scholarly journals Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1987
Author(s):  
Maoqiang Gu ◽  
Anjun Xu ◽  
Hongbing Wang ◽  
Zhitong Wang
Keyword(s):  
Carbon Content ◽  
Real Time ◽  
Process Parameters ◽  
Short Term Memory ◽  
Integral Model ◽  
Case Based Reasoning ◽  
Prediction Errors ◽  
Converter Steelmaking ◽  
Dynamic Prediction ◽  
Time Dynamic

The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.

scholarly journals Application of deep learning in automatic detection of technical and tactical indicators of table tennis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0245259
Author(s):  
Fufeng Qiao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Automatic Detection ◽  
Table Tennis ◽  
Time Dynamic ◽  
Time Motion ◽  
Automatic Detection System

A DCNN-LSTM (Deep Convolutional Neural Network-Long Short Term Memory) model is proposed to recognize and track table tennis’s real-time trajectory in complex environments, aiming to help the audiences understand competition details and provide a reference for training enthusiasts using computers. Real-time motion features are extracted via deep reinforcement networks. DCNN tracks the recognized objects, and the LSTM algorithm predicts the ball’s trajectory. The model is tested on a self-built video dataset and existing systems and compared with other algorithms to verify its effectiveness. Finally, an overall tactical detection system is built to measure ball rotation and predict ball trajectory. Results demonstrate that in feature extraction, the Deep Deterministic Policy Gradient (DDPG) algorithm has the best performance, with a maximum accuracy rate of 89% and a minimum mean square error of 0.2475. The accuracy of target tracking effect and trajectory prediction is as high as 90%. Compared with traditional methods, the performance of the DCNN-LSTM model based on deep learning is improved by 23.17%. The implemented automatic detection system of table tennis tactical indicators can deal with the problems of table tennis tracking and rotation measurement. It can provide a theoretical foundation and practical value for related research in real-time dynamic detection of balls.

scholarly journals Using Real-Time Dynamic Prediction to Implement IoV-Based Collision Avoidance

2019 ◽  
Vol 9 (24) ◽  
pp. 5370
Author(s):  
Che-Cheng Chang ◽  
Wei-Ming Lin ◽  
Chuan-An Lai
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Confidence Region ◽  
Dynamic Prediction ◽  
Internet Of Vehicles ◽  
Time Dynamic ◽  
The Future ◽  
Future Position

For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.

Real-time dynamic prediction model of NOx emission of coal-fired boilers under variable load conditions

Fuel ◽  
2020 ◽  
Vol 274 ◽  
pp. 117811 ◽  
Author(s):  
Tingting Yang ◽  
Kangfeng Ma ◽  
You Lv ◽  
Yang Bai
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Nox Emission ◽  
Variable Load ◽  
Dynamic Prediction ◽  
Time Dynamic ◽  
Load Conditions

Optimization Research on Converter Steelmaking Process Parameters Based on DOE

2013 ◽  
Vol 579-580 ◽  
pp. 128-132 ◽  
Author(s):  
Bi Hong Lu ◽  
Yu Kai Li ◽  
Bao Zhang Qu
Keyword(s):  
Neural Network ◽  
Carbon Content ◽  
Process Parameters ◽  
Rbf Neural Network ◽  
Converter Steelmaking ◽  
Steelmaking Process ◽  
Initial Weight ◽  
End Point ◽  
Significant Factors ◽  
The Ideal

The temperature and carbon content at blow end point should be controlled strictly during the BOF converter steelmaking process. Meanwhile many factors have impact on the temperature and carbon content at blow end point. These factors include initial weight of molten iron, initial weight of scrap steel, oxygen blow duration, the temperature and carbon content when lowering the sublance, as well as the weight of all kinds of addition reagents. In order to determine the optimized process parameters so as to reach the ideal temperature and carbon content at blow end point, this paper built a series of experiment programs based on DOE. According to the experiment programs, authors conducted these experiments with the help of RBF neural network and analyzed each parameters as well as some interactions impact on target. According to the statistical analysis results of experiment data (the SNR), authors extract significant factors and reached an optimized process parameters A3B3C2D1E1F3G2H3J2. According to the RBF neural network, the prediction error of carbon content and temperature is only 0.0063 and 0.0159 respectively. The result proves that DOE is an effective method in optimizing process parameters, and worth promoting and applying in converter steelmaking process.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic
Sign in / Sign up

Export Citation Format

Share Document