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Third international conference on intelligent Traffic Systems and Smart City, ITSSC 2023

Third International Conference on Intelligent Traffic System...

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2023 3rd international conference on intelligent Traffic Systems and Smart City, ITSSC 2023

ISBN: (纸本)9781510672963

The proceedings contain 44 papers. The topics discussed include: short-time passenger flow prediction of new urban rail transit based on graph convolutional neural network;navigation safety evaluation of offshore wind farm waterway based on variation coefficient cloud matter element model;research on the vulnerability of container terminals under the new development pattern of ‘double circulation’;allocation model and algorithm for multiple container areas at U-automated container terminal;research on lane-changing decision model with driving style based on XGBoost;research on path planning of ship avoiding typhoon based on improved artificial potential field method;and a multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling.

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Comparative Analysis of Machine learning Models for Solar Power Prediction Using Time Series Weather data 3

Comparative Analysis of Machine Learning Models for Solar Po...

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

作者： Kane, Sumedh Nilesh Rathore, Akshit Abhijeet Anees, Sanya Kane, Arati Electronics and Communication Engineering Netaji Subhas University of Technology Delhi India Instrumentation and Control Engineering Bharati Vidyapeeth College of Engineering Delhi India

ISBN: (纸本)9798350387193

Smart Grids preside over traditional power grids due to their efficiency and effectiveness. Smart grid implementation has benefited all components of the power system. These grids usually generate energy from renewable energy resources. However, the stability, reliability, and seamless functioning of the grid are adversely affected by the highly weather-dependent characteristics of renewable energy sources. As a result, accurate energy generation forecasting is an essential requirement for the grid's steady and reliable operation. The forecasting techniques for the quantity of electricity produced by solar power plants are discussed in this study. First, the impact of weather on solar power generation is investigated. Second, a machine learning method for solar panel output power prediction based on weather conditions is proposed. Finally, an ensemble approach utilizing different algorithms for machine learning is utilized to boost the accuracy of the proposed model. For the objective of creating prediction models, we utilized a variety of techniques, including temporal convolutional neural networks, fine trees, support vector machines with different kernel functions, artificial neural networks, and long-short-term memory RNNs. Each model's prediction accuracy has been assessed by comparing and contrasting these different methods. A comparative investigation indicated that the Temporal Convolutional Network-based model trumps the others in terms of efficiency. The proposed ensemble model exceeded the conventional individual models after performance comparison with the standard approaches. © 2024 IEEE.

关键词： Prediction models

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Car Damage Identification System Using Convolutional Neural Network 3

Car Damage Identification System Using Convolutional Neural ...

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3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Krishna, Raji Sariki, Tulasi Prasad Sigamsetty, Siri Mahalakshmi Kumar, G. Bharadwaja Jayanthi, D. Asha, A. School of Electrical Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Computer Science Engineering Vellore Institute of Technology Chennai Campus Chennai India Rajalakshmi Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331533205

The number of individuals driving has increased daily, leading to a rise in the number of auto insurance claims. The lengthy process of registering, processing, and determining the outcome of each claim includes the service engineer's manual assessment, which serves as the basis for the damage report, and the physical examination carried out by an insurance company surveyor. It would be advantageous for both the organization and the client if this entire procedure were automated. This system utilizes images of the car in a damaged state as input. It then generates pertinent data, including the locations of the damaged sections (front, rear, and sides) and an assessment of the extent of the damage (none, minor, or substantial). This system uses the Convolutional Neural Network (CNN) Model to analyze images automatically for auto insurance, providing the user with information on the location and extent of the vehicle damage. ImageNet weights and the VGG16 architecture are also used here. Transfer learning is used for a variety of tasks, such as finding and classifying different kinds of auto parts and issues. The logistic regression classifier is trained using feature and label extraction methods. © 2025 IEEE.

关键词： Convolutional neural networks

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A Meta-learning Based Resource Allocation Algorithm in Vehicular Edge Computing Networks 3

A Meta-Learning Based Resource Allocation Algorithm in Vehic...

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

作者： Mo, Wenshuai Zhuang, Lei Xu, Zexi Song, Yanrui Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350380989

With the rapid development of intelligent transportation systems, in-vehicle edge computing networks serve as key infrastructures to provide strong support for vehicle communication and coordination. However, the traditional resource allocation methods face many challenges due to the changing network topology, the uncertainty of data load, and the spatio-temporal relationship between traveling vehicles. Therefore, in this paper, a meta-learning based resource allocation algorithm is designed to overcome these problems. By introducing meta-learning, the aim is to enable the system to quickly adapt to different scenarios and requirements so as to improve the resource utilization. The algorithm not only fully considers the network performance index, but also comprehensively solves the real-time and reliability requirements to achieve comprehensive optimization. The resource allocation algorithm based on meta-learning designed in this paper simulates different car networking tasks during the training process, so that the network learns a more robust resource allocation strategy, which not only fully takes into account the network performance index, but also comprehensively solves the real-time and reliability requirements and achieves comprehensive optimization. Through a large number of simulation experiments and comparisons in Telematics, the algorithm proposed in this paper outperforms the current state-of-the-art algorithms by 20% in terms of request acceptance rate, and leads by 15% in terms of overall gain. The experimental results show that the algorithm in this paper outperforms the current state-of-the-art algorithms in solving the resource allocation problem for car networking applications in edge computing networks. © 2024 IEEE.

关键词： edge computing network Internet of Vehicles meta-learning NFV Resource allocation

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Comparative Analysis of Power Distribution in Conventional Grids and Deep learning-Enhanced Smart Grid 3

Comparative Analysis of Power Distribution in Conventional G...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Blessy, J. Janish Vinoth Raj, R. Nareshkumar, M. Daniel Kamalam, T. Infanta, J.Binu Allen Velammal Institute of Technology Chennai India Saveetha Engineering College Chennai India Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331509675

This study explores the potential of deep learning methodologies to improve the efficiency and performance of power grids compared to conventional systems. The methodologies employed by machine learning algorithms in processing data sourced from smart meters, weather sensors, and energy markets are examined. The main focus of this study is on applications of energy sectors like demand forecasting, optimisation of generation and distribution, and real-time energy consumption management. The advantages of AI powered grids encompass refined demand forecasting, better integration of renewable energy sources, and optimised management of storage systems. This study illustrates the ability of intelligent grids to effectively manage supply and demand, leading to decreased costs and a minimised environmental footprint. The integration of learning-based smart grids in the context of demand response, emphasising the adaptation of energy supply to align with consumer behaviour is observed. Recurrent Neural Network algorithm is proposed to enhance the framework through the integration of Bagging and Boosting techniques in AI driven distribution systems. The results demonstrate that neural networks possess considerable capability to improve grid stability, decrease operational expenses, and alleviate the effects of climate change through more efficient energy systems. This study explores the comparative potentials of artificial intelligence in optimising smart grid energy systems alongside traditional grid frameworks. The findings indicate that the smart grid, enhanced by deep learning technologies, demonstrates significantly greater efficiency compared to conventional grids. © 2025 IEEE.

关键词： Demand response

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Deep Reinforcement learning for Dynamic VNF Profiling and Resource Optimization 3

Deep Reinforcement Learning for Dynamic VNF Profiling and Re...

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

作者： Jaisudthi, Pratchaya Phungkoed, Natthakran Srisuk, Kanyaphak Phueaknumpol, Vasupon Sridee, Pachara Threerapat Computer Engineering Rambhai Barni Rajabhat University Chanthaburi Thailand Computer Network Technology Rambhai Barni Rajabhat University Chanthaburi Thailand

ISBN: (纸本)9798331529222

This paper presents an AI-driven framework for multi-objective Virtual Network Function (VNF) profiling, utilizing deep reinforcement learning (DRL) to optimize key resources such as CPU, memory (MEM), and link capacity (LC) in dynamic network environments for next-generation networks. The proposed DRL model was tested in two scenarios: vFW VNF and Snort Inline Mode VNF, to evaluate its effectiveness in resource optimization. Compared to traditional reinforcement learning (RL), DRL demonstrated approximately a 10% reduction in CPU usage across both scenarios. Additionally, DRL achieved an 8% reduction in memory usage for the vFW VNF scenario and optimized LC allocation by approximately 23% for Snort Inline Mode VNF and 8% for vFW VNF. These results highlight DRL’s superior adaptability and efficiency in complex, fluctuating environments, making it a promising approach for real-time VNF resource management in next-generation networks. ©2024 IEEE.

关键词： Deep reinforcement learning

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Research on High Resolution Remote Sensing intelligent Computing and Agricultural Service Application 24

Research on High Resolution Remote Sensing Intelligent Compu...

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3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Li, Wenjie Zhang, Jinzhong Yang, Zhiping Tu, Wenli Sun, Fengqing Zhang, Yujia Tang, Tao Liu, Li Qilu Aerosp Informat Res Inst Jinan Peoples R China Chinese Acad State Key Lab Remote Sensing Sci Aerosp Informat Res Inst Beijing Peoples R China Ctr Agr Informat Chongqing Chongqing Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Hubei Peoples R China

ISBN: (纸本)9798400709777

High-resolution remote sensing intelligent interpretation is an important way to achieve precise production and rapid updating of agricultural information. The agricultural information integration service platform guided by data, calculation and decision-making has become a research hotspots of agricultural information services, promoting the transformation of remote sensing big data intelligent computing in smart agriculture from theory to practical application. This article took the agricultural remote sensing computing technology at the land parcel scale as the design concept, proposed an intelligent computing integration model of satellite, air and ground spatiotemporal collaboration, and took smart agriculture service of Chongqing city as an application case to introduce the intelligent production, agricultural management and decision-making services of land parcel scale information. From the perspective of crop planting distribution, growth monitoring and other aspects, information technology was used to provide accurate and timely monitoring and sharing services throughout the entire process of agricultural cultivation, planting, management and harvest, and the necessity of researching the intelligent calculation and models of information service was analyzed in this paper. The area accuracy of agricultural information products of rice, corn and rape all are above 90%. The results will provide regional business support for the government to pay timely attention to the agricultural conditions, participate in agricultural decision-making and provide feasible reference for the promotion of rural revitalization and food security in the future.

关键词： Agriculture Remote Sensing intelligent computing Land parcel Deep learning

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Performance Analysis and Improvement of Lithium Battery Charging System Based on Deep learning Algorithms 3

Performance Analysis and Improvement of Lithium Battery Char...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Lv, Hongtu Zhang, Hongyao Li, Gongju College Of Intelligent Systems Science And Engineering Hubei Minzu University Hubei Enshi China Jiangsu Fuyuan Technology Jiangsu Changzhou China Anhui Chery Ruifu Special Vehicle Technology Anhui Wuhu China

ISBN: (纸本)9798350318609

The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models cannot accurately reflect the complexity of actual systems, and limited experimental data limits the understanding of system behavior. Neglecting influencing factors leads to low prediction accuracy and a lack of real-time monitoring mechanisms to cope with dynamic changes in the system. These limitations affect the comprehensive analysis and optimization of system performance. This paper designed a Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) based on convolutional neural networks to extract local and global features from time series data, and combined it with time series prediction to predict charging temperature. In experimental verification, the quadratic mean error of the CNN-GRU algorithm model is 0.011, and the mean absolute deviation is 0.018. The error between the predicted electricity price and the actual value is within 2%. © 2024 IEEE.

关键词： learning algorithms

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intelligent data Pipelines for Predictive Maintenance in Saas products 3

Intelligent Data Pipelines for Predictive Maintenance in Saa...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Dubey, Shubham Priyadarshni, Sweta Covetrus Inc NJ United States Deloitte Uttar Pradesh India

ISBN: (纸本)9798331532420

In the rapid pace of today’s digital world, one thing that is most important is to make sure SaaS works without any setbacks. Downtime or performance may result in less customer satisfaction and huge revenue losses. Predictive maintenance allows one to undertake an immediate, proactive strategy to anticipate events and resolve problems well before the users are affected. However, its implementation in SaaS environments has complexities of its own because these are built up with a very distributed nature, and the volume of data is huge in these *** paper will review how intelligent data pipelines can help in predictive maintenance for SaaS products. By using smart data pipelines to intelligently gather, process, and analyze real-time data from all constituent parts of the SaaS infrastructure, patterns and anomalies may be spotted that predict an impending issue. In this paper, we present a design and implementation of scalable data pipelines with machine learning algorithms on logs, performance metrics, and user behavior data. We demonstrate, through case studies and experimental results, how such intelligent data pipelines reduce downtime, improve system reliability, and enhance customer satisfaction. Application of intelligent data pipes in SaaS operations marks one of the most important steps toward more resilient and effective software services. © 2024 IEEE.

关键词： Predictive maintenance

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Joint Extraction of Entity Relationships in Walnut Disease and Pest Based on Chinese NLP Models 3

Joint Extraction of Entity Relationships in Walnut Disease a...

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

作者： Zhu, Ruiliang Song, Xiangshuai Zhang, Hao Cai, Xuli School of Computer and Electronic Information Guangxi University Nanning Guangxi China

ISBN: (纸本)9798350380989

This study addresses the limited application of deep learning techniques in the field of walnut disease and pest and the challenges posed by complex relationships and diverse entity types in this domain. We propose a deep learning-based method for constructing a knowledge graph in the walnut disease and pest domain, incorporating ontologies to establish a conceptual model for the disease and pest knowledge graph. To overcome issues such as relationship overlap (e.g., one-to-many, many-to-many) and loss of relationship chains, we introduce a novel labeling scheme called 'based on ontology binding BIESO (Begin-Inside-End-Single-Other)' that directly models triplets. By employing a label matching algorithm, we obtain triplet data. We train and predict on the dataset using an end-to-end model consisting of Bidirectional Encoder Representations from Transformers (BERT), Bi-directional Gate Recurrent Unit (BiGRU), and Conditional Random Field (CRF). Experimental results show an F1 score of 75.79%, outperforming models such as BERT-BiLSTM-CRF and word2vec-BiGRU-CRF. We semiautomatically extract unstructured knowledge and store the extracted triplets in a Neo4j graph database, enabling visualization of the knowledge. The research methodology of this knowledge graph can serve as a reference for constructing knowledge graphs in walnut agriculture and developing intelligent question-answering systems for walnut disease and pest. © 2024 IEEE.

关键词： BERTBiGRU-CRF deep learning knowledge graph ontology Walnut disease and pest

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