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Visual defect detection for historical building preservation

Expert Systems with Applications 2025年 291卷

作者： Mengqiu Cheng Xinting Zhang Leihua Xia Junming Chen Jiayuan Xie Zongfang Ma Qing Li School of Mechanical and Electrical Engineering Xi’an University of Architecture and Technology Xi’an China School of Information and Control Engineering Xi’an University of Architecture and Technology Xi’an China Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Faculty of Humanities and Arts Macau University of Science and Technology Macau China Department of Mathematics The University of Hong Kong Hong Kong SAR China

Advancements in automated building defect detection technology are essential for preserving historical architecture. Although existing intelligent technologies have made notable progress in detecting defects in modern residential buildings, their application to historical buildings faces significant challenges due to the complexity of structures, material diversity, and long-term natural and human-induced deterioration. This study focuses on historical buildings in Guangzhou, China, and introduces a dedicated task for detecting defects in such buildings. To support this task, a high-quality, customized dataset has been developed as a research foundation. To address challenges such as image blurriness during data collection and the complex structural characteristics of historical buildings, this study proposes a baseline model named Deblur-DetNet. The model employs a two-stage approach that integrates image deblurring and defect detection, significantly enhancing both accuracy and robustness. Experimental results demonstrate that the proposed method performs exceptionally well in the task of historical building defect detection, particularly in identifying fine-grained defects. This research provides robust technical support for the protection and restoration of historical buildings and establishes a solid foundation for further studies in cultural heritage preservation. Our dataset is available at https://***/file/d/1-9NiAw0hzomWHOA9RMEMXOOwIq5HScPx/view?usp=sharing .

关键词： Computer vision Defect detection Historical building Image deblurring technology

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Bebop 2 Quadrotor as a Platform for Research and Education in Robotics and control Engineering

Bebop 2 Quadrotor as a Platform for Research and Education i...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Piotr Kozierski Jacek Michalski Marek Retinger Rafal Madonski Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering of Poznan University of Technology Poznań Poland Institute of Computing Science (Faculty of Computing and Telecommunications) and with the Institute of Robotics and Machine Intelligence (Faculty of Control Robotics and Electrical Engineering) Poznan University of Technology Poznań Poland International Energy College Energy Electricity Research Center Jinan University Zhuhai China Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In conducting research and teaching in fields related to unmanned aerial vehicles (UAVs), it is particularly important to select a universal, safe, open research platform and tools for rapid prototyping. Ready-to-use, low-cost micro-class UAVs such as Bebop 2 are successfully used in that regard. This article presents how to use the potential of this flying robot with Robot Operating System (ROS). The most important software solutions for the developed experimental testbed FlyBebop are characterized here. Their capabilities in research and education are exemplified using three distinct cases: 1) research results on the method of optimal, in-flight, iterative self-tuning of UAV position controller parameters (based only on current measurements), 2) the use of the reinforcement learning method in the autonomous landing of a single drone on a moving vehicle, 3) planning the movement of UAVs for autonomous video recording along the planned path in the arrangement: "cameraman drone" and "lighting technician drones".

关键词： Drones Robots Education Cameras Sensors Tools

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A hybrid TwinSVM-HHO model for multilingual spam review detection using sentiment features and pre-trained embeddings

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Expert Systems with Applications 2025年 287卷

作者： Al-Zoubi, Ala’ M. Mora, Antonio M. Faris, Hossam Qaddoura, Raneem Department of Artificial Intelligence and Data Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Signal Theory Telematics and Communications ETSIIT and CITIC University of Granada Granada Spain King Abdullah II School for Information Technology The University of Jordan Amman Jordan Research Centre for Information and Communications Technologies of the University of Granada (CITIC-UGR) University of Granada Granada 18010 Spain School of Computing and Informatics Al Hussein Technical University Amman Jordan

The detection of spam reviews in multilingual environments remains a challenging task due to linguistic diversity, data imbalance, and semantic complexity. This paper proposes a novel hybrid model that integrates Twin Support Vector Machine (TwinSVM) with Harris Hawks Optimization (HHO) for simultaneous parameter optimization and feature selection. To enhance semantic understanding, sentiment-based features are incorporated alongside pre-trained word embedding models—BERT, FastText, and MUSE—across English, Arabic, and Spanish datasets. Our approach generates 24 high-quality datasets using embeddings with 100 and 400 dimensions, including a combined multilingual set. Experimental results demonstrate that our proposed HHO-TwinSVM model consistently outperforms conventional classifiers and metaheuristic-enhanced SVMs, achieving accuracy improvements of up to 9.44 % and enhanced robustness in low-resource languages. This integrated framework represents a scalable and adaptable solution for multilingual spam detection. Four detailed experiments were conducted in this study, each designed to address and demonstrate a specific aspect of the proposed approach. Across all experiments, the method outperformed existing algorithms, achieving impressive accuracy rates of 92.9741 %, 89.0314 %, 80.3580 %, and 85.0859 % on Arabic, English, Spanish, and multilingual datasets, respectively. Subsequently, sentiment analysis features were incorporated to further enhance detection performance, resulting in improvements of 1.0994 %, 2.6674 %, 9.4430 %, and 8.7448 %, respectively. A comprehensive analysis of the experimental results, including the influence of reviews and sentiment features, is also presented. © 2025 Elsevier Ltd

关键词： Detection HHO Multilingual Pre-trained Security Sentiment analysis Spam reviews Twin SVM Word embeddings

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Unlocking the potential of digital pathology: Novel baselines for compression

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Journal of Pathology Informatics 2025年 17卷 100421-100421页

作者： Fischer, Maximilian Neher, Peter Schüffler, Peter Ziegler, Sebastian Xiao, Shuhan Peretzke, Robin Clunie, David Ulrich, Constantin Baumgartner, Michael Muckenhuber, Alexander Almeida, Silvia Dias Gőtz, Michael Kleesiek, Jens Nolden, Marco Braren, Rickmer Maier-Hein, Klaus Institute Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) partner site Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Clinic of Diagnostics and Interventional Radiology Section Experimental Radiology Ulm University Medical Centre Ulm Germany Institute of Pathology TUM School of Medicine and Health Technical University of Munich Munich Germany Department of Diagnostic and Interventional Radiology Faculty of Medicine Technical University of Munich Munich Germany Institute for AI in Medicine (IKIM) University Medicine Essen Essen Germany German Cancer Consortium (DKTK) partner site Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany National Center for Tumor Diseases (NCT) NCT Heidelberg a partnership between DKFZ and University Medical Center Heidelberg Heidelberg Germany Research Campus M2OLIE Mannheim Germany PixelMed Publishing Bangor PA United States Munich Center for Machine Learning Munich Germany Helmholtz Imaging German Cancer Research Center Helmholtz Germany

Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological whole slide images (WSIs). Whereas current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and texture disparities, potentially impacting clinical decision-making. Whereas prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets. In addition, we collect an initially uncompressed dataset for an unbiased perceptual evaluation of compression schemes. Our results show that deep learning models fine-tuned for perceptual quality outperform conventional compression schemes like JPEG-XL or WebP for further compression of WSI. However, they exhibit a significant bias towards the compression artifacts present in the training data and struggle to generalize across various compression schemes. We introduce a novel evaluation metric based on feature similarity between original files and compressed files that aligns very well with the actual downstream performance on the compressed WSI. Our metric allows for a general and standardized evaluation of lossy compression schemes and mitigates the requirement to independently assess different downstream tasks. Our study provides novel insights for the assessment of lossy compression schemes for WSI and encourages a unified evaluation of lossy compression schemes to accelerate the clinical uptake of digital pathology. © 2025 The Authors

关键词： Feature similarity Lossy compression Whole slide images

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990–2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet Infectious Diseases 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H.A. El Hafeez, Samar Abd Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwar, Saleha Anwari, Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishnan, Senthi Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background: Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods: We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age–sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings: In 2021, diarrhoeal diseases caused an estimated 1·17 million (95% uncertainty interval 0·793–1·62) deaths globally, representing a 60·3% (50·6–69·0) decrease since 1990 (2·93 million [2·31–3·73] deaths). The most pronounced decline was in children younger than 5 years, with a 79·2% (72·4–84·6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147–221) in 1990 to 51·4 million (39·9–65·9) in 2021. In 2021, an e

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Strain and doping engineerings unlocking power density and cyclability of microspherical TiNb 2 O 7 anodes of lithium-ion batteries

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Journal of Energy Chemistry 2025年 108卷 827-837页

作者： Yang Li Jing Yang Tai Su Kai Zhang Yanjie Li Maykel Manawan Dongwei Ma Chengfu Yang Zhongzhu Liu Zhicong Shi Carlos Ponce de León Albarrán Yong-Wei Zhang Jia Hong Pan Beijing Key Laboratory of Emission Surveillance & Control for Thermal Power Generation North China Electric Power University Beijing 102206 China Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials School of Resources Environment and Materials Guangxi University Nanning 530004 Guangxi China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore Fakultas Teknologi Pertahanan Universitas Pertahanan Indonesia Jawa Barat 16810 Indonesia Tianjin Lishen Battery Co. Ltd Tianjin 300450 China School of Chemical Engineering The University of Adelaide Adelaide 5005 Australia Niobium Product Department CITIC Metal Co. Ltd Beijing 100004 China Institute of Batteries School of Materials and Energy Guangdong University of Technology Guangzhou 510006 Guangdong China Faculty of Engineering and Physical Sciences University of Southampton Highfield Southampton SO17 1BJ UK

The limited ion/electron transport kinetics and insufficient crystalline stability of TiNb 2 O 7 (TNO) present significant challenges to the development of high-performance lithium-ion batteries (LIBs) with fast-charging capabilities and long cycle life. Here we propose a dual-modification strategy combining N-doped carbon (NC) coating and Co 2+ /W 6+ doping, which not only enhances ionic and electronic conductivity but also effectively regulates volume expansion during electrochemical cycling. Upon Li + ion insertion, a significant reduction in the unit cell expansion coefficient of doped TNO is observed, from 7.48% (pristine TNO) to 5.37% (with 3% W 6+ doping) and 4.65% (with 3% Co 2+ doping), along with lowered lattice distortion and improved uniformity in internal strain release. Density functional theory (DFT) simulation demonstrates that Co 2+ and W 6+ ions preferentially substitute Ti 4+ sites in the TNO crystal, leading to the improved electronic conductivity by narrowing the bandgap. Moreover, Co 2+ doping creates lower electron density and wider Li + ion transport channels than W 6+ doping. The optimized 3Co-TNO@NC anode delivers a remarkable power density of 11.0 kW kg −1 at 20 C while maintaining a high reversible capacity of 150.9 mAh g −1 at 10 C after 2000 cycles. It also exhibits excellent compatibility in full cells, paired well with LiFePO 4 (137.9 mAh g −1 after 2000 cycles) and Ni-rich LiNi 0.8 Co 0.1 Mn 0.1 (130.9 mAh g −1 after 500 cycles) cathodes at 5 C, highlighting its potential as a high-safety, low-strain anode material for high-power LIBs.

关键词： Lithium-ion batteries

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Dynamical heterogeneity and universality of power-grids

arXiv

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arXiv 2023年

作者： Hartmann, Bálint Ódor, Géza Papp, István Benedek, Kristóf Deng, Shengfeng Kelling, Jeffrey Institute of Energy Security and Environmental Safety HUN-REN Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary Institute of Technical Physics and Materials Science HUN-REN Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary Faculty of Natural Sciences Chemnitz University of Technology Straße der Nationen 62 Chemnitz09111 Germany Department of Information Services and Computing Helmholtz-Zentrum Dresden-Rossendorf P.O.Box 51 01 19 Dresden01314 Germany

While weak, tuned asymmetry can improve, strong heterogeneity destroys synchronization in the electric power system. This study explores and compares the heterogeneity levels of high voltage (HV) power grids in Europe and North America. We provide an analysis of power capacities and loads of various energy sources from the databases and found heavy tailed distributions with similar characteristics. Graph topological measures and community structures also exhibit strong similarities, while the cable admittance distributions can be well fitted with the same power-laws (PL), related to the length distributions. The community detection analysis shows the level of synchronization in different domains of the European HV power grids, by solving a set of swing equations. We provide numerical evidence for frustrated synchronization and Chimera states and point out the relation of topology and level of synchronization in the subsystems. We also provide an empirical data analysis of the frequency heterogeneities within the Hungarian HV network and find q-Gaussian distributions related to super-statistics of time-lagged fluctuations, which agree well with former results on the Nordic Grid. © 2023, CC BY.

关键词： Synchronization

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An Energy-Efficient Context Aware Solution for Environmental Assessment ⁎

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IFAC-PapersOnLine 2020年第5期53卷 756-761页

作者： Isabela Birs Cristina Muresan Ioan Nascu Clara Ionescu Ghent University Faculty of Engineering and Architecture Research Group of Dynamical Systems and Control Tech Lane Science Park 125 B-9052 Ghent Belgium EEDT Decision & Control Flanders Make Consortium Tech Lane Science Park 131 B-9052 Ghent Belgium Department of Automation Technical University of Cluj-Napoca Memorandumului 28 Cluj-Napoca Romania

The paper focuses on presenting the advantages of context aware cyber-physical systems through an experimental platform capable of assessing its surroundings and self-performing decisions. The context aware paradigm is present in the control law implementation with various advantages such as energy efficiency as well as in the environmental measurements that trigger the robot to perform context-relevant decisions. The platform provides high versatility and the results presented throughout the study can be adapted to a manifold of multidisciplinary fields.

关键词： Submerged Vehicle Context Awareness Environmental Assessment Event-based control

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A spectral small-gain condition for input-to-state stability of infinite networks

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IFAC-PapersOnLine 2020年第2期53卷 5303-5308页

作者： Christoph Kawan Andrii Mironchenko Abdalla Swikir Navid Noroozi Majid Zamani Institute of Informatics Ludwig Maximilian University Munich Germany Faculty of Computer Science and Mathematics University of Passau Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Germany Computer Science Department University of Colorado Boulder USA

This paper presents a tight small-gain theorem for networks composed of infinitely many finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. We illustrate the effectiveness of our result by applying it to traffic networks.

关键词： Nonlinear systems small-gain theorems infinite-dimensional systems input-to-state stability Lyapunov methods large-scale systems

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