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检索条件"任意字段=International Conference on Computer Technologies in Physical and Engineering Applications"

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computer Vision- and Human-Robot Interaction-Supported Assembly for Collaborative Off-Earth Habitat Construction 12th

Computer Vision- and Human-Robot Interaction-Supported Assem...

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12th international conference on Frontiers of Intelligent Computing: Theory and applications, FICTA 2024

作者： Bier, Henriette Hidding, Arwin Micah Prendergast, J. Peternel, Luka Lab Faculty of Architecture and the Built Environment Delft University of Technology Julianalaan 134 DelftBL 2628 Netherlands Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 CD Delft2628 Netherlands

ISBN: (纸本)9789819601424

Rhizome 1.0 and 2.0 are European Space Agency (ESA) co-funded projects that have been implemented with a team from the Architecture, Mechanical engineering, and Aerospace engineering Faculties, TU Delft, and various industrial partners. The focus is on the development of a Martian habitat using 3D-printed components and in situ resource utilization. This paper presents a new multi-modal method developed for the collaborative assembly of building components with the support of computer Vision (CV) and Human-Robot Interaction (HRI) using compliant robotic collaborative manipulators. The building components are Voronoi-based and are fabricated using Design-to-Robotic-Production and -Assembly (D2RP&A). During the collaborative assembly, the robot uses CV to detect the fabricated component and generate autonomous actions to perform the pick-and-place movement. Between the autonomous robot actions, HRI is used by the human to physically guide the robot when grasping and positioning. To evaluate the proposed method, lab experiments were conducted using robotically milled mock-up components from Styrofoam, which were assembled with a collaborative robotic arm. The results indicate that robots can assist humans during the assembly process to implement tasks that are beyond their physical abilities, while robots benefit from human cognitive capabilities during more complex actions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic assembly

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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Artificial Intelligence based Anomaly detection in Distributed energy resources for Smart Grid

Artificial Intelligence based Anomaly detection in Distribut...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Umathe, Shradha Dudhe, Neelima Hiware, Rutuja Vaidya, Prajakta G H Raisoni College of Engineering Department of Electrical Engineering Nagpur India

ISBN: (纸本)9798331529833

The rapid emergence and evolution of Information and Communication technologies (ICT) has greatly impacted the energy distribution industry. This has led to the transformation of the traditional distribution system into a smart grid. Although the design strategies for smart grids have been studied by academic researchers, there is currently no practical guidance available for the industry on how to improve the level of intelligence in the grid. An industrial control system (ICS) is a type of device that monitors and controls the activities of an industrial environment. Unlike other forms of communication, the traffic of ICS is stable, regular, and periodical. Through statistical modeling, we can analyze the various features of this communication to create a statistical profile of its performance. For instance, by observing the inter-arrival and packet direction of this communication, we can identify the distribution of features that are useful in analyzing the communication. Due to the complexity of the issue, many researchers are currently working on developing techniques to detect anomalous intrusions in smart grids. Unfortunately, there is not enough experience in this field to develop effective approaches. Another issue that emerges when implementing pattern recognition techniques is that it can be very challenging to identify the signals. Due to the nature of the data in smart grids, there are often high levels of misclassification. The ability to detect and prevent power outages is very important for the safety and operation of the power grid. Modern technologies have evolved to provide a large and complex cyber physical system (CPS) that can monitor and control the various components of the power grid. The power grid is continuously improving due to the advancements in technology, and the number of sensors and measurement devices that can be used to monitor and control the system will increase. Unfortunately, this increase in data can overwhelm the existing S

关键词： SCADA systems

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Siamese Network for EEG Classification of Motor Execution and Imagery 5

Siamese Network for EEG Classification of Motor Execution an...

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5th IEEE international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Li, Zhi Chen, Cai Peng, Fulai Lv, Danyang Tian, Chongxuan Meng, Xiangwei Zhang, Ningling Wu, Fengxia Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan250000 China Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan250061 China School of Basic Medical Sciences Shandong University Jinan250000 China

ISBN: (纸本)9798331533694

Motor imagery (MI), the mental simulation of movement without physical execution, is a pivotal aspect of brain-computer interface (BCI) research, facilitating direct communication between the brain and external devices. Non-invasive BCIs, particularly those utilizing electroencephalogram (EEG) signals, face inherent challenges, including high-dimensional data, temporal dependencies, and variability across subjects. This study investigates the potential of Siamese networks-A deep learning framework grounded in contrastive learning-for single-channel EEG classification in MI tasks. Through a series of comparative experiments employing identical parameter settings, including ten-fold cross-validation, we demonstrate that Siamese networks outperform convolutional neural networks (CNNs), yielding an average classification accuracy of approximately 10% higher. These findings underscore the superior capability of Siamese networks in capturing subtle temporal patterns and mitigating inter-subject variability, positioning them as a promising alternative to traditional CNN-based approaches in EEG-based MI classification. © 2025 IEEE.

关键词： Electroencephalography

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A Comprehensive Survey and Implementation of Health Kiosk Innovations: AI Integration, IoT applications, and Predictive Healthcare Solutions 3

A Comprehensive Survey and Implementation of Health Kiosk In...

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3rd international conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Amrutesh, Aniverthy Bharadwaj, Shreelakshmi Darshan Kumar, T. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798331515911

Kiosks, initially designed for administrative purposes, have rapidly evolved into vital tools in healthcare, offering a range of services such as diagnostics, telemedicine, and health data management. This paper presents a comprehensive survey of health kiosks, emphasizing their application in healthcare environments, particularly with the integration of AI, IoT, and predictive analytics. Key technologies, including advanced biometric sensors and AI-driven health insights, have significantly enhanced their functionality. The economic impact of deploying health kiosks, their integration with existing healthcare systems, and the importance of adhering to international medical standards are thoroughly examined. A review of academic research, patents, and industry innovations sheds light on the latest advancements, while case studies of real-world implementations demonstrate their growing adoption across different sectors. The paper also provides a comparative analysis of kiosk models globally, addressing challenges in deployment. Future trends such as AI and wearable technology integration, blockchain for secure health data management, and personalized healthcare services are explored, highlighting the potential for health kiosks to further revolutionize healthcare delivery. Considering the Literature available a practically feasible Health Kiosk is implemented with a custom ML model deployed with an accuracy of 95.3% is achieved. © 2025 IEEE.

关键词： Electronic health record

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Model-Based Control for Enhanced Headlamp Performance 1

Model-Based Control for Enhanced Headlamp Performance

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1st international conference on Sustainable Energy technologies and Computational Intelligence, SETCOM 2025

作者： Shivaraj, Ananya Dhull, Asmi Obulesu, Y.P. Vit School of Electrical Engineering Vellore632014 India

ISBN: (纸本)9798331520540

This paper follows a model-based design approach for the closed-loop headlamp control system, primarily with the purpose of improving luminance control as well as the efficiency of energy consumption in automotive applications. The method is centered around a DC-DC boost converter with high output voltage accuracy to ensure optimal power transfer towards headlamps. It can be directly developed in MATLAB/Simulink, which allows faster prototyping and algorithm testing before hardware is even deployed. The closed-loop feedback mechanism continuously compares output voltage to a setpoint and dynamically adjusts the duty cycle of the converter in real-time to maintain uniform lighting. In addition, the converter is validated through simulation to have stable voltage regulation with minimal overshoot and fast settling times. The hardware implementation of the system is carried out using an STM Nucleo board, which brings the reliability of the Simulink model into practical and real-time implementation. This paper indicates that model-based design offers a proper way of optimizing development time thereby ensuring robust headlamp performance under variable conditions. © 2025 IEEE.

关键词： computer debugging

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Robotic Automation Dynamic Hybrid Neuro-Fuzzy and Deep Learning Framework with GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic and CNN-TCN for Accurate IoMT-Based Chronic Kidney Disease Detection

Robotic Automation Dynamic Hybrid Neuro-Fuzzy and Deep Learn...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Allur, Naga Sushma Dondapati, Koteswararao Chetlapalli, Himabindu Kodadi, Sharadha Deevi, Durga Praveen Purandhar, N. Astute Solutions Llc CA United States Everest Technologies OH United States 9455868 Canada Inc ON Canada Infosys TX United States O2 Technologies Inc CA United States code - 69 Andhra Pradesh 517325 India

ISBN: (纸本)9798331513894

Background Information: Chronic kidney disease (CKD) is a world health concern that requires an early detection and careful observation over time. The IoMT is offering continuously streaming data for prognostication;however, the traditional approaches fail in capturing temporal interdependencies, spatial representation of attributes, and uncertainty in data. Objectives: The objective is the engineering of a Dynamic Hybrid Neuro-Fuzzy and Deep Learning Architecture by combining GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic, and CNN-TCN for accurate identification of CKD, ensuring scalability, precision, and minimal latency. Methods: The proposed framework uses GRU-BiLSTM for temporal pattern analysis, Capsule Networks for spatial representation, Type-2 Fuzzy Logic for managing uncertainty, and CNN-TCN for extraction of spatial-temporal features. Data preprocessing and robust feature extraction enhance the prediction pipeline using IoMT. Results: The architecture outperforms the state-of-the-art methods, with 96.5% accuracy, 95.7% precision, and 94.9% recall, while being scalable (50 GB) and having minimal latency of 98.5 ms, making it suitable for real-time IoMT applications. Conclusion: This framework provides a strong, extensible, and adaptive solution for CKD prediction, overcoming the challenges of IoMT and improving patient care. Future work will extend this framework to other chronic diseases and secure deployments through distributed learning. © 2025 IEEE.

关键词： Fuzzy inference

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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd international conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 international conference on Pervasive Computational technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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Intelligent Energy Management Strategies for Renewable Energy Sources 1

Intelligent Energy Management Strategies for Renewable Energ...

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1st international conference on AIML-applications for engineering and Technology, ICAET 2025

作者： Patil, Sangita Deotale, Disha Mali, Ramesh MIT School of Computing MIT ADT University Department of Electrical & Electronics Engineering Pune412201 India MIT School of Computing MIT ADT University Department of Computer Science & Engineering Pune412201 India

ISBN: (纸本)9798350355611

There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. Every time, effective and efficient Energy Management Strategies (EMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. Standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another. © 2025 IEEE.

关键词： Hybrid power

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