scholarly journals Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hui Wang ◽  
Haoqi Tan ◽  
Tian Qu ◽  
Jiupeng Zhang
Keyword(s):  
Prediction Model ◽  
Influencing Factor ◽  
Correlation Method ◽  
Asphalt Mixtures ◽  
Good Prediction ◽  
Modified Asphalt ◽  
Grey Correlation ◽  
Depth Prediction ◽  
Test Conditions ◽  
Good Prediction Accuracy

APA rutting tests were conducted for six kinds of asphalt mixtures under air-dry and immersing conditions. The influences of test conditions, including load, temperature, air voids, and moisture, on APA rutting depth were analyzed by using grey correlation method, and the APA rutting depth prediction model was established. Results show that the modified asphalt mixtures have bigger rutting depth ratios of air-dry to immersing conditions, indicating that the modified asphalt mixtures have better antirutting properties and water stability than the matrix asphalt mixtures. The grey correlation degrees of temperature, load, air void, and immersing conditions on APA rutting depth decrease successively, which means that temperature is the most significant influencing factor. The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.

Bayesian Network for Freeway Traffic State Prediction

2018 ◽  
Vol 2672 (45) ◽  
pp. 124-135 ◽  
Author(s):  
Ho-Chul Park ◽  
Dong-Kyu Kim ◽  
Seung-Young Kho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Bayesian Network ◽  
Traffic Flow ◽  
Sensitivity Analyses ◽  
Good Prediction ◽  
Traffic Operations ◽  
Traffic State ◽  
State Prediction ◽  
Good Prediction Accuracy

Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent a flow breakdown. Therefore, it is necessary to predict the traffic state in such a way as to reflect the stochastic process of traffic flow. To predict accurately the traffic state, machine learning-based models have been widely adopted, but they have difficulty in obtaining insights for traffic state prediction due to black-box procedures of the models. A Bayesian network (BN) is a methodology that is suitable for dealing with problems that involve uncertainty, and it can also improve the understanding of such problems. In this study, we develop a traffic state prediction model using a BN to reflect the dynamic and stochastic characteristics of traffic flow. To improve the BN, which has been used with a simple structure in transportation problems, we propose a modeling procedure using a mixture of Gaussians (MoGs). In the performance evaluation, the BN has better performance than a logistic regression, and it has the same level of performance as an artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provide the understanding of traffic state prediction and the guidelines for improving the model. The BN developed in this study can be considered as a traffic state prediction model with good prediction accuracy and interpretability.

scholarly journals Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Olesya Ajnakina ◽  
Deborah Agbedjro ◽  
Ryan McCammon ◽  
Jessica Faul ◽  
Robin M. Murray ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Absolute Risk ◽  
Large Population ◽  
External Validation ◽  
The United States ◽  
Good Prediction ◽  
Good Prediction Accuracy ◽  
All Cause Mortality ◽  
Development And Validation

Abstract Background In increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10 years. Methods For the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50–75 years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell’s optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ≥50 years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts. Results The model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model’s prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope β=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity. Conclusions A new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

scholarly journals Fast Prediction with Sparse Multikernel LS-SVR Using Multiple Relevant Time Series and Its Application in Avionics System

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yang M. Guo ◽  
Pei He ◽  
Xiang T. Wang ◽  
Ya F. Zheng ◽  
Chong Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Computation Time ◽  
Support Vector ◽  
Good Prediction ◽  
Trend Prediction ◽  
Good Prediction Accuracy ◽  
Fast Prediction

Health trend prediction is critical to ensure the safe operation of highly reliable systems. However, complex systems often present complex dynamic behaviors and uncertainty, which makes it difficult to develop a precise physical prediction model. Therefore, time series is often used for prediction in this case. In this paper, in order to obtain better prediction accuracy in shorter computation time, we propose a new scheme which utilizes multiple relevant time series to enhance the completeness of the information and adopts a prediction model based on least squares support vector regression (LS-SVR) to perform prediction. In the scheme, we apply two innovative ways to overcome the drawbacks of the reported approaches. One is to remove certain support vectors by measuring the linear correlation to increase sparseness of LS-SVR; the other one is to determine the linear combination weights of multiple kernels by calculating the root mean squared error of each basis kernel. The results of prediction experiments indicate preliminarily that the proposed method is an effective approach for its good prediction accuracy and low computation time, and it is a valuable method in applications.

A Link Prediction Model Based on Similarity between Links

2014 ◽  
Vol 651-653 ◽  
pp. 1748-1752
Author(s):  
Fu Li Xie ◽  
Guang Quan Cheng
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Network Science ◽  
The Other ◽  
Prediction Methods ◽  
Good Prediction ◽  
Prediction Problem ◽  
Model Based ◽  
Good Prediction Accuracy

With the development of network science, the link prediction problem has attracted more and more attention. Among which, link prediction methods based on similarity has been most widely studied. Previous methods depicting similarity of nodes mainly consider their common neighbors. But in this paper, from the view of network environment of nodes, which is to analysis the links around the pair of nodes, derive nodes similarity through that of links, a new way to solve the link prediction problem is provided. This paper establishes a link prediction model based on similarity between links, presents the LE index. Finally, the LE index is tested on five real datasets, and compared with existing similarity-based link prediction methods, the experimental results show that LE index can achieve good prediction accuracy, especially outperforms the other methods in the Yeast network.

scholarly journals Forecast of Highway Subgrade Settlement Based on Improved BP Neural Network

2020 ◽  
pp. 118
Author(s):  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Average Error ◽  
Good Prediction ◽  
Settlement Prediction ◽  
Training Samples ◽  
Good Prediction Accuracy ◽  
Neural Network Prediction ◽  
Network Prediction

In order to improve the feasibility and accuracy of the roadbed settlement prediction model, the factor analysis method is combined with the BP neural network method, and an improved BP neural network roadbed settlement prediction model is proposed. Select example data to test the improved BP neural network roadbed settlement prediction model. The test results: The relative average error of the 10 sets of training samples’ predicted and actual roadbed settlements was 4.287%, and the roads of five predicted samples The relative error of subgrade settlement is 1.79%, 1.93%, 6.62%, 7.19%, 4.05%, all less than 10%, which proves that the improved BP neural network prediction model has good prediction accuracy.

scholarly journals Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7202
Author(s):  
Jianfei Huang ◽  
Xinchun Cheng ◽  
Yuying Shen ◽  
Dewen Kong ◽  
Jixin Wang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Prediction Method ◽  
Good Prediction ◽  
Construction Machinery ◽  
Autonomous Operation ◽  
Good Prediction Accuracy ◽  
Wheel Loaders ◽  
The Cost

Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.

scholarly journals Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma

2021 ◽  
Vol 10 (13) ◽  
pp. 2869
Author(s):  
Indah Jamtani ◽  
Kwang-Woong Lee ◽  
Yun-Hee Choi ◽  
Young-Rok Choi ◽  
Jeong-Moo Lee ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Prediction Model ◽  
Risk Group ◽  
Significant Risk ◽  
Risk Scores ◽  
Accurate Information ◽  
Survival Prediction ◽  
Good Prediction ◽  
Recurrence Rates

This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC.

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