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8th International Conference on Control, Automation and Robotics, ICCAR 2022

作者： Lu, Di Wang, Jun-Feng Zhang, Bao-Yi Jiang, Yu Macau University Of Science And Technology Faculty Of Humanities And Arts China College Of Creative Design Shenzhen Technology University Shenzhen China Xiamen University Of Technology Department Of Product Design Xiamen China Southwest University Of Science And Technology Industrial Design Engineering Mianyang China

ISBN: (纸本)9781665481168

This paper aims to clarify the definition in the minds of users about 'easy to use' of daily-use related products and provide guidance for the design and application of daily-use related products. First, the development and present situation of affordance theory and daily-use product design are introduced, and the development status and trends of design innovation are summarized. Then, the design characteristics of daily-use products as the main category are explored based on affordance theory. Besides, statistics and daily-use-related products are sorted out through domestic and foreign online selling channels and categorized according to the form. Afterward, the experimental scores of the subjects' selection of physical linkages are quantitatively conducted. A product design principle of daily-use related products based on affordance is proposed. The results laid a foundation for the design of other product categories with experimental methods. © 2022 IEEE.

关键词： Product design

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Sports Events Recognition Using Multi Features and Deep Belief Network

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Computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Charcoal Briquette Manufactured from Indonesian Sugar Palm Bunches (Arenga longipes Mogea) as Biomass-Based New Renewable Energy

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Journal of Renewable Materials 2025年第3期13卷 639-652页

作者： Hakim, Luthfi Iswanto, Apri Heri Lubis, Yunida Syafriani Wirawan, Adam Jagwani Batubara, Ridwanti Kim, Nam Hum Antov, Petar Rogoziński, Tomasz Hua, Lee Seng Chen, Lum Wei Selvasembian, Rangabhashiyam Jayusman Sutiawan, Jajang Laboratory of Forest Product Technology Faculty of Forestry Universitas Sumatera Utara Deli Serdang 20353 Indonesia Research Center for Applied Botany National Research and Innovation Agency Cibinong16911 Indonesia Department of Forest Biomaterials Engineering College of Forest and Environmental Sciences Kangwon National University Chuncheon24341 Korea Republic of Faculty of Forest Industry University of Forestry Sofia1797 Bulgaria Department of Furniture Design Faculty of Forestry and Wood Technology Poznan University of Life Sciences Poznan60627 Poland Cawangan Pahang Kampus Jengka Kuantan26400 Malaysia Department of Bio and Natural Resource Faculty of Bioengineering and Technology Universiti Malaysia Kelantan Jeli 17600 Malaysia Department of Environmental Science and Engineering School of Engineering and Sciences SRM University-AP Amaravati522240 India Research Center for Biomass and Bioproduct National Research and Innovation Agency Cibinong16911 Indonesia

The utilisation of sugar palm bunches-charcoal briquettes (SPB-CB) represents a significant advancement in biomass energy. This study aimed to analyse the properties of charcoal briquettes produced from SPB (Arenga longipes). The experiment involved categorising the dimensions of charcoal powder into three specific particle sizes: 20–40 mesh, 40–60 mesh, and particles that could pass through a 60-mesh screen. The charcoal powder will be combined with tapioca as a binding agent at three specific concentrations: 11%, 13%, and 15%. The research findings indicate that the samples underwent 60 mesh passes achieved the maximum briquette density, with an average value of 0.58 g/cm3. The highest attainable compressive strength sample value was 27.52 kgf/cm2, which was attained by employing 60 mesh size and 15% adhesive concentration. The calorimetric investigation showed that SPB-charcoal had the highest calorific value of 25.88 MJ/kg, while the SPB-CB had a little lower caloric value of 24.64 MJ/kg. The ash content and volatile matter values showed that the briquettes with the lowest ash content had values of 10.49% and 32.65%, respectively. Furthermore, the carbon fixation values varied between 16.65% and 52.36%. Thermogravimetric analysis indicates that charcoal derived from SPB exhibits superior thermal characteristics compared to charcoal briquettes. However, thermal properties of SPB charcoal do not show significant differences when compared to charcoal briquettes that have been processed with a mesh size of 20–40 and include 11% adhesive. According to this research, it may be inferred that charcoal briquettes made from sugar palm bunches meet the requirements specified in SNI 01-6235-2000. © 2025 The Authors.

关键词： Brinell Hardness

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Surgical skill analysis using explainable AI in endoscopic sinus surgery

Surgical skill analysis using explainable AI in endoscopic s...

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2025 IEEE/SICE International Symposium on system Integration, SII 2025

作者： Yamada, Kaito Suzuki, Masanobu Miyaji, Kou Ebina, Koki Sase, Kazuya Tsujita, Teppei Chen, Xiaoshuai Abe, Takashige Komizunai, Shunsuke Nakamaru, Yuji Senoo, Taku Homma, Akihiro Konno, Atsushi Hokkaido University Graduate School of Information Science and Technology Sapporo Japan Hokkaido University Faculty of Medicine and Graduate School of Medicine Department of Otolaryngology-Head and Neck Surgery Sapporo Japan Tohoku Gakuin University Department of Mechanical Engineering and Intelligent Systems Sendai Japan National Defense Academy of Japan Department of Mechanical Engineering Yokosuka Japan Hirosaki University Graduate School of Science and Technology Hirosaki Japan Hokkaido University Graduate School of Medicine Department of Urology Sapporo Japan Kagawa University Faculty of Engineering and Design Takamatsu Japan

ISBN: (纸本)9798331531614

Endoscopic sinus surgery (ESS) is a standard procedure performed worldwide for diseases of the nose and sinuses, but it is highly technical and requires effective training. A system for evaluating the skill level of surgeons using machine learning based on measurement data from endoscopic sinus surgeries performed with a 3D sinus model has been developed. In this study, an analysis using SHapley Additive exPlanations (SHAP) values was conducted to investigate behaviors that significantly reflect surgeons' skill proficiency. Additionally, feature reduction was performed using a variable reduction method, eliminating features in ascending order of their contributions as calculated by SHAP. This approach aimed not only to mitigate the risk of overfitting due to a decrease in explanatory variables but also to improve the accuracy of classifying surgeons based on skill differences. The results demonstrated that reducing features based on SHAP contributions led to an improvement in classification accuracy. © 2025 IEEE.

关键词： Endoscopy

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The Influence of Input Variable Selection on Deep Learning-Based Sea Ice Parameter Inversion from Multi-Sensor Satellite Data

The Influence of Input Variable Selection on Deep Learning-B...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Chen, Xinwei Patel, Muhammed Cantu, Fernando J. Pena Park, Jinman Turnes, Javier Noa Jiang, Mingzhe Xu, Linlin Scott, K. Andrea Clausi, David A. Huang, Weimin University of Waterloo Vision and Image Processing Lab Department of System Design Engineering Canada Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada

ISBN: (纸本)9798218142186

The automated mapping of Arctic sea ice using satellite data from various sources plays a critical role in maritime shipping and climate science research. Deep learning models, particularly convolutional neural networks (CNNs), have shown significant potential in generating more accurate and reliable automated sea ice maps from synthetic aperture radar (SAR) images. This study focuses on investigating the impact of integrating multi-source data on enhancing the performance of CNN-based sea ice mapping models. By employing a multitask U-Net architecture, the paper aims to estimate three essential sea ice parameters, namely sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE), through different combinations of input data. The AI4Arctic Sea Ice Challenge dataset with multi-sensor satellite data is utilized for model training and evaluation. The experimental findings confirm the effectiveness of incorporating passive microwave data and spatial-temporal information in conjunction with SAR imagery, as well as feature redundancy removal, resulting in improved accuracy for sea ice parameter estimation. © 2023 The Marine technology Society (MTS).

关键词： Sea ice

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Performance Enhancement of Piezoresistive Tactile Sensor Based on Mechanical Metamaterial with Negative Poisson's Ratio

Performance Enhancement of Piezoresistive Tactile Sensor Bas...

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IEEE SENSORS

作者： Mingyu Kang Honggap Choi Keun Park Soonjae Pyo Deparment of Mechanical Design and Robot Engineering Seoul National University of Science and Technology Seoul Republic of Korea Department of Mechanical System Design Engineering Seoul National University of Science and Technology Seoul Republic of Korea

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

This work proposes a piezoresistive tactile sensor based on a metamaterial with a negative Poisson's ratio, utilizing a more cost-effective and straightforward method compared to previous processes for fabricating microstructures. To demonstrate the impact of negative Poisson's ratio on piezoresistive sensor performance, comparison structures with a positive Poisson's ratio are designed, manufactured, and tested. The metamaterial-based sensor exhibits a high sensitivity at low pressures, which is 2.8 times higher than that of a body-centered cubic structure and 10 times higher than a simple cubic structure. Additionally, the fabricated sensor demonstrates excellent flexibility and minimal physical interference due to the integration of flexible electrodes and substrates. The sensor shows potential for various applications, including wearable devices, which have recently gained significant attention.

关键词： Performance evaluation Electrodes Sensitivity Tactile sensors Interference Metamaterials Microstructure Wearable devices Piezoresistance Substrates

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Regulating microstructure and composition by carbonizing in-situ grown metal-organic frameworks on cotton fabrics for boosting electromagnetic wave absorption

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Nano Research 2024年第8期17卷 7290-7300页

作者： Jie Jin Hongsen Long Hu Liu Yan Guo Tiantian Bai Ben Bin Xu Mohammed A.Amin Hua Qiu Mohamed H.Helal Chuntai Liu Changyu Shen Zeinhom M.El-Bahy Zhanhu Guo State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China China State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410000China School of Electronic Engineering North China University of Water Resources and Electric PowerZhengzhou 450046China Complex Conditions of High-end Tire Technology Innovation Center Shuguang Rubber Industry Research&Design Institute Co.LtdGuilin 541004China Integrated Composites Lab Department of Mechanical and Civil EngineeringNorthumbria UniversityNewcastle Upon TyneNE18STUK Department of Chemistry College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Shaanxi Key Laboratory of Macromolecular Science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Department of Chemistry Faculty of Arts and ScienceNorthern Border UniversityRafha 73213Saudi Arabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr City 11884CairoEgypt

High-temperature carbonized metal-organic frameworks(MOFs)derivatives have demonstrated their superiority for promising electromagnetic wave(EMW)absorbers,but they still suffer from limited EMW absorption capacity and narrow *** the advantage of microstructure and chemical composition regulation for the design of EMW absorber,hierarchical heterostructured MoS_(2)/CoS_(2)-Co_(3)O_(4)@cabonized cotton fabric(CF)(MCC@CCF)is prepared by growing ZIF-67 MOFs onto CF surface,chemical etching,and *** from the dual loss mechanism of magnetic-dielectric multicomponent carbonized MOFs,chemical etching and carbonization process can effectively introduce abundant micro-gap structure that can result in better impedance matching and stronger absorption capacity via internal reflection,doped heteroatoms(Mo,N,S)to supply additional dipolar polarization loss,and numerous heterointerfaces among MoS_(2),CoS_(2),Co_(3)O_(4),and CCF that produce promoted conduction loss and interfacial polarization ***,a minimal reflection loss of−52.87 dB and a broadest effective absorption bandwidth of 6.88 GHz were achieved via tunning the sample thickness and filler loading,showing excellent EMW absorption *** research is of great value for guiding the research on MOFs derivatives based EMW absorbing materials.

关键词： metal-organic frameworks hierarchical structure chemical etching sulfurization electromagnetic wave absorption

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference system to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

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EVOLUTIONARY MORPHOLOGY TOWARDS OVERCONSTRAINED LOCOMOTION VIA LARGE-SCALE, MULTI-TERRAIN DEEP REINFORCEMENT LEARNING

arXiv

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arXiv 2024年

作者： Chen, Yenan Zhang, Chuye Gu, Pengxi Qiu, Jianuo Yin, Jiayi Qiu, Nuofan Huang, Guojing Huang, Bangchao Zhang, Zishang Deng, Hui Zhang, Wei Wan, Fang Song, Chaoyang Department of Mechanical and Energy Engineering School of Design Department of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Design Southern University of Science and Technology Shenzhen518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of 'intelligent design under constraints' - hereafter referred to as constraint-driven design intelligence - in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning. (Project GitHub: https://***/ancorasir/BennettWheelLegRL). © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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