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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Xu, Sang Shen, Anwen Tang, Qipeng Luo, Xin Li, Peihe School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350386127

Extended state observer (ESO) has been widely used in torque disturbances compensation due to its outstanding performance. However, achieving accurate compensation for periodic torque disturbances using conventional methods is difficult, which results in significant speed fluctuations and a consequent reduction in the performance of the control system. To resolve the problem, an adaptive aperiodic and periodic disturbances observer based on enhance linear ESO (LESO) is proposed in this paper. The periodic and aperiodic disturbances are accurately estimated by one LESO and another cascading LESO, respectively. And then, the estimated disturbances are used for inner control loop feedforward to suppress speed fluctuation. In addition, a frequency locked loop (FLL) is employed to enable the feedback error gain of the constructed enhanced LESO to adaptively track the frequency of periodic disturbances. Further, the stability of the enhanced LESO and FLL are thoroughly discussed and rigorously proven. Finally, the feasibility and effectiveness of the proposed scheme have been verified on a 5.5 kW PMSM. © 2024 IEEE.

关键词： State estimation

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Topo-Loss: A Novel Loss Function for Learning Multiple Manifold Structures in Image Classification with Deep Neural Networks 5

Topo-Loss: A Novel Loss Function for Learning Multiple Manif...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Ran, Likai Cai, Chao Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350373820

abstract-The real data distribution of a specific category tends to approach a low-dimensional manifold in the Riemannian space, and the manifolds of different categories do not intersect in high-dimensional space. However, existing loss functions used for image classification ignore the inherent manifold structure between samples, resulting in limited network performance. In this paper, we reveal that maintaining the manifold structure between samples while extracting features can significantly improve the efficiency of neural networks in image classification tasks and propose a novel loss function called Topo-Loss. Topo-Loss is calculated by a dual-branch structure network. One of the branches is used to calculate the cross-entropy loss, and the other branch is used to get the cosine loss between the local geodesic distance of samples in the feature space and it in the original space. The optimization goal of the network is to minimize the weighted sum loss of the two branches, thus allows the neural network to maintain the manifold structure between samples during the feature extraction. The visualization experiments show that the DNN gains the ability to learn multiple manifolds with Topo-Loss simultaneously, significantly improving the network's performance in image classification. Meanwhile, our work outperforms other similar works. © 2024 IEEE.

关键词： Image classification

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A Comprehensive Market Power Analysis Method Based on Principal Component Market Power Index 16

A Comprehensive Market Power Analysis Method Based on Princi...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Wang, Wei Hu, Fei Yu, Yaowen Zhao, Yong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350386127

With the ongoing development of electricity markets, the potential for power generators to abuse market power has become a significant concern. Traditional multi-criteria analysis methods can be cumbersome, making existing composite indicators difficult to apply. To address this challenge, Principal Component Analysis (PCA) is utilized to streamline the assessment process by creating a composite Principal Component Market Power Index (PCMPI) based on seven key market power indicators. The results show that the first two principal components explain over 91% of the total variance, effectively simplifying the evaluation. Additionally, generators with higher PCMPI values exhibited bidding behaviors that suggest potential market manipulation, including significant fluctuations in bid prices. This study highlights the effectiveness of PCA in facilitating market power analysis and enhancing the detection of potential market abuse. © 2024 IEEE.

关键词： dimensionality reduction electricity market market power indicators principal component analysis

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Triple Attention Feature Aggregation for Few-Shot Segmentation 4

Triple Attention Feature Aggregation for Few-Shot Segmentati...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zhao, Zeyu Chen, Zhong Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350394375

Few-shot semantic segmentation (FSS) tackles the task of constructing class-agnostic models that can adeptly segment novel classes with limited annotations. However, existing methods suffer from the issue of segmentation results biased towards base classes. Our paper introduces an innovative FSS method by incorporating a Triple Attention Module (TAM) and a transformer-based Feature Aggregation Module (FAM). Our objective is to adaptively enhance the representation of features relevant to novel classes in query images using TAM, mitigating the issue of segmentation results biased towards base classes. Meanwhile, we employ FAM to aggregate support-query feature pair, ultimately achieving pixel-wise semantic matching. The experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that our method achieves a significant performance improvement in few-shot semantic segmentation task. © 2024 IEEE.

关键词： Semantic Segmentation

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

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SAR Ship Detection Based on Multi-Scale Feature Cross Fusion

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Progress in Electromagnetics Research B 2025年 110卷 149-161页

作者： Ren, Xiaozhen Zhou, Peiyuan Liu, Gang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

Synthetic aperture radar (SAR) ship detection plays a significant role in ocean monitoring. However, the current SAR ship detection methods face limitations in detecting small and dense ships. To address these issues, a novel SAR ship detection method based on multi-scale feature cross-fusion (MFCNet) is proposed in this paper. In the proposed model, a feature extraction network with a spatial fusion attention mechanism (FESNet) is designed to improve the capability of the backbone network in feature extraction. A multiintersection spatial pyramid pooling (MISPP) module is proposed to expand the receptive field and enhance the semantic information. Furthermore, a feature cross-fusion network (FCFNet) is designed to comprehensively integrate features of different scales for enhancing SAR ship detection performance. Experimental results demonstrate that the proposed model achieves high detection performance on the SSDD and HRSID datasets, providing more reliable technical support for ship detection in maritime environments. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Synthetic aperture radar

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CropOptimizer: Intelligent Crop Forecasting and Decision Support System 2

CropOptimizer: Intelligent Crop Forecasting and Decision Sup...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jadhav, Balasaheb Ligde, Vaidehi Malpani, Rushikesh Lakhotiya, Rushikesh Savale, Phalguni Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350388916

CropOptimizer is an innovative crop forecasting and decision support system designed to revolutionize agricultural practices. Comprising five key modules - crop recommendation, fertilizer suggestion, disease detection, weather forecasting, and market price tracking - CropOptimizer combines modern technologies to help farmers make informed decisions. The crop recommendation module, leveraging XGBoost, uses soil, climate, and nutrient data to select suitable crops that maximize production potential. The fertilizer suggestion module provides specialized guidance based on specific crop and soil characteristics, ensuring effective nutrient management through XGBoost. The disease detection module employs deep learning with ResNet-9 for image processing to identify plant diseases from leaf images, followed by specific treatment recommendations, achieving high accuracy. Weather forecasting offers a 7-day forecast, including rainfall predictions, based on plant type, state, and city through an API. Market price tracking provides live market prices for various crops, enabling farmers to make informed selling decisions. By integrating these components, CropOptimizer increases crop yield and sustainability, fostering a more resilient agricultural ecosystem and improving food security and economic success. © 2024 IEEE.

关键词： Crop rotation

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Evaluating the Performance of Pre-Trained CNNs for Multi-Class Skin Disease Detection 4

Evaluating the Performance of Pre-Trained CNNs for Multi-Cla...

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4th International Conference on Multimedia Processing, Communication and Information technology, MPCIT 2024

作者： Ubale, Ganesh Kashyap, Aryan Kalal, Sahil Kamble, Omkar Pande, Soham Mhamane, Saniya Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350375466

This study evaluates the performance of pre-trained convolutional neural networks (CNNs) for multi-class skin disease detection. We investigate the efficacy of four models: Xception, InceptionResNetV2, ResNet50, and VGG16. The models are trained and tested on two publicly available datasets: DermNet (6 diseases) and a skin disease classification dataset. We compare the performance of each model based on metrics like accuracy, precision, recall, and F1-score. This research aims to identify the most suitable pre-trained CNN architecture for accurate skin disease classification using readily available datasets. The findings can contribute to the development of automated diagnostic tools to assist dermatologists in early and precise skin disease detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 IEEE.

关键词： Convolutional neural networks

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Decentralized Application using Blockchain 5

Decentralized Application using Blockchain

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kayade, Parth Pardeshi, Aniket Patil, Suyog Raut, Prashant Shetkar, Pranav Barhate, Madhuri Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350367171

This research work introduces a novel social media platform built on blockchain technology, aiming to address contemporary challenges in privacy, security, and content integrity. Leveraging decentralized ledger technology, the platform ensures transparent and secure data storage, granting users unprecedented control over their personal information. Through cryptographic protocols, identity protection is prioritized, reducing vulnerability to data breaches. The proposed system employs smart contracts for content attribution, guaranteeing immutable and authentic user-generated content. Token-based incentives incentivize community participation and amplify content creators' contributions. By emphasizing decentralization and transparency, the developed platform intends to revolutionize the social media landscape, fostering a more resilient, userdriven digital environment. The fusion of blockchain technology and social media heralds a new era, offering a resilient, transparent, and user-centric digital ecosystem for the future. © 2024 IEEE.

关键词： Dapp NodeJS ReactJS Smart Contract Solidity Web3.0

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