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A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

computers, Materials & Continua 2024年第1期78卷 653-676页

作者： Haibo Li Yongbo Yu Zhenbo Zhao Xiaokang Tang College of Computer Science and Technology Huaqiao UniversityXiamen361021China

Accurate forecasting of time series is crucial across various *** prediction tasks rely on effectively segmenting,matching,and time series data *** instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction ***,these events of varying granularity frequently intersect with each other,which may possess unequal *** minor differences can result in significant errors when matching time series with future ***,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction ***,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation *** framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on *** data from a nationwide online car-hailing service in China ensures the method’s *** average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)*** other experiment is conducted on stock data froma public data *** proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained usin

关键词： Time series short-term prediction multi-granularity event alignment event matching

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A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

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High-Confidence Computing 2024年第3期4卷 13-24页

作者： Rongxin Guo Zhenping Guo Zerui Lin Wenxian Jiang College of Engineering Huaqiao UniversityQuanzhou 362000China College of Computer Science and Technology Huaqiao UniversityXiamen 361021China

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence *** solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance(PBFT)in IoT scenarios,a hierarchical consensus protocol called DCBFT is *** all,we propose an improved k-sums algorithm to build a two-level consensus cluster,achieving an hierarchical management for IoT ***,A scalable two-level consensus protocol is proposed,which uses a multi-primary node mechanism to solve the single-point-of-failure *** addition,a data synchronization process is introduced to ensure the consistency of block data after view ***,A dynamic reputation evaluation model is introduced to update the nodes’reputation values and complete the rotation of consensus nodes at the end of each consensus *** experimental results show that DCBFT has a more robust dynamic and higher consensus ***,After running for some time,the performance of DCBFT shows some improvement.

关键词： IoT Hierarchical consensus Clustering Reputation evaluation model Practical byzantine fault tolerance

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

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Tsinghua science and technology 2024年第4期29卷 1232-1247页

作者： Zhuolun Jiang Zefei Ning Hao Miao Li Wang College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyJinzhong 030600China

Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.

关键词： time series forecasting multivariate time series spatio-temporal decomposition

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3D Point Cloud Matching Based Selfie Generation for Chang'e-5

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计算机科学技术学报（英文版） 2025年第1期40卷 85-98页

作者：陈潇瑞杨孟飞张高张伍彭兢顾征邓湘金杨柳枝杨飞杨赟孙守迁童若锋 Min Tang College of Computer Science and Technology Zhejiang UniversityHangzhou 310007China

Generating selfie images on the surface of a celestial body poses several challenges,including the position of the robotic arm,camera field of view,and limited shooting *** address these challenges,the PCMIS(3D Point Cloud Matching Based Image Stitching)algorithm is designed,along with a corresponding shooting *** algorithm estab-lishes a correspondence between depth and color information,enabling the generation of stitching views under any given view ***,the algorithm is accelerated using GPU processing,resulting in a significant reduction in stitching *** algorithm is successfully applied to generate selfie images for the Chang'e-5 mission.

关键词： image stitching 3D point cloud matching Chang'e-5 GPU computing selfie generation

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RVAM16:a low-cost multiple-ISA processor based on RISC-V and ARM Thumb

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Frontiers of computer science 2025年第1期19卷 41-51页

作者： Libo HUANG Jing ZHANG Ling YANG Sheng MA Yongwen WANG Yuanhu CHENG College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

The rapid development of ISAs has brought the issue of software compatibility to the forefront in the embedded *** address this challenge,one of the promising solutions is the adoption of a multiple-ISA processor that supports multiple different ***,due to constraints in cost and performance,the architecture of a multiple-ISA processor must be carefully optimized to meet the specific requirements of embedded *** exploring the RISC-V and ARM Thumb ISAs,this paper proposes RVAM16,which is an optimized multiple-ISA processor microarchitecture for embedded devices based on hardware binary translation *** results show that,when running non-native ARM Thumb programs,RVAM16 achieves a significant speedup of over 2.73×with less area and energy consumption compared to using hardware binary translation alone,reaching more than 70%of the performance of native RISC-V programs.

关键词： multiple-ISA processor architecture binary translation RISC-V embedded

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An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4574-4584页

作者： Fan, Zongwen Gou, Jin Weng, Shaoyuan College of Computer Science and Technology Huaqiao University Xiamen361021 China School of Software Engineering Xiamen Institute of Software Technology Xiamen361024 China

Concrete is a vital component in modern construction, prized for its strength, durability, and versatility. Accurately determining the quantities of concrete components is crucial in civil engineering applications to optimize resources (e.g., manpower and financial resources). In this article, we propose an unbiased fuzzy-weighted relative error support vector machine (UFW-RE-SVM) for reverse prediction of concrete components. First, we add an unbiased term to the target function of UFW-RE-SVM for obtaining an unbiased model. Second, we design a fuzzy-weighted operation to indicate sample importance by incorporating the fuzzy membership values into the UFW-RE-SVM. The nth root operation is introduced to address the exponential explosion issue in the fuzzy-weighted operation. Finally, considering the UFW-RE-SVM is sensitive to its hyperparameters for multioutput prediction, the whale optimization algorithm (WOA) is utilized for hyperparameter optimization for its effectiveness in optimization tasks. We design the fitness function based on the results from multiple components to balance the performance of multioutput predictions. Experimental results show that the performance of our proposed model outperforms existing works in predicting concrete components in terms of mean absolute relative error, standard deviation, and root mean square error. Further, the statistical test shows the WOA and two other metaheuristics can significantly improve the prediction performance. This indicates that the unbiased term, fuzzy-weighted operation, and WOA are effective for improving the proposed model for reverse prediction concrete components. With these promising results, the proposed model could provide decision-makers with a valuable tool for determining concrete component quantities based on desired concrete qualities. © 2020 IEEE.

关键词： Forecasting

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Research on Stock Price Prediction Method Based on the GAN-LSTM-Attention Model

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computers, Materials & Continua 2025年第1期82卷 609-625页

作者： Peng Li Yanrui Wei Lili Yin College of Computer Science and Technology Harbin University of Science and TechnologyHarbin150006China

Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and *** paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price ***,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock *** discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock ***,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation *** results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.

关键词： Stock price prediction generative adversarial network attention mechanism time-series prediction

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Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination

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computers, Materials & Continua 2025年第3期82卷 4669-4690页

作者： Qi Mu Yuanjie Guo Xiangfu Ge Xinyue Wang Zhanli Li College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In low-light image enhancement,prevailing Retinex-based methods often struggle with precise illumina-tion estimation and brightness *** can result in issues such as halo artifacts,blurred edges,and diminished details in bright regions,particularly under non-uniform illumination *** propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the *** introducing multi-scale effective guided filtering,our method surpasses the limitations of traditional isotropic filters,such as Gaussian filters,in handling non-uniform *** dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different *** balanced approach achieves a harmonious blend of smoothing and detail preservation,enabling more accurate illumination ***,we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity,further balancing enhancement effects across different brightness levels in the *** results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various *** exhibits superior quality and objective evaluation scores compared to existing *** method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images,producing enhanced images with precise details and natural,vivid colors.

关键词： Retinex non-uniform low illumination local content-aware effective guided image filtering

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引用

computers, Materials & Continua 2025年第3期82卷 5135-5151页

作者： Jian Feng Yifan Guo Cailing Du College of Computer Science & Technology Xi’an University of Science and TechnologyXi’an710054China

Graph similarity learning aims to calculate the similarity between pairs of *** unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in *** address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive ***,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the *** enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive ***,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original *** goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each *** representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph ***,the graph similarity score is computed according to different downstream *** conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive ***,the model achieves the best overall performance.

关键词： Graph similarity learning contrastive learning attributes structure

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