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AllWeather-UsableWearable Dual Energy Harvester for Outdoor Sustainable Operation

SusMat 2025年第1期5卷 159-170页

作者： Yeongju Jung Jiyong Ahn Ji-Seok Kim Junhyuk Bang Minwoo Kim Seongmin Jeong Jinwoo Lee Seonggeun Han Il-Kwon Oh Seung Hwan Ko Applied Nano and Thermal Science Lab Department of Mechanical EngineeringSeoul National UniversitySeoulRepublic of Korea National Creative Research Initiative for Functionally Antagonistic Nano-Engineering Department of Mechanical EngineeringSchool of Mechanical and Aerospace EngineeringKorea Advanced Institute of Science and Technology(KAIST)DaejeonRepublic of Korea Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoulRepublic of Korea Device Solutions Research Samsung ElectronicsHwaseong-siRepublic of Korea Institute of Engineering Research/Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National UniversitySeoulRepublic of Korea Interdisciplinary Program in Bioengineering Seoul National UniversitySeoulRepublic of Korea

Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable ***,the current lack of engineering design that can harvest energy regardless of weather conditions presents a significant *** this regard,this study introduces a wearable,breathable all-weather usable dual energy harvester(AWuDEH)that can seamlessly generate electrical energy regardless of weather *** this study,the AWuDEH integrated with the thermoelectric generator and the droplet-based electricity generator is *** AWuDEH,especially engineered with a bi-functional top substrate for radiative cooling and electrification,achieves sustainable energy harvesting outdoors,thereby addressing the conventional challenge associated with the necessity for separate energy harvesters tailored to outdoor usage contingent on weather *** device reaches a maximum power output of 14.6μW cm^(-2)under simulated sunny conditions and generates a much more enhanced thermoelectric power of 74.78μW cm^(-2)and a droplet-based electric power of 256.25 mW m^(-2)in rainy *** proof,this study developed self-powered wearable electronics capable of acquiring physiological signals in simulated outdoor *** study presents a promising advancement in wearable technology,offering a potent solution for sustainable energy harvesting independent of weather conditions.

关键词： all-weather usable droplet-based electricity generator hybrid architecture outdoor sustainable operation thermoelectric generator

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare 5

Exploring Innovations in Autonomous Robotics and Mechatronic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Shalini, T.Grace Ganeshabu, L. Chemudugunta, Priyanka Yamsani, Nagendar Kumar, Ch.Ashok Athiraja, A. School of Computing Department of Computational Intelligence Chengalpattu Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Department of Robotics and Automation Thandalam Mevalurkuppam Rajalakshmi Nagar Tamil Nadu Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India Sr University Department of School of Computer Science and Artificial Engineering Warangal India Malla Reddy Engineering College Department of Mechanical Engineering Main Campus Hyderabad Telangana Maisammaguda India Telangana Secunderabad India

ISBN: (纸本)9798331504403

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Putra, Qori Alfatah Sari, Christy Atika Rachmawanto, Eko Hari Cahyo, Nur Ryan Dwi Mulyanto, Edy Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers. © 2023 IEEE.

关键词： Image segmentation

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting 19

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Akl, James Kodate, Shreedhar Calli, Berk Worcester Polytechnic Institute Robotics Engineering Department WorcesterMA01609 United States Worcester Polytechnic Institute Data Science Program WorcesterMA01609 United States

ISBN: (纸本)9798350320695

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our 'Oxy-fuel Cutting Task State' (OCTS) dataset is available at ***/10.5281/zenodo.7734951. © 2023 IEEE.

关键词： Risk assessment

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Estimation of Mediolateral Gait Postural Stability using Time-Series Pelvis Angular Velocities 11

Estimation of Mediolateral Gait Postural Stability using Tim...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

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

ISBN: (纸本)9781665492324

Gait stability indices play an important role in both clinical and commercial applications and are effective for estimating an individual's fall risk. In general, the computation of gait stability indices requires motion-capture systems to measure the positions and angles of multiple body segments. If the stability indices can be estimated from the time-series data of a single body segment, they can be easier to access. We used principal motion analysis to estimate the margin of stability, which is a popular gait stability index, based on the time series of the angular velocities of the pelvis during walking. The linear combination of three principal motions could estimate the minimum margin of stability along the mediolateral direction with a correlation coefficient of 0.58. The angular velocities of the pelvis can be used to estimate gait stability independently or in conjunction with other kinematic information. © 2022 IEEE.

关键词： Time series

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AgileVision Academy: Indoor Drone Navigation and Obstacle Avoidance 14

AgileVision Academy: Indoor Drone Navigation and Obstacle Av...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Jiang, Haolong Gu, Lingdan Wang, Jiayu Lan, Xiao Liu, Qinyao Wang, Fanxin Xi'an Jiaotong Liverpool University University of Liverpool School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Robotics Department of Intelligence Robotics Engineering With Contemporary Entrepreneurialism Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Information and Computer Science Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Mechatronics and Robotics Suzhou China

ISBN: (纸本)9798350356670

Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge by developing advanced algorithms for lightweight drones, enabling them to reach target location and carrying out delivery efficiently and safely. Utilizing camera data to map and avoid obstacles, we create a system that not only maps and perceives the environment but also navigates its path to the destination. Included in the educational content, this project demonstrates the effectiveness of the technology through practical applications and real-world testing, helping the next generation of engineers accumulate technical experience and broaden their understanding of drone navigation algorithms and applications in system integration. It has a milestone impact on education and development of the drone field. © 2024 IEEE.

关键词： Drones

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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2023 International Conference on Networking, Electrical engineering, computer science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Multi-robot Active SLAM based on Submap-joining for Feature-based Representation Environments

Multi-robot Active SLAM based on Submap-joining for Feature-...

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2022 Australasian Conference on robotics and Automation, ACRA 2022

作者： Chen, Shengduo Zhao, Liang Huang, Shoudong Hao, Qi Robotics Institute of University of Technology Sydney Australia Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9781713866480

The ability to acquire knowledge of the environment actively is essential for autonomous system. In this paper, we propose a multi-robot active simultaneous localization and mapping (SLAM) algorithm based on mutual information for feature-based representation environments that do not depend on the grid map. A multi-layer motion planner and virtual landmarks are introduced to improve exploration efficiency and reduce planning time. To improve the system's accuracy and scalability, we also developed a decentralized version of the active SLAM based on the submap-joining approach. Both simulations and real-world experiments are performed to validate the effectiveness of the proposed methods. © 2022 Australasian robotics and Automation Association. All rights reserved.

关键词： Industrial robots

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