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Graph-Based Modeling for Multi-input Temperature Prediction in Power Plant 8th

Graph-Based Modeling for Multi-input Temperature Prediction...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gang, Yusen Peng, Chen Cheng, Chuanliang Shanghai University Shangda Road Baoshan District Shanghai99 China

ISBN: (纸本)9789819602247

The operational efficiency and safety of thermal power generation units are intricately dependent on the precise control of combustion chamber temperatures. Accurate temperature prediction is indispensable for optimizing boiler combustion efficiency and swiftly identifying abnormal operational conditions. In this research, we propose an innovative end-to-end data-driven model designed to address the inherent complexity of multi-input multi-output temperature prediction. By fusing temperature data with a graph-based framework and harnessing spatiotemporal attention mechanisms, our model, underpinned by Graph Convolutional Network and Transformers, adeptly captures intricate spatial relationships among output variables. Empirical validation using real-world plant data attests to the model’s exceptional predictive performance. The results highlight its potential to significantly enhance temperature prediction accuracy in power plant operations. Notably, the model achieves a root mean squared error of 2.257 and a mean absolute error of 1.803 as the best prediction scores. Overall, our proposed approach presents a promising solution for the accurate prediction of temperature, with potential implications for improving operational efficiency and safety in thermal power generation units. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Learning intelligent computing Signal Analysis Signal Processing systems Modelling

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intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

引用

2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Patil, Jay Joshi, Suvarna Mulla, Rahesha Yadav, Kushal Mit School of Computing Mit Adt University Department of Computer Science and Eng. Pune India University Department of Artificial Intelligence and Machine Learning Pune India Indian Institute of Technology Madras Department of Data Science India

ISBN: (纸本)9798331527495

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods. © 2025 ieee.

关键词： Ultrasonic testing

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advanced Spatiotemporal Anomaly Detection in Photovoltaic Power Plants Using Temporal Graph Networks Integrated with Publicly Available Weather data

Advanced Spatiotemporal Anomaly Detection in Photovoltaic Po...

引用

2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Reddy, B.Ravali Awasthy, Neeta Keer, Priyameet Kaur Nijhawan, Ginni Mala, Vajenti Albawi, Ali Srinivas, T. Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331527495

For purposes of performance analysis and minimization of maintenance expenses, it is vital to identify an effective way in which to monitor anomalies in photovoltaic (PV) power plants. This paper proposes a novel framework to address the problem of anomaly detection based on the inherent graph structure of PV plant configurations using Temporal Graph Networks (TGNs). TGNs are built to capture spatial and temporal dependencies of the PV systems which allows detecting discrepancies affecting the power production, which traditional NN might fail not to notice. The model uses external weather data collected from NOAA and tracks how external parameters influence the performance of photovoltaic panels and therefore electricity generation. Our methodology solely employs TGNs to analyse the intricate relationship in the PV plant data to improve the recognition of abnormality associated with the equipment and weather conditions. The integration of NOAA's open source weather data into the extent of analysis further raises sophistication by offering a variety of features and conditions affecting PV results. Our approach achieves high accuracy of anomaly detection and can be considered to be better compared with the baselines that do not take graph dependencies or time aspect into account. This advancement has important implications for enhancing the operation and maintenance recommendations in the PV power plants and hence the reliability of the satellite power systems. Allying our efforts to one highly specialized method, this research presents a new state of the art in leveraging centralized data for anomaly detection in renewable energy. © 2025 ieee.

关键词： Network theory (graphs)

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A Transfer Learning based Deep Learning Framework for Lung Disease Detection Utilizing CXR Images

A Transfer Learning based Deep Learning Framework for Lung D...

引用

2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Kansal, Kajal Kansal, Kanika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Abes Engineering College Department of Computer Science and Engineering - Data Science Uttar Pradesh Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527495

The classification of various lung diseases with the help of chest X-ray images is one of the significant and challenging issues in medical imaging applications. This work proposes a transfer learning-based deep neural network architecture to classify lung diseases using a publicly available dataset. The proposed DenseNet201 model uses the interconnected structure to optimize feature transmission, minimize computation redundancy, and improve the gradient flow. The proposed model achieved a better classification accuracy of 98.34% compared to the other existing methods. These results show that DenseNet201 effectively learns discriminative features from the input medical image data. The proposed framework is suitable for large-scale implementation of lung disease classification. It helps develop more efficient computer-aided diagnostic systems, assisting clinicians in decision-making. © 2025 ieee.

关键词： Lung cancer

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Edge-Cloud Infrastructure for Mushroom Growth Monitoring and Analysis

Edge-Cloud Infrastructure for Mushroom Growth Monitoring and...

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international conference on Computational Cybernetics (ICCC)

作者： Sándor Tarsoly Artúr I. Károly Tamás Csibrák Péter Galambos Antal Bejczy Center for Intelligent Robotics University Research and Innovation Center Obuda University Budapest Hungary

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

This paper presents the development and implementation of an edge-cloud infrastructure for monitoring and analyzing white and brown button mushroom growth. The system includes edge devices with sensors and cameras for onsite data collection and a cloud platform for data storage and processing. The collected data is processed using a Mask R-CNN model trained on synthetic data to identify and analyze individual mushrooms. Over two years of monitoring, it has demonstrated reliability in capturing and processing data in challenging agricultural environments. These results highlight the potential of integrating advanced machine learning with edge-cloud systems to enhance precision agriculture and resource management.

关键词： Precision agriculture Memory Machine learning Sensor systems data models Sensors Resource management Reliability Monitoring Synthetic data

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Design and Simulation of an intelligent Traffic Management System Using Vehicular Networks

Design and Simulation of an Intelligent Traffic Management S...

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P. Tapan Datta D.Lalith Kumar S. Tanoj Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this study , the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the ieee 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks.

关键词： Protocols Vehicle-to-infrastructure Simulation Vehicular ad hoc networks Switches Radio propagation Safety Security Reliability Vehicle dynamics

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Dynamic Quality Management in Wind Power Construction: Integrating Digital Twins with BIM and IoT

Dynamic Quality Management in Wind Power Construction: Integ...

引用

Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Xiaodan Yuan Silu Wang Xu Zhao Jinzhou Electric Power Supply Company State Grid Liaoning Electric Power Supply Co. Ltd Jinzhou China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The increasing complexity of wind power construction projects necessitates advanced quality management systems to address real-time challenges effectively. This paper introduces a digital twin-based framework integrating Building Information Modeling (BIM) and Internet of Things (IoT) technologies to enhance quality management. IoT sensors provide continuous, real-time data acquisition from personnel, materials, equipment, inspection, methods, and the environment, while BIM offers a spatial platform for visualization and analysis. The system's architecture includes automated data collection, parametric information integration, and predictive analytics, enabling comprehensive and proactive quality control. Preliminary results from pilot implementations highlight significant improvements in material efficiency, response times to quality issues, and stakeholder collaboration, demonstrating the system's potential for scalable and adaptive applications in wind power construction.

关键词： Scalability Collaboration Wind power generation Real-time systems Digital twins Internet of Things Time factors Stakeholders Predictive analytics Quality management

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Design of Cloud-Edge-End Collaborative Framework for Power Supply intelligent Inspection Operation Scenario Based on Unmanned Aerial Vehicle

Design of Cloud-Edge-End Collaborative Framework for Power S...

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Wei Wang Ping Qiu State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end" collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing.

关键词： Power supplies Clouds Collaboration Inspection Autonomous aerial vehicles Cameras Electricity supply industry Real-time systems Maintenance Edge computing

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Time Series data Classification using the Knowledge-Augmented Transformer Model

Time Series Data Classification using the Knowledge-Augmente...

引用

international conference on Big data and Smart computing (BIGCOMP)

作者： Hyun-Seok Chung Sun-Young Hyun Chang-Eun Lee Young-Guk Ha SmartLabs Seoul Republic of Korea ETRI Daejeon Republic of Korea Department of Computer Science and Engineering Konkuk University Seoul Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Time Series Classification predicts outcomes by analyzing sequential data that changes over time. As the complexity of time series data increases, particularly in scenarios involving dynamic relationships, existing models face limitations. This paper proposes a classification model using an encoder-based transformer with knowledge augmentation to address these challenges. The model preprocesses time-series data into a graph structure, enabling effective learning of intricate relationships. Relational attention and temporal attention enhance focus on critical relationships and time points, respectively. Additionally, symbolic reasoning through knowledge augmentation allows the model to capture complex, implicit information. Experimental results show that the proposed model outperforms existing SOTA model on complex datasets like SC2EGSet.

关键词： Computational modeling Time series analysis Knowledge based systems Games Predictive models Transformers data models Cognition Complexity theory Faces

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Research on Multi-Sensor Parameter Updating and Firmware Upgrading Method Based on Modbus

Research on Multi-Sensor Parameter Updating and Firmware Upg...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Kai Ding Chaoyue Jiang Ming Tao Renping Xie Jiaxue Li School of Computer Science and Technology Dongguan University of Technology Dongguan China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In industrial systems, sensors are used as key data acquisition devices. Optimizing their performance and functionality is essential for ensuring stable operation and improving system productivity. In order to realize function expansion, performance optimization and security guarantee, parameter updates, and firmware upgrades are indispensable operations. However, traditional methods for parameter updates and firmware upgrades typically rely on manual operations, which has many drawbacks such as low efficiency, high maintenance cost, and a significant risk of errors. These limitations make it difficult to meet the demands for high efficiency and intelligent equipment management in modern industrial systems. We propose a solution for parameter updating and firmware upgrading based on Modbus protocol, designing and realizing related hardware and Supervisory Control Software systems. The system communicates with the Supervisory Control Software through the LoRa module, utilizing the reliability and standardization characteristics of the Modbus protocol to improve the efficiency of parameter updates and firmware upgrades effectively. The research in this paper provides an efficient and reliable realization path for the intelligent management of sensors in industrial systems and provides an important reference for research and application in related fields.

关键词： Protocols Supervisory control Software Hardware Sensor systems Software reliability Maintenance intelligent sensors Thermal stability Microprogramming

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