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21st international conference on Networking, Sensing and control, ICNSC 2024

作者： Gu, Lei Wu, NaiQi Qiao, Yan Zhang, SiWei Institute of Systems Engineering Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology China

ISBN: (纸本)9798350365221

Robotic cluster tools have been applied for wafer fabrication. To improve the productivity of such a tool, one method involves increasing the number of spaces within a process module and the fingers on a robot arm, leading to multi-finger-robotic cluster tools with multi-space process modules. This study addresses the scheduling issue of four-finger-robotic cluster tools with four-space process modules, a commonly seen configuration in modern semiconductor fabs. Residency time constraints make the scheduling issue of such a tool more challenging. This study conducts an analysis of the steady-state operations of such a tool under two presented robot task sequences. Then, based on the temporal properties of the tool, two linear programs are developed to find feasible schedules to minimize the system cycle time. Experiments show the performance of a tool under different robot task sequences by considering different parameters. © 2024 ieee.

关键词： Robotic arms

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Symbiotic Fusion of Evolving Anatomical Landscapes for intelligent Diagnostic Narratives

Symbiotic Fusion of Evolving Anatomical Landscapes for Intel...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Hanyue Zhuang, Yong Xu, Jingyu Hainan University College of Applied Science and Technology China Wuhan University School of Cyber Science and Engineering China Northern Arizona University Az United States

ISBN: (纸本)9798350386226

The transition from 2D to 3D medical imaging presents unprecedented challenges in data analysis and interpretation, particularly due to domain shifts and the complex temporal nature of patient data. We introduce 3DLVR, a groundbreaking framework that leverages advanced domain adaptation techniques to bridge this dimensional gap in automated radiology report generation. At the heart of 3DLVR lies a novel cross-attention-based multi-modal fusion module, 3DLR, which seamlessly integrates longitudinal patient data to adapt to evolving medical conditions and imaging characteristics. Our approach utilizes optimal transport theory to formulate the domain adaptation problem, ensuring robust performance across diverse patient populations and imaging protocols. Experiments on the CT-RATE dataset demonstrate 3DLVR's superior adaptability, with a great improvement in cross-domain performance compared to existing methods. Furthermore, our framework exhibits remarkable generalization capabilities. By advancing the frontier of domain adaptation in medical AI, 3DLVR not only enhances the accuracy and relevance of automated reports but also paves the way for more personalized and adaptive healthcare systems. This work represents a significant step towards realizing the potential of 3D medical imaging in the era of precision medicine, with broad implications for improving diagnostic accuracy, treatment planning, and patient outcomes. © 2024 ieee.

关键词： Spatio-temporal data

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Revolutionizing Face Recognition with IIoT: Smarter, Faster, Connected

Revolutionizing Face Recognition with IIoT: Smarter, Faster,...

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2025 international conference on Computer, Electrical and Communication Engineering, ICCECE 2025

作者： Grover, Yash Sharma, Pulkit Kansal, Nishtha Vivekananda Institute of Professional Studies School of Engineering & Technology Technical Campus New Delhi India

ISBN: (纸本)9798331513894

The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. Automating processes like access control and attendance tracking minimizes human intervention, reduces errors, and streamlines workflows. From a security perspective, facial recognition strengthens access management, reduces unauthorized entries, and enhances real-time surveillance capabilities. In safety-critical industries, this technology can enforce compliance with protective protocols and monitor workers' well-being by detecting signs of fatigue or stress. Additionally, the use of artificial intelligence (AI) and machine learning facilitates real-time data analysis, providing insights to optimize operations and prevent system failures. This paper further examines the broader applications of IoT-driven facial recognition, highlighting promising research areas such as intelligent security systems, biometric authentication, automated surveillance, and smart access control. Practical implementations span smart homes, healthcare monitoring, financial transactions, and public safety, where real-time facial identification enhances both security and operational performance. Despite these advantages, challenges like data privacy concerns and the need for robust technological infrastructure remain critical. Strategies such as data encryption, edge computing, and adherence to privacy regulations like GDPR are essential to protect personal information. As advancements in AI, machine learning, and 5G networks continue, facial recognition systems integrated with IIoT will become increasingly accurate and efficient, paving the way for smarter, more secure, and highly efficient industrial and public environments. © 2025 ieee.

关键词： Access control

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Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models 5

Ensemble-Based Transfer Learning for Multi-Class Plant Disea...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Lakshmi, Popuri Mohana Siva Lakshminadh, K. Reddy, K.V. Narasimha Reddy, Dodda Venkata Narasaraopeta Engineering College Dept of Computer Science and Engineering Narasaraopeta India Narasaraopeta Engineering College Dept of Information Technology Narasaraopeta India

ISBN: (纸本)9798331518097

In the world, plant diseases pose a serious threat to agricultural productivity and food security. Early, accurate, and rapid identification of plant diseases is important for con-trolling loss of crops. In the following research, transfer learning models VGG16, ResNet50, and Xception are applied to attempt overcoming this challenge of multiclass plant disease detection. To improve classification accuracy, we propose an ensemble model that combines the strengths of these pre-trained networks. Multiple plant species and disease categories were experimented on extensively on publicly available plant disease datasets. The results show that ensemble model achieves better precision, precision and recall than individual models and therefore presents a robust solution for identifying several plant diseases together as a pack. Results from the experiment demonstrate that the proposed method could be deployed in real-time agricultural systems and have potential to provide a scalable and efficient diagnostic tool for farmers and agronomists to detect plant diseases and reduce their impact. This work is among the growing body of work in AI based agricultural solutions and indicates that transfer learning and ensemble techniques are promising in precision farming. © 2024 ieee.

关键词： Plant diseases

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Machine Vision Based Predictive Maintenance for Machine Health Monitoring: A Comparative Analysis

Machine Vision Based Predictive Maintenance for Machine Heal...

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2023 international conference on Robotics and automation in Industry, ICRAI 2023

作者： Ul Haq, Ihtisham Anwar, Shahzad Khan, Tahir University of Engineering & Technology Department of Mechatronics Engineering Peshawar Pakistan University of Engineering & Technology AI in Healthcare National Centre of Artificial Intelligence Peshawar Pakistan

ISBN: (纸本)9781665464727

Smart manufacturing was given unparalleled chances by data-driven approaches, speeding up the shift to Industry 4.0 ways of production. Machine learning and deep learning are indispensable in the creation of smart systems that could perform descriptive, analytical, and predictive analytics for monitoring the health of manufacturing processes and equipment. This study discusses the advantages and disadvantages of applying deep learning (DL) to intelligent machining and tool maintenance. The building blocks of a smart monitoring system are unveiled. The primary benefits and drawbacks of ML models are described, and they are contrasted to those of deep learning models. Deep belief networks, Auto-Encoder, recurrent neural network (RNNs) and convolutional neural networks (CNNs), were some of the most prominent DL models covered, their applications in smart manufacturing and tool health monitoring were also examined. intelligent machining could benefit from a data-driven smart manufacturing strategy in six ways: (1) by providing hybrid intelligent models;(2) by managing high-dimensional data;(3) by dealing with big data;(4) by achieving optimal sensor fusion;(5) by avoiding sensor redundancy;and (6) by automating feature engineering. Finally, the data-driven challenges and research needs in smart manufacturing were discussed. There were many obstacles, such as those related to process uncertainty, data nature, data size, and model selection. © 2023 ieee.

关键词： Predictive analytics

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Skill Learning Using a Single Demonstration for Hierarchical Robotic Navigation 25

Skill Learning Using a Single Demonstration for Hierarchical...

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25th ieee international conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Alves, João Bernardo Lau, Nuno Silva, Filipe University of Aveiro IEETA DETI Aveiro Portugal LASI Intelligent System Associate Laboratory Portugal

ISBN: (纸本)9798331538606

Training robots to perform complex navigation tasks traditionally requires extensive datasets of expert demonstrations, which are expensive and time-consuming to collect. This paper introduces a hierarchical reinforcement learning approach that distils meaningful skills using a single expert trajectory. By combining reverse curriculum learning and goal-conditioned reinforcement learning, our method segments complex tasks into manageable sub-tasks and learns these sub-tasks using sparse rewards. A learned skill selection mechanism then dynamically chains the skills based on their estimated values. We evaluate our approach in a high-dimensional autonomous driving scenario, demonstrating skill acquisition and task completion rates that surpass a state-of-the-art baseline that also uses a single demonstration. Our results show that flexible skill extraction and chaining using a reduced amount of data can effectively tackle the challenges of sparse rewards and planning in a robotic navigation environment. © 2025 ieee.

关键词： Demonstrations

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Guest Editorial Special Issue on Development and Learning and on Epigenetic Robotics

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ieee TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第4期14卷 1302-1305页

作者： Ellefsen, Kai Olav Dautenhahn, Kerstin Torresen, Jim Ritmo Univ Oslo Dept Informat Oslo Norway Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Oslo Dept Informat Oslo 0315 Norway Univ Oslo Oslo 0315 Norway

ieee ICDL (the Joint ieee international conference on Development and Learning—formerly named ICDL-EpiRob) is a unique conference gathering researchers from computer science, robotics, psychology, and developmental studies to share knowledge and research on how humans and animals develop sensing, reasoning, and actions. This includes taking advantage of interaction with social and physical environments and how cognitive and developmental capabilities can be transferred to computing systems and robotics. This approach goes hand in hand with the goals of both understanding human and animal development and applying this knowledge to improve future intelligent technology, including robots that will be in close interaction with humans.

关键词： Special issues and sections Meetings Robot learning Robot sensing systems Robot control

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Developing an Adaptive Security Framework for Real-Time Threat Detection and Response in Cloud-Network systems

Developing an Adaptive Security Framework for Real-Time Thre...

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2024 international conference on cybernation and Computation, cyberCOM 2024

作者： Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vimal Kumar, M. Guru Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St.Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha University Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed;conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts. © 2024 ieee.

关键词： Cloud security

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Simulation and Performance Analysis of Passive Cell Balancing Technique for Lithium batteries using Modified Generic model 3

Simulation and Performance Analysis of Passive Cell Balancin...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331515911

Modern society is increasingly adopting Electric Vehicles (EVs) due to fuel scarcity and the need to control pollution. Batteries are the most suitable energy storage devices for EVs;however, they have certain limitations, such as maintaining a safe operating temperature, risks of overcharging and deep discharging, and cell imbalance. Protecting batteries is crucial, and to ensure their safety, Battery Management systems (BMS) are employed. BMS performs tasks such as parameter estimation and cell balancing based on the battery model. In this paper, a Modified Generic Battery Model is developed to provide a more accurate representation of battery characteristics. Passive cell balancing is implemented for both the MATLAB inbuilt battery model and the developed battery model. The variation of the State of Charge (SoC) is analyzed during charging and discharging, with and without cell balancing, in both cases. It is observed that the balancing time is approximately 110 seconds in both cases. However, minor variations in the SoC are noted in the developed generic model, which could be eliminated by improving the control algorithm or using active cell balancing. This validates the accuracy of the developed generic battery model. Additionally, an analysis is performed during simulation to identify the optimum balancing resistor, which is found to range between 0.6-0.65 Ω. © 2025 ieee.

关键词： MATLAB

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Digitization and Secure Process of Forensic Data Management Using Blockchain

Digitization and Secure Process of Forensic Data Management ...

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2023 international conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Indhumathi, R. Mathivanan, P. Mahalakshmi, K. Subha, K. Krishna, Nanda S. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Business Systems Coimbatore India Surya Group of Institutions Department of Computer Science and Engineering Villuppuram India

ISBN: (纸本)9798350315127

cyber threats and data manipulation are the main concerns in this technological society. The investigator finds it challenging to submit evidence to the court because of the information breach as continuity documentation of records is very critical for safeguarding against criminal events. Blockchain-based digital forensic data management systems have been developed to deal with this issue of Privacy preservation for evidence items. Blockchain technology provides authentication, immutability, traceability, resilience, and distributed trust for forensic data. In order to ensure security and ease of use, the system utilizes onion architecture. Data can be stored and analysed whenever required. They provide trust between the examiners and the system. The system is a multi-platform system that provides effective usage where it can be accessed and used from anywhere. The honest information is make sure to follow the crime investigator of the steps. if we use the propose system of block chain, it is can't be altered the evidence during the submission of court. The storing data of each block is verify and added to the blockchian. The creation of safe database was implemented by a decentralised network with bitcoin and networks of cryptocurrency. In proposed system, the blockchain technology helps in can't able to access and altered the data during the investigation periods. This system is used to implements the easily accessed and used in anywhere. © 2023 ieee.

关键词： Blockchain

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