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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Hu, Longteng Xue, Feng Yao, Chen Li, Yunzhou Wei, Jin Wang, Peichen Zhu, Zheng Jia, Zhenzhong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Harbin150090 China

ISBN: (纸本)9798350325706

The wheeled rover is widely used in off-road planetary exploration tasks nowadays. However, complex operating environment like soft deformable terrains on the planet surface demands the high performance of rover. In order to reduce danger, environmental perception technologies need to be used on the rover. Terrain classification is a critical method in environmental perception. In this research, a wheel-vision-based terrain classification method is proposed. This method uses wheel-terrain interaction parameters obtained from inside-wheel cameras which are strongly related to the terrain as input. We use single-wheel testbed to collect and process data to form a dataset. The data is obtained under different slip ratios and different terrains. Then, we train four terrain classification models using machine learning algorithms and analyze their performance. From the results, all performance criteria of the models achieve over 95%, with DT being the optimal method. The feasibility of our method in the case of fewer training samples and the rationality of dataset selection are also verified through the results of different training set sizes and the ablation experiment. © 2023 IEEE.

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Real-Time Performance Monitoring of a CNC Milling Machine using ROS 2 and AWS IoT Towards Industry 4.0 20

Real-Time Performance Monitoring of a CNC Milling Machine us...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Patel, Deep Maiti, Chayan Muthuswamy, Sreekumar Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Chennai Kancheepuram600127 India

ISBN: (纸本)9781665463973

The current trend of Industry 4.0 is bringing significant changes to manufacturing processes, with the Internet of Things (IoT) and Cyber-Physical Systems (CPS) becoming essential platforms for achieving high-quality products and throughput. This paper proposes a novel system design for the digital transformation of a CNC milling machine towards Industry 4.0, using a sensor network based on Robot Operating System 2 (ROS 2) connected to the Amazon Web Services (AWS) IoT platform. The system uses two sensors, an inductive proximity sensor (KD18-3008PA) for spindle speed monitoring and an infrared thermal sensor (MLX90614-DCC) for monitoring the tool bit temperature. Data from the sensors is collected using the ESP32-WROOM, which transmits the collected data to the Raspberry Pi 4 (RPi 4) running micro-ROS agents. The RPi 4 acts as an IoT gateway, publishing data to AWS IoT core, where it is stored and visualized using Amazon QuickSight. The proposed system enables real-time monitoring of the CNC milling machine's performance, facilitating predictive maintenance and improving efficiency. The results show that the proposed system design effectively achieves digital transformation and can be scalable and flexible, making it suitable for various applications and as a foundation for further research in Industry 4.0. © 2023 IEEE.

关键词： Sensor networks

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Dynamic Stability of a Sieve Shaker Used in a Pachouli Oil Refining Process 3rd

Dynamic Stability of a Sieve Shaker Used in a Pachouli Oil R...

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3rd International Conference on Experimental and Computational Mechanics in engineering, ICECME 2021

作者： Kurniawan, Rudi Fuadi, Zahrul Abubakar, Fathurrahman Afdhal, Afdhal Department of Mechanical and Industrial Engineering Universitas Syiah Kuala Banda Aceh Indonesia Undergraduate Study Program Department of Mechanical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9789811936289

One of the problems causing low quality of essential oils is the high content of solvent resulting from traditional extraction process employing low purity metallic components. This extracted oil therefore requires further process to improve the patchouli alcohol content of the oil by removing unwanted components. One of the process is by mixing the oil with bentonite and zeolite. In this study, a sieve shaker is designed to be used at an integrated equipment for patchouli oil refining process. In order to have a dynamically stable shaker, it is necessary to investigate the effect of various components for the stability, particularly the spring stiffness. The simulation was conducted using Autodesk Inventor using a one degree of freedom model. It is shown from the result that, for the defined condition, the sieve shaker shown a stable condition for the spring stiffness value at least 10 kN/m. Spring stiffness lower than the value could cause detachment of the roll follower from the cam, resulting in impact between the components. © 2023, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stiffness

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computerized tomography

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Walking-age estimator based on gait parameters 12

Walking-age estimator based on gait parameters

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Kuroda, Tomohito Okamoto, Shogo Akiyama, Yasuhiro Tokyo Metropolitan University Department of Computer Science Hino Japan Shinshu University Dept. Mechanical Engineering and Robotics Ueda Japan

ISBN: (纸本)9798350340181

Human gait motions differ depending on age. We estimated peoples' age using kernel regression analysis with reported height and weight and representative gait parameters as explanatory variables. The samples were Japanese women aged in their 20s-70s represented in an open database. The estimated ages exhibited a correlation coefficient of 0.65 with the actual ages, and the mean absolute error was 17.7 years. Estimation errors were significantly greater for older adults than for young people. Given that the method involves gait parameters that can be measured using wearable devices, such as inertial measurement units, this technique provides an accessible way for people to independently monitor their physical health in daily life. © 2023 IEEE.

关键词： Regression analysis

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Severe Plastically Deformed Mg–Zn–Zr–RE Alloy Developed as a Biomaterial

Severe Plastically Deformed Mg–Zn–Zr–RE Alloy Developed a...

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Magnesium Technology Symposium held at the TMS Annual Meeting and Exhibition, 2024

作者： Shunmugasamy, Vasanth C. Mansoor, Bilal Mechanical Engineering Program Texas A&M University at Qatar Doha Qatar Department of Materials Science and Engineering Texas A&M University College StationTX United States

ISBN: (纸本)9783031502392

Mg alloysMagnesium alloys (Mg alloys) have high corrosion rateCorrosion Rate that inhibits their application as biomaterialBiomaterials. For safe use as biomaterial, it is essential to control their corrosion ratesCorrosion Rate. In Mg alloys, microgalvanicMicro-galvanic corrosion coupling between the α-Mg matrix and secondary precipitates can exist which results in increased corrosion rate. To address this challenge, we engineered the microstructureMicrostructure of a biodegradable Mg–Zn–RE–Zr alloy by severe plastic deformationPlastic deformation process such as friction stir processingFriction Stir Processing (FSP) (FSP), improving its corrosion resistance and mechanical propertiesmechanical Properties simultaneously. Subjecting the alloy to FSP resulted in refined grains, basal textureTexture and broken and uniformly distributed secondary precipitates. In vitro corrosion of base material showed microgalvanic coupling between precipitate and matrix, resulting in unstable surface layer. The processed alloy showed uniform corrosion owing to formation of stable surface film formation, due to the refined grains, texture, and distribution of precipitates. The results show promising potential of Mg alloyMagnesium alloys (Mg alloys)as biomaterialBiomaterials. © The Minerals, Metals & Materials Society 2024.

关键词： Corrosion resistance

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Real time imaging of acoustic microrobots inside microfluidic and ex-vivo vessels 26

Real time imaging of acoustic microrobots inside microfluidi...

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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Fonseca, Alexia D.C. Heinle, Anna Heinle, Tirza Ahmed, Daniel Acoustic Robotics Systems Lab Department of Mechanical and Process Engineering ETH Zurich Switzerland

ISBN: (纸本)9781733419048

Combined navigation and imaging of microrobots in living vasculatures is essential in realizing targeted drug delivery. However, in the present there are still few microrobots which can show both functionalities simultaneously when implemented inside a vasculature environment. We developed a dual acoustic control and imaging of "swarmbots", in which we can track in real time microrobot navigation inside microfluidic vessels and also inside ex-vivo vessels. We additionally show how to exploit Power Doppler imaging to identify microrobotic movement when the traditional ultrasound imaging does not provide enough contrast. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Controlled drug delivery

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BCLH2Pro:A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

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Energy and AI 2024年第4期18卷 13-26页

作者： Thanadol Tuntiwongwat Sippawit Thammawiset Thongchai Rohitatisha Srinophakun Chawalit Ngamcharussrivichai Somboon Sukpancharoen Department of Mechanical Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Automation Robotics and Intelligent System Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Chemical Engineering Faculty of EngineeringKasetsart UniversityBangkok 10900Thailand Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals(CBRC) Faculty of ScienceChulalongkorn UniversityPathumwanBangkok 10330Thailand Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400SelangorMalaysia Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Center for Alternative Energy Research and Development Khon Kaen UniversityKhon Kaen 40002Thailand

This study optimizes biomass chemical looping processes(BCLpro),a technique for converting biomass to energy,through machine learning(ML)for sustainable energy *** study proposes an integrated Fe_(2)O_(3)-based,BCLpro combining steam gasification for H2 *** Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model.A methodology involving K-Nearest Neighbors(KNN),Extreme Gradient Boosting(XGB),Light Gradient Boosting Machine(LGBM),Support Vector Machine(SVM),Random Forest(RF),and CatBoost(CB)algorithms was employed to predict H_(2)yields in the BCLpro,utilizing 10-fold cross-validation for robust model *** highlight the CB algorithm’s superior performance,achieving up to 98%predictive accuracy,with carbon content,reducer temperature,and Fe_(2)O_(3)/Al_(2)O_(3)mass ratio identified as crucial *** algorithm has been developed into a user-friendly tool,BCLH2Pro,accessible via a web *** tool is designed to assist in reducing costs,optimizing biomass selection,and planning operational conditions to maximize H2 yield in BCLpro *** to the tool can be obtained through the following link:http://***/.

关键词： Artificial intelligence Supervised learning Process simulation Chemical looping Agricultural waste

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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Science China(Technological Sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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Understanding Acoustic Phenomena in Stenosed Coronary Arteries through Bond Graph Modeling of Sound Generation 46

Understanding Acoustic Phenomena in Stenosed Coronary Arteri...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Sandoval, Loreto Rojas Ulloa, René Araya Da Silva, Ana Karoline Almeida De Almeida Nunes, Gustavo Adolfo Marcelino Faria, Rafael Mendes Sepúlveda, José Parra Da Costa, Lindemberg Barreto Mota Acevedo, Luca Rubilar Rosa, Mário Fabrício Fleury Pino, Esteban J. De Siqueira Rodrigues Fleury Rosa, Suélia Universidad de Concepción Electrical Engineering Department Chile University of Brasilia Mechanical Engineering Department Distrito Federal Brasilia70910-900 Brazil University of Brasilia Postgraduate Program in Biomedical Engineering Distrito Federal Brasilia70910-900 Brazil

ISBN: (纸本)9798350371499

Coronary artery stenosis is characterized by the obstruction or narrowing of coronary arteries, impairing blood flow to the cardiac muscle. Cardiovascular diseases rank among the leading global causes of death. Understanding the mechanisms, risk factors, and impacts of coronary artery stenosis (CAD) is essential for developing effective strategies for prevention, early diagnosis, and treatment, aiming to enhance cardiovascular health and reduce adverse consequences associated with this condition. Previous studies explore the potential of acoustic devices in non-invasive CAD detection through the analysis of cardiac sounds. A theoretical model utilized a cardiac microphone and signal processing to generate sounds in partially obstructed coronary arteries. Emphasizing the model's sensitivity to geometric and dynamic parameters, this article integrates Bond Graph (BG) models into the analog sound circuit, aiming to develop a dynamic representation of biological processes in complex systems like coronary arteries. This research seeks to construct a comprehensive mathematical model using a state-space approach to enhance understanding, diagnostics, and interventions in cardiovascular conditions. The utilization of BG demonstrated notable similarity to the pathology of the disease, providing an accurate representation of its effects. The model's sensitivity to geometric and dynamic parameters allowed an in-depth understanding of the specific physiological characteristics of the disease, highlighting its ability to reproduce patterns observed in patients with CAD. © 2024 IEEE.

关键词： Diagnosis

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