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A vector hybrid triboelectric sensor(HTS)for motion identification via machine learning

Nano Research 2023年第7期16卷 10120-10130页

作者： Nannan Zhou Hongrui Ao Xiaoming Chen Shan Gao Hongyuan Jiang School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China

Rapidly responding and cost-effective sensors played a crucial role in industrial ***,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring *** study developed a vector hybrid triboelectric sensor(HTS)with surface nanocrystalline containing triboelectric vibration and rotation units(triboelectric vibration unit(TVU),triboelectric rotation unit(TRU))capable of detecting the vibrational and rotary states of the *** synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS,which contributed to reducing the volume and spatial distribution of the *** on the voltage/current/charge(U-I-Q)signal amplitudes and phase features generated by the TVU,the vibration frequency and orientation of the device can be identified by using a double-layer neural network(D-LNN),in which the accuracy reaches 96.5%and 95.5%***,by combining logistic regression,D-LNN,and linear regression,the accuracy of the TRU for rotary classification exceeds 93.5%in practical *** this study,the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.

关键词： vector triboelectric sensor surface nanocrystalline hierarchical structure sensing properties state detection machine learning

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Topology Optimization of a 3D Printable Prosthetic Foot

Topology Optimization of a 3D Printable Prosthetic Foot

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2024 International Conference on Innovations in Science, engineering and technology, ICISET 2024

作者： Barua, Anurupa Helal-An-Nahiyan, Md. Khulna University of Engineering & Technology Department of Mechatronics Engineering Khulna Bangladesh

ISBN: (纸本)9798350355499

This research tackles a novel prosthetic foot design, simulation and FDM (Fused Deposition Modeling) 3D printing by customizing it for a human of 50 kg body mass. An initial foot model was created in SolidWorks software using actual human foot dimensions and then topology optimization was applied to reduce mass by 70% while maintaining structural integrity under a 500 N external load and a factor of safety of 1.5. The three components of the stance phase-heelstrike, mid-stance, and toe-off-were simulated in a static manner utilizing force on the tibia. The results showed stress levels below the yield strength, confirming the foot's capacity to sustain body weight while standing without failing. Using PLA (Polylactic acid) material, the revised and optimized design was manufactured using a Creality Ender-3 S1 Plus 3D printer. The prosthetic foot weighed 564 grams and had 60% infill after 3D printing. The effectiveness of optimization process is demonstrated by the notable mass reduction from the 2.34 k g mass of the original design model. The PLA material qualities are taken into account in the computations and simulations. This prosthetic foot is a financially sensible option, as indicated by its computed cost of about 5,574 Taka. The presented design shows a significant percentage reduction of material as well as cost in comparison to the commercially available prosthetic feet. © 2024 IEEE.

关键词： Fused Deposition Modeling

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Space Partitioning and Path Planning for Mission Area Exploration Using Multiple UGVs

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Journal of Institute of Control, Robotics and Systems 2024年第3期30卷 197-205页

作者： Choi, Sang-Won Lee, Sang-Hyeon Woo, Sang-Beom Kim, Tae-Hyeong Seoul, Hyeong-Su Kim, Duksu Computer Science and Engineering Korea University of Technology and Education Korea Republic of Computer Mechanical Engineering Korea University of Technology and Education Korea Republic of Computer Mechatronics Engineering Korea University of Technology and Education Korea Republic of

We propose a method for efficient partitioning and exploring complex mission areas using multiple unmanned ground vehicles (UGVs). We introduce two area partitioning algorithms that decompose a given mission area into segments appropriate for UGV navigation. The shape of the area determines the algorithms used. Additionally, we present a path-planning algorithm for each UGV’s entry and mission paths. Consequently, our method generates a collision-free driving plan based on the path planning *** have implemented the proposed method in a simulator and conducted verification tests on various mission area shapes and UGV numbers. Our findings show that it achieves a partitioning quality comparable to a previous Voronoi diagram-based approach while improving performance by up to 15.3 times. Furthermore, the driving plan generated by our method allows all UGVs to navigate their paths safely without colliding. We also validated the effectiveness of the proposed method on a small UGV test bed. These results collectively affirm the practicality and efficacy of the proposed approach. © ICROS 2024.

关键词： Motion planning

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MODELLING AND ANALYSIS OF SELECTED ISSUES OF THE DYNAMICS OF A LAND ROBOT PLATFORM

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Archives of Transport 2024年第4期72卷 23-41页

作者： Dziopa, Zbigniew Zuska, Andrzej Więckowski, Dariusz Faculty of Mechatronics and Mechanical Engineering Kielce University of Technology Kielce Poland Institute of Vehicles and Construction Machinery Engineering Faculty of Automotive and Construction Machinery Engineering Warsaw University of Technology Warsaw Poland

The research object is a land robot designed to perform special tasks. One of the robot's elements is a six-wheeled platform with independent suspension. The platform allows the robot to move on land and overcome obstacles. Any equipment enabling the robot to perform planned tasks can be mounted on it. If the implementation of the mission requires the platform to move in uneven terrain, external forces are generated resulting from the platform's wheels overcoming uneven road surfaces. Disturbances occurring in the system may negatively affect the measuring and actuating devices mounted on the platform. Actuator devices may also cause additional excitations. This may lead to disruptions in the process of the land robot carrying out its intended work. The aim of the work is to analyse selected dynamics issues with particular emphasis on the modal analysis of the land robot platform and, on this basis, to check the correctness of its design. In order to carry out the tasks set, a physical and mathematical model of the platform was developed. The energy method was used to derive the analytical relationships, which requires the determination of the kinetic energy, the potential energy, the dissipative Rayleigh function and external non-potential forces. A modal analysis was carried out to determine the dynamic parameters of the system and its susceptibility to the generated excitations was determined. The considerations carried out allow for the design of a structure that improves comfort for the actuating devices. Thanks to the developed model, it is possible to select the values and distribution of system parameters in such a way as to exclude the phenomenon of beating and resonance. In the general case, the robot's basic motion is coupled to the disturbances generated in the system. That's why the considerations carried out in this paper are so important, which enable the development of robot platform dynamics that minimize the impacts on the robot's realized fundamen

关键词： Kinetic energy

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Electro-discharge machining of microholes on 3d printed Hastelloy using the novel tool-feeding approach

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International Journal of Lightweight Materials and Manufacture 2025年第2期8卷 157-164页

作者： Korgal, Akash Shettigar, Arun Kumar P, Navin Karanth Kumar, Nishanth J, Bindu Madhavi Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal 575025 India Central Research Facility National Institute of Technology Karnataka Surathkal 575025 India Department of Mechatronics Engineering Mangalore Institute of Technology and Engineering Karnataka 574225 India

Hastelloy, a nickel-based superalloy renowned for its exceptional resistance to corrosion at high temperatures, is widely used in sectors such as nuclear, aerospace, chemical processing, and pharmaceuticals. Microelectrical discharge machining (μ-EDM) is crucial for generating microholes and channels on Hastelloy. Since it effectively addresses difficulties like work hardening, high strength & wear resistance, and low thermal conductivity in traditional machining. Microholes play a major role in many critical components for precise control of fluids in fuel injectors, managing heat in turbine blades, controlled gas exchange, etc. The current research investigates the drilling of 8:1 aspect ratio microholes machined by 400 μm diameter electrodes. This study investigated the influence of tool material (tungsten carbide, carbide drill bit, and brass) on μ-EDM performance. Compared to tungsten carbide and carbide drill bits, brass exhibited significantly lower electrode wear, leading to more precise microholes with reduced overcut and taper angle. However, brass also required a substantially longer machining time. Carbide drill bits offered a balance between wear resistance, machining time, and overcut/taper angle. © 2024 The Authors

关键词： High temperature corrosion

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Enhanced Respiratory Tract Auscultation Audio Signal Classification Technique Employing LSTM and RNN 7

Enhanced Respiratory Tract Auscultation Audio Signal Classif...

引用

7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Ul Haq, Ihtisham Ahmad, Mehran Khan, Hamza Ahmad University of Engineering & Technology Peshawar Department of Mechatronics Engineering Pakistan University of Engineering & Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350338461

The complications and fatalities caused by respiratory tract infections are a major public health issue worldwide. Detecting the wide variety of respiratory disorders, from pneumonia to LRTI, URTI, and COPD, requires cutting-edge diagnostic tools. Thanks to advances in AI technology and the improvement of medical diagnostic tools, early detection of such diseases is now possible. In this article we offer a state-of-the-art LSTM-based RNN model that has been meticulously trained on more than 1800 lung audio recordings. We've used data augmentation methods to make sure our dataset is as complete and accurate as possible. In addition, we have combined MFCC and genetic algorithms in our feature extraction method to select the most relevant features for our model. Our findings are conclusive;the suggested MFCC-GA-LSTM model achieved an impressive 98.65% accuracy and an F1 score of 99.16, proving its superiority over prior approaches to classification. The potential for early detection and prevention of these potentially fatal illnesses has been greatly improved because to this novel approach we have developed for diagnosing infections of the respiratory system. © 2023 IEEE.

关键词： Long short-term memory

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Exploration of Various Forging Route Performance of Cast A356 Alloy Reinforced with Fly Ash and MWCNT Hybrid Composites

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Journal of The Institution of Engineers (India): Series D 2023年第1期104卷 259-268页

作者： Soundararajan, R. Sathishkumar, A. Feroz Ali, L. Kaviyarasan, K. Mechanical Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Mechatronics Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India

This proposed study is concerned with casted virgin A356 alloy and stir casted A356 reinforcement with 8 wt% power plant waste fly ash particles and 2 wt% of multi-wall carbon nanotube (MWCNT) hybrid composites further exposed by unidirectional (X), bi-directional (X and Y) and multi-directional forging technique (X, Y and Z). The micrograph and energy dispersive X-ray spectroscopy (EDS) study revealed that the stir-casted hybrid composites with multi-directional forging sample have even distribution of pour-free particles and fine grain refinement in the matrix. The increased hardness and tensile strength evidence the particle bridging inside the matrix as well as a load transfer strengthening mechanism as a result of fly ash and MWCNT representing thermodynamic reactions in the A356 melt. The multi-axial forged samples exhibited 35.21% higher hardness and 15.67% higher tensile strength of values of 96 VHN and 310 MPa over the cast virgin A356 alloy respectively. When compared to other samples, the stir cast hybrid composite with multidirectional forging has a tendency to have a lower wear rate and a higher friction coefficient. This was ascribed due to processing technique and constructive embedding with bonding ability of fly ash and MWCNT reinforcements over the A356 metal alloy. Also, the presence of MWCNT within the matrix clearly indicated good wet ability during sliding. The results exhibit superior resistance to abrasion and adhesive behaviour. Furthermore, the corrosion behaviour of the multi-axis forged hybrid composite samples was superior than any other samples which are evident through corrosion surface analysis using SEM. This outcome was prime consideration, especially in automobile applications. © 2022, The Institution of Engineers (India).

关键词： Tensile strength

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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A Framework for Industrial Inspection System using Deep Learning

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Annals of Data Science 2024年第2期11卷 445-478页

作者： Hridoy, Monowar Wadud Rahman, Mohammad Mizanur Sakib, Saadman Department of Mechatronics & amp Industrial Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Mechanical Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Computer Science & amp Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh

Industrial Inspection systems are an essential part of Industry 4.0. An automated inspection system can significantly improve product quality and reduce human labor while making their life easier. However, a deep learning-based camera inspection system requires a large amount of data to classify the defective products accurately. In this paper, a framework is proposed for an industrial inspection system with the help of deep learning. Additionally, A new dataset of hex-nut products is proposed containing 4000 images, i.e., 2000 defective and 2000 non-defective. Moreover, different CNN architectures, i.e., Custom CNN, Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2, are experimented with the concept of transfer learning on the new hex-nut dataset. Fine-tuning the CNN architectures is performed by freezing the last 14 layers, which provided the optimal architecture, i.e., Xception (last 14 layers trainable, excluding the fully connected layer). The proposed framework can efficiently separate the defective products from the non-defective products with 100% accuracy on the hex nut dataset. Furthermore, the proposed optimal Xception architecture has experimented on a publicly available casting material dataset which produced 99.72% accuracy, outperforming existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Industry 4.0

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A parametric examination of the energetic, exergetic, and environmental performances of the geothermal energy-based multigeneration plant for sustainable products

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International Journal of Hydrogen Energy 2024年 143卷 947-957页

作者： Yilmaz, Fatih Ozturk, Murat Selbas, Resat Isparta University of Applied Sciences Faculty of Technology Department of Mechatronics Engineering Isparta Turkey Isparta University of Applied Sciences Faculty of Technology Department of Mechanical Engineering Isparta Turkey

Geothermal energy is one of the most reliable sources of environmentally friendly energy. In this research, an advanced combined geothermal power plant is not just planned, but thoroughly examined using a comprehensive set of evaluation approaches including thermodynamic, environmental, and exergo-environmental. This system comprises a geothermal cycle, two ORCs with Ammonia fluid, a PEM electrolyzer, an Ammonia synthesis module, and a reverse osmosis desalination. This newly developed configuration can produce clean electricity, green hydrogen, ammonia, freshwater, and heating. To determine the cycle performance and relationship between exergy destruction and the environment, various analyses, which are energetic and exergetic performance, exergy destruction rate, carbon-dioxide emission reduction rate, and environmental index evaluation, are conducted parametrically. The examination results illustrate that the net electricity capability of the modeled plant is 2046 kW. Afterwards, 0.002367 kgs−1 green Hydrogen and 0.009436 kgs−1 ammonia can be produced with this designed system. A total of 4945 kW exergy destruction is observed in this designed system. Additionally, the newly configured cycle has an energetic performance of 26.31% and an exergetic performance of 33.09%. Overall, the comprehensive evaluation of the system instills confidence in its reliability and potential for further carbon dioxide emission reductions. © 2024 Hydrogen Energy Publications LLC

关键词： Desalination

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