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arXiv 2025年

作者： Kuře, Matěj Peichl, Adam Bušek, Jaroslav Olgac, Nejat Vyhlídal, Tomáš Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Prague 6160 00 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic Department of Mechanical Engineering University of Connecticut StorrsCT United States

Non-collocated vibration absorption (NCVA) concept using delayed resonator for in-situ tuning is analyzed and experimentally validated. There are two critical contributions of this work. One is on the scalable analytical pathway for verifying the concept of resonant substructure as the basis of the ideal vibration absorption. The second is to experimentally validate the spatial and spectral tunability of NCVA structures for the first time. For both novelties arbitrarily large dimensions of interconnected mass-spring-damper chains are considered. Following the state of the art on NCVA, control synthesis is performed over the resonant substructure comprising the delayed resonator and a part of the primary structure involved in the vibration absorption. The experimental validation of the proposed NCVA concept is performed on a mechatronic setup with three interconnected cart-bodies. Based on the spectral analysis, an excitation frequency is selected for which a stable vibration suppression can be achieved sequentially for all the three bodies, one collocated and two non-collocated. The experimental results closely match the simulations for complete vibration suppression at the targeted bodies, and thus validating the crucial spatial tunability characteristic as well as the traditional spectral *** Codes 93D20 © 2025, CC BY.

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Improving the Performance of Computer Vision Algorithms for the Multi-Mode Hybrid Drone Delivery System

Improving the Performance of Computer Vision Algorithms for ...

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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Leung, Ronald Goodwill, Chelsy Dong, Alexander Shah, Kareena Katibeh, Mohammad Rutgers University The Governor's School of New Jersey Program in Engineering and Technology New BrunswickNJ United States The Rutgers University Department of Mechanical and Aerospace Engineering New BrunswickNJ United States

ISBN: (纸本)9781665473453

Drone deliveries provide an innovative alternative to current last-mile delivery solutions;however, drones are limited by their short flight time. To overcome this, researchers devised the Multi-Mode Hybrid Drone Delivery System, in which a plane transports a drone to deliver a package. The preexisting system requires improvements to its computer vision algorithms to enable the drone to redock to the plane. This research aims to evaluate and improve the performance of the object detection algorithm. Three tests are performed to establish performance metrics: The Frequency, Distance Tracking, and Horizontal Position Accuracy Tests. Results reveal that decreasing the dimensions of the camera's input significantly increases the processing speed of the computer vision algorithm. A new method is proposed that improves the accuracy of the drone's positional tracking, providing the precision needed to make the redocking process possible. © 2022 IEEE.

关键词： Drones

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A novel photovoltaic/thermal (PVT) system using nano-enhanced micro-encapsulated PCM slurry for steam production: A numerical study

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International Journal of Thermofluids 2025年 27卷

作者： Azmeer, Ahmed Sheikh, Yahya Kanjo, Hasan A. Sabouni, Hamzeh Physical Science and Engineering Department King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mechanical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah United Arab Emirates Materials Science and Engineering Program American University of Sharjah Sharjah United Arab Emirates

As industries reliant on steam-based processes strive to reduce fossil fuel consumption and carbon emissions, interest in renewable technologies, particularly photovoltaic/thermal (PVT) systems that generate both thermal and electrical energy, is increasing. However, the limited thermal performance of PVT systems remains a key barrier. Traditional working fluids, such as water, nanofluids, and phase change material (PCM) slurries, exhibit low thermal conductivity, poor temperature regulation, and inefficient heat storage. To address these issues, a novel working fluid incorporating graphene oxide (GO) nanoparticles into microencapsulated PCM (MPCM) slurry is proposed. The integration of GO nanoparticles not only enhances the dispersion stability of the MPCM slurry but also significantly increases its thermal conductivity, leading to improved heat dissipation from PV cells. A comprehensive CFD investigation is conducted using ANSYS Fluent 2021, employing an Eulerian–Eulerian multiphase model to simulate the three-phase flow. In this model, water serves as the primary phase, while GO nanoparticles and MPCM function as secondary dispersed phases. The results demonstrate that increasing the GO volume fraction from 0 to 0.005 reduces average PV cell temperatures by up to 5°C at 40°C. Additionally, the nano-enhanced MPCM slurry achieves a thermal efficiency of 88.1 % and an electrical efficiency of 10.3 %. It is also found that using MPCM slurry as the working fluid, a 15 % volume fraction of MPCM with a melting point of 40°C achieves the highest thermal efficiency of 90.1 %, outperforming water by 20.1 % and surpassing previous studies on MPCM slurry and nanofluids. © 2025 The Author(s)

关键词： Nanofluidics

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Autonomous Navigation of Rescue Robot on International Standard Rough Terrain by Using Deep Reinforcement Learning

Autonomous Navigation of Rescue Robot on International Stand...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Hayato Matsuo Noritaka Sato Yoshifumi Morita Department of Engineering Electrical and Mechanical Engineering Program Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya Aichi Japan

Rescue robots perform rescue and search operations at disaster sites. These robots should be able to navigate autonomously because remote control is difficult. The objective of this research was to enable rescue robots to navigate autonomously on international standard rough terrain. To achieve this objective, we built a learning environment in a simulator using Unity, a physics engine, and conducted deep reinforcement learning using the machine learning framework of Unity and ML-Agents. A comparative verification with remote control demonstrated that autonomous navigation was superior to remote control in terms of both time and success rates because of the difference in the motion of the robot.

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A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

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PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-Order Recurrent Neural Network for Solving Time-Variant Quadratic programming

PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-...

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International Conference on Control and Automation (ICCA)

作者： Yi Yang Weibing Li Jianshu Zhou Junda Huang Jinfei Hu Richard M. Voyles Xin Ma Department of Mechanical and Automation Engineering Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China School of Engineering Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This study presents a Predefined-Time Convergent Fractional-Order Zeroing Neural Network (PTC-FOZNN) as an innovative solution for Time-Invariant Quadratic programming (TIQP) and Time-Variant Quadratic programming (TVQP) problems. This model represents a noteworthy integration of fractional calculus into recurrent neural networks for cyclic motion planning of robots. The PTC-FOZNN diverges from the conventional Zeroing Neural Network (ZNN) by implementing a conformable fractional derivative that complies with the Leibniz rule-a standard often unmet by existing fractional derivatives. Additionally, it employs a novel predefined-time stabilizer as its activation function. When compared to the traditional ZNN, the PTC-FOZNN, configured with $0 < \alpha\leq 1$ , exhibits enhanced convergence speed and greater positional accuracy for both TIQP and TVQP tasks. Through meticulous theoretical and empirical validation, including simulation trials with a Franka Emika Panda robotic arm, the PTC-FOZNN model has proven its effectiveness in precise trajectory tracking and computational efficacy.

关键词： Recurrent neural networks Trajectory tracking Kinematics Manipulators Planning Quadratic programming Task analysis

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Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes

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Procedia Computer Science 2024年 232卷 2259-2268页

作者： M.O. Okwu O.J. Oyejide J. Oyekale K. Ezekiel C. Maware O.F. Orikpete C.P. Okonkwo Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Department of Mechanical Engineering Universiti Teknologi Petronas Seri Iskandar Malaysia Maintenance Engineering and Asset Management University of Manchester United Kingdom Fugio Cho Engineering Technology 210E Center for Robotics and Manufacturing Building University of Kentucky USA Centre for Occupational Health Safety and Environment (COHSE) University of Port Harcourt Choba Nigeria Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria

Accurate prediction of biogas yield is crucial for optimizing waste-to-energy conversion systems in anaerobic co-digestion processes. In this study, a double input and single output (DISO) fuzzy mamdani model (FMM) was developed for the prediction of biogas yield in a pilot scale of 105-L mesophilic anaerobic sludge bio-digester. The input variables considered are the combination of cow dung and pig waste and the retention time (RT), while the output variable is the experimental biogas yield. Triangular Fuzzy Membership Functions (TFMF) were utilized to define the input and output datasets, and rules were derived from de-fuzzification. Comparative analysis between the FMM's predicted results and experimental values showcased its effectiveness in forecasting biogas yield during the anaerobic co-digestion of the hybrid wastes. Significantly, the FMM consistently produced results with low error values for the sample dataset, underscoring its accuracy even under stochastic conditions. This study emphasizes the FMM's ability to generate predictions with minimal deviations, offering superior results. As a prospect for future research, the implementation of hybrid algorithms may further enhance biogas yield prediction accuracy within waste-to-energy systems.

关键词： Waste-to-Energy Conversion Fuzzy Mamdani Model biodigester co-digestion decomposable waste

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Microfluidic Formation of Porous Microgels with Interior Spiral Canals for Mesenchymal Stem Cell Culture

Microfluidic Formation of Porous Microgels with Interior Spi...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Zhen Zhan Tanyong Wei Zhan Yang Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Soochow University Suzhou China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Stem cell delivery is pivotal in tissue engineering, enabling the regeneration and repair of damaged tissues and advancing therapeutic solutions. As an innovative stem cell delivery system, microgels provide a supportive, protective environment that enhances cell viability and promotes targeted tissue regeneration. However, conventional microgels face limitations due to the lack of microporous structures and interior canals, which hinder efficient nutrient and oxygen exchange, thereby reducing cell viability and proliferation. To overcome these challenges, we developed a droplet microfluidic system for the stable and continuous production of porous microgels with interior spiral canals by introducing a pore-forming agent. This controlled formation process results in microgels with excellent porosity and monodispersity. With their unique structure and biocompatibility, these porous microgels are excellent microcarriers for 3D cell culture, enhancing nutrient and oxygen exchange and providing a conducive environment for cell growth and proliferation.

关键词： Irrigation Spirals Three-dimensional displays Microprocessors Tissue engineering Computer architecture Stem cells Nutrients Microfluidics Surface treatment

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The Role of Compaction Roller in Defining the Layup Quality and Laminate Porosity in Robotic Fiber Placement 24

The Role of Compaction Roller in Defining the Layup Quality ...

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24th International Conference on Mechatronics Technology, ICMT 2021

作者： Ammar, Mohamed M.A. Shirinzadeh, Bijan Monash University Robotics and Mechatronics Research Laboratory Department of Mechanical and Aerospace Engineering ClaytonVIC3800 Australia

ISBN: (纸本)9781665424592

The automated fiber placement has been commonly used to fabricate composite structures with high performance. However, the automated manufacturing process is limited by the developed defects in the final composite components. The current study focuses on the effect of the compaction roller's configurations and stiffness on the manufactured thermoset com-posites. The robotic fiber placement process is applied to fabricate different composite specimens. Several unidirectional and multi-axes specimens have been manufactured using 4 different rollers. The attitude of the compaction roller is also adjusted to be normal and to ensure uniform distribution of the compaction pressure. The surface quality of the manufactured components has been evaluated through image processing and detecting the layup defects. The scanning electron microscopy is used to estimate the change in the material porosity. The samples manufactured using a compliant and perforated roller included defects such as blisters, wrinkles, overlaps, and distortions. Additionally, the material porosity has been significantly improved when using the solid steel roller. © 2021 IEEE.

关键词： Porosity

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Design and Characterization of a Multi-Domain Unmanned Vehicle Operating in Aerial, Terrestrial, and Underwater Environments

Design and Characterization of a Multi-Domain Unmanned Vehic...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Canelon, Dario Westlake, Sam Wang, Youbing Papanikolopoulos, Nikolaos University of Minnesota Department of Mechanical Engineering United States Minnesota Robotics Institute University of Minnesota United States

ISBN: (纸本)9780738131153

In this paper, we demonstrate and validate the flight characteristics of a multi-domain unmanned platform capable of locomotion in the air, water, and land domains. The OmnibotV2 uses four flight motors in a quad-copter configuration for flight, a dual screwdrive system for land and water locomotion, and optionally features a buoyancy control unit for water operations. Experiments were performed to measure how maximum battery voltage, temperature, and motors of different torque-speed characteristics affect the performance of the platform in flight. Screwdrive locomotion similarly also evaluated on land, underwater, and on the water's surface. The transition between the water's surface and the air was validated as was the use of the flight propulsion for moving around in and under the water. A streaming camera unit as well as other sensors were integrated and validated for future applications in environmental monitoring. © 2021 IEEE.

关键词： Antennas

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