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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Kishore S Nandana Santhosh Tina Babu H Ramamoorthy Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The efficacy of deep learning models, specifically Convolutional Neural Networks (CNNs), in accurately recog- nizing and clas- sifying a diverse range of traffic signs. The research utilizes a meticulously curated dataset obtained from Kaggle, comprising annotated images labeled with corresponding ClassIds representing various traffic sign categories. The moti- vation behind this work stems from the critical need for robust and reliable traffic sign recognition systems, which play a vital role in enhancing safety, efficiency, and enabling autonomy in transportation systems. Through systematic experimentation and rigorous validation, the study assesses the performance of CNN models across various evaluation metrics, shedding light on the effectiveness of these deep learning architectures in accurately discerning nuanced visual cues present in traffic sign imagery. Furthermore, the research explores the challenges posed by factors such as variations in lighting conditions, occlusions, and class imbalances, proposing potential mitigation strategies. The proposed methodology utlized transfer learning model ResNet50 which achieved accuracy of 92

关键词： Visualization Technological innovation Systematics Transfer learning Lighting Transportation Safety Convolutional neural networks Reliability Residual neural networks

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Evolutionary Algorithms for Optimizing Wind Turbine Blade Design

Evolutionary Algorithms for Optimizing Wind Turbine Blade De...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu Kishore S K. Nithin Kumar R. Guna Vardhan Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte Deemed to be University Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Wind turbine blade design is a complex multi-objective optimization challenge, requiring a balance between aerodynamic efficiency, structural integrity, and cost minimization. This study proposes a hybrid optimization framework combining Genetic Algorithms (GA) for global exploration and Differential Evolution (DE) for local refinement. The hybrid GA-DE approach accelerates convergence and enhances solution quality by efficiently navigating the large design space. Experimental results show up to a 20% reduction in structural loads while maintaining high energy output, with a 15% faster convergence rate compared to traditional single-algorithm methods. The optimized blade designs achieved improved aerodynamic performance and reduced material costs, offering a more practical solution for next-generation wind energy systems. This research demonstrates the potential of hybrid evolutionary algorithms to drive innovation in renewable energy by delivering cost-effective and high-performance turbine blades, particularly for larger turbines requiring enhanced durability and power output.

关键词： Technological innovation Costs Wind energy Blades Evolutionary computation Aerodynamics Search problems Wind turbines Optimization Genetic algorithms

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A Multimodal Named Entity Recognition Model for Power Equipment Based on Deep Neural Network 3

A Multimodal Named Entity Recognition Model for Power Equipm...

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3rd International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Zhang, Qiang Song, Bochuan Zhao, Changwei Liu, Tongyang Zhang, Fengda Li, Zhuangzhuang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Anhui Electric Power Research Institute Anhui China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9781643684581

Digital empowerment of China's power energy sector is a key factor in increasing its economic and social benefits, and named entity recognition technology is the most fundamental and core task of information extraction technology in the digital empowerment process. Therefore, we propose a multimodal named entity recognition model PE-MNER for power equipment based on deep neural networks. Compared to text multimodality, text and image multimodality can use image information to supplement missing information in the text, thus enabling more accurate entity extraction. The model first obtains a BERT neural network through incremental training, and then extracts Chinese character features through the network. Then, a hierarchical visual prefix fusion network is used to fuse image information. From the comparative experimental results, it can be seen that the proposed model has the best performance compared to the benchmark model, with an improvement of 4.08%~7.20% in the F1 score compared to the benchmark model. © 2023 The Authors.

关键词： Deep neural networks

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Research on Pseudo Measurement Generation Method Based on 1D-CNN and Transfer Learning for Medium-Voltage Distribution Networks

Research on Pseudo Measurement Generation Method Based on 1D...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Zhiqing Sun Shuya Lei Xuan Yi Wense Zhang Yishen Wang Xiabing Ni Qifeng Wang Xiao Liang Yan Huang state grid Hangzhou power supply company Zhejiang China State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Zhejiang China State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Zhejiang China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

With the increasing scale and complexity of new power systems, ensuring the safe and optimized operation of distribution networks becomes particularly important. Due to constraints such as economic factors, the comprehensive deployment of real-time measurement devices in mediumvoltage distribution networks faces significant challenges, resulting in insufficient observability of the distribution network. To address this issue, this paper proposes a method that combines one-dimensional convolutional neural networks (1D-CNN) with transfer learning for pseudo measurement generation. This method first constructs a 1D-CNN pseudo measurement generation model using a large amount of historical measurement data, and then trains and optimizes the 1D-CNN model through transfer learning to achieve pseudo measurement generation even under topological changes. The test results of actual engineering data demonstrate that the proposed method can generate high-frequency pseudo measurement data for nodes in medium-voltage distribution networks in real time, accurately, and efficiently. Additionally, the proposed method exhibits good adaptability when the distribution network topology changes. In this way, this paper provides a new technical path for the perception of the operational status of medium-voltage distribution networks and provides technical support for the safe and optimized operation of distribution networks.

关键词： Transfer learning Distribution networks Medium voltage Real-time systems Data models Time measurement Topology Power systems Convolutional neural networks Observability

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Transformer multimodal data and knowledge encoding method based on attention model 2

Transformer multimodal data and knowledge encoding method ba...

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2nd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2023

作者： Zhang, Peng Zhang, Guoliang Fan, Xiaoyu Gan, Jinrui Wu, Peng Zhou, Fei Lv, Shunli Chen, Long Liu, Xin State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China NARI Group Corporation State Grid Electric Power Research Institute Nanjing215200 China State Grid Anhui Electric Power Co. Ltd. Hefei230061 China

ISBN: (纸本)9781510672840

Transformers are key hub equipment in the power system, and their operation reliability is directly related to the stability of the power grid. In order to ensure the safe and stable operation of the transformer, monitoring devices such as dissolved gas analysis (DGA), partial discharge (PD), and voiceprint vibration are installed to obtain multimodal data such as structured data, visible light pictures, atlas pictures, and voiceprint data. However, existing fault diagnosis methods cannot integrate these multi-modal features to evaluate the health state of the transformer. In order to solve these problems, a transformer multi-modal data knowledge fusion coding method based on attention model is proposed. For structured state quantities such as online monitoring state quantities and test state quantities, Bi-LSTM is used for encoding to obtain structured state quantity codes. For the partial discharge state quantity, multi-dimensional structured data is extracted from the n-q-φ spectrum and discharge waveform spectrum, and Bi-LSTM is used to obtain the partial discharge structured state quantity codes. For RGB images, an encoding structure including a convolution layer, a pooling layer and a coding layer is constructed to obtain partial discharge image codes. Then, structured state quantity codes, partial discharge structured state quantity codes and partial discharge image codes were fused based on the Attention model, and the fused multi-modal data knowledge codes were used in the fault diagnosis process of the transformer. Multimodal data knowledge encoding technology is applied to transformer fault diagnosis. The application results show that the fault diagnosis accuracy can be significantly improved after fusion and encoding of multimodal data. Compared with the traditional structured state quantity diagnosis model, the accuracy of fault diagnosis is improved. 8.75%, up to 92.50%. © 2024 SPIE.

关键词： Failure analysis

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach

Retinal Scan Denoising Using Generative Adversarial Networks...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Gowthami G Mano Shankari J Tina Babu Department of Computer Applications St. Francis De Sales College Bengaluru India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images.

关键词： Noise reduction Noise Process control Media Retina Generative adversarial networks Motion pictures Intelligent systems Medical diagnostic imaging Image denoising

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Topology Identification of Distribution Networks Using Multi-Object Binary Classification Graph Transformer

Topology Identification of Distribution Networks Using Multi...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Siyan Liu Danni Shi State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

With the continuous increase of renewable energy, the distribution network's structure tends to be more complex. The real-time topology is difficult to obtain timely and the topol-ogy categories increases exponentially, which poses challenges to further analysis and the network's computation. Traditional distribution network topology identification methods rely on high computational complexity and require a large number of fault samples. To address these problems, this paper proposed a Multi-Object(MO) binary classification graph transformer method for topology identification. Through mapping the switch status and voltage measurement and branch conductance of the distribution network, a MO classification mechanism is introduced, performing binary classification for each edge with undetermined connection status, and physically map the output of the identification model to the switches status in the distribution network. The method utilized a Graph Transformer-based convolutional neural network which used to extract the nodes and the edges features. Combing the proposed architecture with an online hard example mining mechanism and maximum spanning tree filter strategy, the model collapse caused by imbalanced data is dealt with and the accurate identification is realized. The proposed method is validated by the IEEE 33-bus distribution network system, and the results show the high accuracy for practical topology identification.

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

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Procedia Computer Science 2025年 258卷 1576-1585页

作者： Tina Babu Rekha R Nair Kishore S Sheila Mahapatra Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte University (Deemed to be University) Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD.

关键词： Autism Spectrum Disorder (ASD) Machine Learning Bayesian Optimization Random Forest Early Diagnosis Predictive Modeling

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Power Transformer Health State Prediction Based on Multi-attention Coupling Learning 6

Power Transformer Health State Prediction Based on Multi-att...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhang, Peng Gan, Jinrui Wu, Chunpeng Wu, Peng Qi, Bo Zhou, Fei State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing102209 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China

ISBN: (纸本)9798350346671

Transformer is the key apparatus of the power system, and its operational reliability is directly related to the stability of the power grid. Accurately predicting the future health state of transformers can reserve enough time for formulating outage maintenance and power dispatching plans. The existing transformer health state prediction methods are based on the structure of 'predict-diagnose', which is the superposition of two local optimal methods, with error superposition. To fill this gap, a transformer health state prediction method based on multi-attention coupling learning is proposed in this paper. The 'situational quantity' integrating the transformer operation state change relationship, the correlation relationship between state quantities, the mapping relationship between the state quantity and the state, the fuzzy relationship between the evaluation result and the actual state, and the traditional expert experience is proposed. Then, the structural optimization attention (SOA) model applicable to 'situational quantity' is proposed, and various relationships are treated as individual attention. All kinds of attention are coupled and constrained in the SOA to realize the unification of the prediction process and diagnosis process, obtain the global optimization of prediction and diagnosis, and the problem of error superposition is solved. The actual fault case verification results show that the accuracy of the proposed SOA prediction model can reach 96.33%, which is significantly better than the traditional methods. © 2023 IEEE.

关键词： Global optimization

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning

Review of DDoS Attack Detection in Big Data with Cloud using...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： P. Radhika S. Kamalakkannan P. Kavitha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

Cloud computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible and virtualized sources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined.

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