scholarly journals Research on Optimization Strategy of Regional Haze Governance Based on Synergy Effect——A Case of Central Plains City Group

2020 ◽  
Vol 740 ◽  
pp. 012176
Author(s):  
Zili Peng ◽  
Ganggang Zhang
Keyword(s):  
Optimization Strategy ◽  
Synergy Effect ◽  
Central Plains ◽  
Regional Haze ◽  
City Group

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Simulation Tool for Assignment Models: SIMASI

2014 ◽  
Vol 13 (8) ◽  
pp. 4723-4728
Author(s):  
Pratiksha Saxena ◽  
Smt. Anjali
Keyword(s):  
Optimization Procedure ◽  
Simulation Tool ◽  
Optimization Strategy ◽  
Performance Measurements ◽  
Integrated Simulation ◽  
Assignment Model ◽  
Classical Models ◽  
Simulation Results ◽  
User Friendly ◽  
Assignment Models

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples. 

Hybrid optimization strategy beyond local optima in aerospace panel designs

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 588-593
Author(s):  
K. L. Chan ◽  
David Kennedy ◽  
Fred W. Williams
Keyword(s):  
Hybrid Optimization ◽  
Optimization Strategy ◽  
Local Optima

Examining the Impact and Influencing Channels of Emission Trading Pilot Markets in China

2020 ◽  
Vol 11 (1) ◽  
pp. 136-136
Author(s):  
Qiong Wu ◽  
Kanittha Tambunlertchai ◽  
Pongsa Pornchaiwiseskul
Keyword(s):  
Total Factor Productivity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Emission Trading ◽  
Chinese Government ◽  
Synergy Effect ◽  
Factor Productivity ◽  
Plan Period ◽  
Carbon Emission Trading ◽  
The Impact

The global warming has become a serious issue in the world since the 1980s. The targets for the first commitment period of the Kyoto Protocol cover emissions of the six main greenhouse gasses (GHGs). China is the world's largest CO2 emitter and coal consumer and was responsible for 27.3 percent of the global total CO2 emission and 50.6 percent of the global total coal consumption in 2016 (BP, 2017). As China plays an important role in the global climate change, China has set goals to improve its environmental efficiency and performance. In 2011, the Chinese government for the first time announced an intent to establish carbon emission trading market in China. Eight regional emission trading schemes have been operating since 2013 (seven pilot markets during the 12th Five Year Plan period and one pilot market during the 13th Five Year Plan period) including provinces of Guangdong, Hubei, and Fujian, and cities of Beijing, Tianjin, Shanghai, Shenzhen, and Chongqing. The goal of these regional emission trading pilot markets is to help the government establish an efficient carbon emission trading scheme at national level. Some researchers have been focused on examining the impact of emission trading schemes in China using CGE model by constructing different scenarios and ex-ante analysis using data prior to emission trading pilot markets implementation. While this paper tries to conduct an ex-post analysis with data of 2005-2017 to evaluate the impact of emission trading pilot markets in China at provincial level using difference-in-difference (DID) model. By including both CO2 and SO2 as undesirable outputs to calculate Malmquist-Luenberger (ML) Index to measure green total factor productivity, this paper plans to evaluate the impact of carbon emission trading pilot markets in China via emission reduction, regional green development, synergy effect and influencing channels. This paper tries to answer the following research questions: (1) Do emission trading pilot markets reduce CO2 emission and increase regional green total factor productivity? (2) Is there any synergy effect from emission trading pilot markets? (3) What are the influencing channels of emission trading pilot markets? Keywords: Emission trading, CO2 emissions, Different-in-difference

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