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Enhanced energy efficiency by implementing MHD flow and heat transfer in Cu-Al₂O₃/H₂O hybrid nanoparticles with variable viscosity

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Nano-Structures and Nano-Objects 2024年 40卷

作者： Majeed, Aaqib Ali, Parvez Kouki, Marouan Siddhu, Muhammad Kashif Department of Mathematics The University of Faisalabad Sargodha Road University Town Faisalabad 38000 Pakistan Department of Mechanical Engineering College of Engineering Qassim University Buraydah 51452 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Computer Science The University of Faisalabad Sargodha Road University Town Faisalabad 38000 Pakistan

Hybrid nanofluids are engaged in phase-change materials and thermal energy storage systems to enhance heat transfer during the charging and discharging processes. Improved understanding of how variable viscosity and thermal radiation affect these fluids contributes to more efficient energy management. This study aims to formulate an efficient mathematical model for the two-dimensional flow of a hybrid nanofluid composed of copper (Cu) and alumina oxide (Al₂O₃) suspended with base fluid H₂O to form a hybrid fluid under the influence of thermal radiation. The present study also integrates the effects of variable viscosity and viscous dissipation. Electromagnetic radiation impact due to temperature also amalgamated. The governing PDEs are reformulated into ODEs via tailored similarity transformations. These reformulated equations are then numerically resolved using Bvp4c solver, leveraging the shooting method within MATLAB for precision and efficiency. The most significant results are predetermined relevant parameters, such as the prosperity parameter, magnetic parameter, radiation parameter, slip velocity parameter,Biot number, convention parameter, Eckert number, heat source parameter, Prandtl number on velocity and temperature distribution are inspected graphically and in the form of table. Outcomes illustrate that fluid velocity flattens by increasing magnetic parameters because there exists a Lorentz force that opposes the fluid motion, whereas enhancement is noted via radiation parameter. Compared to conventional nanofluid, temperature curves of hybrid nanoliquid is higher. Furthermore, recent results indicate strong agreement for a specific instance. © 2024 Elsevier B.V.

关键词： Computational model Convective parameter Hybrid nanofluid Magneto-hydrodynamic Porous surface Variable viscosity Viscous dissipation

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction

arXiv

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arXiv 2023年

作者： Chen, Shulu Evans, Antony Brittain, Marc Wei, Peng The Department of Electrical and Computer Engineering George Washington University WashingtonDC20052 United States System Design for Airbus UTM at Acubed An Airbus Innovation Center SunnyvaleCA94086 United States The Technical Staff MIT Lincoln Laboratory LexingtonMA02421 United States The Department of Mechanical and Aerospace Engineering George Washington University WashingtonDC20052 United States

Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations within and around the urban environment. Before the commercialization and adoption of this emerging transportation mode, however, aviation safety must be guaranteed, i.e., all the aircraft have to be safely separated by strategic and tactical deconfliction. Reinforcement learning has demonstrated effectiveness in the tactical deconfliction of en route commercial air traffic in simulation. However, its performance is found to be dependent on the traffic density. In this project, we propose a novel framework that combines demand capacity balancing (DCB) for strategic conflict management and reinforcement learning for tactical separation. By using DCB to precondition traffic to proper density levels, we show that reinforcement learning can achieve much better performance for tactical safety separation. Our results also indicate that this DCB preconditioning can allow target levels of safety to be met that are otherwise impossible. In addition, combining strategic DCB with reinforcement learning for tactical separation can meet these safety levels while achieving greater operational efficiency than alternative solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Leveraging Physiological Markers to Quantify the Transient Effects of Traumatic Stress and Non-Invasive Neuromodulation *

Leveraging Physiological Markers to Quantify the Transient E...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Asim H. Gazi Jesus Antonio Sanchez-Perez Shlok Natarajan Michael Chan Mohammad Nikbakht David J. Lin J. Douglas Bremner Jin-Oh Hahn Omer T. Inan Christopher J. Rozell School of Electrical and Computer Engineering GT Atlanta GA USA School of Interactive Computing GT Department of Biomedical Engineering GT Atlanta GA USA Department of Radiology and Imaging Sciences and the Department of Psychiatry and Behavioral Sciences Emory University School of Medicine Atlanta GA USA Atlanta Veterans Affairs Health Care System Decatur GA USA Department of Mechanical Engineering University of Maryland College Park MD USA

Millions around the world suffer from traumatic stress (stress caused by traumatic memories). Transcutaneous cervical vagus nerve stimulation (tcVNS) has been shown to counteract physiological changes associated with traumatic stress. However, little is known regarding the approximate timecourse of tcVNS effects. This knowledge of how quickly tcVNS takes effect is needed to optimize closed-loop tcVNS systems that can mitigate traumatic stress in a timely manner. To address this gap, we studied N=26 participants with history of prior trauma. Participants wore electrocardiogram, photoplethysmogram, seismocardiogram, and respiratory effort sensors throughout a double-blind protocol involving traumatic stress and active tcVNS (n=12) or sham stimulation (n=14). From the physiological signals, we extracted cardiovascular and respiratory markers and studied their dynamics during the traumatic stress and stimulation conditions. We decoupled the short-term transient responses from longer-term cumulative changes by centering each condition’s response with respect to data immediately prior to the condition. We thereby elucidate a diverse set of transient physiological responses to tcVNS and traumatic stress. These responses demonstrate that tcVNS-induced changes occur within seconds and have the potential to reduce acute physiological manifestations of traumatic *** relevance— Traumatic stress can overpower an individual within seconds and often occurs outside the clinic. This analysis focuses on transient physiological responses to traumatic memories and tcVNS captured using multimodal physiological sensing. We demonstrate that tcVNS-induced changes occur within seconds and have the potential to mitigate some of the short-term effects of traumatic stress.

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Localized space-tim e method of fundamental solutions for three-dimensional transient diffusion problem

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Acta Mechanica Sinica 2020年第5期36卷 1051-1057,I0002页

作者： L.Qiu J.Lin Q.-H.Qin W.Chen International Center for Simulation Software in Engineering and Sciences College of Mechanics and MaterialsHohai UniversityNanjing 210098China College of Engineering and Computer Science Australian National UniversityCanberraACT 2601Australia State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Acoustic Institute of AcousticsChinese Academy of SciencesBeijing 100190China

A localized space-time method of fundamental solutions(LSTMFS)is extended for solving three-dimensional transient diffusion problems in this *** interval segmentation in temporal direction is developed for the accurate simulation of long-time-dependent diffusion *** the LSTMFS,the whole space-time domain with nodes arranged i divided into a series of overlapping subdomains with a simple *** each subdomain,the conventional method of fundamental solutions is utilized and the coefficients associated with the considered domain can be explicitly *** calculating a combined sparse matrix system,the value at any node inside the space-time domain can be *** experi-ments demonstrate that high accuracy and efficiency can be simultaneously achieved via the LSTMFS,even for the problems defined on a long-time and quite complex computational domain.

关键词： Localized spacetime method of fundamental solutions Meshless method Long-time evolution Transient diffusion

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Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

arXiv

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Jigsaw-based Benchmarking for Learning Robotic Manipulation

arXiv

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arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023, CC BY.

关键词： Benchmarking

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xiaobo Liu Fang Wan Sheng Ge Haokun Wang Haoran Sun Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen Guangdong China School of Design Southern University of Science and Technology Shenzhen Guangdong China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong China Department of Computer Science The University of Hong Kong Hong Kong China

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Soybeannet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (Uav) Images

SSRN

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SSRN 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute & Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source at https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images and the labeled soybean dataset at https://***/datasets/jiajiali/uav-based-soybean-pod-images, offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, The Authors. All rights reserved.

关键词： Statistical tests

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SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images

arXiv

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arXiv 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code (https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images) and the labeled soybean dataset (https://***/datasets/jiajiali/uav-based-soybean-pod-images), offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, CC BY-NC-SA.

关键词： Statistical tests

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