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Research Square 2021年

作者： Ostasevicius, Vytautas Paulauskaite-Taraseviciene, Agne Paleviciute, Ieva Jurenas, Vytautas Griskevicius, Paulius Eidukynas, Darius Kizauskiene, Laura Institute of Mechatronics Kaunas University of Technology Studentu 56-122 KaunasLT-51424 Lithuania Department of Applied Informatics Kaunas University of Technology Studentu 50-214 KaunasLT-51368 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-105 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-107 KaunasLT-51368 Lithuania Department of Mechanical Engineering Kaunas University of Technology Studentu 56-301 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-125 KaunasLT-51424 Lithuania Centre of Real Time Computer Systems Kaunas University of Technology Barsausko 59-51423 KaunasLT-51368 Lithuania

The forces acting in the process of single point incremental forming (SPIF) change the geometry of the sheet metal. The tool-workpiece interaction process is non-linear due to the large deformations of the sheet metal, which determine the plastic behaviour, as well as the evolutionary boundary conditions resulting from the contact between the tool and the sheet. Instead of lubricating the contact surface of the forming tool and the sheet metal, an innovative environmentally friendly method to reduce the coefficient of friction by vibrating the sheet has been proposed. The finite element method (FEM) allowed a virtual evaluation of the deformation parameters of the SPIF process in order to determine the destructive loads. The FEM was chosen as a deterministic numerical tool to evaluate the set of defect parameters induced by forming forces. The paper also proposes a method for predicting the formation force using an artificial neural network (ANN), assuming that such a model is generalized to implicit data. In this context, an empirical analysis of the implementation of the ANN technique is performed. © 2021, CC BY.

关键词： Sheet metal

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Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques

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Journal of Energy Storage 2025年 124卷

作者： Almadhor, Ahmad Basem, Ali Singh, Pradeep Kumar Othman, Nashwan Adnan Samad, Sarminah Alhomayani, Fahad M. Abduvalieva, Dilsora Ali, H. Elhosiny Akram, Muhammad Afzal, Abdul Rahman Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University U.P. Mathura281406 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Faculty of Design Informatics and Business University of Abertay Scotland Dundee United Kingdom Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

The transition to renewable energy is crucial to meet growing global energy demands while minimizing environmental impact. Solar energy, a leading renewable source, faces limitations due to its intermittent nature, making energy storage systems essential for continuous supply. Thermal energy storage systems (TESSs) provide a compelling solution, especially by utilizing latent heat storage with phase change materials (PCMs), which efficiently store large amounts of energy. However, PCM-based systems suffer from low thermal conductivity and weak heat transfer performance. Numerous techniques to amend the thermal conductivity of PCMs, whether applied individually or together, show promise for improving the efficiency of TESSs. However, challenges like increased viscosity, decreased latent heat capacity, high costs, and complex manufacturing often limit their practical use. Thus, it is crucial to investigate more straightforward and more cost-effective solutions, like incorporating extended surfaces into the PCM container. In this study, a novel approach is taken to ameliorate the heat transfer surface area in TESSs by incorporating tubes and stands inside the PCM container rather than relying on conventional fins. A new compact TESS design was introduced, featuring fixed tubes supported by aluminum-made stands. Two artificial neural networks (ANN) models were trained with input parameters, including the length of the vertical stands, horizontal stands, and tilted stands, to anticipate the half-melting time and complete melting time of the PCM. In the end, two optimal designs of O-TESS1 and O-TESS2 were proposed by a single-objective optimization, focusing on minimizing the half and complete melting times of the PCM, respectively. A multi-objective optimization was also performed to balance the mentioned objectives. Using the generated data, a Pareto front and TOPSIS-selected designs were developed to visualize the trade-offs between these competing objectives. It took

关键词： Solar heating

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Secure Human Action Recognition by Encrypted Neural Network Inference

arXiv

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arXiv 2021年

作者： Kim, Miran Jiang, Xiaoqian Lauter, Kristin Ismayilzada, Elkhan Shams, Shayan Department of Mathematics Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of School of Biomedical Informatics University of Texas Health Science Center HoustonTX United States SeattleWA United States Department of Computer Science and Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Applied Data Science San Jose State University San JoseCA United States

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2. Copyright © 2021, The Authors. All rights reserved.

关键词： Cryptography

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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

arXiv

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arXiv 2020年

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Denmark Department of Informatics University of Oslo Norway John A. Paulson School of Engineering and Applied Sciences Harvard University United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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On Biased Compression for Distributed Learning

arXiv

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arXiv 2020年

作者： Beznosikov, Aleksandr Horváth, Samuel Richtárik, Peter Safaryan, Mher Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Skolkovo Institute of Science and Technology Moscow121205 Russia School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow141701 Russia

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate O ( δLexp[−µKδL ] + (CKµ+δD) ) , where δ ≥ 1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Energy-efficient Swing-up and MPC Stabilization of an Inverted Pendulum

Energy-efficient Swing-up and MPC Stabilization of an Invert...

引用

International Conference on Process Control (PC)

作者： Martin Gulan Peter Minarčík Jakub Kulhánek Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

This article presents a control framework to swing up and stabilize the inverted pendulum-cart system in its unstable equilibrium and maintain this configuration while changing the reference position of the cart. The swing-up problem is solved by an energy-based approach that respects physical constraints of the system. Stabilization and subsequent cart position tracking is addressed by means of an implicit model predictive controller with guaranteed stability imposed via a terminal set constraint. Viability of the proposed framework is practically demonstrated on a laboratory inverted pendulum system, the states of which were estimated by a continuous-discrete extended Kalman filter.

关键词：

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

arXiv

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arXiv 2023年

作者： Graham, Simon Vu, Quoc Dang Jahanifar, Mostafa Weigert, Martin Schmidt, Uwe Zhang, Wenhua Zhang, Jun Yang, Sen Xiang, Jinxi Wang, Xiyue Rumberger, Josef Lorenz Baumann, Elias Hirsch, Peter Liu, Lihao Hong, Chenyang Aviles-Rivero, Angelica I. Jain, Ayushi Ahn, Heeyoung Hong, Yiyu Azzuni, Hussam Xu, Min Yaqub, Mohammad Blache, Marie-Claire Piégu, Benoît Vernay, Bertrand Scherr, Tim Böhland, Moritz Löffler, Katharina Li, Jiachen Ying, Weiqin Wang, Chixin Snead, David Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir M. Tissue Image Analytics Centre University of Warwick Coventry United Kingdom Histofy Ltd Birmingham United Kingdom Institute of Bioengineering School of Life Sciences Epfl Lausanne Switzerland Dresden Germany The Department of Computer Science The University of Hong Kong Hong Kong Tencent Ai Lab Shenzhen China College of Biomedical Engineering Sichuan University Chengdu China Department of Precision Instruments Tsinghua University Beijing China College of Computer Science Sichuan University Chengdu China Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association Berlin Germany Humboldt University of Berlin Faculty of Mathematics and Natural Sciences Berlin Germany Charité University Medicine Berlin Germany University of Bern Bern Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Softsensor.ai BridgewaterNJ United States PRR.ai TX United States Department of R&d Center Arontier Co. Ltd Seoul Korea Republic of Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Cnrs Ifce Inrae Université de Tours Prc Nouzilly3780 France Institut de Génétique et de Biologie Moléculaire et Cellulaire Illkirch France Centre National de la Recherche Scientifique UMR7104 Illkirch France Institut National de la Santé et de la Recherche Médicale Inserm U1258 Illkirch France Université de Strasbourg Strasbourg France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany School of Software Engineering South China University of Technology Guangzhou China Department of Pathology University Hospitals Coventry Warwickshire Nhs Trust Coventry United Kingdom Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry United Kingdom

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. © 2023, CC BY-NC-SA.

关键词： Grading

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OptoShield: A Low-Cost Tool for Control and Mechatronics Education

OptoShield: A Low-Cost Tool for Control and Mechatronics Edu...

引用

Asian Control Conference

作者： Gergely Takács Tibor Konkoly Martin Gulan Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (纸本)9781728102634

Commercial educational tools for teaching feedback concepts in control systems engineering and mechatronics are generally expensive, large, complex and sensitive instruments; therefore cannot be used for homework assignments outside the classroom. This paper presents a novel open-source teaching aid that can be readily manufactured under AC3 and as such, is financially accessible to students and universities operating on a tight budget. The device proposed in this paper implements a simple optical experiment, allowing students to follow a prescribed brightness level using feedback control concepts. In addition to real-time control, the apparatus is also suitable for teaching signal processing and system identification principles. The educational aid presented here is attached to the popular Arduino microcontroller prototyping platform in a form of a miniature extension shield, thereby using a standardized physical layout and software ecosystem. We describe the hardware, the application programming interface (API) and library written in C/C++ for the Arduino integrated development environment (IDE) and the accompanying Simulink toolbox; moreover present sample exercises for system identification and proportional-integral-derivative (PID) control.

关键词： Light emitting diodes Hardware Education Software packages Resistors Open source software

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Real-time Stabilizing MPC of a 2DOF Helicopter

Real-time Stabilizing MPC of a 2DOF Helicopter

引用

International Conference on Process Control (PC)

作者： Martin Gulan Peter Minarčík Martin Lizúch Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

This paper presents real-time control of a laboratory helicopter model with two degrees of freedom. To accomplish this goal we propose to employ a numerically robust approach to the synthesis of stabilizing model predictive control based on model prestabilization before the prediction phase. The underlying optimal control algorithm involves integral action, input constraints imposed by actuators, and guarantees closed-loop stability and recursive feasibility via a low-complexity terminal set constraint. Performance of the controller is practically demonstrated in the task of tracking a state reference corresponding to a predefined flight trajectory, and compared with linear quadratic control.

关键词： Helicopters Numerical models Predictive models Mathematical model Rotors Computational modeling Propellers

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First measurement of the top quark pair production cross section in proton-proton collisions at s = 13.6 TeV

引用

Journal of High Energy Physics 2023年第8期2023卷 204页

作者： Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. Lechner, L. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Kello, T. Van Mechelen, P. Bols, E.S. D’Hondt, J. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Morton, A. Müller, D. Sahasransu, A.R. Tavernier, S. Van Doninck, W. Van Putte, S. Vannerom, D. Clerbaux, B. Dansana, S. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Lee, K. Mahdavikhorrami, M. Makarenko, I. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. Dobur, D. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Tytgat, M. Van Den Bossche, N. Vermassen, B. Wezenbeek, L. Benecke, A. Bruno, G. Bury, F. Caputo, C. David, P. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mondal, K. Tran, T.T. Vischia, P. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. Moraes, A. Rebello Teles, P. Aldá Júnior, W.L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. Dos Santos Sousa, V. Fonseca De Souza, S. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C.A. Calligaris, L. Fernandez Perez Tomei, T.R. Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Rodozov, M. Shopova, M. Sultanov, G. Dimitrov, A. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Lezki, S. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Milosevic, V. Monti, F. Sharma, R. Tao, J. Wang, J. Zhang, H. Zhao, J Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The first measurement of the top quark pair (tt¯) production cross section in proton-proton collisions at s = 13.6 TeV is presented. Data recorded with the CMS detector at the CERN LHC in Summer 2022, corresponding to an integrated luminosity of 1.21 fb⁻¹, are analyzed. Events are selected with one or two charged leptons (electrons or muons) and additional jets. A maximum likelihood fit is performed in event categories defined by the number and flavors of the leptons, the number of jets, and the number of jets identified as originating from b quarks. An inclusive tt¯ production cross section of 881 ± 23 (stat + syst) ± 20 (lumi) pb is measured, in agreement with the standard model prediction of 924₋₄₀⁺³² pb. © The Author(s) 2023.

关键词： Hadron-Hadron Scattering Top Physics

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