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Contribution to Maintenance 4.0 by Monitoring and Prognosis of Industrial Installations by Digital Twin: Case Study on Wastewater Filtration Pilot 12

Contribution to Maintenance 4.0 by Monitoring and Prognosis ...

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12th IEEE international conference on intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Soufiane, Embarki Yousra, El Kihel El Mahdi, Bouyahrouzi Bachir, El Kihel Ump Laboratory of Industrial Engineering and Seismic Engineering Oujda Morocco LINEACT-CESI Bordeaux France

ISBN: (纸本)9798350358056

The digital solutions have taken an advance during the digital transition in the industrial development, the use of new technologies has allowed to better understand the behavior of the different parts of an industrial installation and to verify the interactions between the adjustment parameters and the optimal functioning of the production units and their impact on the competitiveness of the entity. A digital twin is a digital representation of a physical object (asset or process) that is updated in real time by the transfer of data between this physical object and its digital (virtual) model. What differentiates DT from simulation and other digital models or Computer Aided Design is the automatic bidirectional exchange of data between the digital and physical twins in real time. In this manuscript, we present a digital twin structure for monitoring a water filtration pilot. First, we propose a relational modeling of the digital twin layers of a production system with a detailed architecture of the digital twin based on the technologies used. Finally, a real-time supervision interface of the filtration process parameters is demonstrated as a first step in the implementation of a digital twin. © 2023 IEEE.

关键词： Digital Twin Industry 4.0 Internet Of Things Maintenance 4.0 smart Factories

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IoTPHM: Experimental Evaluation of Internet of Things Powered Patient Health Monitoring System using smart Sensors

IoTPHM: Experimental Evaluation of Internet of Things Powere...

引用

Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： P. Prince C. Rajabhushanam Department of Computer Science and Engineering Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research(BIHER) Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The Internet of Things Powered Patient Health Monitoring (IoTPHM) system represents a significant advancement in remote healthcare by integrating smart sensors, microcontrollers, and IoT technology. This system continuously monitors vital health parameters such as heart rate, blood oxygen saturation (SpO 2 ), body temperature, blood pressure, respiratory rate, and physical activity. Utilizing an ESP32 microcontroller, the system efficiently collects, preprocesses, and transmits data to cloud servers or mobile applications using MQTT and Bluetooth protocols. Preprocessing techniques, such as noise filtering and data normalization, ensure accurate and reliable analysis. The system's sustainability is enhanced through a rechargeable battery with a solar charging option. Evaluation results demonstrate high sensor accuracy, low latency in data transmission, and robust user feedback for comfort and usability. The IoTPHM system supports edge computing for real-time alerts and cloud-based analytics for predictive insights, enabling proactive healthcare management. Its compact, wearable design ensures convenience for continuous monitoring, catering to both clinical and home environments.

关键词： Temperature sensors Cloud computing Microcontrollers Medical services Real-time systems Internet of Things Biomedical monitoring Monitoring intelligent sensors Edge computing

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A smart Home Design with intelligent Patient Monitoring Features by Using Internet of Things (IoT) Enabled Communication Technology

A Smart Home Design with Intelligent Patient Monitoring Feat...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： P. Prince C. Rajabhushanam Department of Computer Science and Engineering Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The integration of Internet of Things (IoT) technology into smart home systems has revolutionized patient monitoring by providing real-time health insights and automated environmental adjustments. This study focuses on designing and evaluating a smart home system with intelligent patient monitoring features, aimed at enhancing patient safety, comfort, and healthcare delivery. The system employs a network of IoT-enabled devices, including wearable health sensors, environmental monitors, and smart cameras, to track vital signs such as ECG, SPO2, and body temperature, along with environmental parameters like air quality, humidity, and temperature. The system achieved a high accuracy rate, with vital sign sensors demonstrating over 98% reliability across various metrics. A communication framework using Wi-Fi and MQTT protocols ensures seamless data transfer and real-time responsiveness. Edge computing enables low-latency data processing, while cloud analytics provide predictive insights for early health issue detection. Key findings demonstrate the system's efficiency in emergency response, with an average detection-to-alert time of 1.4 seconds, and its ability to adapt the environment based on health conditions with over 95% precision. Automated features such as temperature control, fall detection, and personalized lighting enhance patient care and well-being. This work underscores the transformative potential of smart home technologies in healthcare, paving the way for future advancements in personalized health monitoring systems.

关键词： Temperature sensors Patient monitoring smart homes Medical services Real-time systems Sensor systems Internet of Things Wearable sensors intelligent sensors Edge computing

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Impact of AI-powered business intelligence on smart city policy-making and data-driven governance

Impact of AI-powered business intelligence on smart city pol...

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international conference on Green Energy, computing and intelligent Technology 2024 (GEn-CITy 2024)

作者： SM T. H. Rimon Mohammad A. Sufian Zenith M. Guria Niaz Morshed Ahmed I. Mosaddeque Asif Ahamed Management & Marketing Department Qatar University Doha Qatar College of Business Westcliff University Irvine California USA Business Administration Department International American University Los Angeles USA College of Engineering and Technology Westcliff University Irvine California USA

ISBN: (数字)9781837242863

Integrating Artificial Intelligence (AI) with Business Intelligence (BI) systems is revolutionizing the ways of governance in smart cities through data management, real-time analytics, and forecasting. These systems improve urban planning, resource allocation, and utilization, as well as, people and asset protection, and enhance transparency and responsibility. But barriers such as concerns about privacy, weaknesses of algorithms, and legal restrictions, prevent their widespread use. This paper considers the usage, benefits, and obstacles of AI-enabled BI tools in the governance of smart cities considering pressing issues such as integration with blockchain, Explainable AI (XAI), and IoT. Based on a comprehensive literature review, the research aims to highlight the usefulness of AI technologies in developing inclusive sustainable urban spaces.

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Unlocking the Potential of Competition-Based Learning: A Case Study of Kaggle in Big data analytics Education

Unlocking the Potential of Competition-Based Learning: A Cas...

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international conference on smart computing & Communications (ICSCC)

作者： Pengfei Fan Erick Purwanto Na Li Jia Wang Ting Ting Tay Ling Wang Qiufeng Wang Department of Intelligent Science School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China School of Electronic Engineering and Computer Science School of Physical and Chemical Sciences Queen Mary University of London London UK Department of Computing School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Department of Educational Studies Academy of Future Education Xi'an Jiaotong-Liverpool University Suzhou China Educational Development Unit Academy of Future Education Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big data analytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data was collected from students who completed the Big data analytics module. Results indicate high satisfaction levels among participants, with the majority expressing willingness to recommend CBL using Kaggle to future students. Moreover, the CBL approach significantly enhanced student engagement and improved their understanding of Big data analytics concepts and techniques. The matic analysis revealed positive aspects such as real-time scoring and leaderboards, but also highlighted challenges including the lack of structured learning and guidance. Solutions proposed by students include additional support mechanisms, a balance between competition and learning, and diverse competition themes. Overall, this study underscores the potential of CBL using Kaggle to transform Big data analytics education, while also emphasizing the need for tailored support and refinement of course design.

关键词： Ethics Education Collaboration Transforms Big data Real-time systems

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AI-Based Mental Fatigue Recognition and Responsive Recommendation System

AI-Based Mental Fatigue Recognition and Responsive Recommend...

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international conference on data analytics, intelligent computing, and Cyber Security, ICDIC 2020

作者： Kumar, Korupalli V. Rajesh Devi, B. Rupa Sudhakara, M. Keerthi, Gabbireddy Madhavi, K. Reddy Woxsen University Telangana Hyderabad India Annamacharya Institute of Technology and Sciences AP Tirupati India School of Computing and Information Technology Reva University Bangalore India CSE Vignan’s Foundation for Science Technology and Research A.P Guntur India Sree Vidyanikethan Engineering College AP Tirupati India

ISBN: (纸本)9789811941610

Complete comfort is considered the definite one among all the foremost concerns in the present scenario. The organization has been proceeding with more contemplation in upgrading their work comfort. The workers also proceed with many perspectives to enhance their ongoing comfort status. However, comfort is generally related to their everyday activity and conduct, mainly in the worksites where it influences both the levels of stress and mood, resulting in mental fatigue. Specifically, the characteristic of the person’s comfort will be affected by the conduct in a worksite. We suggested a mental fatigue identification system that embraced deep learning techniques for supplying non-intrusive observing systems. The comfort level has been classified based on surveys: pressure and mood. This preparatory study instructs the model to generalized and personalized models of classifications. The model as personalized perspective has been taken as one of the steps to supply a personalized health resolution support system that helps in elevating the awareness in customers and motivates them to upgrade their conduct and eventually put up to the best comfort system. We have attained an accuracy of 87% on the model generic and 94% on the model personalized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Visualization and Analysis of Knowledge Structure of smart Agriculture in the Context of Big data

Visualization and Analysis of Knowledge Structure of Smart A...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Jun Sun Meng Yang Schools of Management Dalian Polytechnic University Dalian China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Big data boosts agricultural production through intelligent transformation; data will become the emerging element of modern agricultural production. Therefore, applying big data technology to agriculture is a new trend in the development of modern agriculture. It can promote the development and progress of agricultural information service technology to a large extent. It can also promote the overall development process of the agricultural field to a large extent. Developing smart agriculture can improve agricultural modernization and promote agricultural transformation and upgrading. smart agriculture can improve the yield and quality of agricultural products, reduce the waste of natural resources, and reduce the pollution of the environment. There is an increasing number of research results related to smart agriculture. However, there is a lack of research on using big data analytics tools to sort out smart agriculture fully. Therefore, based on knowledge mapping, this paper analyzes intelligent agriculture's research hotspots and development trends. This study concludes that precision agriculture, the Internet of Things, big data, artificial intelligence, and cloud computing are the current research hotspots in intelligent agriculture. Furthermore, it is moving toward intelligence and sustainability.

关键词： smart agriculture Technological innovation data visualization Production Big data Market research Agriculture

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Green Mobility Solutions Through intelligent Fleet Management and smart Logistics

Green Mobility Solutions Through Intelligent Fleet Managemen...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Wai Yie Leong Yuan Zhi Leong Rajendra Kumar Persiaran Perdana BBN Putra Nilai INTI International University Nilai Negeri Sembilan Malysia Kallang Avenue Schneider Electric Building Schneider Electric Singapore Pte. Ltd. Singapore Department of CSE Sharda University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

smart logistics and green mobility are the future-oriented solutions to improve efficiency, sustainability, and environmental responsibility in transportation and logistics. Advanced technologies like IoT, AI, and data analytics are utilized by smart logistics to optimize the supply chain process, remove inefficiencies, and ensure real-time tracking and monitoring. Such intelligent use of technology makes the logistics ecosystem's operations more streamlined and leads to better decision-making. Green mobility is an environmentally friendly mode of transportation that aims to decrease the ecological footprint of logistic operations. In this paper, it is seen in the form of electric and hybrid vehicles, alternative fuels, and sustainable transport modes. The green mobility concept focuses on reducing carbon emissions, less dependence on fossil fuels, and promoting environmentally friendly transport.

关键词： Technological innovation data analysis Automation Supply chain management Shared transport Green products Supply chains Routing Real-time systems Sustainable development

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Adaptive Stream Processing Framework for Energy-Efficient smart Greenhouses Using Neuromorphic computing

Adaptive Stream Processing Framework for Energy-Efficient Sm...

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IoT Based Control Networks and intelligent Systems (ICICNIS), international conference on

作者： Akey Sungheetha R Rajesh Sharma Sheila Mahapatra K Sasi Kala Rani A. Ezil Sam Leni R Tamilarasi Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This paper presents an innovative framework combining neuromorphic computing and stream processing analytics for energy-efficient smart greenhouse management. The proposed Adaptive Greenhouse Optimization and Resource Integration (AGORI) methodology addresses key challenges in real-time environmental monitoring, energy optimization, and sustainable agriculture. By leveraging Meta Spark Creator AR for visualization and implementing a novel hybrid algorithm combining Spiking Neural Networks (SNNs) with streaming data analytics, our system achieves 47% improved energy efficiency and 38% better crop yield prediction accuracy compared to traditional methods. The framework was validated across 14 international deployment sites, demonstrating robust performance under diverse climatic conditions. Results show significant improvements in resource utilization and operational sustainability, with particular emphasis on power stability and green energy integration.

关键词： Measurement Neuromorphic engineering Green products Prediction algorithms Energy efficiency Agriculture Sparks Resource management Optimization Crop yield

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V-FARM: Virtualized Farming analytics for smart Decision-Making

V-FARM: Virtualized Farming Analytics for Smart Decision-Mak...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Haitham Mahmoud Matt Bell Neave Anderson Elizabeth Cutter Claire Edwards Adel Aneiba Umar Daraz De Mi College of Computing Birmingham City University Birmingham United Kingdom Animals and Agriculture Department Hartpury University Gloucester UK Business Development Hartpury University Gloucester UK

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

The welfare, productivity, and health of livestock are being impacted steadily by climate change and extreme weather events. For instance, it is predicted that heat stress will cause dairy cows in Southeast England to lose more than 170 kg of milk a year; in the hottest UK regions, this loss could increase to 1,300 kg per cow (18.6% of annual yield) by the 2090s. A solid foundation for evaluating environmental stress is presented in this paper. It is based on agro-environmental indices that are obtained from temperature and humidity data. To improve predictive capabilities, the proposed Virtualized Farming analytics for smart Decision-Making (V-Farm) system uses state-of-the-art synthetic data generation techniques, such as TCGAN and polynomial fitting. A novel approach to lessen the effects of thermal stress in precision livestock farming is to incorporate these indices into automated ventilation systems.

关键词： Climate change Heat treatment Temperature measurement Humidity control Indexes Livestock Precision agriculture Decision making smart agriculture

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