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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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Dead-beat Identification for Model Reference Adaptive control⋆

Dead-beat Identification for Model Reference Adaptive Contro...

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IEEE Conference on Decision and control

作者： Ali Kashani Ahmad Kalhor Babak Nadjar Araabi Claus Danielson Mechanical Engineering department University of New Mexico NM US Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this work, we propose a novel parameter identifier for linear time-invariant systems, which has the fastest possible convergence in principal (i.e., dead-beat). The identifier applies where states and derivatives of states can be measured. The identifier is applied to the certainty-equivalence (indirect) model reference adaptive control that relaxes the conventional assumptions about open-loop stability and persistent excitation in MRAC. Furthermore, in conventional MRAC the parameter convergence rate is proportional to the tracking error, whereas our proposed adaptation law has dead-beat dynamics. This method is previously applied to a Delta robot. In this paper, we present a theoretical analysis of the method by solving the identifier dynamics to get its closed-form solution and examine the effect of perturbations such as white additive noise, uncertainty, and disturbance. The linearized model of an actuated inverted pendulum with unknown parameters is used as a benchmark example.

关键词： Linear systems Adaptation models Uncertainty Closed-form solutions Perturbation methods Benchmark testing Stability analysis

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Automatic Defect Recognition Method of Aluminium Profile Surface Defects 21

Automatic Defect Recognition Method of Aluminium Profile Sur...

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Proceedings of the 7th International Conference on Robotics and Artificial Intelligence

作者： Lei Yang Ge Gao Man Wu Jianyong Li School of Electrical Engineering Zhengzhou University China College of Computer and Communication Engineering Zhengzhou University of Light Industry China Beijing Aerospace Control Center China

ISBN: (纸本)9781450385855

Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects present the characteristics of micro defects and different sizes. Conventional handcrafted-based methods and machine learning-based methods have limited feature expression ability which cause relatively poor detection performance. Recently, with the stronger feature extraction ability, deep learning has got wide applications on defect detection and recognition. Due to the loss information caused by pooling operations, it still exists a certain drawbacks on multi-scale object detection. To address this issue, with the residual neural network (ResNet), a new deep defect recognition network is proposed in this paper for aluminium profile surface defects to construct an end-to-end defect detection scheme. An attention fusion model is proposed to improve the detection precision on multi-scale defects. Experiments show that the proposed defect detection method shows a better detection performance compared with other advanced detection models.

关键词： Deep Network Architecture Aluminium Profile Surface Defects Feature Fusion Attention Module. Object Recognition

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Network Slice Degradation Probability as a Metric for Defining Slice Performance Isolation 21st

Network Slice Degradation Probability as a Metric for Defini...

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21st International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2021 and 14th Conference on Internet of Things and Smart Spaces, ruSMART 2021

作者： Polyakov, Nikita Yarkina, Natalia Samouylov, Konstantin Koucheryavy, Yevgeni RUDN University Miklukho-Maklaya Street 6 Moscow117198 Russia Unit of Electrical Engineering Tampere University Tampere33720 Finland Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences 44-2 Vavilov Street Moscow119333 Russia

ISBN: (纸本)9783030977764

Slice isolation is a key feature of the network slicing technique and refers to protecting slices from negative impact of fault, attack or workload increase in other slices. Dynamic resource slicing policies, although provide efficient multiplexing and resource utilization, may lead to situation when a traffic surge in one slice hinders performance of other slices. The level of performance isolation cannot be specified and evaluated without defining an adequate metric. This paper addresses network slice degradation probability as a metric for defining performance isolation of slice. We use teletraffic theory to derive an analytical expression for the degradation probability in a single slice. © 2022, Springer Nature Switzerland AG.

关键词： Probability

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Design and application of a one-stop mobile application sharing platform for steel enterprises

Design and application of a one-stop mobile application shar...

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第42届中国控制会议

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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Comparative Techno-Economic-Environmental Analysis of HVDC and HVAC Technologies for an Offshore Wind Farm in China

Comparative Techno-Economic-Environmental Analysis of HVDC a...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yingqi Liang Shuangxi Wu Yang Liu Dongqiang Fu Yan Rui Lee Gengrui Guo Tianhao Luo Tianhao Wang Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou China

DC collection systems offer advantages by reducing the weight and size of DC cables without requiring reactive power compensation. This enables the replacement of the bulky 50/60 Hz transformers typically used in AC collection systems on offshore platforms with smaller medium-frequency transformers in DC collection configurations. Nonetheless, challenges persist in implementing high-power DC-DC converters with high-voltage transformation ratios and DC protection methods in DC collection systems. It is worth noting that while HVDC (High Voltage DC) transmission can transfer offshore wind power from collection systems to onshore grids, DC collection systems do not always result in fewer power conversion stages compared to AC collection systems. To tackle these challenges, this paper conducts a comparative analysis of the technological, economic, and environmental aspects of DC and AC collection systems for offshore wind farms, using a wind farm in China as an illustrative example. Our approach involves an innovative method for estimating losses and a technical comparison. Simulation results validate that DC collection systems exhibit higher total losses than AC collection systems. We also explore the impact of collection bus voltages on these losses in DC systems. Additionally, we develop an economic cost assessment method, and the sensitivity analysis results confirm that cost reductions primarily stem from the reduced size of DC cables and offshore platforms rather than improvements in DC protective devices and DC-DC converters. Lastly, we investigate the environmental implications of these systems.

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks

Comparative Analysis of Interval Reachability for Robust Imp...

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IEEE Conference on Decision and control

作者： Alexander Davydov Saber Jafarpour Matthew Abate Francesco Bullo Samuel Coogan Center for Control Dynamical Systems and Computation University of California Santa Barbara School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Implicit neural networks (INNs) are a class of learning models that use implicit algebraic equations as layers and have been shown to exhibit several notable benefits over traditional feedforward neural networks (FFNNs). In this paper, we use interval reachability analysis to study robustness of INNs and compare them with FFNNs. We first introduce the notion of tight inclusion function and use it to provide the tightest rectangular over-approximation of the neural network’s input-output map. We also show that tight inclusion functions lead to sharper robustness guarantees than the well-studied robustness measures of Lipschitz constants. Like exact Lipschitz constants, tight inclusions functions are computationally challenging to obtain, and thus we develop a framework based upon mixed monotonicity and contraction theory to estimate the tight inclusion functions for INNs. We show that our approach performs at least as well as, and generally better than, state-ofthe-art interval-bound propagation methods for INNs. Finally, we design a novel optimization problem for training robust INNs and we provide empirical evidence that suitably-trained INNs can be more robust than comparably-trained FFNNs.

关键词： Training Robustness Mathematical models Feedforward neural networks Computational efficiency Reachability analysis Optimization

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Haessig, Germain Joubert, Damien Haque, Justin Milde, Moritz B. Delbruck, Tobi Gruev, Viktor Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria University of Zurich Institute of Neuroinformatics ETH Zurich Switzerland Western Sydney University International Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350302493

The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired frame-based cameras lack the high dynamic range and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ~12ms and dynamic range to ~72dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with submillisecond latencies over a millionfold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low-cost and low-latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image high dynamic range polarization scenes that vary at high speeds and to observe the dynamical properties of single collagen fibers in a bovine tendon under rapid cyclical ***: https://***/mFuCeTMWEqY © 2023 IEEE.

关键词： Polarization

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