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International Conference on Deep Learning and Artificial Intelligence (IC-DLAI)

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Articles

Graphene-Mediated Electrodeposition: Toward Sustainable Copper Composite Alloys in Graphene Matrix

Hayley Richardson,Charles Bopp,Bao Ha,Reeba Thomas andKalathur S.V. Santhanam

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This paper explores the electrodeposition process facilitated by a graphene bath, aiming to create sustainable copper composite alloys embedded within a graphene matrix. The integration of graphene into electrodeposition processes offers a promising route to enhance the properties of resulting alloys, leveraging graphene's exceptional mechanical, electrical, and thermal properties. This study investigates the fabrication process, properties, and potential applications of the resulting copper composite alloys. By incorporating graphene, the resulting alloys exhibit improved mechanical strength, electrical conductivity, and corrosion resistance compared to conventional copper alloys. Moreover, the sustainable nature of the electrodeposition process underscores its potential for environmentally friendly manufacturing. This paper sheds light on the feasibility and advantages of utilizing graphene-mediated electrodeposition for the production of advanced copper composite alloys with enhanced performance and sustainability.

Sustainable Energy Requalification of Existing Residential Buildings: A Decision Support Tool. Case Study: Trevignano Romano

Luciano Argento,Francesco Buccafurri,Angelo Furfaro,Sabrina Graziano,Antonella Guzzo,Gianluca Lax,Francesco Pasqua andDomenico Saccà

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This paper presents a decision support tool for the sustainable energy requalification of existing residential buildings, with a case study focused on Trevignano Romano. As the need for energy-efficient building retrofits grows, there is a demand for comprehensive tools to guide decision-making processes. The proposed tool integrates various factors such as energy consumption, environmental impact, and economic feasibility to facilitate informed decisions. Through a case study in Trevignano Romano, the tool is applied to assess different requalification strategies and their implications. The results demonstrate the effectiveness of the tool in identifying optimal solutions that balance sustainability goals with practical constraints. This research contributes to the advancement of sustainable building practices by providing a systematic approach to energy requalification tailored to specific contexts.

Triple-Layer Biomimetic Muscle: Simultaneous Sensing and Actuation in Soft Robotics

Francisco García-Córdova,Antonio Guerrero-González,Joaquín Zueco andAndrés Cabrera-Lozoya

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This paper presents a triple-layer biomimetic muscle with simultaneous sensing and actuating capabilities, designed for soft robotics applications. Inspired by natural muscle sneed for energy roposed muscle system integrates sensing and actuation functions within a triple-layer architecture. The outer layers serve as sensors, detecting external stimuli such as pressure and strain, while the inner layer functions as an actuator, generating responsive motion. This unique design enables the muscle to sense its environment and adapt its behavior accordingly, mimicking the versatility and agility of biological muscles. The paper discusses the fabrication process, characterization, and experimental validation of the triple-layer biomimetic muscle. Additionally, potential applications in soft robotics, including human-robot interaction and wearable devices, are explored. Through this research, a novel approach to integrating sensing and actuation capabilities in soft robotic systems is introduced, paving the way for advanced functionalities and enhanced performance in a wide range of applications.

Deformable Hydrogel Microrobots: Exploring Biomedical Applications

Qinghua Cao,Wenjun Chen,Ying Zhong,Xing Ma andBo Wang

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This paper explores the potential biomedical applications of deformable hydrogel microrobots. These microrobots, constructed from flexible hydrogel materials, offer unique capabilities for navigating complex biological environments and performing targeted tasks at the microscale. The paper discusses the fabrication methods, properties, and functionalities of hydrogel microrobots, emphasizing their deformability and biocompatibility. Various biomedical applications are explored, including targeted drug delivery, minimally invasive surgery, tissue engineering, and biosensing. Through a comprehensive review of recent advancements and challenges, this paper highlights the potential of deformable hydrogel microrobots to revolutionize biomedical research and clinical practice. Moreover, it discusses future directions for optimizing their design, control, and integration into biomedical systems, paving the way for innovative solutions to healthcare challenges.

Exploring the Role of Social Robots in Healthcare: A Review of Characteristics, Requirements, and Technical Solutions

Luca Ragno,Alberto Borboni,Federica Vannetti,Cinzia Amici andNicoletta Cusano

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This review delves into the application of social robots in healthcare, examining their characteristics, requirements, and technical solutions. As interest in robotic technology grows within the healthcare sector, understanding the unique capabilities and challenges of social robots is essential. The paper analyzes the characteristics that make social robots suitable for healthcare settings, such as their ability to engage with patients and provide assistance with various tasks. It also discusses the specific requirements that must be met to ensure effective integration into healthcare environments, including safety, usability, and adaptability. Furthermore, the review explores the technical solutions and advancements in robotics technology that facilitate the development of social robots for healthcare applications. By synthesizing recent research and insights, this review provides valuable guidance for researchers and practitioners interested in leveraging social robots to enhance healthcare delivery and patient outcomes.

Monolithic PneuNets Soft Actuators: Design, Production, and Characterization Methodologies for Robotic Rehabilitation

Monica Tiboni andDavide Loda

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This paper presents methodologies for the design, production, and characterization of monolithic PneuNets soft actuators tailored for robotic rehabilitation applications. Soft actuators offer promising advantages for rehabilitation robotics due to their inherent compliance and safety features. The study outlines systematic approaches for the design optimization of PneuNets actuators, encompassing parameters such as geometry, material selection, and manufacturing techniques. It further details the production process, highlighting the monolithic construction method for enhanced structural integrity and performance. Additionally, characterization methodologies are elucidated, encompassing mechanical testing, dynamic response analysis, and compatibility assessment with human motion patterns. Through comprehensive methodologies, this research aims to advance the development of soft actuators for robotic rehabilitation, fostering safer and more effective interventions in healthcare settings.

Reviewing Prognostics and Health Management of Rotating Machinery in Industrial Robots with Deep Learning Applications

Prashant Kumar,Salman Khalid andHeung Soo Kim

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

This review explores the application of deep learning in prognostics and health management (PHM) of rotating machinery within industrial robots. As rotating machinery plays a critical role in the performance and reliability of industrial robots, effective PHM strategies are essential for minimizing downtime and optimizing maintenance schedules. The paper examines recent advancements in utilizing deep learning techniques for predictive maintenance and fault diagnosis in rotating machinery. It provides a comprehensive overview of methodologies, including data-driven approaches, feature extraction, and model training. Furthermore, the review discusses challenges and opportunities in implementing deep learning-based PHM systems for industrial robots. Through synthesizing recent research and insights, this review aims to provide valuable guidance for researchers and practitioners interested in leveraging deep learning for enhanced PHM in industrial robotics.

Assessing Motion Artifact Correction Methods for Cone-Beam Computed Tomography Images: A Focus on Blood Vessel Geometry

Evaluation of Motion Artifact Correction Technique for Cone-Beam Computed Tomography Image Considering Blood Vessel Geometry

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Motion artifacts pose significant challenges in the acquisition and analysis of medical imaging data, particularly in cone-beam computed tomography (CBCT) images where the presence of blood vessels exacerbates the issue. This study aims to evaluate various motion artifact correction techniques specifically tailored to CBCT images, with a particular emphasis on their effectiveness in preserving the geometry of blood vessels. A comprehensive assessment is conducted, considering both qualitative and quantitative metrics to gauge the performance of each technique. The evaluation encompasses a range of motion scenarios and vessel geometries to provide a robust analysis. Results indicate varying degrees of success among the evaluated methods, highlighting the importance of selecting appropriate correction strategies based on the characteristics of the motion and vascular structures involved. Insights gained from this study can inform the development of more effective motion artifact correction algorithms tailored for CBCT imaging, ultimately enhancing the quality and reliability of medical image analysis in clinical settings.

Developing a Vision Transformer-Based Classifier for Assessing Damage Severity in Ground-Level Imagery of Homes Impacted by California Wildfires

Kevin Luo andIe-bin Lian

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

In the aftermath of California wildfires, assessing damage severity to residential properties is crucial for effective response and recovery efforts. This study presents the development of a novel classifier leveraging Vision Transformer (ViT) architecture to analyze ground-level imagery of homes affected by wildfires. The proposed approach aims to accurately classify damage severity levels, ranging from minor to severe, based on visual cues captured in the images. A comprehensive dataset comprising a diverse range of wildfire-affected properties is utilized for training and evaluation. The ViT model demonstrates promising performance in automatically discerning damage severity levels, outperforming traditional convolutional neural networks (CNNs) in certain scenarios. Additionally, transfer learning techniques are explored to enhance the model's generalization capabilities. The findings suggest that ViT-based classifiers offer a promising avenue for rapid and reliable damage assessment in wildfire-affected areas, aiding disaster response teams and policymakers in prioritizing resources and interventions effectively.

Advancing Fidelity Quantification Methods in Computer-Generated Imagery: A Comprehensive Investigation

Alexandra Duminil,Sio-Song Ieng andDominique Gruyer

Year: 2021| Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

In the realm of computer-generated imagery (CGI), ensuring fidelity—faithfulness to reality—is paramount for applications across industries such as entertainment, design, and scientific visualization. This abstract encapsulates our rigorous exploration aimed at refining fidelity quantification methods in CGI. Our investigation scrutinizes existing metrics while also proposing innovative approaches to address their limitations. By dissecting the complexities of CGI fidelity assessment, including challenges like perceptual realism and image complexity, we present novel methodologies tailored to accurately gauge fidelity levels. Through comprehensive experimentation and validation, we showcase the efficacy of our proposed techniques, paving the way for enhanced quality control and advancement in CGI production pipelines. This research not only contributes to the theoretical understanding of fidelity in CGI but also offers practical tools to elevate visual realism and quality across a myriad of applications.


International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

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