As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid pr...As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.展开更多
Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road n...Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.展开更多
BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC...BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS Patients with curatively resected advanced gallbladder adenocarcinoma(T3 and T4)were selected from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.A survival prediction model based on Bayesian network(BN)was constructed using the tree-augmented na?ve Bayes algorithm,and composite importance measures were applied to rank the influence of factors on survival.The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3.The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTS A total of 818 patients met the inclusion criteria.The median survival time was 9.0 mo.The accuracy of BN model was 69.67%,and the area under the curve value for the testing dataset was 77.72%.Adjuvant radiation,adjuvant chemotherapy(CTx),T stage,scope of regional lymph node surgery,and radiation sequence were ranked as the top five prognostic factors.A survival prediction table was established based on T stage,N stage,adjuvant radiotherapy(XRT),and CTx.The distribution of the survival time(>9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)>adjuvant radiation>adjuvant chemotherapy>surgery alone.For patients with node-positive disease,the larger benefit predicted by the model is adjuvant chemoradiotherapy.The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P<0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P<0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients.Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease.展开更多
Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model cou...Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.展开更多
Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality.The fundamental challenges for traffic pre...Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality.The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics.Existing approaches mainly focus on modelling the traffic data itself,but do not explore the traffic correlations implicit in origin-destination(OD)data.In this paper,we propose STOD-Net,a dynamic spatial-temporal OD feature-enhanced deep network,to simultaneously predict the in-traffic and out-traffic for each and every region of a city.We model the OD data as dynamic graphs and adopt graph neural networks in STOD-Net to learn a low-dimensional representation for each region.As per the region feature,we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations.To further capture the complicated spatial and temporal dependencies among different regions,we propose a novel joint feature,learning block in STOD-Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal-aware.We evaluate the effectiveness of STOD-Net on two benchmark datasets,and experimental results demonstrate that it outperforms the state-of-the-art by approximately 5%in terms of prediction accuracy and considerably improves prediction stability up to 80%in terms of standard deviation.展开更多
This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the...This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k andα)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.展开更多
Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual inte...Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is su...The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
Network security situation is a hot research topic in the field of network security. Whole situation awareness includes the current situation evaluation and the future situation prediction. However, the now-existing r...Network security situation is a hot research topic in the field of network security. Whole situation awareness includes the current situation evaluation and the future situation prediction. However, the now-existing research focuses on the current situation evaluation, and seldom discusses the future prediction. Based on the historical research, an improved grey Verhulst model is put forward to predict the future situation. Aiming at the shortages in the prediction based on traditional Verhulst model, the adaptive grey parameters and equal- dimensions grey filling methods are proposed to improve the precision. The simulation results prove that the scheme is efficient and applicable.展开更多
Dephosphorization is essential content in the steelmaking process,and the process after the converter has no dephosphorization function.Therefore,phosphorus must be removed to the required level in the converter proce...Dephosphorization is essential content in the steelmaking process,and the process after the converter has no dephosphorization function.Therefore,phosphorus must be removed to the required level in the converter process.In order to better control the end-point phosphorus content of basic oxygen furnace(BOF),a prediction model of end-point phosphorus content for BOF based on monotone-constrained backpropagation(BP)neural network was established.Through the theoretical analysis of the dephosphorization process,ten factors that affect the end-point phosphorus content were determined as the input variables of the model.The correlations between influencing factors and end-point phosphorus content were determined as the constraint condition of the model.200 sets of data were used to verify the accuracy of the model,and the hit ratios in the range of±0.005%and±0.003%are 94%and 74%,respectively.The fit coefficient of determination of the predicted value and the actual value is 0.8456,and the root-mean-square error is 0.0030;the predictive accuracy is better than that of ordinary BP neural network,and this model has good interpretability.It can provide useful reference for real production and also provide a new approach for metallurgical predictive modeling.展开更多
In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time in...In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model （DTHMM）. The expectation maximization （EM） algorithm is introduced to derive the maximumlikelihood estimation （MLE） of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator （C-A） delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.展开更多
Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, g...Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.展开更多
As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet...As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.展开更多
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,...Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.展开更多
In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level...In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.展开更多
BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managi...BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.展开更多
Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling a...Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.展开更多
基金funded by National Key Research and Development Plan(2018YFC0406703)Supported by the National Natural Science Foundation of China(51779277)+4 种基金Chinese Academy of Water Sciences(SD0145B072021)Supported by the State Key Laboratory of Flow Water Cycle Simulation and Regulation,SKL2022ZD05Support provided by the fund of State Key Laboratory of Water Cycle and Water Security,IWHR(Grant No.SKL2024YJZD05)Support provided by the fund of Power China(DJ-ZDXM-2020-50)Support provided by the fund of Research and Application of Intelligent Simulation and Intelligent Control Technology for Structural States of Gravity DAMS in Jingling Reservoir Project,Zhejiang Province(JLSKFW-2024113).
文摘As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m^(3),showing significant potential for improving resource efficiency and supporting sustainable construction practices.
基金Sponsored by National Key Research and Development Program of China(Grant No.2020YEB1600500).
文摘Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.
基金Supported by the National Natural Science Foundation of China,No.81572420 and No.71871181the Key Research and Development Program of Shaanxi Province,No.2017ZDXM-SF-055the Multicenter Clinical Research Project of School of Medicine,Shanghai Jiaotong University,No.DLY201807
文摘BACKGROUND The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma(GBC)after curative resection remain unclear.AIM To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy.METHODS Patients with curatively resected advanced gallbladder adenocarcinoma(T3 and T4)were selected from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.A survival prediction model based on Bayesian network(BN)was constructed using the tree-augmented na?ve Bayes algorithm,and composite importance measures were applied to rank the influence of factors on survival.The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3.The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy.RESULTS A total of 818 patients met the inclusion criteria.The median survival time was 9.0 mo.The accuracy of BN model was 69.67%,and the area under the curve value for the testing dataset was 77.72%.Adjuvant radiation,adjuvant chemotherapy(CTx),T stage,scope of regional lymph node surgery,and radiation sequence were ranked as the top five prognostic factors.A survival prediction table was established based on T stage,N stage,adjuvant radiotherapy(XRT),and CTx.The distribution of the survival time(>9.0 mo)was affected by different treatments with the order of adjuvant chemoradiotherapy(cXRT)>adjuvant radiation>adjuvant chemotherapy>surgery alone.For patients with node-positive disease,the larger benefit predicted by the model is adjuvant chemoradiotherapy.The survival analysis showed that there was a significant difference among the different adjuvant therapy groups(log rank,surgery alone vs CTx,P<0.001;surgery alone vs XRT,P=0.014;surgery alone vs cXRT,P<0.001).CONCLUSION The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients.Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease.
基金Item Sponsored by Fundamental Research Funds for Central Universities of China(FRF-BR-10-027B)
文摘Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat sta- tus and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could over- come the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are es- tablished and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish tempera- ture, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blo- wiag station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.
基金supported by the National Natural Science Foundation of China,Grant/Award Number:62401338by the Shandong Province Excellent Youth Science Fund Project(Overseas),Grant/Award Number:2024HWYQ-028by the Fundamental Research Funds of Shandong University.
文摘Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality.The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics.Existing approaches mainly focus on modelling the traffic data itself,but do not explore the traffic correlations implicit in origin-destination(OD)data.In this paper,we propose STOD-Net,a dynamic spatial-temporal OD feature-enhanced deep network,to simultaneously predict the in-traffic and out-traffic for each and every region of a city.We model the OD data as dynamic graphs and adopt graph neural networks in STOD-Net to learn a low-dimensional representation for each region.As per the region feature,we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations.To further capture the complicated spatial and temporal dependencies among different regions,we propose a novel joint feature,learning block in STOD-Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal-aware.We evaluate the effectiveness of STOD-Net on two benchmark datasets,and experimental results demonstrate that it outperforms the state-of-the-art by approximately 5%in terms of prediction accuracy and considerably improves prediction stability up to 80%in terms of standard deviation.
基金Project(52108433)supported by the National Natural Science Foundation of ChinaProject(HSR202004)supported by the Open Foundation of National Engineering Research Center of High-Speed Railway Construction Technology(CSU),China+3 种基金Projects(2024RC3170,2021RC4031)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(2024JJ5018,2024JJ5427)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(KQ2402027)supported by the Changsha City Natural Science Foundation,ChinaProjects(2021-Special-08,2022-Special-09)supported by the Science and Technology Research and Development Program Project of China Railway Group Limited。
文摘This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k andα)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.
基金funded by the National Natural Science Foundation of China,grant number 624010funded by the Natural Science Foundation of Anhui Province,grant number 2408085QF202+1 种基金funded by the Anhui Future Technology Research Institute Industry Guidance Fund Project,grant number 2023cyyd04funded by the Project of Research of Anhui Polytechnic University,grant number Xjky2022150.
文摘Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving,social robotics,and intelligent surveillance systems.Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents.These interactions are critical to trajectory prediction accuracy.While prior studies have employed Convolutional Neural Networks(CNNs)and Graph Convolutional Networks(GCNs)to model such interactions,these methods fail to distinguish varying influence levels among neighboring pedestrians.To address this,we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions.Specifically,we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians.These features are then fused and processed by the Bidirectional Graph Attention Network(Bi-GAT),which models the bidirectional interactions between the target pedestrian and its neighbors.The model computes node attention weights(i.e.,similarity scores)to differentially aggregate neighbor information,enabling fine-grained interaction representations.Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-theartmethods regarding Average Displacement Error(ADE)and Final Displacement Error(FDE),highlighting its strong prediction accuracy and generalization capability.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the National Natural Science Foundation of China(No.52178436)the Shanghai Collaborative Innovation Research Center for Multi-network&Multi-modal Rail Transit,China.
文摘The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
基金the National Natural Science Foundation of China(No.60605019)
文摘Network security situation is a hot research topic in the field of network security. Whole situation awareness includes the current situation evaluation and the future situation prediction. However, the now-existing research focuses on the current situation evaluation, and seldom discusses the future prediction. Based on the historical research, an improved grey Verhulst model is put forward to predict the future situation. Aiming at the shortages in the prediction based on traditional Verhulst model, the adaptive grey parameters and equal- dimensions grey filling methods are proposed to improve the precision. The simulation results prove that the scheme is efficient and applicable.
基金supported by the National Natural Science Foundation of China(No.51974023)Key R&D Program Projects in Jiangxi Province(20171ACE50020).
文摘Dephosphorization is essential content in the steelmaking process,and the process after the converter has no dephosphorization function.Therefore,phosphorus must be removed to the required level in the converter process.In order to better control the end-point phosphorus content of basic oxygen furnace(BOF),a prediction model of end-point phosphorus content for BOF based on monotone-constrained backpropagation(BP)neural network was established.Through the theoretical analysis of the dephosphorization process,ten factors that affect the end-point phosphorus content were determined as the input variables of the model.The correlations between influencing factors and end-point phosphorus content were determined as the constraint condition of the model.200 sets of data were used to verify the accuracy of the model,and the hit ratios in the range of±0.005%and±0.003%are 94%and 74%,respectively.The fit coefficient of determination of the predicted value and the actual value is 0.8456,and the root-mean-square error is 0.0030;the predictive accuracy is better than that of ordinary BP neural network,and this model has good interpretability.It can provide useful reference for real production and also provide a new approach for metallurgical predictive modeling.
基金supported in part by the National Natural Science Foundation of China (60774098 60843003+3 种基金 50905172)the Science Foundation of Anhui Province (090412071 090412040)the University of Science and Technology of China Initiative Foundation
文摘In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model （DTHMM）. The expectation maximization （EM） algorithm is introduced to derive the maximumlikelihood estimation （MLE） of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator （C-A） delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.
基金Project supported by National Basic Research Program of China (Grant Nos 2009CB320505 and 2009CB320504)National High Technology Research and Development Program of China (Grant Nos 2006AA01Z235, 2007AA01Z206 and 2009AA01Z210)
文摘Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet-grey-chaos （WGC）, for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM（1,1） and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.
基金Supported by the National Defence Science and Industry Committee(41314020201)
文摘As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.
文摘Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.
文摘In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.
基金Supported by the Chinese Nursing Association,No.ZHKY202111Scientific Research Program of School of Nursing,Chongqing Medical University,No.20230307Chongqing Science and Health Joint Medical Research Program,No.2024MSXM063.
文摘BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.
文摘Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.
