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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Li, Chen Qi, Xuelei Zhao, Juntao Liao, Xuanhong Lin, Zitian Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Shenyang China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong High School Attached to Northeast Normal University International Curriculum Center Changchun China School of Automation Science and Engineering South China University of Technology Guangzhou China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel relocation recognition algorithm for the multi-UAV cluster. First, the autonomous localization algorithm based on visual-inertial fusion is proposed. Then, the algorithm for vocabulary updating and relocating is designed. Finally, practical testing is implemented to verify and improve the efficiency of exploration ability and the accuracy of the autonomous navigation and positioning in the complex overlapping regions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels

arXiv

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arXiv 2022年

作者： Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling Huawei Noah’s Ark Lab Hong Kong The School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China The Center for Intelligent and Networked Systems Department of Automation BNRist Tsinghua University Beijing100084 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink scheduling for an access point (AP) over multiple unknown channels with heterogeneous AoI requirements defined by AoI-dependent costs. The AP serves N users by using M channels without the channel state information. Each channel serves only one user in each decision epoch. The optimization objective is to minimize the time-averaged AoI-dependent costs plus additional communication transmission costs over an infinite horizon. This decision-making problem can be formulated as a Markov decision process, but it is computationally intractable because the size of the state space grows exponentially with respect to the number of users. To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle’s index theory to design an index-based scheduling policy algorithm. We derive an analytic formula for the indices, which reduces the computational overhead and facilitates online adaptation. Our numerical examples show that our index-based scheduling policy achieves comparable performance to the optimal policy and outperforms several other heuristics. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

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A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

arXiv

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arXiv 2022年

作者： Lin, Yijing Gao, Zhipeng Du, Hongyang Niyato, Dusit Kang, Jiawen Deng, Ruilong Shen, Xuemin Sherman State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China College of Control Science and Engineering Zhejiang University China Department of Electrical and Computer Engineering University of Waterloo Canada

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands. © 2022, CC BY.

关键词： Semantics

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

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

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

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Preconditioning for the integration of a spatiotemporal pharmacodynamic system

Preconditioning for the integration of a spatiotemporal phar...

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92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022

作者： Tebbens, Jurjen Duintjer Lanzendörfer, Martin Matonoha, Ctirad Papáček, Štěpán Department of Computational Mathematics Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Praha-Libeň 182 07 Czech Republic Department of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Kralove Charles University Heyrovského 1203 Hradec Králové500 05 Czech Republic Institute of Hydrogeology Engi neering Geology and Applied Geophysics Faculty of Science Charles University Albertov 6 Praha 2 128 43 Czech Republic Department of Control Theory Institute of Information Theory and Automation of the Czech Academy of Sciences Pod Vodárenskou věží 4 Praha-Libeň 182 00 Czech Republic

We address efficient modeling of the intracellular action of a drug binding to the nuclear pregnane X receptor. The binded complex enters the cell nucleus and acts on DNA, resulting in enhanced production of an enzyme which metabolizes, among others, co-administered drugs. Pharmacodynamic modeling is traditionally based on a compartmental approach, which simplifies the complex processes in the human body through the definition of a small number of compartments representing organs, tissues, cells, cytoplasm or abstract units. Inside a compartment, the concentrations of all substances are assumed to be distributed homogeneously, i.e. there is no spatial dependence. They lead to a system of ODEs for the time-dependence of the concentrations of the active substances. We will present an extension of a model from the literature for the action of the tuberculose-drug Rifampicin. The extension consists of a first attempt to add spatial resolution for substances that are active in the cytoplasm. Spatial resolution can be benecifial to analyze important issues like, among others, local exceeding of toxic drug levels, delay of transport and drug-drug interactions. We address efficient solution of the linear systems arising when numerically integrating the resulting PDE’s and consider some preconditioning techniques based on properties of the underlying biochemical network. © 2023 Wiley-VCH GmbH.

关键词： Linear systems

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Modeling and control of General Hydraulic Excavator for Human-in-the-loop automation

Modeling and Control of General Hydraulic Excavator for Huma...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Guangda Chen Yinghao Gan Jiayi Chen Shuanwu Shi Wei Chen Yingfeng Chen Rong Xiong Changjie Fan Fuxi Robotics in NetEase Hangzhou P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou P.R. China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou P.R. China Department of Automation Zhejiang University of Technology Hangzhou P.R. China

As labor shortages and safety regulations become more prominent, the need for human-in-the-loop automation of excavators is increasing. To meet this demand, we have developed a comprehensive modeling method for the excavator arm using nonlinear optimization approaches, including a simplified model that maps the task space to the joint space, as well as an equivalent model that maps the joint space to the actuator space. These models were then used to build a feedforward-PID joint velocity controller and a joint trajectory controller combined with position feedback, which forms the core of our proposed semi-automatic control system for the excavator arm. Our deployment scheme is simple and efficient, and has been deployed on two excavators of different makes and sizes. Experiments show that our deployment scheme performs well on both excavators, with an average error of 0.05 rad/s for the velocity controller and less than 5 cm for the trajectory controller. Using our semi-automatic system, we have completed demonstration experiments for precise digging and grading operations. A demonstration video can be found at https://***/N6I0WZGSF68.

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Guidance control for Parallel Parking Tasks

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 301-306页

作者： Jiyuan Tan Chunling Xu Li Li Fei-Yue Wang Dongpu Cao Lingxi Li Key Laboratory of Urban Intelligent Traffic Control Technology North China University of TechnologyBeijing 100144China Beijing Transport Institute Beijing 100073China Department of Automation BNRistTsinghua UniversityBeijing 100084China Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100080China Driver Cognition and Automated Driving Laboratory University of WaterlooWaterloo N2L 3G1Canada Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA

Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies(called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies(called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.

关键词： Automatic parking guidance control trajectory planning

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SvTI: SFC-driven Queue Injection in Virtual Time-sensitive Network via Augmented Topology

SvTI: SFC-driven Queue Injection in Virtual Time-sensitive N...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yajing Zhang Qimin Xu Cailian Chen Mingyan Li Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China College of Computer Science Chongqing University Chongqing P. R. China

ISBN: (纸本)9781665435413

Driven by the coexisted heterogeneous communication protocols and diverse quality of service (QoS) requirements of industrial applications, time-sensitive networking (TSN) has been proposed as a promising and unified standard to provide deterministic transmission. However, the traffic scheduling of the TSN network should be orchestrated in a coordinated manner, which is challenging to respond to dynamic applications rapidly. For this, network function virtualization (NFV) has been introduced to eliminate the tight coupling between functions and devices, thus enabling a more flexible network resource allocation. However, NFV cannot be simply applied in a TSN network due to the inherent complex characteristics of TSN. To address this issue, we develop SvTI, a service function chain (SFC) driven queue injection scheme in the NFV-enabled TSN network. The basic idea of SvTI is to jointly model virtual network functions (VNFs) embedding and the TSN multi-queue characteristic by constructing an ordered augmented topology according to the SFC. Thus, the VNF embedding and TSN queue injection problem can be transformed into a shortest path routing problem. Besides, considering the QoS requirements of applications, a TSN queue-based topology division mechanism is proposed to simplify the augmented topology further and improve the algorithm efficiency. Simulation results show that our SvTI obtains a smaller graph size than other works and is more scalable.

关键词： Network topology Service function chaining Simulation Quality of service Dynamic scheduling Routing Topology

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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