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Investigation of a 7-Axis Suspended Robotic Arm for Automated Spraying System 3

Investigation of a 7-Axis Suspended Robotic Arm for Automate...

引用

3rd International Conference on Mechatronics and Automation technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science ZhaoQing University ZhaoQing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

This study successfully develops a 7-axis suspended robotic arm for an automated spraying system, considering factors such as flexible operation modes, spray area dimensions, workpiece geometry, and the degrees of freedom required for effective operation. By using SolidWorks software for verification, the endpoints of the robotic arm can reach all positions within the workspace. The design and fabrication of the arm are completed according to specifications for structural strength, load capacity, rated torque output, and rotational speed. Kinematic analyses, both forward and inverse, are applied to determine the posture and position of the 7-axis robotic arm, facilitating the planning and design of the spraying trajectory. The control system architecture utilizes a multi-axis servo control board, integrated with a manifold deformation control method for trajectory tracking. Simulations and experiments are conducted to use a car shell workpiece provided by the manufacturer. The results from the simulations of MATLAB analyses show that the maximum tracking error is less than 0.4 mm. In experiments with the robotic arm, the maximum error is less than 0.7 mm. The results of the simulation and experiment are quite close, indicating that the deformation control method effectively operates the posture and movement of the 7-axis suspended robotic arm. Therefore, the 7-axis suspended robotic arm developed in this study will be effectively utilized in the production line of the automated spraying system. © 2024 The Authors.

关键词： MATLAB

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Towards Unveiling Vulnerabilities and Securing IoT Devices: An Ontology-Based Approach 29

Towards Unveiling Vulnerabilities and Securing IoT Devices: ...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Bhole, Mukund Kastner, Wolfgang Sauter, Thilo Institute of Computer Engineering Tu Wien Vienna Austria Institute of Computer Technology Tu Wien Austria Dep. of Integrated Sensor Systems Danube University Krems Austria

ISBN: (纸本)9798350361230

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem. © 2024 IEEE.

关键词： Ontology

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities 29

IT Security Solutions for IT/OT Integration: Identifying Gap...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

ISBN: (纸本)9798350361230

In the contemporary landscape of convergence between Information technology (IT) and Operational technology (OT), maintaining asset visibility within the Industrial Control Systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403-1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries. © 2024 IEEE.

关键词： Data acquisition Industry 4.0 IT Security Solutions IT/OT Integration Operation technology

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Reliability assessment of models for risk stratification: A knowledge-based approach 24

Reliability assessment of models for risk stratification: A ...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Zhao, Zhan Gil, Paulo Loureiro, Joao Petrella, Lorena Henriques, Jorge University of Coimbra CISUC Dep. of Informatics Engr. Coimbra Portugal University of Coimbra CISUC Portugal NOVA School of Sci. and Technology CTS Dep. of Electrical and Computer Engr. Portugal

ISBN: (纸本)9798331530631

The present work proposes a framework aiming to develop reliability metrics for model predictions, integrating a priori clinical knowledge with data and emphasising feature relevance. The approach combines clinical knowledge, represented as rules, with selected features, such as risk factors, where their significance is captured through assigned weights. Validation of the proposed methodology is conducted using the Global Registry of Acute Coronary Events (GRACE) risk model, applied to a dataset from the Centro Hospitalar e Universitário de Coimbra, focusing on risk assessment. The results demonstrate that these reliability metrics are closely linked to the performance of individual GRACE predictions, enabling the identification of cases or groups where the GRACE model yields reliable or unreliable predictions. This framework provides a valuable supplementary metric to existing global risk models. © 2024 IEEE.

关键词： Prediction models

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda 3

Energy Access Assessment Report Focusing on Energy Poverty a...

引用

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Khalat, Abdurazag Cape Peninsula University of Technology Dep. of Electrical Electronics and Computer Engineering Cape Town South Africa Sabratha University Dep. of Electrical and Electronics Engineering Sabratha Libya

ISBN: (纸本)9798350319897

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today's Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented. © 2023 IEEE.

关键词： Smart power grids

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor 2

Logistic Regression Classification for Assessing the Risk of...

引用

2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alzubi, Dalia Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Aljaidi, Mohammad Alazab, Rami Gharaibeh, Hasan Al-Naamneh, Qais Concordia University Dep. Information and Systems Engineering Montreal Canada Trine University Dep. of Business Administration IN United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Jordan University of Science and Technology Dep. Surgery and Urology Irbid Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses © 2023 IEEE.

关键词： Tumors

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ERROR SENSITIVITY MODULATION BASED EXPERIENCE REPLAY: MITIGATING ABRUPT REPRESENTATION DRIFT IN CONTINUAL LEARNING 11

ERROR SENSITIVITY MODULATION BASED EXPERIENCE REPLAY: MITIGA...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Sarfraz, Fahad Arani, Elahe Zonooz, Bahram Advanced Research Lab NavInfo Europe Netherlands Dep. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment. Deep neural networks (DNNs), however, exhibit catastrophic forgetting and the learned representations drift drastically as they encounter a new task. This alludes to a different error-based learning mechanism in the brain. Unlike DNNs, where learning scales linearly with the magnitude of the error, the sensitivity to errors in the brain decreases as a function of their magnitude. To this end, we propose ESMER which employs a principled mechanism to modulate error sensitivity in a dual-memory rehearsal-based system. Concretely, it maintains a memory of past errors and uses it to modify the learning dynamics so that the model learns more from small consistent errors compared to large sudden errors. We also propose Error-Sensitive Reservoir Sampling to maintain episodic memory, which leverages the error history to pre-select low-loss samples as candidates for the buffer, which are better suited for retaining information. Empirical results show that ESMER effectively reduces forgetting and abrupt drift in representations at the task boundary by gradually adapting to the new task while consolidating knowledge. Remarkably, it also enables the model to learn under high levels of label noise, which is ubiquitous in real-world data streams. Code: https://***/NeurAI-Lab/ESMER. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Errors

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

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2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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Knowledge Representation of Asset Information and Performance in OT Environments 28

Knowledge Representation of Asset Information and Performanc...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Bhole, Mukund Kastner, Wolfgang Sauter, Thilo TU Wien Institute of Computer Engineering Vienna Austria TU Wien Institute of Computer Technology Austria Danube University Dep. of Integrated Sensor Systems Krems Austria

ISBN: (纸本)9798350339918

To make a management decision for the Operational technology (OT) environment, obtaining OT asset information from a plant is essential. Key Performance Indicators (KPIs) can play a crucial role in evaluating the performance of the manufacturing process, minimizing production costs, assessing process effectiveness, and investigating various settings to improve performance, operations, and quality in OT. This paper aims to provide a detailed examination of how Asset Information Mining Sources (AIMSs) support the process for acquiring both static and runtime OT asset information. Once we have obtained accurate OT asset information, we can calculate the KPIs and incorporate them into a knowledge representation using an ontology-based model. This model can assist the management team in making informed decisions and provide a roadmap for exploring further automation possibilities. © 2023 IEEE.

关键词： Knowledge representation

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