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Mechanism design optimization through CAD-based Bayesian optimization and quantified constraints

Discover Mechanical Engineering 2024年第1期3卷 1-18页

作者： Ben Yahya, Abdelmajid Ramos Garces, Santiago Van Oosterwyck, Nick De Boi, Ivan Cuyt, Annie Derammelaere, Stijn Department of Electromechanics CoSysLab University of Antwerp Antwerp Belgium AnSyMo/CoSys Flanders Make The Strategic Research Center For The Manufacturing Industry Antwerp Belgium Department of Electromechanics InViLab University of Antwerp Antwerp Belgium Department of Computer Science University of Antwerp Antwerp Belgium Division of Computing Science and Mathematics University of Stirling Stirling United Kingdom

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the resu

关键词： Bayesian optimization Dimensional synthesis Mechanical systems Motion control

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Enhancing Cyclone center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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The Impact of Language Expressions and visual Exploration on Neurodivergent Artist Quotes in Educational Setting 9

The Impact of Language Expressions and Visual Exploration on...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Indrawati, Desi Sjaerodji, Yuda Suryasa Darisman, Aris Waridha, Imaniar Rizki Bina Nusantara University Language Center Faculty of Humanities Computer Science Department West Java Bandung Indonesia School of Design Bina Nusantara University Visual Communication Design Study Program West Java Bandung Indonesia

ISBN: (纸本)9798350383010

In recent years, neurodiversity has gained popularity among those proponents of people's inclusion, dignity, and rights. Demonstrations and images help them learn more effectively. Give written instructions, visual clues, and brief spoken instructions when feasible. Furthermore, the researchers included seventeen student populations, eleven officers and staff, and twelve lecturer populations. For two days in a row, they attended the interactive quotation display by neurodivergent artists, and in response, they contributed drawings that reflected their interpretations of the quotes. An interview served as the second method of gathering data. The researchers also conducted a structured interview with four students, four officers, and three lecturers acting as spokespeople. Quotes from the works of Neurodivergent Artists such as Nintendo, Love, Amsterdam, Suzanne, Kacamata (Glasses), Teman (Friends), Mudik (Homecoming), Menjahit (Sewing), and Iggle can be found in the Interactive Installation activity. Using a mixed-methods approach, the researchers collected and examined the data (Creswell, 2014). It is an approach that blends qualitative and quantitative research techniques. According to the findings, the Amsterdam quote is liked by lecturers, staff, and students alike. In addition, the Love quote sparks curiosity in both students and instructors, indicating a mutual admiration for its meaning. © 2024 IEEE.

关键词： Students

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Differentiable quantum computing for large-scale linear control 24

Differentiable quantum computing for large-scale linear cont...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Connor Clayton Jiaqi Leng Gengzhi Yang Yi-Ling Qiao Ming C. Lin Xiaodi Wu Joint Center for Quantum Information and Computer Science and Department of Computer Science University of Maryland Joint Center for Quantum Information and Computer Science and Department of Mathematics University of Maryland and Department of Mathematics and Simons Institute for the Theory of Computing UC Berkeley Joint Center for Quantum Information and Computer Science and Department of Mathematics University of Maryland Department of Computer Science and Center for Machine Learning University of Maryland

ISBN: (纸本)9798331314385

As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significant overhead as problem dimensions grow. In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation. Specifically, we build a quantum-assisted differentiable simulator for efficient gradient estimation that is more accurate and robust than classical methods relying on stochastic approximation. Compared to the classical approaches, our method achieves a super-quadratic speedup. To the best of our knowledge, this is the first end-to-end quantum application to linear control problems with provable quantum advantage.

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Bayesian Ensembled Knowledge Extraction Strategy for Online Portfolio Selection

Bayesian Ensembled Knowledge Extraction Strategy for Online ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kumar, Abhishek Segev, Aviv University of Oulu Center for Ubiquitous Computing Oulu Finland University of Helsinki Department of Computer Science Helsinki Finland University of South Alabama Department of Computer Science Mobile United States

ISBN: (纸本)9781665480451

Online portfolio selection, one of the major fundamental problems in finance, has been explored quite extensively in recent years by machine learning and artificial intelligence communities. Recent state-of-the-art methods have focused on Mean Reversion significantly and have demonstrated outstanding performance. Another version of the same phenomenon, Median Reversion, has also performed well and demonstrated its ability to be robust against noises and outliers. Another important characteristic is Momentum. In this paper, a Bayesian ensembling approach to extract knowledge from both Mean Reversion and Median Reversion simultaneously based on the momentum associated with each one is proposed for the online portfolio selection task. The proposed method demonstrates its effectiveness by outperforming current state-of-the-art algorithms on several datasets. © 2022 IEEE.

关键词： Artificial intelligence

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Exploring the Impact of Interactive Technologies on Student Engagement in Blended Learning Environments at Higher Education Institutions

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 233-257页

作者： Ghai, Anupriya Sharma Altinay, Zehra Altinay, Fahriye Jasola, Sanjay Dagli, Gokmen Yikici, Betul School of Computing Graphic Era Hill University Uttarakhand Dehradun India Faculty of Education Societal Research and Development Center Near East University Mersin 10 Nicosia99138 Cyprus Department of Computer Science DBS Global University Uttarakhand Dehradun India Faculty of Education University of Kyrenia Mersin 10 Kyrenia99138 Cyprus

Adopting new technologies in higher education is essential for enhancing students’ skills and knowledge. Blended learning, an innovative approach integrating traditional classroom instruction with online components, has emerged as an effective educational solution. Incorporating online elements into blended learning models has shown great promise in enriching the learning experience. This study employs a structural equation model to investi-gate the key factors influencing students’ adoption of blended learning in higher educational institutions. The study identifies significant relationships among various factors, highlight-ing their roles in shaping student experiences and intentions. These findings provide prac-tical recommendations for educators and institutions aiming to optimize blended learning environments, ultimately fostering active student participation and satisfaction. This study contributes to the continuous improvement of educational practices and student outcomes within blended learning models, particularly with the integration of massive open online courses (MOOCs). © 2025 by the authors of this article. Published under CC-BY.

关键词： Contrastive Learning

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