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共 20256 条记录，以下是661-670 订阅

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Study on intelligent Decision Architecture of Online data Acquisition and Analysis System 3

Study on Intelligent Decision Architecture of Online Data Ac...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yu, Chenglong Liu, Daoyuan Yan, Hongpo Li, Qiang Gong, Maozhen Beijing Institute of Computer Technology and Application Beijing China Jincheng Nanjing Engineering Institute of Aircraft System Nanjing China

ISBN: (纸本)9798331529222

To better improve the production capacity and control efficiency of discrete manufacturing workshops, a decision system architecture based on online acquisition and data analysis is proposed, and its architecture and operation process are described. Furthermore, the key supporting function such as manufacturing data detection and acquisition based on internet of things, data analysis for production control, production optimization decision based on data analysis were further studied. Ultimately, a prototype system was built, as well as its functionality was validated by the past actual data. It has good reference value to provides technical support and software foundation for subsequent research and performance verification. ©2024 IEEE.

关键词： Smart manufacturing

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Portable soil analyzer to promote Digital Agriculture 3

Portable soil analyzer to promote Digital Agriculture

引用

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

作者： Vaibhavi, D. Harshitha, T. Hegde, Meghana D. Samruddhi, D. Gadad, Veena Sowmyarani, C.N. Ramakanth Kumar, P. R V College of Engineering Dept of Ece Bengaluru India R V College of Engineering Dept of Cse Bengaluru India R V College of Engineering Dept of Aiml Bengaluru India

ISBN: (纸本)9798331515911

Digital agriculture is revolutionizing the way food is grown, managed, and distributed, offering a path to more efficient and sustainable agricultural practices that can address global challenges like climate change and food security. This includes efficient management of soil health and water usage. It is critical for maximizing crop yields and ensuring sustainable practices. This presents the design and implementation of a smart portable soil analyzer that can measure key soil properties including pH, moisture, and nutrient levels. The device is designed to interface with a comprehensive management system that provides real- time weather updates and monitors irrigation systems based on soil moisture data and weather conditions. The proposed system enhances farm productivity, sustainability, and profitability. © 2025 IEEE.

关键词： Smart agriculture

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Leveraging Multi-Model learning for Enhanced Grayscale Image Colorization 3

Leveraging Multi-Model Learning for Enhanced Grayscale Image...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pavithra, R. Yogapriya, M. Shalini, B. Gowri, R. Kandavel, N. Thirumalai Murugan, R. Department of CSE-AIML Chennai Institute of Technology Tamil Nadu Chennai India Department of CSE Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

This study addresses the challenge of grayscale image colorization, which often suffers from inconsistent results and an inability to manage complex patterns in single-model approaches. A multi-model ensemble approach is presented for grayscale image colorization, which integrates five advanced deep learning models. These five models were selected rather than two or three to increase the robustness across multiple datasets and to capture a greater range of features. By combining the results from models trained in both RGB and Lab color spaces, the proposed method improves visual quality and color accuracy. The study demonstrated that the ensemble approach is much better than a single-model approach when considering the colorization accuracy and the fine texture. Colorization plays a vital role in preserving historical photo restoration, enriching digital archives, and improving visualization across education, science, and the arts. By transforming grayscale images into accurate color representations, it also paves the way for future advancements in digital storage and media restoration. © 2025 IEEE.

关键词： RGB color model

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Dynamic Planning and intelligent Decision-Making Model of Power Grid Project Reserve Integrating Multi-Source data and Deep learning 3

Dynamic Planning and Intelligent Decision-Making Model of Po...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Meifang Yang, Lina Yang, Liu Wang, Tao Jia, Junqiang Hu, Junjun State Grid Xinjiang Information and Communication Company Xinjiang Urumqi830000 China State Grid Electric Power Research Institute Nanjing Jiangsu Nanjing210000 China

ISBN: (纸本)9798331519032

In this paper, a dynamic planning and intelligent decision-making model of power grid project reserve based on multi-source data and deep learning is proposed to meet the new challenges of power grid project reserve and decision-making under the background of current energy transformation and smart grid construction. In the process of data fusion, hierarchical fusion strategy is adopted, including data preprocessing, feature extraction and feature level fusion. The deep learning module adopts a model combining convolutional neural networks (CNN) and long short-term memory (LSTM) to capture the spatial and temporal features of power grid data. CNN is used to extract local spatial features such as voltage fluctuations and current changes, while LSTM captures the temporal variations of these features. The fused eigenvectors are used as the input of the deep learning model, and the data base is provided for the dynamic programming algorithm by accurately predicting the power grid operation state and load demand. According to the prediction results, the dynamic programming algorithm comprehensively considers the project cost, income, implementation time and resource constraints, and optimizes the reserve strategy of power grid projects. Through the combination of dynamic planning and deep learning, the optimal allocation of resources and efficient management of projects are realized, aiming at minimizing operating costs, maximizing power grid stability and optimizing return on investment. The experimental results show that, compared with the traditional methods, the model has significantly improved the prediction accuracy and decision-making optimization effect, with the prediction accuracy reaching 91.8% and the decision-making optimization effect reaching 84.1%. This research not only improves the scientific and intelligent level of power grid project reserve, but also provides strong support for the sustainable development of smart grid. © 2024 IEEE.

关键词： deep learning dynamic planning intelligent decision-making multi-source data power grid

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PV Fault Detection Using CNN For Enhancing Reliability Of Solar Power Plants 3

PV Fault Detection Using CNN For Enhancing Reliability Of So...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Alam, Sameer Kaushik, Shivam Shaique, Shaikh Mohd Rafiuddin, Nidal Zhcet Amu Department Of Electrical Engineering UTTAR PRADESH Aligarh India

ISBN: (纸本)9798350387193

Solar energy presents a promising avenue for clean and abundant electricity generation. However, issues such as equipment malfunctions or inefficiencies can hinder its effectiveness. This study introduces a Convolutional Neural Networks based approach for efficient anomaly detection in photovoltaic cells, by employing deep learning Convolutional Neural Networks. The primary focus of this study is on optimizing the efficiency of anomaly detection in PV cells. The proposed project employs a comprehensive and diverse dataset comprising large sets of infrared thermal images, encompassing both anomalous and healthy infrared images of PV modules. This extensive dataset is utilized to train the Convolutional Neural Networks model, enabling precise testing of new IR image data. The model is designed such that it differentiates between healthy and faulty IR images from a given dataset. The paper conducts a thorough analysis of the proposed project, highlighting its advantages in terms of accuracy, reliability, and efficiency. This research contributes to optimizing solar power generation, ensuring its reliability and longevity. © 2024 IEEE.

关键词： Convolutional neural networks

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Agricultural Crop Recommendation System 3

Agricultural Crop Recommendation System

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Balakrishnan, D. Kumar, Anumula Praneeth Sai Kiran Reddy, Kristipati Kumar, R. Ravindra Aadith, K. Madhan, Sudarsi Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798350338607

The Agricultural Crop Recommendation System using machine learning is a software application that utilizes artificial intelligence algorithms to provide recommendations to farmers on the best crops to plant based on their specific farming conditions. The system collects and analyzes various data sources, such as soil quality, climate, and historical crop yields, to make personalized crop recommendations. The system is easy to use and can be accessed through a web-based platform or mobile app. The implementation of this system can help farmers to optimize their crop production, reduce costs, and increase efficiency in agricultural *** implementation of a machine learning-based Agricultural Crop Recommendation System holds immense potential to transform the agricultural sector. By leveraging data-driven insights, this system can offer farmers recommendations that are both sustainable and profitable, enabling them to make informed decisions and enhance their overall productivity. © 2023 IEEE.

关键词： Crops

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Research on the Automatic Recheck System and Maintenance Technology for Dismantled Electric Energy Meters 3

Research on the Automatic Recheck System and Maintenance Tec...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Meng, Fanling Li, Jianping Li, Zhendong Li, Xi Li, Yan Xiang, Shigui State Grid Qinghai Electric Power Company Marketing Service Center Xining China Yantai Dongfang Wisdom Electric Co. Ltd. Yantai China

ISBN: (纸本)9798350380989

This paper designs an intelligent management system for millions of dismantled and returned electricity meters to achieve automatic sorting and evaluation of dismantled and used electricity meters. The system includes storage connection unit, automatic loading and unloading unit, system conveying unit, automatic disassembly unit, automatic identification unit, appearance inspection unit, labeling unit, manual labeling unit, automatic sorting unit and scrap disposal unit. By categorizing the faults in the installation components of the dismantled energy meter, the maintenance value is determined based on the fault situation, maintenance cost, and remaining life. © 2024 IEEE.

关键词： automatic sorting cost analysis dismantling the electricity meter intelligent management system

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A Multi-Model Approach to Oral Pathology Classification and Dental X-Ray Analysis 3

A Multi-Model Approach to Oral Pathology Classification and ...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sumalatha, M.R. Tejasree, M.S. Sowmiya, S. Parthasarathi, D. Sathish Kumar, M. Dept. of Information Technology Anna University MIT Campus Chennai India

ISBN: (纸本)9798331527549

The timely detection and accurate localization of oral diseases, such as oral cavity cancer and dental anomalies, is critical for improving preventive dental care and patient outcomes. This paper presents an AI-based system that employs a dual-architecture approach for analyzing oral and dental X-ray images. An ensemble of ResNet-50, EfficientNet-B1, and InceptionResNetV2 is used for oral disease classification, leveraging their complementary strengths to ensure accurate predictions across various conditions. For dental X-ray analysis, the Detection Transformer (DETR) model is utilized to detect and localize abnormalities in complex radiographic structures. This integrated system enhances diagnostic accuracy and provides early detection, treatment suggestions, and clinical decision support through a user-friendly interface and chatbot. The proposed approach improves accessibility and efficiency in oral health diagnosis, contributing to better patient outcomes and facilitating early intervention in dental care. © 2025 IEEE.

关键词： Artificial Intelligence Clinical Decision Support Deep learning Dental Anomalies Detection Transformer (DETR) Ensemble learning Oral Disease Detection Preventive Healthcare

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Deep learning Based Modelling for Degradation Analysis and SOH Estimation of Commercial Lithium-Ion Batteries 3

Deep Learning Based Modelling for Degradation Analysis and S...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Ahmed, Mujeeb Kumar, Deepak Jamil, Majid Faculty of Engineering & Technology Jamia Millia Islamia Department of Applied Science Delhi India Delhi Technological University Centre of Excellence for Electric Vehicles and Related Technologies Department of Electrical Engineering Delhi India Faculty of Engineering & Technology Jamia Millia Islamia Department of Electrical Engineering Delhi India

ISBN: (纸本)9798350387193

Lithium-ion batteries (LIBs) are widely used in electric vehicles due to their high energy and power efficiency. Accurate SOH estimation is essential to ensure the effectiveness and stability of the Battery Management System (BMS). Traditional methods for evaluating battery characteristics are inaccurate, leading to inaccurate SOH estimates. Recent research examines the ability of deep learning (DL) models to estimate battery states. Long-term cycling experiments were conducted using two types of commercial LIBs cells with different cathode material compositions. The DL models were evaluated using root mean square error, mean squared error, mean absolute error, R2, and maximum error. The experimental findings consistently confirm the advantages of the suggested approach, which includes excellent estimate accuracy and satisfied adaptability to various battery chemistries. © 2024 IEEE.

关键词： State of charge

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Defect Detection and Identification in PCBs Using Single Stage Object Detection Models 3

Defect Detection and Identification in PCBs Using Single Sta...

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

作者： Aniruddh, D.P. Deepa, N.P. Pathak, Atharva Katti, Hriday Ayush, B.N. Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331515911

In industrial electronics, detection of defects in PCBs is an essential part in ensuring quality and reliability of printed circuit boards. Conventional methods of identifying such defects are sometimes ineffective, as they may not detect the defects efficiently owing to human error. Machine learning (ML) methods can be a better alternative to these traditional methods. Convolutional neural networks (CNNs) are well known for detecting abnormal patterns and irregularities in datasets efficiently. By training ML models using large datasets of PCB image samples, the models can effectively recognize the presence of such defects. The defects may include mouse bites, short circuits, spur defects and so on that may sometimes go unnoticed by the human eye. The implementation of ML models for this purpose not only improvises on accuracy, but also reduces the time taken. This paper focuses on a solution to detecting PCB defects using ML models. © 2025 IEEE.

关键词： Convolutional neural networks

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