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Subseasonal Prediction of April Siberian-Arctic Heatwaves Using a Dynamical-Statistical Approach 认领 引用
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作者 Yan XIA Fei XIE +4 位作者 Jianping LI Yongyun HU Yi HUANG Jianchun BIAN Chuanfeng ZHAO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第5期907-918,共12页
Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness ... Siberian-Arctic heatwaves(SAHs)disrupt ecosystems by increasing wildfires,thawing permafrost,and threatening Arctic communities.As SAHs become more frequent and intense,accurate prediction is crucial for preparedness and mitigating their impacts.We demonstrate that April surface temperatures in the Siberian Arctic can be predicted one month in advance with a skill of 0.75(1979-2022)using a regression model based on Arctic stratospheric ozone,the Arctic Oscillation,and sea ice in the Kara Sea.This model successfully predicts six of seven SAHs,identifying three driven by extreme ozone depletion and three by significant sea-ice loss.Additionally,from 1979 to 1997,warming was primarily caused by ozone depletion,while from 1998 to 2022,sea-ice loss became the main factor.Our findings indicate that SAHs are predictable and recommend this model for real-time monitoring and forecasting,highlighting its potential to enhance preparedness and reduce adverse effects. 展开更多
关键词 subseasonal prediction Siberian-Arctic heatwaves stratospheric Arctic ozone sea ice multiple linear regression
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Nondestructive prediction of leaf area in colored cotton cultivars:a comparative approach using machine learning models with an interactive web interface 认领 引用
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作者 SILVA RIBEIRO João Everthonda SILVA Antonio Gideilson Correiada +7 位作者 ALMEIDA OLIVEIRA Pablo Henriquede SANTOS COÊLHO Esterdos FAGUNDES Rislayne Ingrid MELO Ramon Silva SOUZA MORAIS Carlos Danielde SANTOS Diego Mendonça SILVEIRA Lindomar Mariada BARROS JÚNIOR Aurélio Paes 《Journal of Cotton Research》 CAS CSCD 2026年第1期89-106,共18页
Background Leaf area is a crucial indicator of plant growth and physiology,with direct measurements being destructive to the plant.This study aimed to develop and compare machine learning models[support vector regress... Background Leaf area is a crucial indicator of plant growth and physiology,with direct measurements being destructive to the plant.This study aimed to develop and compare machine learning models[support vector regression(SVR),adaptive neuro-fuzzy inference system(ANFIS),and deep multilayer perceptron(DMLP)]and linear regression(LRM)for the nondestructive prediction of leaf area in five colored cotton cultivars.A total of 1334 leaves were sampled,and their length(L),width(W),and leaf area(LA)were determined via digitized images.The models were developed using 70%of the data for training and 30%for validation.Their performance was evaluated using the coefficient of determination(R2),root mean square error,mean absolute error,mean absolute percentage error,and Willmott's index of agreement.Results The results showed that the machine learning models,notably the ANFIS(triangular membership function),the DMLP(2-16-16-1 configuration),and the SVR[radial basis function(RBF)kernel],significantly outperformed the linear regression models in leaf area estimation accuracy.The ANFIS and DMLP models achieved the highest R2(0.9793,test),followed by the SVR model(R2=0.9790,test),all with minimal errors.Among the linear models,the LRM(using the L×W product)was the most effective(R2=0.9783).Conclusions On the basis of the performance criteria of the models,the machine learning models are more accurate for the nondestructive estimation of leaf area in colored cotton.The best-performing model(SVR with RBF kernel)was made available in an interactive web application,aiming to optimize crop management with accurate and nondestructive data. 展开更多
关键词 Colored cotton Leaf area Nondestructive prediction Linear regression Support vector regression Precision agriculture
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Visual field prediction using K-means clustering in patients with primary open angle glaucoma 认领 引用
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作者 Junyoung Lee Jihun Kim +5 位作者 Hwayoung Kim Sangwoo Moon EunAh Kim Sanghun Jeong Hojin Yang Jiwoong Lee 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第1期63-68,共6页
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to... AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data. 展开更多
关键词 K-means clustering hierarchical ordered partitioning and collapsing hybrid pointwise linear regression visual field prediction
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Intention Prediction-Based Automated Vehicle Control Mechanism Using Social-Pooling LSTM and Pass-Through Time Window Optimization 认领 引用
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作者 Donghee Oh Chris Lee Juneyoung Park 《Computers, Materials & Continua》 SCIE EI 2026年第7期1694-1713,共20页
This study presents a novel integrated framework for autonomous vehicle control at unsignalized intersections in mixed traffic environments,addressing the critical challenge of coordinating Society of Automotive Engin... This study presents a novel integrated framework for autonomous vehicle control at unsignalized intersections in mixed traffic environments,addressing the critical challenge of coordinating Society of Automotive Engineers(SAE)level 4 connected and autonomous vehicles(CAVs)and manually driven vehicles(MVs).The combination of driving intention prediction with a Social Long Short-Term Memory(Social LSTM)and a scheduling algorithm with optimization-driven Pass-through Time Windows(PTWs)is adopted to address traffic flow uncertainty.The Social LSTM model with spatial pooling layers to capture complex multi-vehicle interactions and predict surrounding vehicles’trajectories and maneuver intentions using naturalistic driving data from the CitySim dataset was applied.Unlike conventional approaches that treat prediction and control separately,this framework leverages high-confidence trajectory predictions to inform proactive scheduling decisions for conflict mitigation.The PTW scheduling algorithm formulates intersection management as a constrained optimization problem,dynamically allocating non-overlapping temporal windows for vehicle entering and exiting while considering vehicle dynamics,safety gaps,and deceleration constraints.Comprehensive simulation analysis across varying traffic volumes and CAV market penetration rates reveals significant improvements in both safety and operational efficiency.The scheduling algorithm has notably reduced traffic delay times while maintaining balance with safety measures.This finding provides a fundamental basis for infrastructure-based cooperative driving research,serving as a contributing factor for the development of advanced traffic management systems during the mixed-traffic period. 展开更多
关键词 Scheduling algorithm linear optimization trajectory prediction deep learning social LSTM model connected and autonomous vehicle
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Physics-Informed Graph Learning for Shape Prediction in Robot Manipulate of Deformable Linear Objects 认领 引用
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作者 Meixuan Wang Junliang Wang +2 位作者 Jie Zhang Xinting Liao Guojin Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第6期154-165,共12页
Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent fle... Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent flexibility of DLO and their complex deformation behaviors,such as bending and torsion,it is challenging to predict their dynamic characteristics accurately.Although the traditional physical modeling method can simulate the complex deformation behavior of DLO,the calculation cost is high and it is difficult to meet the demand of real-time prediction.In addition,the scarcity of data resources also limits the prediction accuracy of existing models.To solve these problems,a method of fiber shape prediction based on a physical information graph neural network(PIGNN)is proposed in this paper.This method cleverly combines the powerful expressive power of graph neural networks with the strict constraints of physical laws.Specifically,we learn the initial deformation model of the fiber through graph neural networks(GNN)to provide a good initial estimate for the model,which helps alleviate the problem of data resource scarcity.During the training process,we incorporate the physical prior knowledge of the dynamic deformation of the fiber optics into the loss function as a constraint,which is then fed back to the network model.This ensures that the shape of the fiber optics gradually approaches the true target shape,effectively solving the complex nonlinear behavior prediction problem of deformable linear objects.Experimental results demonstrate that,compared to traditional methods,the proposed method significantly reduces execution time and prediction error when handling the complex deformations of deformable fibers.This showcases its potential application value and superiority in fiber manipulation. 展开更多
关键词 Deformable linear objects Fiber Physics-informed graph neural network(PIGNN) Shape prediction
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Efficient Prediction of Refractive Index and Abbe Number in Polymers Using Density Functional Theory 认领 引用
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作者 Lu-Kun Feng Ai-Wei Zhang +3 位作者 Guo-Hua Huang Cai-Zhen Zhu Ming-Liang Wang Jian Xu 《Chinese Journal of Polymer Science》 SCIE EI CAS CSCD 2025年第8期1468-1482,共15页
Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(... Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(RI)and Abbe number of polymers.Using the LorentzLorenz equation,the frequency-dependent polarizability and molecular volume were calculated to estimate RI.Wavelength-dependent RI values were used to derive the Abbe numbers.Our results show a strong correlation with experimental data,with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number,enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results.By categorizing polymers into classes such as poly(methyl methacrylate)(PMMA)-,polyethylene(PE)-,polycarbonate(PC)-,polyimide(PI)-,and polyurethane(PU)-based materials,this method enables precise predictions and reduces discrepancies using linear correction functions.This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials. 展开更多
关键词 Optical polymers Refractive index prediction Abbe number prediction Coupled perturbed DFT Linear correction
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The improved local linear prediction of chaotic time series 认领 引用 被引量：3
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作者 孟庆芳 彭玉华 孙佳 《Chinese Physics B》 CAS 2007年第11期3220-3225,共6页
Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously... Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method. 展开更多
关键词 local linear prediction Bayesian information criterion state space reconstruction,chaotic time series
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An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks 认领 引用 被引量：4
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作者 沈毅 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第5期632-636,共5页
In this paper, we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabaisi-Albert （BA） scale-free networks. This strategy uses local knowledge of traffic condit... In this paper, we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabaisi-Albert （BA） scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion. 展开更多
关键词 linear prediction congestion networks
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Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method 认领 引用 被引量：4
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作者 张银兵 赵俊渭 +1 位作者 郭业才 李金明 《Journal of Marine Science and Application》 2011年第1期113-120,共8页
The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with ... The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR. 展开更多
关键词 linear prediction blind equalization channel identification second order statistics MMSE
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Multi-view BLUP:a promising solution for post-omics data integrative prediction 认领 引用 被引量：1
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作者 Bingjie Wu Huijuan Xiong +3 位作者 Lin Zhuo Yingjie Xiao Jianbing Yan Wenyu Yang 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2025年第6期839-847,共9页
Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various as... Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data. 展开更多
关键词 Multi-view data Best linear unbiased prediction Similarity function Phenotype prediction Differential evolution algorithm
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Nonlinearly correlated failure analysis and autonomic prediction for distributed systems 认领 引用
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作者 Lu Xu Wang Huiqiang +2 位作者 Lv Xiao Feng Guangsheng Zhou Renjie 《High Technology Letters》 EI CAS 2011年第3期290-298,共9页
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit... In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems. 展开更多
关键词 failure prediction nonlinear correlation analysis feature extraction locally linear embedding autonomic computing
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KLT-based local linear prediction of chaotic time series 认领 引用
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作者 Meng Qingfang Peng Yuhua Chen Yuehui 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第4期694-699,共6页
In the reconstructed phase space, based on the Karhunen-Loeve transformation （KLT）, the new local linear prediction method is proposed to predict chaotic time series. ＆ noise-free chaotic time series and a noise ad... In the reconstructed phase space, based on the Karhunen-Loeve transformation （KLT）, the new local linear prediction method is proposed to predict chaotic time series. ＆ noise-free chaotic time series and a noise added chaotic time series are analyzed. The simulation results show that the KLT-based local linear prediction method can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracies of the KLT-based local linear prediction method are superior to that of the traditional local linear prediction. 展开更多
关键词 Karhunen-Loeve transformation local linear prediction phase space reconstruction chaotic time series.
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Research on a non-linear chaotic prediction model for urban traffic flow 认领 引用 被引量：7
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作者 黄鵾 陈森发 +1 位作者 周振国 亓霞 《Journal of Southeast University(English Edition)》 EI CAS 2003年第4期410-413,共4页
In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconst... In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control. 展开更多
关键词 traffic flow chaotic theory phase reconstruction non linear genetic algorithm prediction model
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Dynamic Wavelength and Bandwidth Allocation Using Adaptive Linear Prediction in WDM/TDM Ethernet Passive Optical Networks 认领 引用 被引量：1
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作者 陆奕奕 郭勇 何晨 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第2期173-178,共6页
Hybrid wavelength-division-multiplexing(WDM)ime-division-multiplexing(TDM) ethernet passive optical networks(EPONs) can achieve low per-subscriber cost and scalability to increase the number of subscribers. This paper... Hybrid wavelength-division-multiplexing(WDM)ime-division-multiplexing(TDM) ethernet passive optical networks(EPONs) can achieve low per-subscriber cost and scalability to increase the number of subscribers. This paper discusses dynamic wavelength and bandwidth allocation(DWBA) algorithm in hybrid WDM/TDM EPONs.Based on the correlation structure of the variable bit rate(VBR) video traffic,we propose a quality-ofservice (QoS) supported DWBA using adaptive linear traffic prediction.Wavelength and timeslot are allocated dynamically by optical line terminal(OLT) to all optical network units(ONUs) based on the bandwidth requests and the guaranteed service level agreements(SLA) of all ONUs.Mean square error of the predicted average arriving rate of compound video traffic during waiting period is minimized through Wiener-Hopf equation.Simulation results show that the DWBA-adaptive-linear-prediction(DWBA-ALP) algorithm can significantly improve the QoS performances in terms of low delay and high bandwidth utilization. 展开更多
关键词 ethernet passive optical network （EPON） dynamic bandwidth allocation （DBA） dynamic wavelength and bandwidth allocation （DWBA） variable bit rate （VBR） adaptive linear prediction grouting
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Gaussian process based model predictive tracking control with improved iLQR 认领 引用
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作者 Li Heng Zhu Gongcai +1 位作者 Liu Andong Ni Hongjie 《High Technology Letters》 EI CAS 2026年第1期49-59,共11页
This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity p... This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment. 展开更多
关键词 model predictive control Gaussian process iterative linear quadratic regulator trajectory tracking
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Impact of high-frequency atmospheric noise on ENSO predictability 认领 引用
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作者 Guangyu Chi Ang Li +1 位作者 Yishuai Jin Xiaopei Lin 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第4期49-59,共11页
This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El NiñoSouthern Oscillation(ENSO).Based on the community climate system model version 4(CCSM4),two set... This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El NiñoSouthern Oscillation(ENSO).Based on the community climate system model version 4(CCSM4),two sets of comparative experiments were conducted:control(CTRL)and interactive ensemble(IE)simulations with reduced atmospheric noise.The analysis combining the linear inverse model(LIM)and the recharge oscillator model(ROM)shows that the IE method significantly improves the predictability of ENSO by effectively suppressing high-frequency atmospheric noise.Specifically,the LIM correlation coefficient of IE data is significantly improved compared to CTRL data within a 12-month forecast time frame.Mechanistic analysis revealed that under the IE mode,the system exhibits stronger thermocline feedback,with both the regulatory effect of thermocline depth anomalies on sea surface temperature(SST)and their response to SST significantly enhanced.This indicates that the long-term stability signals represented by subsurface heat content are more easily extracted under reduced noise conditions,thereby providing additional predictive information for ENSO forecasting. 展开更多
关键词 atmospheric noise predictability of ENSO linear inverse model recharge oscillator model subsurface heat content
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LINEAR PREDICTION APPROACH IN AIRBORNE ADAPTIVE ARRAYS 认领 引用
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作者 Su Jie Li Chunsheng Zhou Yinqing 《Chinese Journal of Aeronautics》 CAS 1996年第1期64-70,共7页
To cope with the time-varying and Dopper-broadened clutter in airborne phase array radars, it is required that the signal processing should be adaptive and two-dimensional both in time and in space. However, the optim... To cope with the time-varying and Dopper-broadened clutter in airborne phase array radars, it is required that the signal processing should be adaptive and two-dimensional both in time and in space. However, the optimum two-dimensional adaptive processing is hard to realize real-timely because it requires a large amount of computation. From the idea of approximating the clutter process by using an auto regressive process, a linear prediction approach is proposed to realize the adaptive space-time processing of airborne adaptive array signals. The research shows that the clutter process can be well approximated by a low-order AR process, so a low-order linear prediction receiver can get a sub-optimum performance at a very low expense. Besides, the low-order linear prediction receiver has additional degrees of freedom to cope with other colored noises and interferences. In consideration of the many advantages of the linear prediction receiver in both algorithms and realizations, it has a good prospect in its application to air borne adaptive array signal processing. 展开更多
关键词 signal processing phased arrays radar linear prediction adaptive filters
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Extended linear regression model for vessel trajectory prediction with a-priori AIS information 认领 引用
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作者 Christiaan Neil Burger Waldo Kleynhans Trienko Lups Grobler 《Geo-Spatial Information Science》 SCIE EI CSCD 2024年第1期202-220,共19页
As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Au... As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Automatic Identification System(AIS).An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally.This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location predictions.In this paper,we propose a novel strategy for predicting future locations of vessels making use of historic AIS data.The proposed method uses a Linear Regression Model(LRM)and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement.In areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the LRM.The proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time. 展开更多
关键词 Automatic Identification System(AIS)data Linear Regression Model(LRM) trajectory mining spatial map historic data trajectory prediction
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Linear extrapolation for prediction of tensile creep compliance of polyvinyl chloride 认领 引用
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作者 谢刚 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第5期587-589,共3页
The universal creep equation is successful in relating the creep （ε） to the aging time （t） , coefficient of retardation time （β） , and intrinsic time （ to ）. This relation was used to treat the creep experim... The universal creep equation is successful in relating the creep （ε） to the aging time （t） , coefficient of retardation time （β） , and intrinsic time （ to ）. This relation was used to treat the creep experimental data for polyvinyl chloride （ PVC ） specimens at a given stress and different aging times. The βgs found by the “polynomial fitting” method in this work instead of the “middle - point” method reported in the literature. The unified master line was constructed with the treated data and curves according to the universal equation. The master line can be used to predict the long- term creed behavior and lifetime by extrapolating. 展开更多
关键词 linear extrapolation prediction tensile creep compliance polyvinyl chloride （ PVC ）
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基于DLinear-LSTNet的航天空间站质谱仪状态预测方法 认领 引用
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作者 杨思婷 盛云龙 +2 位作者 戚娜 庄须叶 王晓格 《传感器与微系统》 北大核心 2026年第9期183-187,共5页
针对传统方法难以有效监测航天质谱仪运行状态的问题,提出一种基于分解线性模型和长短期时间序列网络(DLinear-LSTNet)的航天空间站质谱仪状态预测模型。首先,使用序列分解策略将时序数据分解成趋势序列和季节性序列;其次,采用线性模型... 针对传统方法难以有效监测航天质谱仪运行状态的问题,提出一种基于分解线性模型和长短期时间序列网络(DLinear-LSTNet)的航天空间站质谱仪状态预测模型。首先,使用序列分解策略将时序数据分解成趋势序列和季节性序列;其次,采用线性模型和LSTNet分别对季节性和趋势序列进行建模;最后,将2个模型的输出进行融合,得到最终的预测结果。两类传感器数据预测的实验结果表明:相较于常见传统预测模型,DLinear-LSTNet具有更稳定的预测性能和更高的预测精度。 展开更多
关键词 航天质谱仪 时间序列预测 序列分解策略 线性模型 长短期时间序列网络
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