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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： M. Alp Merzi Veysel Gazi Giuseppe Fedele Luigi D’ Alfonso Antonio Bono Dept. Electrical and Electronics Eng. Faculty of Engineering Marmara University Istanbul Turkey Dept. Control and Automation Eng. Faculty of Electrical and Electronics Engineering Yildiz Technical University Istanbul Turkey Dept. of Informatics Modeling Electronics and Systems Eng. University of Calabria Rende Italy

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

In a recent paper [1], we developed a target capturing strategy for swarms composed of double integrator agents. In this paper, we build on the results in [1] and focus on the implementation of the methodology for a swarm composed of quadrotor UAVs. We utilize both MATLAB and ROS environments under realistic conditions for that purpose. In ROS, we utilize Ultra-Violet LED blinkers based Ultra-Violet Direction And Ranging (UVDAR) system for relative localization. The sensing and localization errors serve as realistic errors for the assumptions in [1] for the uncertainties of the velocity and acceleration of the target. We verify in a realistic scenario that the agents converge to a user-defined ellipsoidal ring around the target despite the uncertainties and the more realistic agent dynamics.

关键词： Location awareness Uncertainty Ultraviolet sources Light emitting diodes Distance measurement Sensors Aircraft

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Semi-automated Synthesis of control System Software Through Graph Search 20th

Semi-automated Synthesis of Control System Software Through ...

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20th Polish control Conference, PCC 2020

作者： Gawron, Tomasz Kozlowski, Krzysztof Institute of Automation and Robotics Faculty of Control Robotics and Electrical Engineering Poznan University of Technology ul.Piotrowo 3a Poznań61-138 Poland

ISBN: (纸本)9783030509354

As the field of automation and robotics develops, control software driving the robotic systems becomes more and more complex. Currently, a predominant approach to modularization and structuring of software in the robotics community, which is embodied by ROS (Robot Operating System) and similar frameworks, is to decompose software into a set of functional modules (i.e. nodes, processes, classes). Particular modules comprising the system are chosen manually by the programmer and they exchange data using loosely coupled publish/subscribe communication mechanisms. While such an approach is helpful and currently highly developed, it seems to lack the means of verifying correctness of the whole control software system (i.e. is there a source of data for all input signals of a given module?, are its outputs actually used by other modules in the system?). Manual specification of system structure and verification of its correctness can be tedious and prone to errors. To help alleviate this, we propose a system composition algorithm utilizing graph search methods. It processes a set of modules implemented by the programmer and produces a graph of modules representing the system with input and output signals specified by the programmer. Such an algorithm can be used with nearly arbitrary control software frameworks. © 2020, Springer Nature Switzerland AG.

关键词： C++ (programming language)

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Fast calibration of agent-based model using mean-field approach 1

Fast calibration of agent-based model using mean-field appro...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Lu, Wanze Xiong, Gang Liu, Xudong Liu, Sheng Ye, Peijun Zhang, Liguo Beijing University of Technology Faculty of Information and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665433372

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software. © 2021 IEEE.

关键词： Calibration

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

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arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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An interpretability analysis of travel decision learning in cyber-physical-social-systems 1

An interpretability analysis of travel decision learning in ...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Guo, Miao Ye, Peijun Liu, Xudong Liu, Sheng Xiong, Gang Zhang, Liguo Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

In the few decades, the rise of Internet and social media has fundamentally changed the management, control, and operation modes of modern engineering and social systems like defense, energy, critical health care and ... 详细信息

ISBN: (纸本)9781665433372

关键词： Deep learning Social networking (online) Scalability Perturbation methods Neurons Transportation Transforms

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Sustainability and Predictive Accuracy Evaluation of Gel and Embroidered Electrodes for ECG Monitoring

SSRN

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SSRN 2024年

作者： Othman, Ghada Ben Kumar, Atal Anil Hassine, Faten Ben Copot, Dana Sidhom, Lilia Kamavuako, Ernest N. Trabelsi, Mohamed Ionescu, Clara Mihaela Chihi, Inès Ghent University Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Zwijnaarde9052 Belgium Department Of Engineering Faculty of Science Technology and Medicine University of Luxembourg Kirchberg Luxembourg Faculty of Sciences of Tunis Tunis Tunisia National Engineering School of Bizerte Carthage University Tunis LAPER- Faculty of Sciences of Tunis El Manar University Tunis Tunisia Technical University of Cluj-Napoca Department of Automation Memorandumului no. 28 Cluj-Napoca400114 Romania Department of Informatics King’s College London London United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait City Kuwait

Despite standards on electrocardiogram (ECG) monitoring in medical diagnostics, signal acquisition is prone to noisy artefacts and rely greatly on the quality of skin contact and signal transducing interference. Electrodes, serving as indispensable conduits in ECG signal acquisition, act as the crucial interface between the human body and recording instrumentation. The usage of traditional gel electrodes may provoke skin irritation, by contrast, the advent of embroidered electrodes, a contemporary innovation, holds the promise of more comfort monitoring. However, the sustainability quotient and predictive precision of these novel electrodes require more in-depth investigation. This paper endeavors a comprehensive evaluation of both gel and embroidered electrodes concerning ECG signal acquisition. Simultaneously, the study aims to construct a polynomial regression model leveraging advanced machine learning (ML) tools. The inferred model predicts embroidered signals based on experimental data obtained from gel electrodes. The methodology encompasses systematic data collection, preprocessing, insightful analysis, and the application of data-driven techniques. The findings of this study highlight the viability of embroidered electrodes as a compelling alternative, transcending traditional paradigms. Employing ML tools, the developed model achieves predictive accuracy, reflected in robust R2 values extending up to 94.9%. © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Trajectory following of a vehicle via computationally improved explicit nonlinear model predictive controller

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AIP Conference Proceedings 2024年第1期3129卷

作者： Volkan Bekir Yangin Özgen Akalin Yaprak Yalçin Istanbul Technical University Faculty of Mechanical Engineering Department of Mechanical Engineering 34469 Istanbul Türkiye Istanbul Technical University Faculty of Electrical and Electronics Engineering Department of Control and Automation Engineering 34469 Istanbul Türkiye

Model predictive control is one of the advanced methods to solve trajectory following problems of autonomous ground vehicles due to its predictive capability. It can deal with multiple inputs, outputs, and set-point signals. However, this technique causes a heavy computational burden to the controller due to its multi-objective optimization approach, limiting its real-time applications. In this study, an explicit nonlinear model predictive controller (E-NMPC) is proposed to control front steering angle and rear wheel tractive torques simultaneously to provide trajectory tracking of a vehicle in NATO double lane change (DLC) maneuvers with consideration of reduced computation time. Artificial neural networks enabling offline learning processes are used to generate a computationally-efficient control law without going through extensive optimization methods. Namely, main contribution of this study is establishment of a neural-network based E-NMPC technique for the considered control problem that removes the need of online optimization and replace the classical NMPC that requires online-optimization with high computational load. The simulated results revealed that time elapsed during one execution cycle can be significantly reduced with the proposed method for various cases, compared to the classical online NMPC method.

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hanying Wang Ziyan Zhao Jiaqi Liang Xingyang Li Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Département de mathématiques et génie industriel Polytechnique Montréal Montréal Qc Canada Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao Hebei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

关键词： Intelligent automation Adaptation models Mechatronics Metaheuristics Search problems Scheduling Resource management Internet of Things Electronic commerce Robots

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Improvement of People Counting by Pairing Head and Face Detections from Still Images 4

Improvement of People Counting by Pairing Head and Face Dete...

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4th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2021

作者： Ha, Thi-Oanh Tran, Hoang-Nhat Nguyen, Hong-Quan Tran, Thanh-Hai Nguyen, Phuong-Dung Doan, Huong-Giang Nguyen, Van-Toi Vu, Hai Le, Thi-Lan Hanoi University of Science and Technology MICA Hanoi Viet Nam Hanoi University of Science and Technology School of Electronics and Telecommunications Hanoi Viet Nam Hanoi University of Science and Technology MICA Hanoi Viet Nam Electric Power University Control and Automation Faculty Hanoi Viet Nam Phenikaa University Faculty of Electrical and Electronics Engineering Hanoi Viet Nam

ISBN: (纸本)9781665419109

Video or image-based people counting in real-time has multiple applications in intelligent transportation, density estimation or class management, and so on. This problem is usually carried out by detecting people using conventional detectors. However, this approach can be failed when people stay in various postures or are occluded by each other. In this paper, we notice that even a main part of human body is occluded, their face and head are still observable. We then propose a method that counts people based on face and head detection and pairing. Instead of deploying only face or head detector, we apply both detectors as in many cases the human does not turn his/her face to camera then head detector takes advantage. Otherwise, face detector produces reliable results. The fact of combining both head and face detection results will lead to duplicated responses for one person. We then propose a simple yet effective alignment technique to pair a face with a head of a person. Subsequently, the remaining heads and faces which are not paired with any other faces or heads will be added to our people counter to increase the true positive rate. We evaluate our proposed method on four datasets (Hollywood, Casablanca, Wider Face, and our own dataset). The experimental results show an improvement of average precision and recall comparing to the original head or face detectors. © 2021 IEEE.

关键词： Face recognition

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A novel signal processing algorithm for measurement while drilling(MWD) system

A novel signal processing algorithm for measurement while dr...

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第40届中国控制会议

作者： ShiLong Cheng Yule Hu 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 Faculty of Engineering China University of Geosciences

Measurement while drilling(MWD) is a widely used signal transmission technology in the drilling *** present,the domestic pulse identification method is relatively simple,using the basic threshold method to identify,only set the threshold which will leading a number of missing detection mud pulse in the complex working *** this paper,a novel data processing algorithm is proposed for measurement while drilling *** method designed in this paper identifies the start time of data frame by using correlation coefficient and synchronization head model,then,based on the principle that the amplitude of mud pulse signal is larger than that of noise pulse signal,the maximum pulse signal in the length of a group of mud pulse series is extracted as mud pulse *** simulation is executed using the data from the actual drilling process and the results show the effectiveness of the proposed method.

关键词： Measurement while drilling Pulse recognition Correlation coefficient Sliding time window

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