scholarly journals Study on the Distribution Characteristic and Engineering Behavior of Moraine Layers in Northwest Yunnan

Author(s):  
Masashi WATANABE ◽  
Satoquo SEINO ◽  
Shogo TOKUNAGA ◽  
Kazuhiro FUJIWARA ◽  
Taro ARIKAWA

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.


Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 730
Author(s):  
Wen Yu ◽  
Xiaojin Wen ◽  
Wei Liu ◽  
Jiangan Chen

In this study, the carbothermic reduction and nitridation mechanism of vanadium-bearing titanomagnetite concentrate are investigated in terms of phase transformation, microstructure transformation, and thermodynamic analyses. The differences in the reaction behavior of titanomagnetite and ilmenite in vanadium-bearing titanomagnetite concentrate, as well as the distribution characteristic of V in the roasted products, are emphatically studied. It is observed that the reaction sequences of titanomagnetite and ilmenite transformations into nitride are as follows: Fe3−xTixO4→Fe2TiO4→FeTiO3→M3O5→(Ti, V)(N, C); FeTiO3→M3O5→Ti(N, C). The reduction of M3O5 to TiN is the rate-limiting step of the entire reaction, and metal iron is an important medium for transferring C for the reduction of M3O5. Titanomagnetite is faster to convert into nitride than ilmenite is, and the reasons for this are discussed in detail. During the entire roasting process, V mainly coexists with Ti and seems to facilitate the conversion of titanium oxides into (Ti, V)(N, C).


2007 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Ruth Sherman ◽  
Renee Mullen ◽  
Haomin Li ◽  
Zhendong Fang ◽  
Yi Wang

Author(s):  
Lingling Zhang ◽  
Yu Zhang ◽  
Shengji Pei ◽  
Yanfei Geng ◽  
Chen Wang ◽  
...  

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