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SSRN 2023年

作者： Tao, Liujun Wu, Huaiyu Zheng, Xiujuan Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China

Accurate prediction of remaining useful life (RUL) is very important for the safe and stable operation of battery systems. Considering that it is difficult to directly measure the capacity of lithium-ion batteries during operation, this paper proposes an online hybrid cascaded data-driven prediction algorithm for RUL. Firstly, health indicators (HIs) are extracted from the charge-discharge voltage and incremental capacity curves. Then, the correlation between HIs and battery capacity degradation is quantified by grey relational analysis. And kernel principal component analysis is used to eliminate redundant information of HIs. Secondly, the kernel extreme learning machine based on sparrow search algorithm (SSA-KELM) is selected as the first-level capacity prediction model to establish the relationship between HIs and capacity. The bidirectional long short-term memory network is selected as the secondary-level capacity prediction model, which fuses the preliminary capacity predictions of SSA-KELM. Finally, the battery dataset from NASA and Oxford are used to verify the proposed method. The results demonstrate that the proposed method has strong generalization ability and high prediction accuracy. Quantitatively, the RMSE and MAPE of NASA batteries are within 0.03, while the errors of Oxford batteries are within 0.003. The RUL prediction errors of all lithium-ion batteries are within two cycles. © 2023, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

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Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Siwen Chen Xiujuan Zheng Shanghai United Imaging Healthcare Co. Ltd Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. Then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness.

关键词： Lithium-ion batteries Time-frequency analysis Accuracy NASA Predictive models Feature extraction Robustness Numerical models Fourier series Convolutional neural networks

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Continual Forgetting for Pre-Trained Vision Models

Continual Forgetting for Pre-Trained Vision Models

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hongbo Zhao Bolin Ni Junsong Fan Yuxi Wang Yuntao Chen Gaofeng Meng Zhaoxiang Zhang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

For privacy and security concerns, the need to erase un-wanted information from pre-trained vision models is becoming evident nowadays. In real-world scenar-ios, erasure requests originate at any time from both users and model owners. These requests usually form a sequence. Therefore, under such a setting, selective information is expected to be continuously removed from a pre-trained model while maintaining the rest. We define this problem as continual forgetting and identify two key challenges. (i) For unwanted knowledge, efficient and effective deleting is crucial. (ii) For remaining knowledge, the impact brought by the forgetting procedure should be minimal. To address them, we propose Group Sparse LoRA (GS-LoRA). Specifically, towards (i), we use LoRA modules to fine-tune the FFN layers in Transformer blocks for each forgetting task independently, and towards (ii), a simple group sparse regularization is adopted, enabling automatic selection of specific LoRA groups and zeroing out the others. GS-LoRA is effective, parameter-efficient, data-efficient, and easy to implement. We conduct extensive experiments on face recognition, object detection and image classification and demonstrate that GS-LoRA manages to forget specific classes with minimal impact on other classes. Codes will be released on https://***/bjzhb666/GS-LoRA.

关键词： Continuing education Privacy Computer vision Codes Large language models Face recognition Object detection

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Event-Triggered Finite-Time Course Control for Unmanned Surface Vehicles with Quantization

Event-Triggered Finite-Time Course Control for Unmanned Surf...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yiming Sun Yuqi Liu Xi Jin Changqing Xia Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Liaoning China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper concerns the event-triggered finite-time course control problem of an unmanned surface vehicle (USV) under the network environment. To facilitate investigation, the nonlinear dynamic of the course control system is firstly modeled by a class of state-dependent uncertain systems. Then a output feedback framework of course control is established for the USV subject to wave-induced disturbance, where an event generator with dynamic triggering scheme and a logarithmic quantizer are introduced to save communication resources. By means of slack variable technique, a novel co-design criterion of output feedback controller and event-triggered scheme is developed to guarantee the course control system finite-time bounded with a desired ${\mathcal{H}}_{\infty}$ performance. Finally, the effectiveness of the proposed method is verified through a practical simulation case.

关键词： Industrial electronics Uncertain systems Quantization (signal) Simulation Control systems Nonlinear dynamical systems Vehicle dynamics

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Flapping-Wing Robot Achieves Multi-Domain Locomotion Using Transformable Wheel Mechanism

Flapping-Wing Robot Achieves Multi-Domain Locomotion Using T...

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IEEE International Conference on robotics and Biomimetics

作者： Xiangxiao Chai Shihua Wang Erzhen Pan Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The difficulty of moving from aquatic to aerial environment is one of the major challenges limiting the development of trans-medium multi-domain locomotion vehicles. In this paper, the concept of the transformable wheel, a feature commonly found in autonomous land vehicles (AL V s), is introduced into the design. Additionally, a flapping-wing robot equipped with multi-domain locomotion capability, which can not only realize aerial flight and surface navigation on water, but also ascend from water to land for subsequent takeoff is presented. The paper first introduces the system design of the robot and proposes the corresponding multi-domain locomotion strategy. Subsequently, the design of the transformable wheel mechanism is described in detail and the force analysis for the water-exit is conducted. The feasibility of the proposed strategy is experimentally validated, confirming the prototype's multi-domain locomotion performance finally. This research explores the feasibility of introducing the transformable wheel mechanism into trans-medium multi-domain vehicle platforms and provides a design example.

关键词： Legged locomotion Torque Limiting Navigation Propellers Roads Wheels Prototypes Motors System analysis and design

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Design and Implementation of Industrial Real-Time Ethernet Redundant Network Based on Parallel Redundancy Protocol

Design and Implementation of Industrial Real-Time Ethernet R...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Mingxu Gang Bingjun Yan Bo Hu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Industrial real-time Ethernet technology is widely used in modern industrial control systems due to its high communication rate and low application cost. However, in complex network environments, industrial real-time Ethernet may encounter data collisions, data loss, and other issues, which reduce the overall reliability of industrial control systems. To address these issues, a method for designing and implementing industrial real-time Ethernet redundancy networks based on parallel redundancy protocols is proposed. The method first analyzes the redundant data frame structure, dual-connected node (DANP) structure, and redundancy implementation principle of parallel redundancy protocols (PRP). Then, aiming at the standard redundancy discard algorithm principle and its shortcomings, an improved sliding window redundancy discard algorithm is proposed to adapt to complex network environments. Based on the discard algorithm, dual-connected redundancy nodes are designed and implemented. Finally, the dual-connected redundancy nodes are used as the core to construct industrial real-time Ethernet redundancy networks. The test results show that in a PRP-based industrial real-time Ethernet redundant network, when a single redundant network fails, the system can switch without delay, greatly improving the reliability of the industrial control system communication network

关键词： Protocols Industrial control Computer network reliability Redundancy Ethernet Complex networks Reliability engineering

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Resource Allocation for Reliable Transmission in IRS-Assisted Industrial Wireless Sensor Networks 18th

Resource Allocation for Reliable Transmission in IRS-Assis...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Wang, Chuqing Zheng, Meng Liang, Wei Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819621880

Intelligent Reflecting Surface (IRS) is one of the promising technologies for improving transmission reliability of Industrial Wireless Sensor Networks (IWSNs) by creating a virtual line-of-sight (LoS) link to bypass obstacles between transceivers. In this paper, we consider an IRS-assisted IWSN and propose a resource allocation method for reliable transmission in the IRS-assisted IWSN. First, a transmission reliability optimization problem in terms of the reflective beamforming at the IRS and the timeslot and power allocated to sensors is formulated. Second, the formulated optimization problem is transformed into an equivalent but simplified problem. In subsequent, a particle swarm optimization based method is utilized to obtain a near-optimal solution. Simulation results demonstrate that this work yields higher transmission reliabilities than existing benchmark schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Distributed Event-Triggered Formation Control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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A new SPICE modeling approach of annular MOSFET in 350 nm commercial CMOS process

A new SPICE modeling approach of annular MOSFET in 350 nm co...

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Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： He Cheng Zhijia Yang Chao Zhang Zhipeng Zhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Annular gate MOSFETs have emerged as potential solutions for radiation-hardened electronics, demonstrating exceptional total ionizing dose tolerance. However, accurate modeling of annular MOSFETs is crucial when designing analog integrated circuits for use in radiation environments, especially when implementing commercial CMOS processes. To enable circuit simulation using tools like SPECTRE/SPICE, foundry-provided PDKs must incorporate a rigorously verified compact model of annular MOSFET behavior. TCAD simulation can then be used to numerically evaluate the current characteristics of the annular MOSFET against the refined compact model constructed from the foundry-provided PDK. In this paper, by aligning the current characteristics of a standard NMOS between TCAD and SPICE simulations, we can verify the material process conditions. Then, based on the material process parameters, the model of the annular gate MOSFET is established using the TCAD simulator. Finally, reasonable agreements are obtained between the TCAD and SPICE simulation results for the I DS -V GS and I DS -V DS characteristics.

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