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Semidefinite programming for manipulating acoustic traps in real time (SMART)

Scientific Reports 2025年第1期15卷 1-26页

作者： Zehnter, Sebastian Endres, Kevin Kronbichler, Martin Andrade, Marco A. B. Funke, Felix Ament, Christoph Chair of Control Engineering University of Augsburg Augsburg 86159 Germany Chair of High-Performance Scientific Computing University of Augsburg Augsburg 86159 Germany Faculty of Mathematics Ruhr University Bochum Bochum 44801 Germany Institute of Physics University of São Paulo São Paulo 05508-090 Brazil

Sound waves can be used for trapping and manipulating objects immersed in liquids or air. However, most acoustic levitation techniques are limited to particles with diameters much smaller than the acoustic wavelength or require time-consuming optimisation-based methods that hinder the dynamic manipulation of objects. Here, we present an approach based on semidefinite programming to manipulate levitated objects in real time. To demonstrate this technique, a phased array consisting of 256 ultrasonic transducers operating at 40 kHz is used for rotating a non-spherical Rayleigh object or to translate Mie spheres along various trajectories. In contrast to previous optimisation-based approaches, the proposed method can determine the emission phases of each transducer in real time, strongly facilitating the implementation of a model-based closed-loop control in future acoustic levitation systems. This is a fundamental step for manipulating levitated objects precisely and at high speeds. © The Author(s) 2025.

关键词： Acoustic levitation Acoustic traps Block-coordinate minimisation Burer–Monteiro method Contactless handling Phase recovery Phase retrieval problem Real time Semidefinite programming Sound pressure field

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OntoBPR: An ontology-based framework for performing building permit reviews using standardized information containers

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Advanced engineering Informatics 2025年 66卷

作者： Hagedorn, Philipp Fauth, Judith Zentgraf, Sven Seiß, Sebastian König, Markus Brilakis, Ioannis Chair of Computing in Engineering Ruhr-Universität Bochum Universitätsstraße 150 North Rhine-Westphalia Bochum44801 Germany Department of Engineering University of Cambridge 7a JJ Thomson Ave CambridgeCB3 0FA United Kingdom Chair of Construction Engineering and Management Bauhaus-Universität Weimar Marienstraße 7a Thuringia Weimar99423 Germany

Building permitting is essential for ensuring the safety, sustainability, and societal alignment of construction projects. Despite interest from both practitioners and researchers, the process remains largely manual and fragmented. Ontologies offer a promising solution by managing complexity and enabling automation through semantic information, though current ontologies in the building permit domain are limited to specific aspects like building code checking. On the process level, the OntoBPR framework integrates multiple domain-specific ontologies for a seamless digital permitting process and provides a workflow to automate the lifecycle of the permit review. Therefore, it suggests integrating the submitted building application using standardized information containers. The paper explores how digital applications can be submitted, reviewed, verified for completeness, and forwarded to authorities, and how permit review results can be gathered to support decision-making and automate notification issuance, and it provides a demonstration in a case study. In conclusion, OntoBPR formalizes a multi-layered ontology that advances and aligns the partitioned building permit process and provides an adaptable framework to harmonize diverse legal, informatics, and procedural aspects. © 2025 The Authors

关键词： Ontology

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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第4期2卷 100088-100088页

作者： Islam, Md. Milon Islam, Md. Zabirul Asraf, Amanullah Al-Rakhami, Mabrook S. Ding, Weiping Sodhro, Ali Hassan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Information Science and Technology Nantong University Nantong226019 China Department of Computer Science Kristianstad University KristianstadSE-29188 Sweden Shenzhen Institutes of Advanced technology Chinese Academy of Sciences Shenzhen China

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://***/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://***/Asraf047/COVID19-CNN-RNN. © 2023 The Authors

关键词： COVID-19

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DeepTrust: A deep learning approach for measuring social media users trustworthiness

arXiv

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arXiv 2021年

作者： Alrubaian, Majed Al-Qurishi, Muhammad Omar, Sherif Mostafa, Mohamed A. Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Chair of Pervasive and Mobile Computing Collage of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Veracity of data posted on the microblog platforms has in recent years been a subject of intensive study by professionals specializing in various fields of informatics as well as sociology, particularly in the light of increasing importance of online tools for news spreading. On Twitter and similar sites, it’s possible to report on ongoing situations globally with minimal delay, while the cost of such reporting remains negligible. One of the most important features of this social network is that content delivery can be customized to allow users to focus only on news items covering subject matters they find interesting. With this in mind, it becomes necessary to create verification mechanisms that can ascertain whether the claims made on Twitter can be taken seriously and prevent false content from spreading too far. This study demonstrates an innovative System for verification of information that can fulfill the role described above. The System is comprised of four mutually connected modules: a legacy module, a trustworthiness classifier;a module managing user authority, and a ranking procedure. All of the modules function within an integrated framework and jointly contribute to an accurate classification of messages and authors. Effectiveness of the solution was evaluated empirically on a sample of Twitter users, with a strict 10-fold evaluation procedure applied for each module. The findings indicate that the solution successfully meets the primary objectives of the study and performs its function as expected. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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On Sir David Cox's publications

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Significance 2022年第2期19卷 39-40页

作者： Mario Cortina Borja Julian Stander Giovanni Sebastiani Chair of the Significance editorial board and professor of biostatistics in the Population Policy and Practice Teaching and Research Department at the Great Ormond Street Institute of Child Health University College London. Associate professor of mathematics and statistics at the School of Engineering Computing and Mathematics University of Plymouth. Senior researcher at the Istituto per le Applicazioni del Calcolo “M. Picone” of the Italian National Research Council.

Mario Cortina Borja, Julian Stander , and Giovanni Sebastiani analyse some of the tens of thousands of citations for the hundreds of papers produced by David Cox in a publishing career that spanned more than 70 years

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Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

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Computers, Materials & Continua 2021年第7期68卷 1161-1181页

作者： Said Ali Hassan Khalid Alnowibet Prachi Agrawal Ali Wagdy Mohamed Department of Operations Research and Decision Support Faculty of Computers and Articial IntelligenceCairo UniversityGiza12613Egypt Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadhSaudi Arabia Trafc Safety Technologies Chair Vice Deanship of Scientic ResearchKing Saud UniversityRiyadhSaudi Arabia Department of Mathematics and Scientic Computing National Institute of TechnologyHamirpur177005India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt School of Engineering and Applied Sciences Wireless Intelligent Networks Center(WINC)Nile UniversityGizaEgypt

The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is *** decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen *** better illustrate the problem,objective,and problem constraints,a real application case study is *** case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in *** hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working *** objective function is formulated in order to maximize the total amount of delivered quantities during the specied time *** case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing *** has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.

关键词： Safeguarding substances mitigation against pandemic outbreaks supply chain management metaheuristic optimization

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Finite cell method for functionally graded materials based on V-models and homogenized microstructures

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Advanced Modeling and Simulation in engineering Sciences 2020年第1期7卷 1-33页

作者： Wassermann, Benjamin Korshunova, Nina Kollmannsberger, Stefan Rank, Ernst Elber, Gershon Chair for Computation in Engineering Technical University of Munich Arcisstr. 21 München 80333 Germany Chair of Computational Modeling and Simulation Technical University of Munich Arcisstr. 21 München 80333 Germany Institute for Advanced Study Technical University of Munich Lichtenbergstr. 2a Garching 85748 Germany Center for Graphics and Geometric Computing Technion Israel Institute of Technology Haifa 3200003 Israel

This paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach. © 2020, The Author(s).

关键词： Additive manufacturing Direct simulation Finite cell method Functionally Ggraded material Homogenization V-models V-reps

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After COVID-19: Crises, Ethics, and Socio-Technical Change

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 248-251页

作者： Calvo, Rafael A. Deterding, Sebastian Flick, Catherine Lutge, Christoph Powell, Alison Vold, Karina V. Imperial College London Dyson School of Design Engineering LondonSW7 9EG United Kingdom De Montfort University Centre for Computing and Social Responsibility LeicesterLE1 9BH United Kingdom Institute for Ethics in Artificial Intelligence Technical University of Munich Peter Löscher Chair of Business Ethics Munich80333 Germany London School of Economics and Political Science Department of Media and Communications LondonWC2A 2AE United Kingdom Institute for the History and Philosophy of Science and Technology Schwartz Reisman Institute for Technology and Society University of Toronto TorontoONM5S 1A1 Canada

Crises like the COVID-19 pandemic accelerate social and technological changes and put new political and public demands on science, technology, and innovation systems. Their urgency clashes with the deliberate slowness of existing responsible innovation and technology ethics practices and processes, while many fast and ad-hoc changes and infrastructures brought about during a crisis become permanent fixtures. This special issue brings together work reflecting in different ways how COVID-19 as an exemplary crisis is creating new challenges for technology ethics. From epidemiological models to contact tracing, biometric recognition, and machine learning-based assisted diagnosis, the different articles show the quandaries of our increasingly datafied health systems. © 2020 IEEE.

关键词： Special issues and sections COVID-19 Ethics Pandemics Technological innovation Decision making Public healthcare Law Fake news Social networking (online) Fingerprint recognition

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A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system

A new approach for the detection of pneumonia in children us...

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作者： Chagas, João Victor S. das de A. Rodrigues, Douglas Ivo, Roberto F. Hassan, Mohammad Mehedi de Albuquerque, Victor Hugo C. Filho, Pedro P. Rebouças Federal Institute of Education Science and Technology of Ceará LAPISCO FortalezaCE60040-215 Brazil Research Chair of Pervasive and Mobile Computing College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science Federal Institute of Education Science and Technology of Ceará FortalezaCE60040-215 Brazil Department of Teleinformatics Engineering Federal University of Ceará FortalezaCE60020-181 Brazil

Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in chest X-ray images. The dataset used has 6000 chest X-ray images of children, and three medical specialists performed the validations. In this work, twelve different architectures of Convolutional Neural Networks (CNNs) trained on ImageNet were adapted to operate as the resource extractors. Subsequently, the CNNs were combined with consolidated learning methods, such as k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The results showed that the VGG19 architecture with the SVM classifier using the RBF kernel was the best model to detect pneumonia in these chest radiographs. This combination reached 96.47%, 96.46%, and 96.46% for Accuracy, F1 score, and Precision values, respectively. Compared to other works in the literature, the proposed approach had better results for the metrics used. These results show that this approach for the detection of pneumonia in children using a real-time IoT system is efficient and is, therefore, a potential tool to aid in medical diagnoses. This approach will allow specialists to obtain faster and more accurate results and thus provide the appropriate treatment. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Conceptual Model of Hardware & Software Co-design for Multicore Systems on Chip

Conceptual Model of Hardware & Software Co-design for Multic...

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Mediterranean Conference on Embedded computing (MECO)

作者： Vladimir Ruchkin Grigory Soldatov Alexei Koryachko Boris Kostrov Ekaterina Ruchkina Ryazan State University Ryazan Russia CAD of computing facilities Chair Ryazan State Radio-Engineering University Ryazan Russia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

The paper suggests co-design of hardware and software of multicore microprocessing structures (MCMPS) for the cyber-physical systems (CPSs) including Internet of Things (IoT) in a single key and complex, closely interacting tools. The development task is performed by using set-theoretic analysis of all internal algorithms with the goal of open up in a step of the compilation independent of equivalence classes in type of clusters. As a result we solve the task of open up the set of all possible cluster structures defines the analysis of explicit parallelism and for choosing the MCMPS in order to design strategy for implementation in a CPS. The suggested co-design is based on formalization of one of the type models of knowledge - frames for design of expert system on technical parameters. In experimental example is analysis. The authors propose a sample using such methodology for analysis of algorithms for monitoring natural and anthropogenic emergencies or forest-fire monitoring, description of the expert system user interface and intellectual telecommunications structures on the base of the MC 127.05 module, which is a heterogeneous multiprocessor system based on K1879BM8Y chip.

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