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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Li, Zairan Wang, Dan Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation China

Optimizing the curvature shape of shoe last is an important research content and a promising direction in current shoe-last design. The emergence of plantar pressure imaging method provided a new technical mean for shoe-last design. The point-cloud dataset was acquired from the plantar pressure experiments which may be used for generating comfort shoe-last curvature, and the key zones on last curvature may be classified and has potential benefit to the designers to produce comfort shoe. In this paper ensemble learning technologies are applied for classifying those points through the proposed weights-updated algorithms. The proposed weights-updated algorithm with boosting operation performed high effectiveness by comparing to k-NN, random forest (RF) and Bayers (NB) in classification accuracy. The proposed boosting based ensemble learning method will be potential applied in interactive design of industry and be beneficial to produce comfort shoes. © 2020 IEEE.

关键词： Learning systems

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Discrete Modes Faults Diagnosis for Hybrid Dynamical Systems Using Petri Nets 21

Discrete Modes Faults Diagnosis for Hybrid Dynamical Systems...

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21st IEEE International Conference on Sciences and Techniques of Automatic control and Computer engineering, STA 2022

作者： Arichi, Fayssal Cherki, Brahim Djemai, Mohamed Engin, Seref Naci Ecole Supérieure en Sciences Appliquées Tlemcen Algeria Algeria LAMIH CNRS Umr 8201 UVHC ValenciennesF-59313 France QUARTZ Laboratory Cergy95000 France Yildiz Technical University Faculty of Electrical and Electronics Engineering Department of Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9781665482615

This paper investigates the problem of diagnosing discrete faults for a class of hybrid dynamical systems, where the evolution of the discrete state is led by a set of discrete modes governed by a partially observed Petri net which means that we can know the activation of some discrete modes and the firing of some switching conditions. The considered class may have several modes evolving simultaneously. The modes can be associated with normal or faulty operating conditions. Therefore, the knowledge of the current mode represents the coeur part of the proposed diagnosis approach. We present at first an asymptotic observer to detect both the activation of discrete modes and the switching conditions. Then, we propose an algorithm to diagnose faults using the estimated discrete states provided by the observer. Simulation results show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Petri nets

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High Step-down Converters for Battery Charging in Photovoltaic Applications 35

High Step-down Converters for Battery Charging in Photovolta...

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35th International Conference on Power Electronics and electrical Drives, EDPE 2021

作者： Malović, Zvonimir Vuger, Nikola Šunde, Viktor Ban, Željko Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

This paper describes non-isolated high step-down converters used to connect photovoltaic modules to the battery storage of a bicycle charging station. Due to the high voltage difference between input and output, some problems may occur with conventional non-isolated DC-DC buck converters. The characteristics of alternative topologies of high step-down converters have been explored. Ideal converter models were created in the PLECS simulation software package based on which the voltage and current stress of the semiconductor switches were determined. Based on measured voltage and current relations, commercial switches were selected. Based on catalogue data, electro-thermal models of the switches were created. A comparative analysis of converters based on conduction and switching losses was performed. Converters were also compared based on the number of components and complexity of control. ©2021 IEEE.

关键词： Batteries Conduction losses Duty cycle Photovoltaic module Step-down converters Switching losses

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UPS with PFC input stage for railway applications with improved immunity on input overvoltage and energy strikes 35

UPS with PFC input stage for railway applications with impro...

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35th International Conference on Power Electronics and electrical Drives, EDPE 2021

作者： Šolc, Ivan Hećimović, Ante Šunde, Viktor Ban, Željko Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

The main topic of this article is uninterruptible power supply (UPS) in railway applications, focusing on the power factor correction (PFC) stage of UPS and its interaction with overvoltages and energy strikes. The PFC topology used in this article is a two-phase interleaved DC/DC boost converter with diode bridge rectifier connected to the 25 kV, 50 Hz contact line via 25 kV/0.23 kV transformer. Since overvoltages and energy strikes can occur in the contact line due to thyristor locomotive commutation or self-induction, these interferences can disturb the normal operation of the PFC module and in some cases even destroy the module. A simulation model of the PFC is created, tested with simulated overvoltages, and three modifications/improvements to the existing topology are proposed. ©2021 IEEE.

关键词： Battery charger Energy strike Interferences Overvoltage Power factor correction (PFC) Railway Uninterruptible power supply (UPS)

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Fuel Composition's Impact on Small Jet Engine Mathematical Modelling

Fuel Composition's Impact on Small Jet Engine Mathematical M...

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International Conference on Computational Cybernetics (ICCC)

作者： Ladislav Fözö Peter Klamo Rudolf Andoga Peter Korba Martin Vencel Marcel Behún Department of Aviation Engineering Faculty of Aeronautics Technical University of Košice Košice Slovakia Kando Kalman Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Avionics Faculty of Aeronautics Technical University of Košice Košice Slovakia Faculty of Mining Ecology Process Control and Geotechnology Institute of Earth Resources Technical University of Košice Košice Slovakia

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

The aim of this paper is to highlight the impact of applying alternative fuels on the mathematical models of jet engines. The analysis focuses on the class of small jet engines, while the presented findings can be generalized to other classes of aircraft engines. The results are illustrated in the characteristics of jet engine components and define the influence of alternative fuels on them. To this end, the paper provides an introductory overview of methods for developing mathematical models while considering the application of sustainable aviation fuels as well as insights to measurement of emission characteristics of turbojet engines.

关键词： Reviews Production Solids Mathematical models Combustion Fuels Aircraft propulsion Sustainable development Jet engines Testing

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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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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Hanying Zhao, Ziyan Liang, Jiaqi Li, Xingyang Liu, Shixin College of Information Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan China Polytechnique Montréal Département de Mathématiques et Génie Industriel MontréalQCH3T 1J4 Canada Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Shenyang110819 China School of Control Engineering/School Northeastern University at Qinhuangdao Hebei Qinhuangdao066004 China

ISBN: (纸本)9781665410205

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. © 2024 IEEE.

关键词： Integer programming

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In-situ controller Autotuning by Bayesian Optimization for Closed-loop Feedback control of Laser Powder Bed Fusion process

arXiv

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arXiv 2024年

作者： Kavas, Barış Balta, Efe C. Tucker, Michael R. Krishnadas, Raamadaas Rupenyan, Alisa Lygeros, John Bambach, Markus Department of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland Control and Automation Group Inspire AG Zürich8005 Switzerland Department of Information Technology and Electrical Engineering Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland ZHAW Centre for Artificial Intelligence Zürich University of Applied Sciences Switzerland

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro

关键词： controllers

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An Optimizing Method Based on Combining Edge Computer and Long Short-Term Memory Networks Applied to Solar Power Forecasting

An Optimizing Method Based on Combining Edge Computer and Lo...

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2021 IEEE International Future Energy Electronics Conference, IFEEC 2021

作者： Nguyen, Minh-Ngoc Pham, Manh-Hai Wu, Yuan-Kang Nguyen, Ngoc-Trung Phenikaa University Faculty of Electrical and Electronic Engineering Hanoi Viet Nam Electric Power University Faculty of Energy Technology Hanoi Viet Nam National Chung-Cheng University Department of Electrical Engineering Chiayi Taiwan Electric Power University Control and Automation Faculty Hanoi Viet Nam

ISBN: (纸本)9781665434485

Along with the rapid development of solar energy sources on the global power system,the challenges related to the weather instability of this power type are also of increasing concern. Intending to solve these challenges,the study of solar power forecasting has become one of the most important problems in the power system field. Most of the published forecast algorithms aim to minimize prediction errors. However,in reality,convenience and data processing time are two necessary goals to commercialize a forecasting software. Recently,IoT is emerging as an effective solution to solve the above multi-objective problem. Especially,in the current trend of real-time forecasting,the application of Edge Computer in the IoT environment is bringing new opportunities for the goal of reducing data processing time while ensuring acceptable forecast errors. Moreover,Edge Computer also reduces the risk of network congestion due to its distributed processing characteristics. This paper introduces a new methodology using Edge Computer combined with the LSTM algorithm applied to the solar power forecasting problem. Real-time calculation results show that the accuracy can be improved by the good choice of the converted data type while the execution time is halved compared to without using Edge Computer. © 2021 IEEE.

关键词： Solar energy

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Detection and Description of Local Features of a Thermal Image

Detection and Description of Local Features of a Thermal Ima...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： I. I. Naumov R. R. Ibadov A. G. Gazaryan Faculty of Automation Mechatronics and Control Don State Technical University Rostov-on-Don Russia Department of Electrical Engineering and Electronics Don State Technical University Rostov-on-Don Russia Institute of Advanced Mechanical Engineering “Rostselmash” Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Thermal images present challenges for traditional computer vision methods due to the lack of clear textures and low contrast. In this paper, we present a hybrid architecture based on a Siamese triplet network and transformers, which provides an accuracy improvement of 42 % over classical methods SIFT, SURF and 28% over state-of-the-art CNN approaches SuperPoint, D2-Net. Key innovations include a thermal-adaptive descriptor, dynamic triplet loss, and a fusion module that integrates spatial and thermal features. Evaluations on industry-standard KAIST, FLIR, Thermal Urban datasets demonstrate excellent performance, with the method detecting 91 % of keypoints in daytime scenes and remaining robust in adverse conditions such as fog, rain, and low visibility. Moreover, the algorithm runs at 45 fps on embedded systems, making it suitable for real-time applications. The algorithm has potential applications in autonomous driving, surveillance, and medical diagnostics.

关键词： Technological innovation Accuracy Heuristic algorithms Surveillance Thermal engineering Computer architecture Transformers Feature extraction Convolutional neural networks Thermal stability

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