The mining method optimization in subsea deep gold mines was studied. First, an index system for subsea mining method selection was established based on technical feasibility, security status, economic benefit, and ma...The mining method optimization in subsea deep gold mines was studied. First, an index system for subsea mining method selection was established based on technical feasibility, security status, economic benefit, and management complexity. Next, an evaluation matrix containing crisp numbers and triangular fuzzy numbers(TFNs) was constructed to describe quantitative and qualitative information simultaneously. Then, a hybrid model combining fuzzy theory and the Tomada de Decis?o Interativa Multicritério(TODIM) method was proposed. Finally, the feasibility of the proposed approach was validated by an illustrative example of selecting the optimal mining method in the Sanshandao Gold Mine(China). The robustness of this approach was demonstrated through a sensitivity analysis. The results show that the proposed hybrid TODIM method is reliable and stable for choosing the optimal mining method in subsea deep gold mines and provides references for mining method optimization in other similar undersea mines.展开更多
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been success...Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.展开更多
This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s ...This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.展开更多
基金Project(2018dcyj052) supported by Survey Research Funds of Central South University,ChinaProject(51774321) supported by the National Natural Science Foundation of ChinaProject(2018YFC0604606) supported by the National Key Research and Development Program of China
文摘The mining method optimization in subsea deep gold mines was studied. First, an index system for subsea mining method selection was established based on technical feasibility, security status, economic benefit, and management complexity. Next, an evaluation matrix containing crisp numbers and triangular fuzzy numbers(TFNs) was constructed to describe quantitative and qualitative information simultaneously. Then, a hybrid model combining fuzzy theory and the Tomada de Decis?o Interativa Multicritério(TODIM) method was proposed. Finally, the feasibility of the proposed approach was validated by an illustrative example of selecting the optimal mining method in the Sanshandao Gold Mine(China). The robustness of this approach was demonstrated through a sensitivity analysis. The results show that the proposed hybrid TODIM method is reliable and stable for choosing the optimal mining method in subsea deep gold mines and provides references for mining method optimization in other similar undersea mines.
文摘Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
文摘This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.
