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63rd IEEE Conference on Decision and control, CDC 2024

作者： Gramlich, Dennis Gao, Sheng Zhang, Hao Scherer, Carsten W. Ebenbauer, Christian Rwth Aachen University Chair of Intelligent Control Systems Aachen52074 Germany Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai200092 China University of Stuttgart Chair of Mathematical Systems Theory Stuttgart70569 Germany

ISBN: (纸本)9798350316339

We present a simple and effective way to account for non-convex costs and constraints in state feedback synthesis, and an interpretation for the variables in which state feedback synthesis is typically convex. We achieve this by deriving the controller design using moment matrices of state and input. It turns out that this approach allows the consideration of non-convex constraints by relaxing them as constraints on the expected value of quadratic functions, and that the variables in which state feedback synthesis is typically convexified can be identified with blocks of these moment matrices. The employed relaxation can lead to constraint violation along sample paths. © 2024 IEEE.

关键词： State feedback

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Stochastic Unit Commitment: Model Reduction via Learning

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Current Sustainable/Renewable Energy Reports 2023年第2期10卷 36-44页

作者： Liu, Xuan Conejo, Antonio J. Constante Flores, Gonzalo E. Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Department of Integrated Systems Engineering The Ohio State University ColumbusOH43210 United States Davidson School of Chemical Engineering Purdue University West LafayetteIN47907 United States

Purpose of Review: As weather-dependent renewable generation increases its share in the generation mix of most electric energy systems, a stochastic unit commitment becomes the natural day-ahead scheduling tool. However, such a tool is generally computationally intractable if a detailed uncertainty description is considered. Taking this into account, we proposed a learning method to make the stochastic unit commitment problem tractable. Recent Findings: Recent advances in statistical learning and machine learning to address optimization problems can be advantageously applied to the rather intractable stochastic unit commitment problem. Considering these advances, we explore simple learning techniques to drastically reduce the size of a stochastic unit commitment problem without significantly altering its optimal solution. Summary: The considered stochastic unit commitment problem is formulated as a two-stage stochastic programming problem. The first stage represents commitment decisions, while the second one represents the operation conditions under different scenarios. Taking into account historical solved instances (or proxies for them), we reduce the size (measured by numbers of constraints and variables) of the stochastic unit commitment problem by (i) fixing unchanged binary variables and by (ii) eliminating inactive inequality constraints. Our numerical results show that the reduced problem generally requires significantly less time to solve while obtaining high-quality solutions, which are very close to or indistinguishable from the one obtained by solving the original problem. We use an Illinois 200-bus system to illustrate and characterize the performance of the proposed problem-reduction method. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Stochastic programming

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Verification of Railway control systems Using Model Checking and CTL, Explained Through a Case Study

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Periodica Polytechnica Transportation engineering 2024年第4期52卷 402-411页

作者： Lukács, Gábor Bartha, Tamás Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Kende street 13-17. BudapestH-1111 Hungary

Systematic faults can often occur during the development of a system. The later such faults are discovered, the more expensive it can be to correct them. In systems engineering practice, there are many methods and tools to reduce the likelihood of systematic faults. In this paper, we present the application of a formal model–based verification technique – called model checking – to assist railway engineers in designing and verifying the safety-related functionality of railway control systems. The proposed process is part of a specification-verification environment that facilitates the construction of correct, complete, consistent, and verifiable functional specifications during development. The results and experience in model checking are illustrated by a case study of a vehicle detection point, a common component in this domain. The model checking of the case study has been performed in the widely used UPPAAL modeling and simulation framework, which can also verify formal properties and generate a counterexample in case of a property violation. By analyzing the counterexample, the designer can gain insights into the system's behavior and identify potential design flaws or failures. Model checking can be used to achieve a higher quality functional specification that is typically more complete and/ or contains fewer faults compared to the traditional development approach. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Railroads

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Dualband 5G Antenna for Wireless Power Transmission

Dualband 5G Antenna for Wireless Power Transmission

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2023 Seminar on Fields, Waves, Photonics and Electro-Optics: Theory and Practical Applications, FWPE 2023

作者： Ignatovich, Vladislav O. Galtsov, Yaroslav O. Institute of Radio Engineering Systems and Control Department of Antennas and Radio Transmitting Devices Taganrog Russia

ISBN: (纸本)9798350370447

In this paper, the results of a simulation of a dualband 5G antenna for wireless power transmission using Altair FEKO software are presented. Frequency bands were selected as 4.4-5.0 n 5.15-5.925 GHz, these bands are declared by standard 5G Sub-6 GHz (bands n79 and n46). The construction of the antenna was developed as complex-shaped patch antenna. Research of the main characteristics was made, and also the parameterization task was solved for this antenna by the criterion of minimizing the SWR. Patch has got SWR 1.058 and 1.273 at bands we're studying. At the end, the prototyping of this model was carried out. Experimental studies of the frequency dependence of the SWR of the antenna found out that the results of nature measurement don't confront the results of computer simulation. © 2023 IEEE.

关键词： Microstrip antennas

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Circularity Reinforcement of Critical Raw Materials in Europe: A Case of Niobium

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Circular Economy and Sustainability 2024年第4期4卷 2449-2472页

作者： von Rennenberg, Theresa Yu, Yifei Fraccascia, Luca Yazan, Devrim Murat Department of Industrial Engineering and Business Information Systems University of Twente Drienerlolaan 5 Enschede 7522 NB Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy

Critical Raw Materials attract increasing attention due to their depleting reserves and low recyclability. Niobium, one of the most rare and vital elements, is primarily found in Brazil. This research explores the potential impact of Circular Economy (CE) strategies on mitigating niobium's criticality within Europe. First, a niobium supply chain is designed and analysed by Enterprise Input–Output modelling. Second, the supply risk is calculated based on the criticality matrix proposed by the European Commission under three scenarios associated with resources, technologies, and policies. The results show that urban mining is a potential solution to reduce niobium’s criticality and mitigate its environmental impacts. A higher recycling input rate and/or a mix of recycling and substitution strategies is necessary to offset niobium’s criticality. Aligned with the CE action plan, the research offers a scientific foundation to strategically prevent the risk of niobium supply shortages. © The Author(s) 2024.

关键词： Circular economy Critical raw materials Enterprise input–output Niobium supply chain Scenario analysis

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Hierarchical Learned Auctions for Communication-Sparse Satellite Environments

Hierarchical Learned Auctions for Communication-Sparse Satel...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Lehman, Hannah Valasek, John Vehicle Systems & Control Laboratory Aerospace Engineering Department Texas AM University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Multiagent coordination research tends to focus on strategies such as Multiagent Reinforcement Learning which makes extensive use of inter-agent communication. This works well in abstracted simulations, but may be challenging or non-feasible to implement in real-world environments because many problems of interest can have sparse, intermittent, or contested communication frameworks. This paper extends the Hierarchical Auctions for the Coordination of Heterogeneous Agents (HACHA) approach to investigate hierarchical auctions and machine learning to facilitate teaming between agents in a system where some agents enter and leave the communication graph at regular intervals, akin to a satellite. Results presented in the paper show that the method is able to identify, distribute, and monitor tasks despite communication challenges. This paper shows that allowing multiple children to propagate auctions results in lower average search times and higher success rates. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Satellite communication systems

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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