Research of Mobile Terminal QOS Analysis Model Based on Time and Location

2012 ◽  
Vol 263-266 ◽  
pp. 1008-1011
Author(s):  
Xiao Ming Bai ◽  
Lin Lai
Keyword(s):  
Mobile Terminal ◽  
The Other ◽  
Quality Analysis ◽  
Signal Quality ◽  
Analysis Model ◽  
Communication Industry ◽  
Related Data ◽  
Model Based ◽  
Network Quality

With the communication industry becoming more and more competitive, the communication operators is paying more and more attentions on the network quality. The paper proposed the terminal signal quality analysis model based on the time and location which uses the technology of the terminal routing tracking and the signal data analysising.The model starting with the time and location,combines with the signal data of the station and the terminal,associates to related data of the other systems,come to the signal qality of the terminal during the move.

scholarly journals Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

2018 ◽  
Vol 159 ◽  
pp. 01061
Author(s):  
Endra Oey ◽  
Dadang Gunawan ◽  
Dodi Sudiana
Keyword(s):  
Compressive Sensing ◽  
Projection Matrix ◽  
Signal Quality ◽  
Tight Frames ◽  
Analysis Model ◽  
Mutual Coherence ◽  
Model Based ◽  
Equiangular Tight Frames ◽  
Simulation Results ◽  
Matrix Design

Co-sparse analysis model based-compressive sensing (CAMBCS) has gained attention in recent years as alternative to conventional sparse synthesis model based (SSMB)-CS. The equivalent operator as counterpart of the equivalent dictionary in the SSMB-CS is introduced in the CAMB-CS as the product of projection matrix and transpose of the analysis dictionary. This paper proposes an algorithm for designing suitable projection matrix for CAMB-CS by minimizing the mutual coherence of the equivalent operator based on equiangular tight frames design. The simulation results show that the CAMB-CS with the proposed projection matrix outperforms the SSMB-CS in terms of the signal quality reconstruction.

scholarly journals The research of regression model in machine learning field

2018 ◽  
Vol 176 ◽  
pp. 01033 ◽  
Author(s):  
Shen Rong ◽  
Zhang Bao-wen
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Reference Value ◽  
The Other ◽  
Analysis Model ◽  
Model Based ◽  
Set Up ◽  
Library Function

The paper herein will analyze the sale of iced products affected by variation of temperature. Firstly, we will collect the data of the forecast temperature last year and the sale of iced products and then conduct data compilation and cleansing. Finally, we will set up the mathematical regression analysis model based on the cleansed data by means of data mining theory. Regression analysis refers to the method of studying the relationship between independent variable and dependent variable. Linear regression model that corresponds to the practical situation is proposed in the paper, which is to set up simple linear regression model based on practical problem and then to implement the following with the help of the latest and most popular Python3.6. Python3.6 boasts the features of pure object-oriented, platform independence and concise and elegant language. So we will call the corresponding library function to predict the sale of iced products according to the variation of temperature, which will provide the foundation for the company to adjust its production each month, or even each week and each day. As a result, the situation of overproduction can be avoided. Moreover, the other situation as the profit will be affected by the lack of production since the rise of temperature will also be avoided. So the regression model also has reference value for the other fields of marketing.

Regional effect of monetary policy based on GVAR model and machine learning

2020 ◽  
pp. 1-13
Author(s):  
Zengming Zhao ◽  
Wenting Chen
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Monetary Policy ◽  
Stress Test ◽  
Analysis Model ◽  
Conduction Model ◽  
Regional Effect ◽  
Macroeconomic Factors ◽  
Model Based ◽  
Gray Correlation

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

Research on SUE DTA Model with Constant Demand Based on Probit Model

2011 ◽  
Vol 374-377 ◽  
pp. 2605-2609
Author(s):  
Lei Shi ◽  
Li Gao
Keyword(s):  
Logit Model ◽  
Existence And Uniqueness ◽  
Probit Model ◽  
The Other ◽  
A Algorithm ◽  
Model Based

Logit model is among the most important model in SUE DTA study. A lot of work have been done based on Logit model. As the other very important SUE DTA model, Probit model has not been the focus of many researcher. This paper presents a SUE model based on Probit model, which aims at building up the Probit model with constant demand. The existence and uniqueness of the model is presented, Finally, a algorithm is given.

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