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Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

Computers, Materials & Continua 2023年第10期77卷 845-866页

作者： Yixia Chen Mingwei Lin College of Computer and Cyber Security Fujian Normal UniversityFuzhou350117China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal UniversityFuzhou350117China

The consensus scheme is an essential component in the real blockchain *** Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,***,how to study the knowledge representation of the collective voting information and then select delegates is a new open *** ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain *** is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain *** rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility ***,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its ***,the superiority of the proposed DPoS consensus algorithm is *** results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.

关键词： Linguistic term set MCDM aggregation operator

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Review and prospect of remote sensing monitoring methods for inland black and malodorous water bodies 24

Review and prospect of remote sensing monitoring methods for...

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Liao, Yansong Huang, Fenghua The Academy of Digital China Fuzhou University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China College of Artificial Intelligence Yango University China

ISBN: (纸本)9798400710285

Black and malodorous water bodies represent a severe water pollution phenomenon that urgently needs addressing due to the discomfort and health risks they pose. Since the release of the"Action Plan for Prevention and Control of Water Pollution" in 2015 and the subsequent"Action Plan for the Treatment of Black and Malodorous Water Bodies in Urban Areas during the 14th Five-Year Plan Period" in 2022 by the Chinese government, monitoring efforts have become increasingly crucial. Remote sensing technology offers advantages such as high dynamics, low cost, and a macroscopic perspective in ecological environment monitoring, making it advantageous over traditional physical and chemical methods in monitoring black and malodorous water bodies. This paper summarizes remote sensing methods for identifying black and malodorous water bodies, including extraction of information through spectral, spatial, index, and temporal features, and analyzes the monitoring feasibility of these methods while pointing out potential issues. Finally, future research directions for identifying black and malodorous water bodies are discussed. © 2024 Copyright held by the owner/author(s).

关键词： Environmental monitoring

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Overview of indoor positioning technology 24

Overview of indoor positioning technology

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Xu, Zeyu Huang, Fenghua The Academy of Digital China Fuzhou University China College of Artificial Intelligence Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China

ISBN: (纸本)9798400710285

This paper introduces the classification of indoor positioning technology, and elaborates the principles of three mainstream indoor positioning technologies based on external source, natural source and multi-source fusion and their development respectively. By analyzing the problems of existing indoor positioning technologies, it summarizes the difficulties and future trends of the current development of indoor positioning technologies at home and abroad. The comprehensive study shows that exploring combined positioning techniques and better positioning algorithms, and combining them with machine learning (including deep learning) to further improve indoor positioning accuracy, establishing a standard system and performance evaluation system for indoor positioning techniques, and focusing on user privacy are the main future development directions of indoor positioning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Fast Three-Dimensional NMR Spectrum Reconstruction Using a Block Hankel Matrix Based on Sliding Windows 17

Fast Three-Dimensional NMR Spectrum Reconstruction Using a B...

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17th International Conference on Complex Medical engineering, CME 2023

作者： Fu, Hao Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350316117

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Three-dimensional NMR spectra offer higher resolution. The Low Rank Hankel Matrix (LRHM) method reconstructs three-dimensional undersampled NMR data by transforming each 2D indirect plain slice into a block Hankel matrix. However, this reconstruction process involves a considerable time overhead. In this work, we introduce a block Hankel matrix based on a sliding window method specifically designed for 3D NMR, to accelerate spectrum reconstruction from NUS signals. This approach can effectively reduce the size of the Hankel matrix for faster reconstruction. Experiments on both synthetic and real data demonstrate that the proposed method achieves the fastest reconstruction speed compared to other methods, without compromising the quality of the spectra. © 2023 IEEE.

关键词： Nuclear magnetic resonance spectroscopy

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NMR Metabolite Spectrum Denoising with Improved U-Net++ 17

NMR Metabolite Spectrum Denoising with Improved U-Net++

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17th International Conference on Complex Medical engineering, CME 2023

作者： Fang, Wenjie Fu, Hao Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350316117

Nuclear Magnetic Resonance (NMR) spectroscopy serves as a significant tool for capturing essential information in chemical reactions. However, in practical applications, NMR signals are frequently afflicted by Gaussian noise, leading to challenges in sequence analysis and quantification. In this paper, we have devised a deep neural network named NMRDnet, with the Unet++ architecture at its core, incorporating modules such as CBAM and soft-thresholding. This network was trained using a simulated dataset based on the exponential characteristics of NMR spectroscopic signals. The experimental results show that the NMRDnet network can effectively remove the noise and restore the original signal. It is superior to the low-rank Hankel matrix denoising method in maintaining peak signal strength and reducing signal distortion. © 2023 IEEE.

关键词： Nuclear magnetic resonance spectroscopy

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Optimizing Resource Allocation in the Internet of Vehicles: An Intelligent Vehicle-Edge-Cloud Collaboration Approach 20th

Optimizing Resource Allocation in the Internet of Vehicle...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Chen, Weijie Lin, Li Xiong, Jinbo Lin, Jiayi Huang, Ruihong Wang, Xing College of Computer and Cyber Security Fujian Normal University Fuzhou China Fujian Provincial University Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9789819628636

The proliferation of the Internet of Vehicles (IoV) has spurred the development of computationally intensive and latency-sensitive applications in vehicles. However, limited onboard computing resources hinder their execution. Vehicle edge computing addresses this by providing readily accessible resources from edge servers. However, efficient resource allocation remains a challenge due to limited edge server capacity and dynamic traffic patterns. This paper proposes a novel three-layer collaborative intelligence framework that leverages edge and cloud resources to minimize system latency in IoV. We employ a deep reinforcement learning algorithm to adapt to dynamic traffic conditions and make optimal resource allocation decisions. Extensive experiments demonstrate a significant reduction in latency (26.75%) compared to baseline methods. © IFIP International Federation for Information Processing 2025.

关键词： Deep reinforcement learning

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PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

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Computers, Materials & Continua 2025年第1期82卷 913-928页

作者： Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang New Engineering Industry College Putian UniversityPutian351100China Engineering Research Center of Big Data Application in Private Health Medicine(Putian University) Fujian Province UniversityPutian351100China Computer College Guangdong University of Science&TechnologyDongguan523668China

In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关键词： Polyp detection YOLOv7 SPD-Conv CBAM deconvolution

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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Clustering Network Based on Pre-Clustering center Codebook 18

Clustering Network Based on Pre-Clustering Center Codebook

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18th International Conference on Intelligent Systems and Knowledge engineering, ISKE 2023

作者： An, Qiulong Wu, Jianning Huang, Tianqiang Huang, Liqing Luo, Haifeng Ye, Feng College of Computer and Cyber Security Fujian Normal University Digital Fujian Institute of Big Data Security Technology Fujian Provincial Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (纸本)9798350318401

Most existing deep clustering models attempt to group similar datas by using autoencoders to simultaneously minimize the clustering loss and reconstruction losses. However, these methods do not impose any constraints on the latent space, resulting in intermediate features that are not suitable for clustering. Moreover, they suffer from poor clustering performance when the number of clusters is large. To address this problem, this paper proposes an autoencoder network with preclustered center codebooks for clustering, two codebooks to represent the cluster centers and center offsets, respectively, making the network features more compatible with the clustering task. Finally, We conducted experiments on CIFAR-10 and STL-10 datasets, the results indicate the effectiveness and superiority of the proposed network model. © 2023 IEEE.

关键词： Network coding

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Bursaphelenchus xylophilus disaster monitoring based on deep learning and Remotely sensed images 2

Bursaphelenchus xylophilus disaster monitoring based on deep...

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2nd International Conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2022

作者： Li, Bingzheng Huang, Fenghua Digital China Research Institute Fuzhou350108 China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University Fuzhou350015 China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou350015 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

With the increasingly severe epidemic of forest diseases and insect disasters in China, the monitoring of forest diseases and insect pests has attracted more and more attention from the academic community. This paper first introduces the changes in the spectral characteristics and identification methods of Bursaphelenchus xylophilus disease. Secondly, the research status, research hotspots, and development trends of Bursaphelenchus xylophilus disease monitoring based on hyperspectral remote sensing are elaborated. It then discusses the development of deep learning and its research status in pest identification in combination with hyperspectral remote sensing and introduces three of the most popular methods of current Bursaphelenchus xylophilus disaster monitoring: You Only Look Once (YOLO), Faster Region-CNN (Faster R-CNN), and Single Shot MultiBox Detector (SSD). Finally, the main work and problems of the existing literature research are summarized to guide future work in this field. © 2022 IEEE.

关键词： Disasters

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