期刊：
Journal of Circuits, Systems and Computers,2021年30(06):2150097:1-2150097:28 ISSN：0218-1266
作者机构：
[Zhang, Yuzuo; Zheng, Shijue] Cent China Normal Univ, Dept Comp Sci & Technol, Wuhan 430079, Peoples R China.;[Zhang, Xinyan; Li, Yuanhao] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China.
关键词：
Neural network models;model integration;NOx emissions;power station boiler
摘要：
In the coal-fired power generation system, it is necessary to predict the NOx emissions of power station boilers when it comes to the step to spray ammonia to ensure that NOx emissions do not exceed national standards. Using traditional machine learning algorithms in the modeling of power station boilers will require features selection and steady-state extraction, which is not suitable for practical applications. In order to reduce the NOx prediction error rate under variable operating conditions, a multi-model fusion algorithm S3LX combined with linear regression, XGBoost, and long-short-term memory recurrent neural network is proposed to model the NOx emission prediction of power station boilers. The preprocessing data scheme suitable for power station boiler data sets is proposed and implemented in this paper, which can perform numerical processing, data cleaning and data standardization for boiler's data and features. A 7-day historical operating data set of a unit in Guangzhou Shajiao C Power Plant was used as the training set and test set and was used to build the NOx emission prediction model after data preprocessing. Results show that compared with traditional machine learning algorithms, S3LX has good prediction ability under varying conditions with an average error of 4.28%. Compared with the average prediction error of the multi-layer perceptron 9.16%, SVM 7.37%, S3LX makes the error significantly reduced and satisfies the actual engineering demand.
期刊：
ICETT '20: Proceedings of the 2020 6th International Conference on Education and Training Technologies,2020年:Pages 1–4
作者机构：
[Feng Xiaoqi; Chen Xingnan; Guo Xiaoyu; Zheng Shijue] School of Computer Science, Central China Normal University, Wuhan Hubei, China
会议名称：
978-1-4503-8798-9
会议时间：
May, 2020
会议地点：
Macau China
会议论文集名称：
ICETT 2020: Proceedings of the 2020 6th International Conference on Education and Training Technologies
摘要：
The "AR" teaching innovation model for Chinese as a foreign language uses virtual space for teaching Chinese as a foreign language, provides learners with a new way and builds a virtual space for learners to explore independently. Based on the innovative teaching model, the article models the "AR" visual training process for teachers of Chinese as a foreign language. The model comprehensively represents the five parts of the training process. Aiming at the "AR" visual training process model, the article establishes the teaching effect evaluation model, which is analyzed from the subjective factors and the objective factors respectively. The results verify the effectiveness of the "AR" visual training process model and show that the training process model is helpful to the implementation of the "AR" teaching innovation model for Chinese as a foreign language.
摘要：
With the implementation of "One Belt One Road" strategy, cultural relics on Silk Road have been introduced to classes. Aiming at the disadvantages of traditional history education such as monotonous mode and insufficient intuitiveness, this paper combines Kinect sensor with cultural relics on Silk Road and explores a multilingual virtual teaching mode of cultural relics which supports somatosensory interaction. It changes the past teaching mode which is dull and abstract and injects new, economical and practical vitality into Silk Road culture, which can help with the inheritance and development of Silk Road culture in the new era.
摘要：
As a population-based intelligence algorithm, fireworks algorithm simulates the fireworks' explosion process to solve optimisation problem. A comprehensive study on enhanced fireworks algorithm (EFWA) reveals that the explosion operator generates too much sparks for the best firework limits the exploration ability. A hybrid version of EFWA (HFWA_DE) is proposed by adding the differential evolution (DE) operator. In HFWA_DE, the population is divided into two subpopulations, then each subpopulation evolves with FWA operator and DE operator separately and exchanges the elitist individual. Experiments on 20 well-known benchmark functions are conducted to illustrate the performance of HFWA_DE. The results turn out HFWA_DE outperforms some state-of-the-art FWAs on most testing functions.
