scholarly journals College Oral English Teaching Reform Driven by Big Data and Deep Neural Network Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hui Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Colleges And Universities ◽  
Feature Representation ◽  
Model Parameters ◽  
Network Technology ◽  
Teaching Environment ◽  
English Teaching ◽  
Spoken English ◽  
Neural Network Technology

The ultimate goal of English teaching is to cultivate the students’ ability to communicate information in English, master good language learning methods, and become independent language learners and users. Therefore, successful English language teaching needs to be achieved through language communication training between teachers and students and between students. This article investigates the importance of promoting the reform of oral English teaching in China’s English teaching environment. We believe that to promote the reform of oral English teaching, an oral teaching environment must be available. However, the current common problem in oral English teaching in colleges and universities is that the spoken conversation objects are not standard enough, or there is no person who can talk to. Therefore, an intelligent spoken dialogue system based on big data and neural network technology is particularly important, and the quality of dialogue depends on accurate spoken speech evaluation. We first extracted six features of pronunciation quality, fluency, content richness, topic relevance, grammar, and vocabulary richness. Secondly, we propose an evaluation model that connects specific TDNN layers in a feedforward manner, using the feature representation of target words in different TDNN layers, which can obtain richer context information and greatly reduce the amount of model parameters. Finally, we conducted a simulation experiment. The experimental results show that the proposed model is accurate in evaluating spoken English and can effectively assist the reform of spoken English teaching in colleges and universities, and its performance is better than SVM by 9.2%.

scholarly journals Application of Flipped Classroom Model Driven by Big Data and Neural Network in Oral English Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yujun Zeng
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Flipped Classroom ◽  
Learning Ability ◽  
Network Technology ◽  
English Teaching ◽  
Model Driven ◽  
English Classes ◽  
Students Attitudes ◽  
Neural Network Technology

With the advancement of big data and neural network technology, flipped classroom informatization has shifted the traditional order of knowledge transfer and internalization, emphasizing students’ autonomous learning before class, knowledge absorption, and knowledge completion in class with the assistance of teachers. Students’ internalization and consolidation create the conditions for individualized learning. In foreign teaching, the benefits and feasibility of the flipped classroom have been demonstrated, and it is a promising new teaching model. Although recent research on oral English teaching in Chinese universities has yielded promising results, students’ classroom activity and participation remain low, learning initiative is lacking, and opportunities and time for oral training are insufficient. This article uses flipped classroom, big data, and neural network technology to teach college oral English classes, with the goal of determining whether the flipped classroom model can help students improve their oral English proficiency and self-learning ability, as well as exploring students’ attitudes toward the flipped classroom model. This paper first proposes a big data and deep neural network-based algorithm for detecting oral English pronunciation errors, which can be used for self-correction of students in the flipped classroom mode to improve the quality of oral English teaching. Finally, we also conducted simulation experiments, and the experimental results show that our algorithm is 4.12% better than SVM.

scholarly journals UNORGANIZED AIR RELEASES WITH RADIOACTIVE AEROSOLS FROM THE NEW SAFE CONFINIMENT OF CHNPP INTO THE ENVIRONMENT

2021 ◽  
pp. 181-186
Author(s):  
P.G. Krukovskyi ◽  
Ye.V. Diadiushko ◽  
D.J. Skliarenko ◽  
I.S. Starovit
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Technology ◽  
Building Structures ◽  
Flow Rates ◽  
Radioactive Aerosols ◽  
Computational Performance ◽  
Lack Of Information ◽  
Chernobyl Npp ◽  
Neural Network Technology

The New Safe Confinement (NSC) of the Chernobyl NPP, which isolates the destroyed reactor and the “Shelter Object” from the environment, is not airtight, so the problem is the lack of information on the flow of unorganized air with radioactive aerosols outside the NSC. This work presents computational model of the hydraulic state of the NSC, which allows to determine these flow rates through the leaks in the shells and building structures under the walls of the NSC. In addition to the developed model, the NSC hydraulic state model, created by neural network technology, was tested, which showed similar results and much higher computational performance, which allows its use for analysis and prediction of NSC`s hydraulic state in real time.

Prospects for the implementation of neural network technologies in radar information processing systems

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Rybakov ◽  
M.H. Aksyaitov
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Technology ◽  
Network Methods ◽  
External Conditions ◽  
Network Technologies ◽  
Radar Information ◽  
Neural Network Technologies ◽  
Neural Network Technology

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

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