Dynamic prediction models of rock quality designation in tunneling projects

2021 ◽  
Vol 27 ◽  
pp. 100497
Author(s):  
Arsalan Mahmoodzadeh ◽  
Mokhtar Mohammadi ◽  
Hunar Farid Hama Ali ◽  
Sazan Nariman Abdulhamid ◽  
Hawkar Hashim Ibrahim ◽  
...  
2020 ◽  
Vol 39 (1) ◽  
pp. 653-662
Author(s):  
Zhou Wang ◽  
Qing Liu ◽  
Haitao Liu ◽  
Shizhong Wei

AbstractThe precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved in the basic oxygen furnace (BOF) steelmaking process. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. This paper presents the characteristics of different end-point carbon prediction models. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. With the development of information technology and auto-detection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. Future contributions to the development and application of intelligent end-point carbon prediction in BOF steelmaking are still arduous tasks. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high-efficiency, and stable production for BOF steelmaking in the future.


2019 ◽  
Vol 3 (1) ◽  
pp. 14-25
Author(s):  
Kuang Junwei ◽  
Hangzhou Yang ◽  
Liu Junjiang ◽  
Yan Zhijun

Purpose Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model. Design/methodology/approach A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval. Findings The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model. Originality/value In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.


2019 ◽  
Vol 53 (3) ◽  
pp. 1485-1494 ◽  
Author(s):  
Jun Zheng ◽  
Xiaohong Wang ◽  
Qing Lü ◽  
Jianfeng Liu ◽  
Jichao Guo ◽  
...  

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