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The Future of Smart Factories: Real-Time Automation and Big Data Analytics on the Cloud 15

The Future of Smart Factories: Real-Time Automation and Big ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, K. Ranjith Arora, Arpit Goud, Dilip Ambika, Krovvidi S B Sandeep, V. More, Priyanka Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India NNRG Engineering College India ADITYA COLLEGE of ENGINEERING and TECHNOLOGY Department of CSE-AI&ML & DS Surampalem India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The evolving future of manufacturing will be all about intelligent factories featuring state-of-the-art technologies. Smart factories combine connected automation and big data in the cloud to streamline production, increase productivity, and lower operational costs. Real-time automation utilizes state-of-the-art sensors, internet of Things (IoT) devices, and artificial intelligence (AI) technology to monitor and control machines or processes in real time independently. This results in faster potential decision-making and bright maintenance works, thus decreasing downtime risk and increasing overall productivity. Big data analytics in the cloud enables real-time collection and analysis of extensive amounts of production processes, machine performance, and supply chain management-related information. The data can then be utilized to discover areas that need improvement, predict maintenance requirements, and even optimize the level of inventories in the sense that it would save costs while at the same time becoming more efficient. Also, real-time automation combined with significant cloud data analytics results in improved communication and collaboration from one production stage to another, hence smooth operations. In other words, it helps create an even more agile and adaptable manufacturing environment where processes can be fine-tuned on the fly to deal with dynamic market shifts. © 2024 IEEE.

关键词： Smart manufacturing

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.

关键词： Parallel processing systems

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Design Of A High-Performance Timer Circuit For Adaptive On-Time Buck Converter 4

Design Of A High-Performance Timer Circuit For Adaptive On-T...

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4th international conference on Electronic Information engineering and computer technology, EIECT 2024

作者： Gao, Chengzhuo Huang, Xiaobing Zhang, Xiaojun Wei, Hongmei Wu, Xinchun School of Information Science and Technology Southwest Jiaotong University Chengdu610097 China Technology Co. Ltd Chengdu610095 China Chengdu University of Technology Chengdu610059 China

ISBN: (纸本)9798331528850

This paper presents a timer circuit designed for an adaptive on-time (AOT) Buck DC-DC converter. The circuit utilizes input voltage feedforward compensation and output feedback techniques to maintain a constant switching frequency despite fluctuations in input and output voltages, thereby achieving fixed-frequency pseudo pulse width modulation. The design is based on a 0.18 μm BCD process and is validated through Spectre simulations. The simulation results indicate that when the input voltage varies from 4V to 14V, the output voltage change will not exceed 18mV, and the output voltage recovery time is less than 12μs. Furthermore, when the load current jumps between 1A-2A, the output undershoot/overshoot voltage will not exceed 29m V, and the output voltage recovery time is within 27us. Additionally, the incorporation of input voltage feedforward technology enhances the speed of the input linear transient response. © 2024 IEEE.

关键词： Buck converter

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Design and implementation of distributed computer cluster management and scheduling system based on ThinkPHP 24

Design and implementation of distributed computer cluster ma...

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4th international conference on Control and Intelligent Robotics, ICCIR 2024

作者： Chen, Zhong Yang, Guoyan Huang, Feijiang School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China

ISBN: (纸本)9798400709937

With the rapid development of information technology, distributed computer clusters play an increasingly important role in processing large-scale data and complex computing tasks, and the efficient management and scheduling of clusters is the key to achieve their performance optimization. The purpose of this paper is to design and implement a distributed computer cluster management and scheduling system based on ThinkPHP framework to improve resource utilization and system performance. This paper elaborates on the system requirements analysis, architecture design, key technology implementation and database design, which not only provides a new implementation scheme for distributed cluster management, but also provides a practical reference for the application of ThinkPHP framework in the field of distributed systems. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cluster computing

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Multi-party secure communication using blockchain over 5G

Multi-party secure communication using blockchain over 5G

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international conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Archana, K. Kareem, Z.H. Al-Farhani, Liwa H. Bagyalakshmi, K. Jenvi, Ignatia K. Majella Kumar, Ashok Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Maharashtra Pune India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq System Analysis Control and Information Processing dep Academy of Engineering RUDN University Moscow Russia Sri Ranganathar Institute of Engineering and Technology Coimbatore India Mathematics Department Saveetha School of Engineering SIMATS Saveetha Nagar Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9781032669663

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

关键词： Blockchain

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Application of wind energy resource assessment system based on big data storage 5

Application of wind energy resource assessment system based ...

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5th international conference on computer Vision and Data Mining, ICCVDM 2024

作者： Shi, Qi Lv, Guannan Ai, Bo Li, Benshuai Wang, Peipei Feng, Wenjun Shang, Hengshuai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China Qingdao Yuehai Information Service Limited Qingdao266555 China Qilu Institute of Technology College of Civil Engineering Shandong Province Jinan273100 China

ISBN: (纸本)9781510682863

This paper constructs an interactive offshore wind power resource assessment system based on the 30-year data of ERA5 wind farm from the European Center for Medium-Range Weather Forecasts (ECMWF), combined with the offshore wind power big data storage technology of Apache Cassandra database and the wind energy resource assessment model, and following the current national standards and technical specification documents. In this paper, 6 wind turbines in Changyi wind farm are randomly selected for authenticity, reasonableness and completeness analysis and verification, which verifies the feasibility of constructing wind farms in this sea area, and provides scientific site selection basis and wind resource assessment reference for the construction of offshore wind farms in China. © 2024 SPIE.

关键词： Offshore wind farms

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Work-Integrated Learning: Elevating Educational Outcomes and Career Prospects in Higher Education 13

Work-Integrated Learning: Elevating Educational Outcomes and...

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13th IEEE international conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Kiruthika, Usha Kanaga Suba, Raja S. Valusa, Bhavani National Institute of Technology Tiruchirappalli Department of Computer Science and Engineering Tamil Nadu India Srm Institute of Science and Technology Tiruchirappalli Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350376234

Work-integrated learning (WIL) combines academic learning with practical work experiences, helping students transition smoothly from theory to real-world application. This paper explores the effectiveness of WIL programs in enhancing employability, fostering industry-ready skills, and promoting a holistic educational experience. This paper focuses on analyzing the statistical outcomes of WIL and how it can be improved in universities. It highlights the challenges, such as misalignment between academic curricula and industry needs, while proposing strategies for overcoming these barriers. Some of the case studies of WIL implementations show how various methodologies can guide universities in effectively incorporating work-integrated learning into their curricula. Through the analysis of prior studies and statistical data, this research aims to identify the best practices and key factors that contribute to the successful implementation of WIL. Ultimately, this study aims to contribute to the development of more effective, responsive, and impactful educational frameworks that better prepare students for their professional futures. © 2024 IEEE.

关键词： apprenticeships co-ops curriculum externships virtual internships work integrated learning

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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Fault diagnosis of Electro-hydrostatic actuators based on CWT and CNN-Transformer 3

Fault diagnosis of Electro-hydrostatic actuators based on CW...

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3rd international conference on Intelligent Mechanical and Human-computer Interaction technology, IHCIT 2024

作者： Li, Jiatong Tu, Chaofan Wang, Xingjian Wang, Zhaoyang Xiu, Wanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Tianjin300131 China China Tianjin Navigation Instrument Research Institute Tianjin300131 China

ISBN: (纸本)9781510683105

Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detection methods in practical systems, making fault diagnosis of EHA a great challenge. To address this issue, this paper proposes a fault diagnosis method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Transformer. Firstly, the collected one-dimensional time series signal of the sensor is converted into a two-dimensional time-frequency map through CWT. Then, a hybrid module of CNN-Transformer is used to extract local and global fault features, and finally the classification results are output through softmax. Conduct EHA fault simulation experiments to validate the proposed method. The research results indicate that the proposed method has a fault diagnosis accuracy of 96.33%, achieving high-precision diagnosis of EHA faults. © 2024 SPIE.

关键词： Wavelet transforms

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