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Research on Model Construction and Algorithm of intelligent Analysis and Fault Diagnosis of Power Equipment 3

Research on Model Construction and Algorithm of Intelligent ...

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3rd International Conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Chen, Longxiang Li, Lihua Yang, Ning Yang, Yang State Grid Corporation of China Beijing100000 China China Electric Power Research Institute Beijing100000 China

ISBN: (纸本)9798331519032

In this paper, a hierarchical intelligent analysis and fault diagnosis model of power equipment is proposed, which includes data acquisition layer, feature extraction layer, fault diagnosis layer and result output layer. Key parameters such as current and voltage are collected in real time by sensors, and fault-related features are extracted by signal processing technology and machine learning algorithm. In the feature extraction layer, wavelet transform (WT) and principal component analysis (PCA) are used to reduce the dimension of features, so as to fully capture the effective information in fault signals. In the fault diagnosis layer, the deep learning (DL) model is adopted, and a large number of historical fault data are trained to realize high-precision fault identification. Aiming at the shortcomings of existing algorithms, this paper proposes a hybrid fault diagnosis algorithm combining DL and physical model. The algorithm improves the diagnosis accuracy and real-time performance by combining the feature learning ability of DL model and the prior knowledge of physical model. Specifically, a dual-stream network structure is designed. One stream processes the original data, and the other stream processes the data converted by the physical model. Finally, the features of the two streams are weighted and fused at the high level of the network for classification and diagnosis. The experimental results show that the hybrid fault diagnosis algorithm proposed in this paper is superior to other algorithms in accuracy, precision, recall and F1 score, which shows its effectiveness and superiority in dealing with complex fault diagnosis problems. In the experiment with 14, 000 training data, 4, 000 verification data and 2, 000 test data, the accuracy of the algorithm is 0.957, the precision is 0.943, the recall rate is 0.962 and the F1 score is 0.952. © 2024 ieee.

关键词： Construction equipment

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Pyramidal Gravitational Search clustering for Hybrid Gene Expression data 3

Pyramidal Gravitational Search clustering for Hybrid Gene Ex...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Kukreti, Swapnil Singh, Urshi College of Engineering Roorkee Uttarakhand India Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9798331523893

Since many real-world performance concerns are becoming bigger, more complex, and more dynamic, natural-inspired algorithms are becoming more and more popular. Due to the scale and complexity of the problems, it is necessary to develop an optimization strategy whose effectiveness is measured by finding the nearly optimal solution in a reasonable amount of time. The bio-inspired metaheuristic techniques are based on swarm intelligence, biological, physical, and chemical features, depending on the source of inspiration. Cancerous tumors form and propagate largely as a result of genetic factors. A high-dimensional data processing difficulty exists with the identification of the pertinent genes. The Pyramid-GSA hybrid framework was developed to tackle the issue of dimensionality reduction through a cyclical gene-reduction method. This framework integrates two advanced modules: a high-throughput filtering component and an iterative wrapper technique inspired by the gravitational search algorithm (GSA). Using a recursive selection approach, top-performing genes are retained in each reduction cycle, facilitating efficient dimensionality compression while preserving essential genetic features. This methodology aims to optimize the gene subset to enhance classification accuracy and minimize interference from non-informative genes. Testing was conducted on a multi-class breast cancer microarray gene expression dataset, with comparisons to established feature selection methods to ensure a comprehensive analysis. The various approaches are assessed and contrasted using datasets from the UCI repository using a set of assessment metrics. Findings demonstrate that in spite of initialization and the use of stochastic operators, for the most favorable feature combination, the suggested version of GSA aims to search the feature space for the optimal feature combination. © 2025 ieee.

关键词： Transcription

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Hybrid Deep Learning Framework for Dynamic and Energy-Efficient Workload Migration in Cloud computing Environments 3

Hybrid Deep Learning Framework for Dynamic and Energy-Effici...

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3rd International Conference on Communication, Control, and intelligent systems, CCIS 2024

作者： D'Souza, Mithun Kaur, Chamandeep Bisht, Ankur Singh Nimma, Divya Dhanalakshmi, G. Faizal, M. K. Mohamed St. Joseph's University Department of Computer Science Bangalore India Jazan University Department of Computer Science & Information Technology Jizan Saudi Arabia Graphic Era Hill University Department of Cse Bhimtal India Graphic Era Deemed to Be University Uttarakhand Dehradun India University of Southern Mississippi Data Analyst in Ummc United States Panimalar Engineering College Department of Information Technology Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

Resource allocation and energy efficiency optimization have become increasingly crucial needs in the fast-evolving landscape of cloud computing, driven by increasing user demands and shrinking environmental windows. The paper introduces a Hybrid Deep Learning Framework that operates with Dynamic and Energy-Efficient Workload Migration. Using the combination of both Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), the workload management can be improved in the cloud environment to a very great extent. With real-time data from a cloud workload, the use of the proposed framework focuses on Min-Max normalization during pre-processing, which adapts uniform scaling of the features given: CPU usage, memory utilization, and energy consumption. This preprocessing stage enables the CNN to effectively derive meaningful patterns and characteristics of resource demands and energy trends. The complex sequences are learned in the LSTM network that is used for predicting future workload behaviors based on temporal dependencies in the data. With this impressive accuracy of 98%, the hybrid approach widely outperforms existing methodologies including standalone CNN and LSTM models, which thereby proves its effectiveness in the predictions of workload shifts and optimization of energy usage. The implementation of the framework in Python further enhances the performance of prediction besides providing a feedback loop mechanism for refining adaptation of the model based on evaluation metrics. This iterative improvement process allows the framework to evolve and respond to changed dynamics in workloads, thereby contributing to more sustainable and efficient cloud computing infrastructures. With a focus on both operational efficiency and environmental sustainability, this hybrid deep learning framework, it demonstrates an attractive solution towards the challenges of modern cloud resource management problems. © 2024 ieee.

关键词： Convolutional neural networks

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 ieee.

关键词： Vehicle safety

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Research on Industrial IoT data Storage and Access Security Countermeasures Based on Solana Blockchain and IPFS Technology 3

Research on Industrial IoT Data Storage and Access Security ...

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3rd International Conference on intelligent computing and Next Generation Networks, ICNGN 2024

作者： Lai, Jiaxin Zha, Weixiong Department of Industrial Engineering and Management East China Jiaotong University East China Jiaotong University City Country Jiangxi Nanchang China Department of Transportation Engineering East China Jiaotong University East China Jiaotong University City Country Jiangxi Nanchang China

ISBN: (纸本)9798331529222

With the rapid development of Industrial Internet of Things (IIoT) technology, an increasing number of devices are connecting to the internet, generating vast amounts of sensitive data and creating data silos that hinder resource coordination. Current centralized storage systems for IIoT data are prone to single points of failure, privacy breaches, and security issues. To address these problems, this paper proposes an IIoT data storage and access solution based on Solana blockchain and InterPlanetary File System (IPFS) technology, aiming to achieve efficient, secure, and scalable data management. The Chainlink Verifiable Random Function (VRF) protocol generates verifiable and tamper-proof random numbers for data verification and consensus. Encryption algorithms and digital signatures implement data encryption, decryption, access control, and privacy protection, while zero-knowledge proof (ZKP) ensures data security and privacy during access. Leveraging IPFS, a high-bandwidth, distributed, peer-to-peer off-chain storage solution is constructed. Smart contracts define rules and automate data access and management operations, enhancing system security and automation. Simulation experiments demonstrate significant efficiency, security, and scalability advantages over traditional centralized solutions, making it suitable for IIoT applications with large data volumes. Future work will focus on optimizing node communication, expanding experiments, and real-world deployment to further enhance system performance. ©2024 ieee.

关键词： Information management

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An Aircraft Identification System Using Convolution Neural Networks 12

An Aircraft Identification System Using Convolution Neural N...

引用

12th ieee International Conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Turchenko, Viktor Turchenko, Iryna Vasylkiv, Nadiia West Ukrainian National University Department for Information Computer Systems and Control Ternopil46000 Ukraine McMaster University Department of Computing and Software HamiltonL8S 4L8 Canada Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350358056

The task of aircraft identification using their registration number is important as visual identification remains the only method of their identification in case of air traffic control systems or radar failure. In this paper, an analysis of known solutions was carried out and an aircraft identification system was developed using a pre-trained YOLO (You Only Look Once) deep convolutional neural network and Optical Character Recognition (OCR) technology. The system consists of three modules: the M1 module is designed for the detection of region of interest-tail, the M2 module is designed for the detection of region of interest-registration number, the M3 module is designed for the classification of the registration number. 2309 aircraft images were used for training, 426 for validation and 408 for testing from the Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) dataset. Our results show that the mean average precision for the M1 and M2 YOLOv7 models on the test set were 0.92 and 0.85 respectively, and for the module M3 OCR technology the classification error was 0.2. © 2023 ieee.

关键词： aircraft convolutional neural networks deep learning detection transfer learning YOLOv7

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 ieee.

关键词： Support vector machines

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Enhancing Road Safety: The Role of intelligent Driver Drowsiness Detection systems 3

Enhancing Road Safety: The Role of Intelligent Driver Drowsi...

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3rd ieee International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Khare, Shanu Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350354348

The analysis of this research paper is touching the road safety concerns by focusing on developing a system which catches the drowsy state of the person who is driving which emerges as a big reason that causes up to 30due to this. Throughout the paper, the study discusses about literature review and various methodologies that include physiological, behavioural, and multimodal fusion approaches showing the evolution and history of drowsiness detection technologies. The methodology includes using of various sensor technologies and algorithms to collect data from the driver and process it to analyse data in real time. Eye movement and facial expressions are extracted which helps the system to identify signs of drowsiness with greater accuracy. When the driver is detected as drowsy a mechanism is triggered to alert the driver, averting accidents. Even with some challenges, the system is a big step towards increasing road safety utilizing advanced technologies to reduce the risk of driver fatigueless. © 2024 ieee.

关键词： Highway accidents

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intelligent data Storage in Electronic Voting Machine using Blockchain System 3

Intelligent Data Storage in Electronic Voting Machine using ...

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3rd International Conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2024

作者： Jacob, Suma Sira Varghese, Lijo Jacob Jaisiva, S. Dilip Kumar, S. Lakshana, R. Keerthana, R. Sri Krishna College of Technology Department of AI & ML Tamil Nadu Coimbatore India Sri Krishna College of Technology Department of EEE Tamil Nadu Coimbatore India

ISBN: (纸本)9798350391565

Despite the purported benefits of electronic balloting projects, the widespread adoption of electronic voting techniques and procedures is taking longer than expected. A variety of economic, cultural, and technological barriers restrict the development and implementation of electronic polling. Despite this, there is still a substantial challenge to be overcome in terms of analysing and harmonizing electronic elections under multiple legislative and regulatory regimes. Today, web-based voting is growing increasingly popular. It has a strong potential of reducing administrative costs and increasing voter registration. Voters can cast their ballots from any location with access to the Internet, eliminating the need to print ballots or construct voting centres. Despite the benefits, online voting systems are viewed with caution since they pose unexpected risks. With only one vulnerability, significant vote manipulation is feasible. When used in voting, digital ballot devices must be reliable, accurate, secure, and user-friendly. © 2024 ieee.

关键词： Blockchain

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Methodology for Extracting Dynamic On-Resistance Testing data in Gallium Nitride-Based Power Transistors 3

Methodology for Extracting Dynamic On-Resistance Testing Dat...

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3rd International Conference on Smart Cities, Automation and intelligent computing systems, ICON-SONICS 2023

作者： Tampubolon, Marojahan Simatupang, Joni Welman Tanaya, Prianggada Indra Universitas Multimedia Nusantara Department of Electrical Engineering Tangerang Indonesia President University Department of Electrical Engineering Cikarang Indonesia Universitas Multimedia Nusantara Deparment of Electrical Engineering Tangerang Indonesia

ISBN: (纸本)9781509062805

This paper presents a methodology for extracting dynamic on-resistance from testing data retrieved from an oscilloscope. It can be applied to various testing conditions with multiple pulse measurements. The method is primarily composed of data selection methodology, filtering techniques, and rds(on) detection near the rising and falling edges. This approach accelerates data extraction and reduces the workload for engineers during the characterization stage of GaN devices, enabling a quicker and efficient analysis. The proposed method has been tested under various conditions and successfully detected the desired locations when configured properly. The case study for PGA26E07BA device confirmed that the rdS(on) value is influenced by the switching frequency and duty cycle with the maximum shift for on-resistance near the rising edge was found at 200kHz, with 90% duty cycle, with the value around 22%. © 2023 ieee.

关键词： data mining

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