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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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An Augmented Reality Application for Upper-Limb Rehabilitation Supported by a Robotic Arm 6

An Augmented Reality Application for Upper-Limb Rehabilitati...

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6th Experiment at International Conference, exp.at 2023

作者： Rodrigues, Jose Carlos Restivo, Maria Teresa Menezes, Paulo University of Porto LAETA-INEGI Faculty of Engineering Porto Portugal University of Porto LAETA-INEGI Porto Portugal A3ES Lisbon Portugal University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

This work looks at the potential use of a robotic arm and augmented reality application, with a head-mounted display, in an upper-limb rehabilitation context. A sample application using a robotic simulator and a cross-sectional study using a real robotic arm are shown. Results from the usability evaluation are positive and encourage further development, but some identified problems must be corrected before further evaluations. © 2023 IEEE.

关键词： Robotic arms

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A Battery Control Strategy for Sequential Utilization of Energy Storage systems in Power Batteries 7

A Battery Control Strategy for Sequential Utilization of Ene...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Li, Bowen Cui, Shijie Zhu, Lizhong Shenyang Ligong University College of Automation and Electrical Engineering Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350339161

With the widespread adoption of energy storage systems utilizing power batteries, battery lifespan degradation has become a primary constraint on system performance. To address this issue, this study proposes a lifespan-optimized battery control strategy. Firstly, a battery lifespan calculation model for the energy storage system is established, taking into detailed consideration key parameters such as battery State of Charge (SOC). Secondly, a control strategy based on SOC for charging and discharging is designed, aiming to minimize battery lifespan consumption to the greatest extent possible. Finally, experimental results demonstrate that the proposed control strategy significantly improves the lifespan of the energy storage system compared to existing strategies. This research provides an effective means of control for the long-term stable operation of power battery systems. © 2024 IEEE.

关键词： Charging (batteries)

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Optimized Rewinding of Oversized Induction Motors

Optimized Rewinding of Oversized Induction Motors

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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Fernando J. T. E. Ferreira Dept. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

When an oversized and/or faulty three-phase squirrel-cage induction motor (SCIM) needs to be rewound, there is an opportunity to redesign its stator winding as a function of its actual load cycle, aiming at improving its efficiency, power factor, and reliability. This paper proposes a systematic methodology to optimize the stator winding of in-operation, line-operated, fixed-speed SCIMs, with two goals, namely, (i) optimization of the fundamental component of magnetizing flux to the actual load cycle, reducing the original core saturation level and nominal power of the motor, and (ii) optimization of the pitch and turns of the coils to minimize the winding copper losses and the airgap magnetomotive force harmonic content. Hybrid computing approaches using the first principles and nondeterministic methods can be used for winding optimization. The proposed methodology has been applied to three oversized 5.5 kW, 4-pole SCIMs, driving industrial ceramic paste mixers, and led to a 30-45% reduction in the line current, a 10-25% reduction in the input active power, and a 20-35% increase in the power factor, enabling an upgrade of the efficiency class from "IE0"/IE1 to IE3/IE4. The decrease of the motor line current reduces the Joule losses in the upstream cables and transformers, freeing power transmission capacity in the power supply network. The proposed strategy is a cost-effective alternative to the replacement of old oversized motors with new well-sized motors, avoiding mechanical readaptation of the fixing and coupling systems and allowing for higher efficiency and power factor levels than those of new well-sized motors. It also fosters copper recycling, the electric motor circular economy, and the local economic activity associated with the motor repair/rewinding sector.

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Anti-Disturbance Path Following of Unmanned Surface Vehicles Based on Fuzzy Active Disturbance Rejection Control 5

Anti-Disturbance Path Following of Unmanned Surface Vehicles...

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5th International Conference on Intelligent Autonomous systems, ICoIAS 2022

作者： Ma, Chuang Wang, Wei Er, Meng Joo Gong, Huibin Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China College of Intelligent Systems Science Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

With the view of achieving path following of unmanned surface vehicles (USVs) in a complex marine environment, a novel path following algorithm based on fuzzy active disturbance rejection control (FADRC) is proposed. Firstly, an improved LOS (IMLOS) guidance law is designed deal with influences of sideslip angle on path following so as to improve robustness of the guidance system. Secondly, to circumvent the problem that the traditional ADRC method cannot automatically adjust system parameters, a FADRC controller which can realize disturbance compensation without accurate model parameters is designed. FADRC can estimate and eliminate internal and external disturbances. The IMLOS and FADRC are integrated to form a composite path following controller. The simulation results demonstrate that the proposed FADRC. © 2022 IEEE.

关键词： Unmanned surface vehicles

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An Automatic Extraction of Features from Water Bodies by using Contrast Vision Transformer 2

An Automatic Extraction of Features from Water Bodies by usi...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Madhumitha, A. Al-Fatlawi, Muhammed Sai Srinivas, T. Aditya Ramachandra, A.C. Venkatasubramanian, R. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with Artificial Intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies’ extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVT extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVT provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively. © 2024 IEEE.

关键词： Median filters

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Impact of UAV Body Dynamics on Coverage Probability in 5G FR2

Impact of UAV Body Dynamics on Coverage Probability in 5G FR...

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IEEE Conference on Wireless Communications and Networking

作者： Pawan Srivastava Rajesh Kumar Samantaray M. P. R. S. Kiran Department of Electrical Engineering Connected Intelligence Lab IDRP on Robotics and Mobility Systems Indian Institute of Technology Jodhpur Rajasthan India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The next-generation wireless communication technologies, such as 5G Frequency Range 2 (FR2), WLANs, etc., are utilizing the millimeter-wave (mmWave) spectrum for increased data rates. For example, applications involving unmanned aerial vehicles (UAVs) for coverage extension in 5G comprise a huge amount of user data to be transmitted to the gNodeB (gNB). Hence, utilizing mmWave communication technologies such as 5G FR2 to establish the link between the UAV and gNB can be a suitable option. However, one of the critical technologies of 5G FR2 is the utilization of directional beamforming, and the impact of the axial unsteadiness of UAV s on the coverage needs to be carefully analyzed. This paper studies and models the effect of beam misalignment manifested due to axial disturbances in UAVs while hovering at a fixed altitude on the network performance. Firstly, a stochastic model for beam misalignment is developed by collecting real-time inertial data from a hovering UAV over multiple flights. Second, an analytical formulation of the coverage probability in a directional UAV to gNB mmWave communication link is formulated. The performance analysis shows that the analytical model matches the simulation outcomes with less than 2% error.

关键词： Wireless communication 5G mobile communication Millimeter wave technology Stochastic processes Autonomous aerial vehicles Real-time systems Performance analysis

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Artifact-Based Domain Generalization of Skin Lesion Models 17th

Artifact-Based Domain Generalization of Skin Lesion Models

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17th European Conference on Computer Vision, ECCV 2022

作者： Bissoto, Alceu Barata, Catarina Valle, Eduardo Avila, Sandra Institute of Computing University of Campinas Campinas Brazil Institute for Systems and Robotics Instituto Superior Técnico Lisbon Portugal School of Electrical and Computing Engineering University of Campinas Campinas Brazil Recod.ai Lab University of Campinas Campinas Brazil

ISBN: (纸本)9783031250682

Deep Learning failure cases are abundant, particularly in the medical area. Recent studies in out-of-distribution generalization have advanced considerably on well-controlled synthetic datasets, but they do not represent medical imaging contexts. We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context. First, we partition the data into levels of increasingly higher biased training and test sets for better generalization assessment. Then, we create environments based on skin lesion artifacts to enable domain generalization methods. Finally, after robust training, we perform a test-time debiasing procedure, reducing spurious features in inference images. Our experiments show our pipeline improves performance metrics in biased cases, and avoids artifacts when using explanation methods. Still, when evaluating such models in out-of-distribution data, they did not prefer clinically-meaningful features. Instead, performance only improved in test sets that present similar artifacts from training, suggesting models learned to ignore the known set of artifacts. Our results raise a concern that debiasing models towards a single aspect may not be enough for fair skin lesion analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pipelines

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A Multi-Gas Detection System Based on Convolutional Neural Network

A Multi-Gas Detection System Based on Convolutional Neural N...

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第43届中国控制会议

作者： Si-Wen Chen Hong-Yue Li Hui-Rang Hou Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The emission of carbon oxides（COX）, nitrogen oxides（NOX）, sulfur compounds, and volatile organic compounds（VOCs） from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared（NDIR） multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network（1D-CNN） and depthwise separable convolutional neural network（DS-CNN）. The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust.

关键词： Non-Dispersive Infrared Multi-gas Detection System Pattern Recognition Convolutional Neural Network

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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