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China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Changdong Wang Shiqi Jiang Richard Xian-Ke Gao Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China A*STAR Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz.

关键词： Magnetic flux Accuracy Flexible printed circuits Magnetic cores Fixtures Electromagnetic interference Permeability measurement Length measurement Permeability Inductors

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Data-driven approximation of control invariant set for linear system based on convex piecewise linear fitting

arXiv

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arXiv 2022年

作者： Xu, Jun Chen, Fanglin School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Engineering Harbin Institute of Technology Shenzhen518055 China

control invariant set is critical for guaranteeing safe control and the problem of computing control invariant set for linear discrete-time system is revisited in this paper by using a data-driven approach. Specifically, sample points on convergent trajectories of linear MPC are recorded, of which the convex hull formulates a control invariant set for the linear system. To approximate the convex hull of multiple sample points, a convex piecewise linear (PWL) fitting framework has been proposed, which yields a polyhedral approximation with predefined complexity. A descent algorithm for the convex PWL fitting problem is also developed, which is guaranteed to converge to a local optimum. The proposed strategy is flexible in computing the control invariant set in high dimension with a predefined complexity. Simulation results show that the proposed data-driven approximation can compute the approximated control invariant set with high accuracy and relatively low computational cost. © 2022, CC0.

关键词： Linear systems

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An e-Business Services Composition Framework

An e-Business Services Composition Framework

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International Conference on Communications (COMM)

作者： Eugen Pop Daniela Gî fu Automatics and Industrial Informatics Department Faculty of Automatic Control and Computers University &#x201C Politehnica&#x201D of Bucharest R&#x0026 D Institute for Automation-IPA SA Bucharest Romania Institute of Computer Science of Romanian Academy - Iasi Branch Faculty of Computer Science of Alexandru Ioan Cuza University of Iasi Iasi Romania

E-business offers to economic agents the opportunity to increase the efficiency of their activity, promote and offer their products and services on a large and diversified market. Often a single service, separately, does not meet all the needs of various categories and groups of customers. In such a situation, service composition can provide more technical, commercial, informational facilities than each separate component service can offer. There are many web-based frameworks used for e-business, but still with limitations. The paper presents a Services Composition Framework and a wise algorithm for e-business models, based on REST and SOAP web services. It is intended to be used for the development of e-business models, comprising stock market exchange, e-commerce, e-banking, etc. The customers can access the framework services, through Smartphones, on mobile and wireless communication channels. Within a Quality-of-Service performance analysis of the e-business Service Composition Framework, the response time and throughput for multiuser access simultaneous requests were evaluated. A stock market exchange service is considered, as an e-business service example, while Bucharest Stock Exchange information is the digital content source for data transactions.

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Metafruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

SSRN

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SSRN 2024年

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset and detection framework are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, The Authors. All rights reserved.

关键词： Fruits

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MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

arXiv

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arXiv 2024年

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, CC BY-NC-SA.

关键词： Fruits

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Barrier function-based adaptive synergetic control with state constraints for manipulator with uncertainty and input saturation

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Ain Shams Engineering Journal 2025年第9期16卷

作者： Muhammad Auzan Yong-Lin Kuo Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei 10607 Taiwan Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta 55281 Indonesia Center of Automation and Control National Taiwan University of Science and Technology Taipei 10607 Taiwan

This study proposes a barrier function-based adaptive nonsingular terminal synergetic control to mitigate chattering and uncertainty in a nonlinear system with hard state constraints and actuator saturation. The controller addresses the uncertainty by utilizing a barrier function-based evolution constraint. Furthermore, the controller incorporates a barrier function-based macrovariable to modify the control behavior and constrain the system states within predefined ranges. The design is presented for an n-th-order nonlinear system, and the stability is proven using the Lyapunov stability theorem. Simulation with constant reference demonstrates the effectiveness of the proposed controller in constraining the system states, and experiments with impulse, sine, and bounded random uncertainty illustrate the controller's ability to handle uncertainty without explicitly defining the worst-case assumption. Moreover, implementation on a robotic manipulator demonstrates the advantage of the proposed method compared to four adaptive controllers in terms of continuous control law, chattering-free response, simplicity in tuning parameters, and tracking performance.

关键词： Adaptive control Barrier function Input saturation Manipulator robot State constraint Synergetic control

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An IoT and Information Mining Algorithm-Based Self-Monitoring and Analysis System for Solar Power Plants

An IoT and Information Mining Algorithm-Based Self-Monitorin...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Kruti Jani Navdeep Dhaliwal Rajesh Singh A. Bhasha Jayapal Lande Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq Shri Chimanbhai Patel Post Graduate Institute of Computer Application Ahmedabad Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University India Dept of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

Because of their affordability and sustainability, renewable sources of energy are receiving a lot of study interest. Particularly, solar power plants are regarded as a type of renewable sources of energy that may be employed in many areas since, while requiring less space than traditional systems, they demand less money for installation and maintenance. The majority of small generating units control space occupancy by putting the machinery on an exposed terrace. However, acres of land is needed for the building of huge power producing stations. It is difficult for human workers to maintain such a huge region of a power plant. The suggested algorithm would assist human workers in identifying the consistency of energy production and failure or faulty areas in solar energy systems using IoT and database mining (DM) techniques. This enables immediate fault repair action to be taken, which increases producing station effectiveness.

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FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion

arXiv

引用

arXiv 2023年

作者： Teng, Siyu Li, Luxi Li, Yuchen Hu, Xuemin Li, Lingxi Ai, Yunfeng Chen, Long Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computer Science BNU-HKBU United International College Zhuhai China The School of Artificial Intelligence Hubei University Wuhan China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research. Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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A visual-inertial vehicle navigation system based line feature

A visual-inertial vehicle navigation system based line featu...

引用

2020 International Conference on Artificial Intelligence and Electromechanical automation, AIEA 2020

作者： Guang, Xingxing Gao, Yanbin Liu, Pan Li, Guangchun Li, Ningbo Leung, Henry College of Automation Harbin Engineering University Harbin China Department of Electrical and Computer Engineering University of Calgary Calgary Canada Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9781728182889

In this paper, an integrated navigation system based on visual and inertial sensors for vehicles is proposed to solve the low accuracy position estimation problem caused by the unavailability signal of GNSS. In the proposed method, the visual sensor provides not only the attitude information by visual attitude method but also the motion information by visual odometer algorithm. Because the visual information is used in two separate ways, the proposed integrated navigation method has better robustness in dynamic conditions. It is verified by experiments in dynamic conditions. The experimental results show that the attitude errors of the proposed method can be reduced an order of magnitude than the previous visual attitude aided inertial navigation systems. For position estimation, the proposed method can improve the accuracy, stability, and reliability of the vehicle navigation system. © 2020 IEEE.

关键词： Inertial navigation systems

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Evaluation of Deep Training and Adapting Algorithms in Medic...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Girija Rani Karetla KVB. Ganesh A Suresh Kumar K. Radha K. Shivakumar Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq School of Computer Data and Mathematical Sciences Western Sydney University Australia KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of CSE Graphic Era Deemed to be University Dept. of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least - square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal- to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods.

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