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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Siva Ramkumar M. Sivaramkrishnan Kaparthi Uday Mohammad Kanan C. Mohan Raj Jayant Giri Dept of ECE SNS College of Technology Dept of EEE Karpagam College of Engnieering India EIE Department CVR College of Engineering Ibrahimpatanam Hyderabad Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department or EEE Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Coimbatore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation Systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS.

关键词： Navigation Heuristic algorithms Transportation Transforms Traffic control Electric vehicle charging Smart grids Delays Vehicle dynamics Testing

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A Novel FBG-based Tri-Axis Tip Force Sensor: Design, Modeling, and Application

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IEEE/ASME Transactions on Mechatronics 2025年

作者： Wang, Zechao Qiu, Yufu Chen, Jianghua Yan, Yongjun Ding, Qingpeng Cheng, Shing Shin Beihang University School of Automation Science and Electrical Engineering Beijing 100081 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Hong Kong Hong Kong The Chinese University of Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Shun Hing Institute of Advanced Engineering Multi-Scale Medical Robotics Center Hong Kong Hong Kong

This article presents the design, modeling, and application of a miniature tri-axis tip force sensor with fiber Bragg grating (FBG) for cardiac ablation. A compliant mechanism (CM) composed of thin-walled beams is designed to convert the distal forces applied at the tip of the sensor to the variations of the wavelengths of the FBGs. The CM design reserves the middle channel for an optical fiber used in laser ablation (LA). The differential principle is utilized to eliminate the impact of the environmental temperature on orthogonal forces (f_x,f_y, and f_z). The uniqueness condition for the measurement of forces and temperature is established. To enable rapid iteration of sensor design, an accurate and efficient reduced-order global transfer matrix method is proposed to develop the unified statics/dynamics model of the sensor. Static calibration experiments reveal resolutions of 11.6mN, 4.7mN and 6.2mN for - x, - y, and - z axes, respectively. The force ranges are 0-2N and - 1~ 1N for axial and flexural directions. Modal test shows the 1^st natural frequency of 1071.6 Hz. Linearity, repeatability, and hysteresis errors are below 5.3% FS. A novel application involving LA with the designed sensor on an ex vivo tissue from a pig heart was conducted to help understand the thermomechanical interaction between the laser and the tissue. © 1996-2012 IEEE.

关键词： Cardiac ablation fiber Bragg grating (FBG) transfer matrix method (TMM) tri-axis force sensor

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Enhancing Thermal Properties: A Comprehensive Study on Cellulose Nanocrystal Nanofluids 2nd

Enhancing Thermal Properties: A Comprehensive Study on Cellu...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Kareem Ahmad Aqeel, Ahmad Abdul Hajjaj, Sami Mohamed, Hassan Gomaa, Mohamed R. Alsboul, Monther Selangor Kajang43000 Malaysia School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Taicang Jiangsu Province Suzhou215400 China School of Engineering and Technology Sunway University Selangor Darul Ehsan No. 5 Jalan Universiti Bandar Sunway47500 Malaysia Selangor Kajang43000 Malaysia Mechanical Engineering Department Faculty of Engineering Al-Hussein Bin Talal University Ma’an71111 Jordan Physics Department College of Science Al Hussein Bin Talal University Maan71111 Jordan

ISBN: (纸本)9789819639489

Nanofluids, characterized by a base fluid infused with nanoparticles, have gained substantial interest due to their superior thermal properties, primarily enhanced heat transfer capabilities. This study investigates the dispersion of cellulose nanocrystal (CNC) nanoparticles in base fluid and its subsequent impact on thermal properties. CNC nanoparticles obtained from Blue Goose Biorefineries Inc. Were dispersed in distilled water and ethylene glycol (EG) at varying volume concentrations (0.25%, 0.50%, 0.75%). The prepared nanofluids underwent thorough stability analyses utilizing techniques such as ultrasonication. The CNC nanofluids ‘ thermal conductivity, density, viscosity, and specific heat capacity were systematically measured using instrumentation. The study found that adding cellulose nanocrystal (CNC) nanoparticles to an 80:20 water-ethylene glycol mixture significantly improves the thermal conductivity, viscosity, and density of the base fluid. Thermal conductivity increased linearly with higher CNC concentrations (0.25%, 0.5%, 0.75%) and temperatures (25–55 ℃), peaking at 0.815 W/m·K at 55 ℃ with 0.75% CNC. Viscosity decreased with temperature but increased with higher CNC concentrations. Density also increased with CNC concentration and decreased with temperature, while specific heat capacity showed minimal changes with concentration but a slight increase with temperature. These enhancements make CNC nanofluids beneficial for heat transfer applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Viscosity

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FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation

arXiv

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arXiv 2025年

作者： Zhou, Bingyang Zheng, Chunran Wang, Ziming Zhu, Fangcheng Cai, Yixi Zhang, Fu Laboratory Department of Mechanical Engineering University of Hong Kong Hong Kong Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency significantly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system’s robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system’s capability for scalable deployment on resource-constrained edge computing platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Kalman filters

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Large-Scale Distributed Multidisciplinary Design Optimization of the NASA Lift-Plus-Cruise Air Taxi Concept

Large-Scale Distributed Multidisciplinary Design Optimizatio...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： van Schie, Sebastiaan P. C. Ruh, Marius L. Fletcher, Andrew Warner, Michael Sperry, Mark Scotzniovsky, Luca Orndorff, Nicholas C. Xiang, Ru Yan, Jiayao Zhao, Han Krokowski, Joshua J. Chen, J.S. Sarojini, Darshan Gill, Hyunjune Lee, Seongkyu Tagg, Andrew Anderson, Ryan Green, Eric Joseph, Cibin Ning, Andrew Cheng, Zeyu Cao, Zhi Mi, Chunting Guibert, Alexandre T. R. Cronk, Ashley Kim, H. Alicia Meng, Y. Shirley Silva, Christopher Hwang, John T. University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Structural Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Aerospace and Ocean Engineering Department Virginia Tech 1600 Innovation Dr BlacksburgVA24060 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Mechanical Engineering Brigham Young University ProvoUT84602 United States Department of Electrical & Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science & Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60607 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Large-scale gradient-based Multidisciplinary Design Optimization (MDO) can aid in the exploration of high-dimensional design spaces for novel air vehicle concepts, thereby leading to more efficient and economic designs. This paper builds on past works where we demonstrated large-scale physics-based MDO capabilities and applied these to NASA’s lift-plus-cruise electric air taxi concept. We extend this comprehensive mid-fidelity system-level optimization problem with high(er)-fidelity subsystem-level optimizations of various aircraft systems and mission phases, with the aim of further enhancing the accuracy and scope of the aforementioned system-level optimization problem: 1) Power minimization during the transition mission phase;2) Electrical powertrain topology optimization;3) Cell chemistry modeling and thermomechanical battery pack topology optimization;4) Shell-based coupled aero-elastic wing structure optimization. An Analytical Target Cascading-like distributed MDO architecture allows us to couple the system- and subsystem-level optimizations in order to arrive at a consistent and feasible design. We find that the system-level design is most heavily impacted by the reduced battery pack-level energy densities that stem from power peaks in the mission profile. These power peaks seem to result from the inefficient lift rotor blade designs that are needed to satisfy noise constraints. We draw conclusions based on trends in our results and give recommendations for future MDO studies of electrical air taxi vehicle concepts. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shape optimization

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Insights into the role of metal dopant on ammonia decomposition over graphene-supported Ni6, Ni5Pt, and Ni5Rh clusters

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International Journal of Hydrogen Energy 2025年 130卷 168-175页

作者： Yusuf, Alfani Budiantono, Reva Juangsa, Firman Bagja Aziz, Muhammad Budiyanto, Muhammad Arif Saputro, Adhitya Gandaryus Mahyuddin, Muhammad Haris Graduate Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia PLN Nusantara Power UMRO Jl. Pluit Karang Ayu Barat No. 1 Jakarta14450 Indonesia Thermal Science and Engineering Research Group Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Jl Ganesha 10 Bandung40132 Indonesia Institute of Industrial Science The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8505 Japan Department of Mechanical Engineering Universitas Indonesia Depok16424 Indonesia Quantum and Nano Technology Research Group Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Research Center for Nanoscience and Nanotechnology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia

Ammonia (NH3) is a promising hydrogen (H2) carrier, owing to its high hydrogen content and ease of storage. However, efficient catalytic decomposition of NH3 to H2 remains challenging. This study investigates the catalytic activity of graphene-supported Ni6, Ni5Pt, and Ni5Rh clusters for NH3 decomposition to N2 and H2 using density functional theory (DFT) calculations. The results show that the addition of Pt and Rh dopants weakens the adsorption of intermediates formed during the NH3 dehydrogenation step and slightly increases the activation energies. Interestingly, however, this drawback becomes a key factor in enhancing the activity of the subsequent N–N association and H–H recombination steps, which are critical for the reaction to cycle. Our electronic structure analysis reveals that the decreased activity in the NH3 dehydrogenation step but increased activity in the N2 and H2 formation steps are due to the ligand effect exerted by Pt and Rh. Specifically, since the metal dopants have higher electronegativities than Ni, the number of electrons at the preferred Ni-edge site decreases and eventually weakens the adsorption. Furthermore, the spatially broad nature of the Pt-5d and Rh-4d orbitals delocalizes the cluster's electron distribution, which weakens the 2N and 2H adsorption and thus eases the N2 and H2 formation. This study provides insights into the role of noble metal dopants in optimizing Ni-based catalysts for NH3 decomposition, paving the way for efficient H2 utility technologies. © 2025 Hydrogen Energy Publications LLC

关键词： Dehydrogenation

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Influence of directional solidification parameters on the amount of quasicrystalline phase of an AlCuFe alloy

Journal of Alloys and Metallurgical Systems

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Journal of Alloys and Metallurgical Systems 2025年 9卷

作者： dos Santos Barros, Thayza Pacheco Caluête, Rafael E. da Silva, Aline Karla Barbosa de Oliveira, Danniel Ferreira Feitosa, Francisco Riccelly Pereira de Lima, Bruno Alessandro Silva Guedes de Lima, Danielle Guedes Federal University of Paraíba Graduate Program in Mechanical Engineering PB João Pessoa58051-900 Brazil Federal University of Paraíba Department of Mechanical Engineering PB João Pessoa58051-900 Brazil Federal University of Paraíba Department of Materials Engineering João Pessoa58051-900 Brazil

In this work, a casting processing route via unidirectional solidification is proposed, where the severity of cooling can be increased to maximize the amount of quasicrystalline phase. For this, the alloy with nominal composition Al62.5Cu25Fe12.5 was melted and solidified unidirectionally under optimized processing conditions. Our results evidenced a dependence of the quasicrystalline phase percentage, obtained for an AlCuFe alloy, as a function of the position of the ingot and the type of mold used during the directional solidification process. This analysis expands the spectrum of studies on structural properties, for alloys of the AlCuFe system, indicating the cooling conditions, during the directional solidification process, necessary to maximize the amount of quasicrystalline phase. © 2024 The Authors

关键词： Iron alloys

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Modulated magnetic and piezoresponse phase by applied electric field in 2D layered materials at room temperature

引用

Results in Physics 2025年 70卷

作者： Lim, Joong Yeon Kim, Seonghwan Kim, Young-Seong Department of Mechanical Robotics and Energy Engineering Dongguk University Jung-gu Seoul 04620 South Korea Department of Mechanical and Manufacturing Engineering Schulich School of Engineering University of Calgary Calgary T2N 1N4 AB Canada

A hybrid perovskite material with a Two-dimensional (2D) layered (C4H9NH3)2PbIBr3 crystal structure is presented that exhibits magnetoelectricity and piezoelectricity coupling properties at room temperature. The inter... 详细信息

A hybrid perovskite material with a Two-dimensional (2D) layered (C₄H₉NH₃)₂PbIBr₃ crystal structure is presented that exhibits magnetoelectricity and piezoelectricity coupling properties at room temperature. The interaction between ferromagnetism and piezoelectricity was achieved by harmonizing their respective ferroic orders. 2D layered organic–inorganic hybrid perovskites demonstrated controlled multifunctional ferroic attributes, with each characteristic established. The magnetic and piezoresponse phase rotation was controlled by modulating the electric-field strength using the magnetoelectricity and piezoelectricity. The responsive and phase-controllable behavior of ferromagnetism and piezoelectricity in 2D hybrid perovskites was effectively managed by an external electric field induced by the coupling between ferroic orders at room temperature. This discovery offers a promising avenue for future research on hybrid perovskites with novel 2D layered-crystal structure. © 2025 The Author(s)

关键词： Hybrid perovskite Layered van der Waals Magnetoelectricity Multifunctional ferroic coupling Piezoelectricity

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Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations

arXiv

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arXiv 2025年

作者： Li, Chenzui Wu, Xi Liu, Junjia Teng, Tao Chen, Yiming Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa16163 Italy

Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partners’ states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduces an innovative impedance regulation skill learning framework for achieving HRC in multiple physical collaborative tasks. The framework is designed to adjust the robot compliance to the human partner’s states while adhering to reference trajectories provided by human-human demonstrations. Specifically, electromyography (EMG) signals from human muscles are collected and analyzed to extract limb impedance, representing compliance behaviors during demonstrations. Human endpoint motions are captured and represented using a probabilistic learning method to create reference trajectories and corresponding impedance profiles. Meanwhile, an LSTM-based module is implemented to develop task-oriented impedance regulation policies by mapping the muscle synergistic contributions between two demonstrators. Finally, we propose a whole-body impedance controller for a human-like robot, coordinating joint outputs to achieve the desired impedance and reference trajectory during task execution. Experimental validation was conducted through a collaborative transportation task and two interactive Tai Chi pushing hands tasks, demonstrating superior performance from the perspective of interactive forces compared to a constant impedance control method. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

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