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Research on Modeling Method of Digital Twin of Lithium Battery Based on Data-Model Hybrid Drive 4

Research on Modeling Method of Digital Twin of Lithium Batte...

引用

4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhang, Yuxin Wu, Jianhua Sun, Shanxun Ye, Tao Energy and Electricity Research Center Jinan University Zhuhai China Guangdong Electrical and Intelligent Control Engineering Technology Research Center Institute of Rail Transportation Jinan University Zhuhai China

ISBN: (纸本)9798350359589

Lithium battery, as an important part of new energy storage, its scientific and safe use and management has become a key factor to achieve low-carbon technology. Lithium-ion battery is a highly nonlinear system, and its operating state is susceptible to environmental interference and other disturbances, resulting in errors. Aiming at the error caused by the disturbance of the running state of lithium battery, this paper proposes a method of digital twin modeling of lithium-ion battery which integrates the mechanism model and the data-driven model. The adaptive particle swarm optimization support vector machine regression (SVR) algorithm was used to conduct data-driven modeling for the errors between the mechanism model and the battery entity data, and the errors were corrected to obtain a real-time and high-precision battery model, which realized condition monitoring and offline simulation display. Moreover, the accuracy of the model was verified through experiments, proving that it can lay a foundation for the safe and intelligent management and control of the battery system. © 2024 IEEE.

关键词： data-driven digital twin lithium-ion battery mechanism model support vector machine regression

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT 2

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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2nd international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Vishwanath, K. Reddy, Rachit Roy, Sritama Vellore Institute of Technology Department of Computer Science Chennai600127 India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350328202

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena. © 2024 IEEE.

关键词： Buoys

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Research on intelligent Automatic Building Model Based on computer BIM System 4

Research on Intelligent Automatic Building Model Based on Co...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Fan, Chong Zhang, Wei Hubei University of Technology Art Design College Hubei China

ISBN: (纸本)9798350342710

This topic based on BIM model, Vue framework, B/S architecture will be distributed underground water source heat pump, lighting control, fresh air, envelope structure, video monitoring and other intelligent subsystems integrated into a unified platform. In this way, integrated management is realized. The system has achieved the goals of centralized management, systematic operation and maintenance, digitized information and three-dimensional visualization. This paper introduces a way to transform the basic category mode of construction industry into the expanded mode of Chengdu topographic map. In the whole building level from the information integration, visualization, positioning automation three aspects of comprehensive improvement. By conducting field visits to the operation manager and testing the prototype system, the reasons for the failure of the air conditioning system can be found more deeply. © 2023 IEEE.

关键词： Construction industry

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An IoT-Edge Prototype for Wastewater Treatment Plant monitoring 4

An IoT-Edge Prototype for Wastewater Treatment Plant Monitor...

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4th international conference on Embedded and distributed Systems, EDiS 2024

作者： Benhariga, Abderrahmane Loukil, Lakhdar Zahaf, Houssam Eddine University of Oran 1 Computer science Department Algeria Nantes University Iut of Nantes France

ISBN: (纸本)9798331519872

Wastewater treatment process presents significant challenges, particularly in achieving efficient treatment and realtime monitoring to ensure environmental safety. Traditional methods often fail to provide timely data, resulting in delays in identifying and addressing issues. This paper aims to address these challenges by employing Internet of Things (IoT) technology at the Edge level, specifically focusing on the physical layer with low-cost and low-power hardware solutions. We propose an IoT infrastructure for Mascara (a city in north-western Algeria) wastewater treatment plant, integrating various sensors to measure critical wastewater parameters such as pH, dissolved oxygen, conductivity, temperature, and turbidity. The infrastructure employs sensor kits equipped with real-time data displays and communication mediums to transfer data to a gateway for storage. The objective of this work is to demonstrate the effectiveness of IoT in enhancing the quality of wastewater treatment monitoring, thereby enabling plant operators to exercise better control over the treatment process. The successful implementation of the system underscores the significant potential of IoT technologies to revolutionize wastewater management, ultimately leading to more sustainable and efficient water resource management. © 2024 IEEE.

关键词： Wastewater treatment

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Detection of distributed Denial of Service Attack on controller in Software Defined Network 1

Detection of Distributed Denial of Service Attack on Control...

引用

1st IEEE international conference on Smart Power control and Renewable Energy, ICSPCRE 2024

作者： Raghul Kannan, A. Naveen Sundar, G. Narmadha, D. Karunya Institute of Tehnology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377002

A network which is said to be controlled based on a centralized controller falls under the category of software-defined network which is intelligent too. Contrary to conventional networks, SDN is easier to monitor and more vulnerable to breaches of security. Early attack susceptibility in SDN is directly dependent on the planes including application plane, data plane, and control plane. The contemporary issues on the plane include DDoS, unauthorized access, DoS, and etc. There are certain techniques consisting of HTTPS, tunneling the connection are available to address the issue in SDN context. When it comes to controller monitoring, a secure coding model is necessary, as generic controller monitoring solutions do not utilize deep learning policies to secure the model during its training phase. Henceforth, Convolution Neural Network for Anomoly Detection(CNN-AD) leverages the CICDDOS-2019 dataset to identify and mitigate DDoS attacks, offering a robust solution that incorporates deep learning policies to enhance the security of SDN controllers during their training phase. CNN-AD method shows 97.51 % of accuracy. This research not only provides a critical advancement in SDN security but also introduces an innovative approach to utilizing deep learning for real-time attack detection and prevention. © 2024 IEEE.

关键词： Denial-of-service attack

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Target Identification and Detection Using Hyperpectral Signature Transformation 13

Target Identification and Detection Using Hyperpectral Signa...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Wilson, Oshin Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States Department of Mechanical Engineering BME University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

Hyperspectral imaging has emerged as a powerful technique for target detection and material identification across various fields, from remote sensing to environmental monitoring. This paper proposes a spectral signature matching method for detecting a known target (i.e., sea water) in hyperspectral images. By analyzing pixel-by-pixel spectral characteristics, spectral matching algorithm computes the similarity between the spectral signature of each pixel and that of known target materials. The proposed method employs the Jasper Ridge hyperspectral data from an urban landscape to demonstrate the algorithm’s effectiveness, with results revealing high accuracy in target detection even in heterogeneous environments where various land cover types (like water, vegetation, urban structures, soil, and rocks) are intermixed within the same area. This highlights the algorithm’s robustness in discerning similar spectral features. The experimental results demonstrate that the Jeffries-Matusita Spectral Angle Mapper (JMSAM) outperformed the Spectral Angle Mapper (SAM) and the Normalized Spectral Similarity Score (NS3) algorithms, by achieving the lowest MSE of 0.056933. © 2025 IEEE.

关键词： Remote sensing

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Dynamic Offloading control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： distributed Resource Allocation intelligent Waste Management Waste Sorting Wireless Communication Network

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Collaborative Governance Empowered by Digital Technology - Construction of Precision intelligent control and Early Warning Platform for Construction Solid Waste 11th

Collaborative Governance Empowered by Digital Technology - C...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： You, Yi Chew, Boon Cheong Fan, Hua Institute of Technology Management and Entrepreneurship Universiti Teknikal Malaysia Melaka Malacca76100 Malaysia Construction College Taizhou Vocational & Technical College Taizhou318000 China Faculty of Technology Management and Technopreneurship Universiti Teknikal Malaysia Melaka Malacca76100 Malaysia Hangzhou Environmental Sanitation and solid waste disposal guarantee Center Hangzhou310000 China

With the continuous acceleration of China's urbanization process, construction solid waste, as an important component of urban solid waste, has caused pollution to the urban ecological environment to a certain extent through its utilization and disposal. The article established a precision intelligent control and early warning platform for construction solid waste, achieving precise supervision of the entire process of construction solid waste disposal, which is of great significance for promoting urban green development and improving urban ecological environment. The experimental results showed that the monitoring coverage rate of the intelligent control early warning platform based on digital technology reached up to 91.8%. © 2024 The Authors. Published by Elsevier B.V.

关键词： Collaborative Management Construction Solid Waste Digital Technology Early Warning Platform Precision intelligent control

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A Review on environmental Sustainability Using Machine Learning Applications 5

A Review on Environmental Sustainability Using Machine Learn...

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

作者： Nirmala, E. Yadav, Manish Naveen, Palanichamy Maragatharajan, M. Suresh, M. Bhargava, Pranjal School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kothri Kalan India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Madhya Pradesh Kothri Kalan India KPR Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331518097

environmental sustainability reduces the destruction of natural resources in the hands of mother earth for next generation. Due to the development of cities and its social and economic factors leads the major role of any such destruction. It affects huge volume of biological system in terms of environmental pollution, change in climate and respective population growth. Henceforth, the general population is severely affected with their quality of life in the present scenario. This research study discusses about various resources that are affected severely due to the environmental factors have stated with the proven evident. Along with this various prediction technology using machine learning procedures are discussed in detail to prevent any such destruction by improving the present style of work. The prediction of any such destruction exploiting resources like land, water, mineral and planetaries creates its impact in the form of climatic changes, as a result of this we are facing drought, earthquakes and landslide. Apart from preventing techniques over plantation of greeneries, it is necessary to implement techniques towards efficient use of resources monitoring is essential features. All these things are now essentially managed with the help of computer aided technology. Further the effective usage of environmental factors results into safe future for the next generation. This can be achieved by using AI with SUV to increase the prediction accuracy. Further this article is directed towards future direction to prevent and use the natural resources is discussed for the benefit of population around the world. © 2024 IEEE.

关键词： Climate change

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On development of a pervasive computing aided assistive systems for the elderly in Internet of Things 2

On development of a pervasive computing aided assistive syst...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yao, Di Xing, Yutao Luo, Chenyao Song, Huaju Beijing100083 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China School of Environmental Science Nanjing XiaoZhuang University Nanjing210017 China

ISBN: (纸本)9781510689053

With the rapid development of sensors, wireless communication, and computing technology, Internet of Things (IoT) has gained significant research interest. In particular, increasing people-oriented applications and systems have been developed to improve the quality of life with intelligent services. In this paper, we propose a situational awareness system based on pervasive computing. In response to the current situation of a large number of elderly people living alone or lacking care, an intelligent assistance system model for elderly people living alone is proposed, aiming to solve problems such as forgetting to turn off lights caused by memory decline, falls caused by mobility impairment, and loss of dementia elderly people. Furthermore, we adopt the OSGi architecture to implement the proposed system. In addition, a virtual device module is designed to shield underlying device heterogeneity ensuring seamless collaboration between devices and services. To further enhance the system's capabilities, optical sensors are integrated for intelligent health monitoring, enabling real-time tracking of vital signs such as heart rate and respiratory rate, as well as early detection of potential health anomalies. To make services transparent to users, we explore the integration methods of distributed sensors and intelligent devices in pervasive computing environments. Experimental results indicate that the system can effectively improve the comfort and safety of elderly people's lives, enhance the convenience of daily affairs and risk prevention and control capabilities. Therefore, the developed system not only provides a technical framework for intelligent assistance systems for the elderly, but also has significant implications for the application of pervasive computing in the field of social welfare. © 2025 SPIE.

关键词： Assistive technology

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