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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Zhiren Chen Ziyu Qiu Nuo Chen the Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Faculty of Computer Science Dalhousie University Halifax Canada Department of Mechanical Engineering Advanced Control and Mechatronics Lab Dalhousie University Halifax Canada

This project developed a novel attention algorithm for multi-stack bidirectional encoder-decoder RNN (including GRU and LSTM) sequence-to-sequence models, particularly for language translation tasks. The attention mechanism utilizes matrix rearranging and multiplication to compute the significance of the vectors in the encoder output to the vectors in the current decoder hidden states when predicting each word. Our approach achieved 98% of the performance of fine-tuned pretrained T5-small, with 30% to 50% fewer parameters depending on vocabulary size, making our model an ideal choice in cases of single-processor training, low processor resource, limited memory, small dataset, or tasks not supported by pretrained transformers.

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Autonomous driving of trucks in off-road environment

arXiv

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arXiv 2023年

作者： Caldas, Kenny A.Q. Barbosa, Filipe M. Silva, Junior A.R. Santos, Tiago C. Gomes, Iago P. Rosero, Luis A. Wolf, Denis F. Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel consumption, and increasing operational efficiency. The main contribution of this paper is the experimental validation of a path tracking control strategy, composed of longitudinal and lateral controllers, on an off-road scenario with a fully-loaded heavy-duty truck. The longitudinal control strategy relies on a Non-Linear Model Predictive controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints. The lateral controller is based on a Robust Linear Quadratic Regulator (RLQR), which considers a vehicle model subject to parametric uncertainties to minimize its lateral displacement and heading error, as well as ensure stability. Experiments were carried out using a fully-loaded vehicle on unpaved roads in an open-pit mine. The truck followed the reference path within the imposed constraints, showing robustness and driving smoothness. © 2023, CC BY.

关键词： controllers

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Study on Degradation of Benzo[a]pyrene in Marine Environment by Chlorella vulgaris LH-1 with Heterotrophic Ability 44

Study on Degradation of Benzo[a]pyrene in Marine Environment...

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44th AMOP Technical Seminar on Environmental Contamination and Response 2022

作者： Chen, Qingguo Song, Zhao Chen, Yu Zhu, Baikang Liu, Mei Li, Yijing Chen, Zhi Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control Zhejiang Ocean University Zhoushan China School of Marine Science and Technology Zhejiang Ocean University Zhoushan China Department of Building Civil and Environmental Engineering Faculty of Engineering and Computer Sciences Concordia University MontrealQC Canada

After long-term weathering, the most toxic components of residual spilled oil in seawater are polycyclic aromatic hydrocarbons. Benzo[a]pyrene (BaP) is a typical polycyclic aromatic hydrocarbon with high molecular weight, wide distribution, stable properties, and strong carcinogenicity in marine environment. Marine microalgae play an important role in the removal of BaP. Previous studies have mainly focused on the photodegradation of BaP by microalga. However, there are few research on microalgae with heterotrophic ability to degrade BaP. In this study, a microalga, Chlorella vulgaris (C. vulgaris) LH-1, with heterotrophic ability to degrade BaP was studied, the effect of BaP concentration on microalgae growth was explored, and the possible degradation mechanism of BaP by this kind of microalgae was proposed. The results showed that low concentration BaP (-1) had less obvious influence on the growth of C. vulgaris under the mixotrophic condition (with illumination), but a higher concentration BaP (＞ 5 mg L-1) had a significant inhibitory effect on the growth of this microalgae. Under heterotrophic cultivation (without illumination), low concentration BaP (-1) promoted the growth of C. vulgaris LH-1, while high concentration BaP (＞20 mg L-1) led to inhibit the growth of this microalga significantly. The degradation rates of mixotrophic and heterotrophic C. vulgaris LH-1 were 62.56~74.13%, and 52.07~71.67%, respectively, when BaP concentration range from 0.5 to 2 mg/L. Microalgae RNA transcriptome sequencing analysis showed that the gene expression of functional enzyme of C. Vulgaris LH-1 such as phenol 2-monooxygenase activity, protocatechuate 3,4-dioxygenase activity, catechol 1,2-dioxygenase activity, styrene degradation, and benzoate degradation were up-regulated in the process of BaP degradation. Based on the results of metabolites detected by high performance liquid chromatography/ quadruple time-of-flight mass spectrometry (HPLC/ Q-TOF), C. Vulgaris LH-1 may de

关键词： Degradation

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An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

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Sensors 2024年第10期24卷 3098页

作者： Chi, Tsun-Kuang Chen, Tsung-Yi Lin, Yu-Chen Lin, Ting-Lan Zhang, Jun-Ting Lu, Cheng-Lin Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Department of Electronic Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information Management Chung Yuan Christian University Taoyuan320317 Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Quezon1108 Philippines

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. © 2024 by the authors.

关键词： Deep learning

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Non-collocated Vibration Suppression using Delay-Based controllers with Multiple Fixed Delays*

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IFAC-PapersOnLine 2023年第2期56卷 180-185页

作者： Adrian Saldanha Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.

关键词： vibration suppression time-delay systems DDAE optimization

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Population games on dynamic community networks

arXiv

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arXiv 2022年

作者： Govaert, Alain Zino, Lorenzo Tegling, Emma The Department of Automatic Control Lund University LundSE-221 00 Sweden Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

In this letter, we deal with evolutionary game-theoretic learning processes for population games on networks with dynamically evolving communities. Specifically, we propose a novel mathematical framework in which a deterministic, continuous-time replicator equation on a community network is coupled with a closed dynamic flow process between communities that is governed by an environmental feedback mechanism, resulting in co-evolutionary dynamics. Through a rigorous analysis of the system of differential equations obtained, we characterize the equilibria of the coupled dynamical system. Moreover, for a class of population games -matrix games-a Lyapunov argument is employed to establish an evolutionary folk theorem that guarantees convergence to the evolutionary stable states of the game. Numerical simulations are provided to illustrate and corroborate our findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Continuous time systems

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Differential Flatness and Manipulation of Elasto-flexible Cables Carried by Aerial Robots in a Possibly Viscous Environment

Differential Flatness and Manipulation of Elasto-flexible Ca...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse Toulouse France

This work considers a system composed of two quadrotors manipulating a deformable and extensible cable. The dynamic model is presented, and it is based on a discrete representation of the cable, which is decomposed into lumped masses interconnected by linear springs through passive spherical joints. A set of flat outputs is found for the system. The flatness is exploited to design a method to manipulate the cable, which is then tested through numerical simulations.

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Parameter fault estimation in distributed heating/cooling systems 22

Parameter fault estimation in distributed heating/cooling sy...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Wijaya Kurniawan Katalin M. Hangos Lorinc Marton Faculty of Computer Science University of Brawijaya Indonesia) and Departement of Electrical Engineering and Information Systems University of Pannonia Hungary Departement of Electrical Engineering and Information Systems University of Pannonia Hungary and Institute for Computer Science and Control Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Romania)

ISBN: (纸本)9781450397117

In this paper, the problem of fault estimation and localization in the connecting dynamic elements of distributed heating and cooling systems are treated. The fault represents the physical parameter change related to the heat transfer between the system and the external environment. First, based on the bi-linear dynamic state space model of the system in the presence of a fault, structural observability analysis using Signed Directed Graph (SDG) has been performed to investigate the sensor placement problem. Then, a nonlinear observer with a parameter adaptation algorithm was proposed for fault estimation. The simulation results show that it can successfully detect and estimate the fault. Fault localization along the length of the element has also been attempted, but it has been found that the localization cannot be performed using practically changeable input variables. Frequency domain analysis is presented to discuss this phenomenon.

关键词： Nonlinear Observer Heating/cooling system Fault diagnosis Structural observability Parameter estimation

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A Distributed control framework for the optimal operation of DC microgrids

A Distributed control framework for the optimal operation of...

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IEEE Conference on Decision and control

作者： Zao Fu Michele Cucuzzella Carlo Cenedese Wenwu Yu Jacquelien M. A. Scherpen School of Cyber Science and Engineering Southeast University Nanjing China Faculty of Science and Engineering University of Groningen Groningen the Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Automatic Control Laboratory ETH Zürich Zürich Switzerland

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper we propose an original distributed control framework for DC microgrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on duality, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC microgrid and the designed distributed control system in a power preserving way, we steer the DC microgrid’s state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework.

关键词： Simulation Perturbation methods Decentralized control Optimal control Microgrids Games Power markets

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