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2022 International Conference on Optical and Photonic engineering, icOPEN 2022

作者： Mo, Chen Cai, Zewei Xi, Jiangtao Tong, Jun Liu, Xiaoli Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

ISBN: (纸本)9781510661905

Lensless light-field imaging is the process to encode the light field information of object through an optical encoder, and then recover the light field information of object through a reconstruction algorithm. In traditional lens-based light field imaging, each microlens in microlens-array corresponds to an angular sampling, and it needs to adjust the microlens-array to alter the angular resolution. In this paper, a commercial holographic diffuser is used as an optical encoder, and a flexible overlapping segmentation method of angular sampling is proposed for the point spread function (PSF). The surface microstructures of holographic diffuser allow any region of it to encode and recover light information of object independently, corresponding to an angular sampling. Calibration of PSF for the lensless imaging system is done firstly, and encoded image of object is captured;then the PSF is divided into different regions as sub-PSFs, corresponding to different angular samplings;after that, light field images of object is reconstructed with corresponding sub-PSFs through reconstruction algorithm;with these light field images, digital refocusing can be achieved finally. Different from evenly segmentation of angular sampling in microlens-array, the overlapping segmentation method divides PSF into sub-PSFs while adjacent sub-PSFs overlap each other. This improves angular resolution of imaging system and ensures low error in reconstruction of light field images. Experiments show that, the overlapping segmentation method can guarantee the reconstruction accuracy of lensless light-field imaging system while flexibly adjusting and improving the angular resolution. © 2022 SPIE.

关键词： Optical transfer function

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Regression coefficient estimation from remote sensing maps

arXiv

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arXiv 2024年

作者： Lu, Kerri Kluger, Dan M. Bates, Stephen Wang, Sherrie Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Mechanical Engineering MIT United States Institute for Data Systems and Society MIT United States

Remote sensing map products are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and — because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs — errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. In this paper, we apply prediction-powered inference (PPI) to regression coefficient estimation. PPI generates unbiased estimates by using a small amount of randomly sampled ground truth data to correct for bias in large-scale remote sensing map products. Applying PPI across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Empirically, we observe effective sample size increases of up to 17-fold using PPI compared to only using ground truth data. This is the first work to estimate remote sensing regression coefficients without assumptions on the structure of map product errors. Data and code are available at https://***/Earth-Intelligence-Lab/ uncertainty-quantification. © 2024, CC BY.

关键词： Regression analysis

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position Control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics Automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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Contribution to the Sensors Faults Diagnosis of a Variable Speed Wind Turbine: Multimodel Approach

Contribution to the Sensors Faults Diagnosis of a Variable S...

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International Conference on systems and Control (ICSC)

作者： Yosra Sayahi Moez Allouche Mariem Ghamgui Sandrine Moreau Driss Mehdi Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Computer Science and Automatic Control for Systems University of Poitiers France

This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer's bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods.

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Incentive Design for Eco-driving in Urban Transportation Networks

Incentive Design for Eco-driving in Urban Transportation Net...

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European Control Conference (ECC)

作者： M. Umar B. Niazi Jung-Hoon Cho Munther A. Dahleh Roy Dong Cathy Wu Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

关键词： Transportation Europe Greenhouse gases

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Why Go Full? Elevating Federated Learning Through Partial Network Updates

arXiv

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arXiv 2024年

作者： Wang, Haolin Liu, Xuefeng Niu, Jianwei Guo, Wenkai Tang, Shaojie State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo BuffaloNY United States Zhongguancun Laboratory Beijing China

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead. © 2024, CC BY.

关键词： Federated learning

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Self-temperature compensation approach for fiber specklegram magnetic field sensor based on polarization specklegram analysis 27

Self-temperature compensation approach for fiber specklegram...

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27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

We proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis, and designed a fiber specklegram magnetic field sensor with high stability and good r... 详细信息

ISBN: (纸本)9781957171142

关键词： Polarization

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Temporal Inconsistency-Based Active Learning

Temporal Inconsistency-Based Active Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tianjiao Wan Yutao Dou Kele Xu Zijian Gao Bo Ding Dawei Feng Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

Deep supervised learning has demonstrated strong capabilities; however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus reducing the human labeling effort. Previous AL research has focused on employing recently trained models to design sampling strategies, based on uncertainty or representativeness. Drawing inspiration from the issue of model forgetting, we propose a novel AL framework called Temporal Inconsistency-Based Active Learning (TIR-AL). In this framework, multiple snapshots of the models across consecutive cycles are jointly utilized to select samples with higher temporal inconsistency, by computing the proposed self-weighted nuclear norm metric. Furthermore, we introduce a consistency regularization term to mitigate the issue of forgetting. Together, these components make full use of the potential of data and facilitate effective interaction within the AL loop. To demonstrate the efficacy of TIR-AL, we conducted a set of experiments illustrating how our approach outperforms state-of-the-art methods without incurring any additional training costs.

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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