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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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Neural-Network-Based Filtering with Encoding Mechanisms: Applications to Multiple Unmanned Aerial Vehicles

Neural-Network-Based Filtering with Encoding Mechanisms: App...

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2023 China automation Congress, CAC 2023

作者： Zhu, Kaiqun Xu, Xinpeng Cao, Zhiru Sun, Ran Yang, Ya University of Macau State Key Laboratory of Internet of Things for Smart City Macau China Shanghai Electro-Mechanical Engineering Institute Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China University of Shanghai for Science and Technology Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9798350303759

In this paper, the neural-network-based filtering problem is investigated for multi-sensor systems with dynamic encoding mechanisms. The sensor nodes and the remote filter are connected through bandwidth-constrained communication networks. To alleviate the communication burden, a novel dynamic encoding-based data compression-decompression mechanism is proposed so as to encode the data into a limited number of bits. Then, with the aid of the neural network learning method, a neural-network-based set-membership filter is developed for estimating the system states. Sufficient conditions are obtained to ensure that the filtering error remains within the bounded ellipsoidal set. In addition, the neural network tuning parameters and the filter gains are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed filtering algorithm is verified through a scenario of maneuvering target tracking using multiple unmanned aerial vehicles. © 2023 IEEE.

关键词： Antennas

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Dynamic Event-Triggered Consensus on Matrix-Weighted Networks: Leadless Case 42

Dynamic Event-Triggered Consensus on Matrix-Weighted Network...

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42nd Chinese control Conference, CCC 2023

作者： Dou, Lintao Wei, Haoyu Huang, Jian Pan, Lulu Shao, Haibin Li, Dewei College of Intelligent Science National University of Defense Technology Changsha410003 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581543

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Multi agent systems

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Towards resilient average consensus in multi-agent systems:a detection and compensation approach

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Frontiers of Information Technology & Electronic engineering 2024年第2期25卷 182-196页

作者： Chongrong FANG Wenzhe ZHENG Zhiyu HE Jianping HE Chengcheng ZHAO Jingpei WANG Department of Automation Shanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310027China Technology and Engineering Center for Space Utilization Chinese Academy of SciencesBeijing100094China

Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent *** this paper,we study the problem of resilient average consensus for multi-agent systems with misbehaving *** protect consensus value from being influenced by misbehaving nodes,we address this problem by detecting misbehaviors,mitigating the corresponding adverse impact,and achieving the resilient average *** types of misbehaviors are considered,including attacks,accidental faults,and link *** characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection compensation based consensus(D-DCC)algorithm with a decaying fault-tolerant error *** scenarios wherein information sets are intermittently available due to link failures,a stochastic extension named stochastic detection compensation based consensus(S-DCC)algorithm is *** prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient accurate average consensus and unbiased resilient average consensus in a statistical sense,***,the Wasserstein distance is introduced to analyze the accuracy of ***,extensive simulations are conducted to verify the effectiveness of the proposed algorithms.

关键词： Resilient consensus Multi-agent systems Malicious attacks Detection Compensation

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization 63

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： You, Jiawei Dai, Xinliang Jiang, Yuning Yin, Haoyu Shi, Yuanming Jones, Colin N. ShanghaiTech University School of Information Science and Technology China Epfl Switzerland Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Epfl Automatic Control Laboratory Switzerland Washington University in St. Louis Department of Electrical and Systems Engineering United States

ISBN: (纸本)9798350316339

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Predictive control systems

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EXAMINATION OF WATERPROOF POCKETS AS PROTECTIVE CARRIERS FOR SMART GARMENTS

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Tekstil ve Muhendis 2022年第126期29卷 80-87页

作者： Akkan, Taner Kara, Şükran Ağin, Berk Yeşİlpinar, Sevil Şenol, Yavuz Dokuz Eylul University Izmir Vocational School Department of Mechatronics Izmir Turkey Dokuz Eylul University Faculty of Engineering Department of Textile Eng. Izmir Turkey İstanbul Technical University Control & Automation Engineering Dept. İstanbul Turkey Dokuz Eylul University Faculty of Engineering Electrical and Electronics Eng. Dept. Izmir Turkey

The importance of smart garments is increasing day by day for entertainment, sports, medical and military fields. Both hard electronics and textile based soft electronics find application in these areas. In our previous study, we focused on crew loss detection system that was applicable for team sports such as sailing. We designed a two pieces electronic system in which a master system was fixed on the boat and a slave system was implemented in the sailing garments. This study brought a new requirement;the need of a waterproof case that would protect the electronic slave system from sea water in case the sailors fall overboard. As a result of this need, in this study, we have designed and tested waterproof pockets that could serve as waterproof carriers for the slave system of sailing garments. For this purpose, studies were carried out using two different approaches. In the first approach, 15 types of pockets were developed by using three different fabric materials and 5 different seam sealing techniques. These pocket prototypes were tested by exposing them to the water for 12 h. In the second approach, an electronic circuit was developed by using a humidity sensor to test the pocket samples those were immersed to water. According to the results of the first approach, samples containing polyester base fabric, polyurethane and polyester membrane and double-sided sealing tape gave the best results. By converting the best pocket design into a garment sleeve, the humidity sensor containing test apparatus was tested and measurements were taken successfully from the system © 2022. Tekstil ve *** Rights Reserved

关键词： Humidity sensors

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Expression recognition based on convolutional difference contextual feature fusion

Expression recognition based on convolutional difference con...

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2022 International Conference on Electronic Information engineering and Computer Science, EIECS 2022

作者： Lu, Shengfu Yang, Chuang Li, Mi Kang, Jiaming Faculty of Information Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Department of Automation Faculty of Information Technology Beijing University of Technology Beijing China Department of Automation Faculty of Information Technology Beijing China

ISBN: (纸本)9781510663312

Facial expression recognition has always been a focus of study in emotion computing. With the aim of solving the problem that previous facial expression recognition studies are susceptible to individual differences and background noise, as well as the loss of facial expression features in deep networks of deep convolutional networks, we propose a facial expression method based on convolutional difference contextual feature fusion. The convolutional difference module can carry out difference operation between emotional image and neutral image in the input image, eliminate individual difference and background noise in expression features and improve the accuracy and generalization of the network. Finally, convolutional difference and contextual features are combined to improve the accuracy of the network further. Experiment results of 4 public facial expression datasets illustrate that compared with the existing facial expression recognition methods, this method has higher accuracy, can extract facial expression features more effectively, and has a better generalization ability. © 2023 SPIE.

关键词： Convolutional neural networks

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Automated Program for Estimation of Uncertainty in Medical Measurements

Automated Program for Estimation of Uncertainty in Medical M...

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Systems and Technologies of the Digital HealthCare (STDH)

作者： Svetlana Khan Sarsenbek Zhussupbekov Igor Fedorenko Department Automation and control Engineering and Telecommunications NJSC Almaty University of Power Almaty Republic of Kazakhstan

To estimate the uncertainty of measurement, analytical methods are widely used, a review and comparative analysis of which is given in the work. Among the existing methods for estimating the uncertainty in measurement, the most common is the classical method (GUM). The formula for the total standard uncertainty of the output quantity includes the values of partial derivatives (sensitivity coefficients), which, in the case of complex model equations that are often encountered in biological measurements, cause certain difficulties. In this paper, the main attention is paid to the numerical method of Kragten, which involves a modification of the law of propagation of uncertainties (the GUM method) with respect to the sensitivity coefficient. The paper presents the development of an automated program for calculating the uncertainty in measurement by the Kragten method, for which a Certificate was received on entering information into the State Register of Rights to Copyrighted Objects.

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

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 MI 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

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. 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 across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, 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 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection

arXiv

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arXiv 2025年

作者： Pulli, Tessa Hönig, Peter Thalhammer, Stefan Hirschmanner, Matthias Vincze, Markus Automation and Control Institute TU Wien Vienna Austria Department of Industrial Engineering University of Applied Sciences Technikum Wien Vienna Austria

Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the highest contrast, which leads to stable and prominent features. We propose a novel approach by incorporating edge detection in a pre-processing step for the tasks of object detection and object pose estimation. We conducted experiments to investigate the effect of edge detectors on transparent objects. We examine the performance of the state-of-the-art 6D object pose estimation pipeline GDR-Net and the object detector YOLOX when applying different edge detectors as pre-processing steps (i.e., Canny edge detection with and without color information, and holistically-nested edges (HED)). We evaluate the physically-based rendered dataset Trans6D-32 K of transparent objects with parameters proposed by the BOP Challenge. Our results indicate that applying edge detection as a pre-processing enhances performance for certain objects. © 2025, CC BY.

关键词： Robot vision

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