Mobile radio prediction model considering long term fading in urban microcellular environment

2005 ◽  
Author(s):  
Kihong Song ◽  
Yeonho Chung
Keyword(s):  
Prediction Model ◽  
Mobile Radio

scholarly journals A Clinical Prediction Model for Long-Term Functional Outcome after Traumatic Spinal Cord Injury Based on Acute Clinical and Imaging Factors

2012 ◽  
Vol 29 (13) ◽  
pp. 2263-2271 ◽  
Author(s):  
Jefferson R. Wilson ◽  
Robert G. Grossman ◽  
Ralph F. Frankowski ◽  
Alexander Kiss ◽  
Aileen M. Davis ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Prediction Model ◽  
Functional Outcome ◽  
Traumatic Spinal Cord Injury ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Cord Injury

A Novel Long-Term Prediction Model for Hemispherical Resonator Gyroscope's Drift Data

2014 ◽  
Vol 14 (6) ◽  
pp. 1886-1897 ◽  
Author(s):  
Chenglong Dai ◽  
Dechang Pi ◽  
Zhen Fang ◽  
Hui Peng
Keyword(s):  
Prediction Model ◽  
Term Prediction ◽  
Hemispherical Resonator ◽  
Long Term Prediction

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

2020 ◽  
Author(s):  
Tianyu Xu ◽  
Yongchuan Yu ◽  
Jianzhuo Yan ◽  
Hongxia Xu
Keyword(s):  
Prediction Model ◽  
Feature Fusion ◽  
Forecast Model ◽  
Data Sets ◽  
Good Prediction ◽  
Data Set ◽  
Rainfall Prediction ◽  
Improve Model ◽  
Probability Prediction

Abstract Due to the problems of unbalanced data sets and distribution differences in long-term rainfall prediction, the current rainfall prediction model had poor generalization performance and could not achieve good prediction results in real scenarios. This study uses multiple atmospheric parameters (such as temperature, humidity, atmospheric pressure, etc.) to establish a TabNet-LightGbm rainfall probability prediction model. This research uses feature engineering (such as generating descriptive statistical features, feature fusion) to improve model accuracy, Borderline Smote algorithm to improve data set imbalance, and confrontation verification to improve distribution differences. The experiment uses 5 years of precipitation data from 26 stations in the Beijing-Tianjin-Hebei region of China to verify the proposed rainfall prediction model. The test set is to predict the rainfall of each station in one month. The experimental results shows that the model has good performance with AUC larger than 92%. The method proposed in this study further improves the accuracy of rainfall prediction, and provides a reference for data mining tasks.

scholarly journals Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

2019 ◽  
Vol 9 (18) ◽  
pp. 3819 ◽  
Author(s):  
Wenhao Guo ◽  
Xiaoqing Zuo ◽  
Jianwei Yu ◽  
Baoding Zhou
Keyword(s):  
Prediction Model ◽  
Early Warning ◽  
Computational Efficiency ◽  
Prevention And Control ◽  
Apriori Algorithm ◽  
Mapreduce Framework ◽  
Frequent Item ◽  
And Control ◽  
Frequent Item Sets

In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

A mechanistic evaluation of modified asphalt paving mixtures

1994 ◽  
Vol 21 (6) ◽  
pp. 954-965 ◽  
Author(s):  
N. Ali ◽  
Shaher Zahran ◽  
Jim Trogdon ◽  
Art Bergan
Keyword(s):  
Prediction Model ◽  
Pavement Performance ◽  
Pavement Distress ◽  
Modified Asphalt ◽  
Significant Difference ◽  
Actual Field ◽  
Asphalt Paving ◽  
Polymer Modified Asphalt ◽  
Pavement Performance Prediction

The main purpose of this study was to facilitate decisions concerning the effectiveness of modifiers in mitigating pavement distress and improving long-term overall pavement performance in actual field conditions, by utilizing short-term laboratory results and a mathematical prediction model. The modifiers investigated were carbon black, neoprene latex, and polymer modified asphalt (STYRELF). The statistical general linear model (GLM) and the Fisher least significant difference (LSD) were used for the analysis of data. The results of the study indicate that the effect of the modifier on the paving mixture properties was insignificant at low temperatures (down to −17 °C), but significant at high temperatures (up to 60 °C) where the synergistic effect of the modifier on the paving mixture was pronounced. The VESYS IIIA pavement performance prediction model was utilized to assess the effects, if any, of the modifier on the pavement's overall performance. All the modifiers improve, to some degree, the overall pavement performance. Key words: modifiers, asphalt, paving mixtures, pavements, polymer asphalt.

Sign in / Sign up

Export Citation Format

Share Document