset unmanned aerial vehicles (UAVs)

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Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第6期10卷 3241-3251页

作者： Zhang, Xingwei Zheng, Xiaolong Mao, Wenji Zeng, Daniel Dajun Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China

The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations' magnitudes are small enough that will not induce humans' perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models' recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans' perception;hence, Hashing Fake can be applied on real-world social websites to protect subscribers' privacy against malicious retrieval software.

关键词： Perturbation methods Semantics Computational modeling Codes Task analysis Privacy Software Adversarial perturbation deep neural network (DNN) privacy protection semantic retrieval

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A Cerebellum-Inspired Prediction and Correction Model for Motion control of a Musculoskeletal Robot

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2023年第3期15卷 1209-1223页

作者： Zhang, Jinhan Chen, Jiahao Wu, Wei Qiao, Hong 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

It is an important issue that how to regulate the existing control models of musculoskeletal robots to improve the ability of motion learning and generalization. In this article, based on the motion modulation function of the cerebellum, a cerebellum-inspired prediction and correction model is proposed to carry out feedforward regulation of the original controller. First, drawing on the reservoir computing mechanism of the cerebellar granular layer, the cerebellum prediction model is established by using the echo state network. Incremental learning for the network is achieved using the replay method, which is able to process control signals with different distributions. The cerebellum prediction network can accurately predict the motion results of the robot under the action of the time-series control signals. Second, referring to the neural pathways of the cerebellum, the cerebellum correction model is constructed. The network learning rules are designed by drawing on the long-term potentiation and depression processes of cerebellar synaptic plasticity. The characteristics of the parameters in the network weight update equations are further analyzed. And the hyperparameter update rules of the correction network weights are proposed. To simulate the function of the cerebellum involved in the limb ballistic movement, the adaptive adjustment method for cerebellum correction duration is proposed. The cerebellum correction network can accurately modulate the original control signals by using the motion prediction results. In experiments, a musculoskeletal robot is used to verify the movement effects under the control of the cerebellum model. The results show that the cerebellum-inspired model can effectively improve the motion accuracy and learning efficiency of the musculoskeletal robot, and enhance the motion generalization ability and system robustness of the robot.

关键词： Biologically inspired control cerebellum-inspired model motion generation motion learning musculoskeletal robot reinforcement learning

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Distributed Dynamic Event-Triggered control for Euler-Lagrange Multiagent systems With Parametric Uncertainties

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1272-1284页

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

This article investigates the distributed dynamic event-triggered control of networked Euler-Lagrange systems with unknown parameters. Using the designed dynamic event-triggered control algorithm, the leaderless consensus problem and the containment problem of networked Euler-Lagrange systems are solved, and the estimations of unknown parameters are updated by an adaptive updating law as well. The stability analysis is given based on an appropriate Lyapunov function and the distributed control problem is theoretically solved by the designed control algorithm. The Zeno behavior of the designed dynamic event-triggered method is excluded in a finite-time interval. Compared to some existing results for the event-triggered control of networked Euler-Lagrange systems, these event-triggered methods can be seen as the special cases of the dynamic event-triggered method proposed in this article. Simulation results based on UR5 robots of V-rep show that the proposed method can provide an increase (4.46 +/- 3.36%) of the average lengths of event intervals compared to the one of the existing event-triggered methods, which leads to a lower usage of the communication resource. Meanwhile, the time of achieving the consensus/containment and the steady-state control performance are not affected.

关键词： Vehicle dynamics Nonlinear dynamical systems Symmetric matrices Laplace equations Heuristic algorithms Directed graphs Robot sensing systems Consensus containment dynamic event-triggered control Euler-Lagrange system multiagent systems (MASs)

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A feedforward compensation approach for cable-driven musculoskeletal systems

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ROBOTICA 2023年第4期41卷 1221-1230页

作者： Fan, Yerui Yuan, Jianbo Wu, Yaxiong Qiao, Hong Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

关键词： biomimetic robots control of robotic systems force control humanoid robots serial manipulator design and kinematics

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Memory-based Human Postural Regulation control: An Asynchronous Semi-Markov Model Approach

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INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2023年第10期21卷 3357-3367页

作者： Ma, Chao Fu, Hang Wu, Wei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article investigates the human postural regulation problem from the dynamical system perspective, which is also applicable for human-like robotics. More precisely, since the dynamical human posture parameters may change caused by varying load or environment abrupt, the semi-Markov jump process is employed to model the human standing postural dynamics with multiple modes. Furthermore, a novel memorized regulation strategy is developed for guaranteeing the stable standing such that the past memory information can be well utilized. In particular, the asynchronous regulation procedure is considered for better describing the human postural model with mismatched jumping modes. By model transformation and stochastic analysis, mode-dependent regulation criteria with state feedback model are established by convex optimization approach, based on which the mode-dependent regulation gains are designed accordingly. Finally, the feasibility and effectiveness of our proposed regulation strategy is verified via an illustrative example of quiet upright standing posture.

关键词： Asynchronous regulation convex optimization human postural regulation memory-based regulation semi-Markov jump system

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Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4254-4258页

作者： Wu, Wansen Yang, Weiyi Li, Juanjuan Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Peng, Yong Wang, Fei-Yue Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Chinese Acad Sci Inst Automat 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

The precise characterization and modeling of Cyber-Physical-Social systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This perspective reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by foundation intelligence and its associated technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the "6A-goal" of ACS and propose potential avenues for future research.

关键词： Crowdsensing Sensors Task analysis Robot sensing systems Decentralized autonomous organization Automation Artificial intelligence Cyber-physical-social systems (CPSS) crowdsensing intelligence (CSI) autonomous crowdsensing

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Data-driven finite-time adaptive attitude tracking control for spacecraft

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Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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