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检索条件"任意字段=3rd International Conference on Informatics in Control, Automation and Robotics"

共 2674 条记录，以下是31-40 订阅

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2017 3rd international conference on control, automation and robotics, ICCAR 2017

2017 3rd International Conference on Control, Automation and...

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3rd international conference on control, automation and robotics, ICCAR 2017

ISBN: (纸本)9781509060870

The proceedings contain 152 papers. The topics discussed include: particle swarm optimization for high-DOF inverse kinematics;learning and correcting robot trajectory keypoints from a single demonstration;a clothoid based real-time interpolation concept for path smoothing;evaluation on path planning with a view towards application;a revised Gaussian distribution sampling scheme based on RRT∗ algorithms in robot motion planning;autonomous low limb exoskeleton to suppress the body weight;maximal operation time estimation for modular and self-reconfigurable robots with output current constraints;modeling and simulation of 5DOF robot manipulator and trajectory using Matlab and CATIA;inverse kinematics analysis of the three-legged reconfigurable spherical robot ii;mobile robot path planning in complex environments using ant colony optimization algorithm;a framework for the kinematic modeling of soft material robots combining finite element analysis and piecewise constant curvature kinematics;mobile robot positioning system for precision manufacturing: the laser lighthouse revisited;development and assessment of a powered 3D-printed prosthetic hand for transmetacarpal amputees;and minimalist mechanical design and characterization of an android for human robot interaction.

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TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device 3

TinyML Speech Classification Embedded Ai Module for Hand Reh...

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3rd international conference on Mechatronics, control and robotics, ICMCR 2025

作者： Numsomran, Arkorn Numsomran, Arjin Chaoraingern, Jutarut Triam Udom Suksa Pattanakarn Suvarnabhumi School Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Instrumentation and Control Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Robotics and AI Engineering Bangkok Thailand

ISBN: (纸本)9798331544546

Recent advancements in artificial intelligence and machine learning have significantly improved healthcare, especially in the development of assistive technologies for rehabilitation. This paper introduces a method for hand rehabilitation that utilizes the capabilities of tiny machine learning to enhance speech classification in a rehabilitation device. The proposed method employs a CNN model capable of classifying speech signals that are indicative of different hand movement patterns. Patients emit these speech signals during prescribed hand exercises, which are crucial for their rehabilitation process. The main focus of this study is on training and deploying a speech classification system that can work in the resource-limited environment of TinyML platforms. We detail the process of capturing speech data, preprocessing it, and extracting the most features relevant to different hand movements. Our results show that using TinyML to help with hand rehabilitation works. The method we came up with shows how TinyML could change the way rehabilitative devices are controlled, and it also shows us what personalized and easy-to-use rehabilitation tools might look like in the future. © 2025 IEEE.

关键词： CNN Embedded AI Hand Rehabilitation Device Speech Classification TinyML

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Semantic Web for BIM-Based Automated Project Management and control: A Review 20th

Semantic Web for BIM-Based Automated Project Management and ...

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20th international conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Palihakkara, Asha Dulanjalie Osorio-Sandoval, Carlos Tizani, Walid Centre for Structural Engineering and Informatics University of Nottingham NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9783031842078

The integration of semantic web technologies in the Architecture, Engineering, and Construction (AEC) industry has gained momentum in recent years, with promising studies being conducted for various use cases. Furthermore, its necessity has been implied in the Building Information Modelling (BIM) Maturity Level 3, which focuses on fully integrated collaboration and open data standards and transfer. The main drivers of implementing semantic web technologies in a multi-data environment like the AEC industry are to overcome interoperability issues, link across domains, and facilitate logical inference and proof. The construction execution phase encompasses multiple stakeholders delivering data from multiple domains in heterogeneous formats, which needs to be put to better use for optimal project delivery. Thus, this study explores how semantic web technology can be applied for BIM-based project management and control during the execution phase through a comprehensive review of the literature to identify existing semantic web applications and their limitations to determine the gaps in knowledge. The development of ifcOWL ontology has spurred attention on developing domain ontologies to be integrated with the ifcOWL for the seamless automation of construction activities. The study identified several ontology-based semantic web applications for project control in a BIM-based environment, for example, 4Dcollab ontology for Synchronous Collaboration Sessions for decision making, LinkOnt for lookahead planning enabled construction constraint checking, CPPC ontology for construction product control, etc. Furthermore, many studies were recorded related to semantic web technology-based 4D BIM use cases, such as automated schedule generation, automated construction sequencing, collaboration, and constraint checking. A consistent idea across these studies is the emphasis on the need for knowledge modelling leveraging data accumulated through both pre-construction and construction

关键词： Project management

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Implementation of an Embedded Electronic control System for a Functional Prosthetic Hand

Implementation of an Embedded Electronic Control System for ...

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Mechatronics, control and robotics (ICMCR), international conference on

作者： Magno Parra Victoria E. Abarca Dante A. Elias Biomechanics and Applied Robotics Research Laboratory Pontificia Universidad Católica del Perú Lima Peru

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

The advancement of electronics enables the development of more complex functional prosthetics with more capabilities and functionality. The prosthetics currently developed do not leave the laboratory environment due to their complexity or the requirement to operate in conjunction with laboratory computing equipment. Therefore, it is essential to develop prosthetics that can operate outside of the laboratory environment while maintaining maximum functionality. Consequently, an electronic control system is developed that can be embedded in a functional prosthetic hand, which is then trained in real-time through electromyography (EMG) using a classification algorithm based on machine learning. The system allows the prosthetic hand to assume six different positions and be controlled by electromyography, recognizing flexion and extension movements of the wrist. This results in a control system that can be fully embedded in the prosthetic hand, with an appropriate weight and size to maintain the aesthetics and functionality of the prosthesis.

关键词： Wrist Mechatronics Laboratories control systems Robot sensing systems Electromyography Real-time systems Classification algorithms Time factors Prosthetic hand

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Optimizing Logistics in E-Commerce Using Deep Random Forest for Enhanced User Satisfaction 3

Optimizing Logistics in E-Commerce Using Deep Random Forest ...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Nisha, G.Anne Sreetha, P. Seetha, A. Manickam, K. Jagannathan, Sharath Kumar Sekar, S.D. Kyoto Computer Gakuin Department of Informatics Kyoto Japan Kpr Institute of Engineering and Technology Department of Biomedical Engineering Tamil Nadu India S. A. Engineering College Department of Information Technology Thiruverkadu Chennai India Sona College of Technology Department of Mathematics Tamil Nadu India Data Science Institute Saint Peter's University NJ United States R.M.K.Engineering College Department of Mechanical Engineering TamilNadu Chennai India

ISBN: (纸本)9798331542375

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages. © 2025 IEEE.

关键词： Marketplaces

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Design a High-Frequency Dual Active Bridge Converter for Discrete Time Frequency

Design a High-Frequency Dual Active Bridge Converter for Dis...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： M. Karthik G. Hemalatha M. Mathankumar L. Sreevidya R. Deepalakshmi V. Kandasamy Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The dual active bridge (DAB) has become a crucial interface element in today's sophisticated systems. Renewable energy sources produce DC power, which is then stored in batteries, ultra-capacitors, and other energy storage devices. DAB is a crucial part of power exchange between two different buses because of its galvanic separation and bidirectional power transfer capabilities. The operation of the DAB is compatible with a broad range of input voltages and output loads. The creation of DAB is covered in most works. A well-known semiconductor integrated circuit with programmable control parameters is used in this straightforward, inexpensive method.

关键词： Time-frequency analysis Renewable energy sources Costs Bridge circuits Switched reluctance motors Stability analysis Batteries Telecommunication computing Time-domain analysis Stress

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Robotic Drug Delivery in the Healthcare Sector

Robotic Drug Delivery in the Healthcare Sector

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Disruptive Technologies (ICDT), international conference on

作者： Mishthi Rastogi Jagjeevan Kanoujiya Bhakti Agarwal Manipal Institute of Technology Manipal India Symbiosis Institute of Business Management Nagpur Symbiosis International University Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper examines the role of robotics in healthcare, with a focus on robotic drug delivery systems. robotics has significantly advanced drug administration by improving precision, reducing side effects, and increasing patient compliance. Technologies such as RoboCap and PillCam have revolutionized drug delivery by being able to deliver treatment where other methods cannot reach. Pill like robotics can enter the GI tract by swallowing where they perform drug delivery and diagnostics better than conventional practices. It also looks at the applicability of microrobotics in minimally invasive surgeries, which therefore can include targeted drug delivery. However, challenges remain, especially in controlling robotic devices within the body. Ethical concerns, costs, and technical issues need addressing as these technologies evolve. Future research should focus on refining control systems and AI to further enhance robotic drug delivery and improve healthcare outcomes, particularly in underserved regions like India.

关键词： Drugs Performance evaluation Technological innovation Targeted drug delivery Refining Medical services Drug delivery Artificial intelligence Robots Remote control

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Deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection 3rd

Deep Learning Precision Farming: Identification of Banglades...

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3rd international conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Siddiki, Marjuk Ahmed Rony, Mohammad Abu Tareq Naim Hossain, Md. Saha, Pritom Islam, Mohammad Shariful Ahmed, Ishtiak Satu, Shoykth Shaharior Ahammad, Mejbah Nazmul Alam, Shah Md. Computer Science and Engineering Daffodil International University Dhaka Bangladesh Noakhali3814 Bangladesh Noakhali3814 Bangladesh Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Computer Science and Engineering Shaikh Burhanuddin Post Graduate College Dhaka Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University TempeAZ United States

ISBN: (纸本)9789819739363

The dynamic potential of Convolutional Neural Networks (CNN) enhanced by Transfer Learning offers a promising avenue for object recognition within the realm of agriculture. To explore this potential in the context of Bangladeshi agriculture, this study leverages CNN enhanced by Transfer Learning to identify Bangladeshi-grown fruits from a dataset featuring 24 distinct types, including Mango, Jackfruits, and Lychee, among others. Utilizing an extensive dataset that encapsulated high-quality images of these fruits, we embarked on a comparative analysis of six advanced CNN architectures: VGG19, Xception, ResNet152V2, MobileNet, DenseNet201, and NASNetLarge. The results were illuminating. While VGG19 lagged behind with an accuracy of 48%, most models showcased impressive performance, with DenseNet201 emerging as the frontrunner at a remarkable 95% accuracy. The findings not only underscore the transformative role of Transfer Learning in optimizing CNN performance, especially when faced with limited labeled datasets, but also spotlight the applicability of these models in revolutionizing quality control and management within the Bangladeshi agricultural sector. As the demand for automated and accurate agricultural systems gains traction globally, the insights from this research offer a foundation for further exploration and application in similar contexts, demonstrating the scalability and adaptability of Transfer Learning-enhanced CNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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TinyML Speech Classification Embedded Ai Module for Hand Rehabilitation Device

TinyML Speech Classification Embedded Ai Module for Hand Reh...

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Mechatronics, control and robotics (ICMCR), international conference on

作者： Arkorn Numsomran Arjin Numsomran Jutarut Chaoraingern Triam Udom Suksa Pattanakarn Suvarnabhumi School Bangkok Thailand Department of Instrumentation and Control Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and AI Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

关键词： Hands Training Technological innovation Tiny machine learning Medical treatment Speech recognition Transforms Speech enhancement Feature extraction Robots

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