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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Huang, Shufen Liu, Changhui Zhang, Yinglin Wuhan Institute of Technology College of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350353174

Aspect-level sentiment analysis tasks aimed at identifying the sentiment polarity of multiple specific aspects of a sentence. Currently most researches based on syntactic analysis have ignored the effect of different relationship types in the dependency tree to the classification results, and most of them are unable to distinguish the importance of node features to the target aspect items. To address these problems, we propose a graph convolutional network model (RD-GCN) that combines relational types and syntactic distances, where the model mainly consists of a Dependency Graph Convolutional Network (DepGCN), a Syntactic Distance Graph Convolutional Network (DisGCN), and an Affine Interaction Module. The DepGCN imports relationship type and dependency probability matrix in the feature transfer process so as to abundantly extract the structural information between the nodes;The DisGCN obtains the importance scores of each word in the sentence according to the syntactic distance matrix from the dependency tree, and setting the distance thresholds to reduce the weight proportion of the long-distance features. The RD-GCN model firstly uses the BiLSTM network as an embedding layer to obtain the initial vectors of the text, then inputs them into two different graph convolutional networks with different syntactic analyses to extract the node features respectively, finally, the affine interaction layer is used as a bridge to make the two different node features interactively learning, to improve the efficiency of the model. The experimental results show that this model on the NLP&CC2012 Chinese dataset improves the Acc and F1 values to 84.36% and 82.58% compared with the ATAE-LSTM and BiGCN comparison models. The Acc and F1 values on the two Chinese datasets of SemEval-2016 task 5 reached 83.60%, 71.99%, and 84.62%, 72.71%, respectively. © 2024 IEEE.

关键词： Network theory (graphs)

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CAIF: Cross-Attention Framework in Unaligned Infrared and Visible Image Fusion 4

CAIF: Cross-Attention Framework in Unaligned Infrared and Vi...

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Wang, Jiao Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350356502

Infrared and visible light image fusion has gained significant attention due to its advantages in challenging environments. While visible light cameras capture rich texture details under normal conditions, they perform poorly in situations like nighttime or smoke. Infrared sensors, however, can effectively highlight key targets even in extreme conditions by detecting thermal radiation. Most existing fusion methods assume perfectly aligned source images, overlooking geometric distortions and pixel shifts caused by varying camera parameters. To address this, we propose a unified framework for infrared and visible light image registration and fusion. This framework effectively handles misaligned images and introduces semantic constraints to enhance the performance in advanced vision tasks. Additionally, a cross-attention-based feature fusion module is integrated into the network. Experimental results show that our method not only improves fusion quality under misalignment but also enhances the performance of subsequent vision tasks. © 2024 IEEE.

关键词： Image fusion

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Research of news text classification method based on hierarchical semantics and prior correction

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8185-8203页

作者： Sun, Ping Song, LinLin Yuan, Ling Yu, Haiping Wei, Yinzhen Wuhan Vocational College of Software and Engineering Hubei Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan China

News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms. © 2024 - The authors. Published by IOS Press.

关键词： Semantics

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Research on pressure control system of evaporation tank based on adaptive fuzzy PID 2

Research on pressure control system of evaporation tank base...

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2nd International Conference on Physics, Photonics, and Optical engineering, ICPPOE 2023

作者： Xiong, Qi Huang, Wei Wang, Duanlian Wen, Zhiyuan College of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510674684

For the field of fiber optic prefabricated rod manufacturing, raw material evaporation tank pressure control system of nonlinearity, hysteresis and other issues, this paper proposes to combine PID control algorithm and fuzzy control algorithm, designed based on the adaptive fuzzy PID evaporation tank pressure control system. The simulation results in Matlab show that, through the traditional PID and adaptive fuzzy PID control algorithm comparison analysis, obtained adaptive fuzzy PID control system response speed, small overshooting, good stability and other characteristics, can well meet the requirements of the control of evaporation tank pressure system. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fuzzy control

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Li-Ion Transport Mechanisms in Selenide-Based Solid-State Electrolytes in Lithium-Metal Batteries:A Study of Li_(8)SeN_(2),Li_(7)PSe_(6),and Li_(6)PSe_(5)X(X=Cl,Br,I)

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Energy & Environmental Materials 2024年第5期7卷 37-47页

作者： Wenshan Xiao Mingwei Wu Huan Wang Yan Zhao Qiu He International School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China College of Materials Science and Engineering Sichuan UniversityChengdu 610065China The Institute of Technological Sciences Wuhan UniversityWuhan 430072China

To achieve high-energy-density and safe lithium-metal batteries(LMBs),solid-state electrolytes(SSEs)that exhibit fast Li-ion conductivity and good stability against lithium metal are of great *** study presents a systematic exploration of selenide-based materials as potential SSE ***,Li_(8)SeN_(2)and Li_(7)PSe_(6)were selected from 25 ternary selenides based on their ability to form stable interfaces with lithium ***,their favorable electronic insulation and mechanical properties were ***,extensive theoretical investigations were conducted to elucidate the fundamental mechanisms underlying Li-ion migration in Li_(8)SeN_(2),Li_(7)PSe_(6),and derived Li_(6)PSe_(5)X(X=Cl,Br,I).Notably,the highly favorable Li-ion conduction mechanism of vacancy diffusion was identified in Li6PSe5Cl and Li_(7)PSe_(6),which exhibited remarkably low activation energies of 0.21 and 0.23 eV,and conductivity values of 3.85×10^(-2)and 2.47×10^(-2)S cm^(-1)at 300 K,*** contrast,Li-ion migration in Li_(8)SeN_(2)was found to occur via a substitution mechanism with a significant diffusion energy barrier,resulting in a high activation energy and low Li-ion conductivity of 0.54 eV and 3.6×10^(-6)S cm^(-1),*** this study,it was found that the ab initio molecular dynamics and nudged elastic band methods are complementary in revealing the Li-ion conduction *** both methods proved to be efficient,as relying on only one of them would be *** discoveries made and methodology presented in this work lay a solid foundation and provide valuable insights for future research on SSEs for LMBs.

关键词： Li-ion transport lithium argyrodites lithium-metal battery selenides solid-state electrolytes

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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Constructal design of printed circuit recuperator for S-CO_(2)cycle via multi-objective optimization algorithm

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science China(Technological sciences) 2024年第1期67卷 285-294页

作者： DAN ZhiSong FENG HuiJun CHEN LinGen LIAO NaiBing GE YanLin Institute of Thermal Science and Power Engineering Wuhan Institute of TechnologyWuhan 430205China College of Power Engineering Naval University of EngineeringWuhan 430033China Hubei Provincial Engineering Technology Research Center of Green Chemical Equipment Wuhan 430205China School of Mechanical&Electrical Engineering Wuhan Institute of TechnologyWuhan 430205China AECC Hunan Aviation Powerplant Research Institute Zhuzhou 412002China

Based on a constructal theory,the structure design of a printed circuit recuperator with a semicircular heat transfer channel for supercritical CO_(2)cycle is carried ***,a complex function composed of weighted sum of the reciprocal of total heat transfer rate and total pumping power consumption is regarded as an optimization objective,and total volumes of the recuperator and heat transfer channel are regarded as *** optimal heat transfer channel radius and minimum complex function of the recuperator are *** turns out that heat transfer rate,pumping power consumption,and complex function under the optimal construct of recuperator are reduced by 15.10%,82.44%,and 32.33%,*** exists the optimal single plate channel number which results in the double minimum complex ***,for the purpose of minimizing the reciprocal of heat transfer rate and pumping power consumption,NSGA-II algorithm is used to achieve multi-objective optimization,and the minimum deviation index derived by the decision-making methods is 0.076,which can be taken as multi-objective optimal design scheme for printed circuit recuperator with semicircular heat transfer *** findings presented here can serve as theoretical recommendations for the structure design of printed circuit recuperator.

关键词： constructal theory supercritical CO_(2)cycle printed circuit heat exchanger heat transfer rate pumping power consumption multi-objective optimization

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A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

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Digital Communications and Networks 2024年第2期10卷 481-497页

作者： Sheng Hao Huyin Zhang Fei Yang Chenghao Li Jing Wang School of Computer Science Central China Normal UniversityWuhan430079PR China Hubei Key Laboratory of intelligent Robot(Wuhan Institute of Technology) Wuhan430079PR China School of Computer Science Wuhan UniversityWuhan430079China School of Information Science and Engineering Shandong Normal UniversityJinan250000China School of Computer Science Hubei University of TechnologyWuhan430079China

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware *** research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)*** designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network *** solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 ***,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,*** on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC *** the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into ***,we prove the proposed analytical model has the ***,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

关键词： Hybrid power-line/Visible light communication (HPVC)networks MAC protocol CSMA/CA IEEE 802.15.7 IEEE 1901 Performance analysis

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Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion

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Plant Phenomics 2023年第4期5卷 825-835页

作者： Yanan Li Yuling Tang Yifei Liu Dingrun Zheng School of Computer Science and Engineering School of Artificial IntelligenceWuhan Institute of TechnologyWuhan 430205China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhan 430073China

Automated counting of grape berries has become one of the most important tasks in grape yield ***,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep *** collection of data required for model training is also a tedious and expensive *** address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is *** algorithm uses VGG16 as the backbone to extract image *** tasks based on density mutual exclusion are *** tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled *** addition,a density difference loss is *** feature representation is enhanced by amplifying the difference of features between different density *** experimental results on the field grape berry dataset show that CDMENet achieves less counting *** with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,*** code is available at.

关键词： Grape mutual backbone

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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