Motivation is one of the most important factors affecting students' performance of English learning, which is widely concerned by foreign language teachers and researchers for a long time. However, how to promote students' motivation in learning English by knowing their English learning motivation types at the initial stages and the factors that influence their sustaining motivation in the long process of English learning is still in need of exploration in the Chinese context. The paper aims to investigate Chinese non-English majors' motivation in English learning to facilitate teachers' understanding of ways to increase it.
Classification, as one of the main task of machine learning, corresponds to the core work of granular computing, namely granulation. Most of granular computing models and related classification methods are uniquely classifying by granule features, but not considering granule structure, especially in information area with widespread application of algebraic structure. In this paper, we propose a granular computing based classification method from algebraic granule structure. First of all, to pre-process the original data in the algebraic granule structure area, we formulate the algebraic structure based granularity with granule structure of an algebraic binary operator. Then, we propose a novel granular computing based classification method as well as related classifying algorithm with congruence partitioning granules and homomorphicly projecting granule structure. Finally, compared with tolerance neighborhood model and quotient space model, we prove that the proposed classification method is much more effective and robust while classifying the algebraic structure based granularity. The proposed granular computing based classification method provides an approach for classifying algebraic structure based granularity, and combines granular computing theory and classification theory of machine learning.
Abstract. In the lightning monitoring systems, positioning calculation is directly related to the results of the detection accuracy. In this paper, the concept of the particle swarm optimization (PSO) algorithm for lightning location estimation was brought in. The PSO overcome the disadvantages of iterative method, such as the difficulty in finding initial and going to diverge. The numerical simulation results show that: the algorithm can obtained lightning point steadily and accurately, and converge quickly. Therefore, the PSO algorithm on lightning location is feasible.
Abstract. Distributed generation (DG) has attracted extensive attention for the emergence of new energy, environmental protection requirements, and flexible and reliable. The addition of DG will have a great impact on the loss and voltage of distribution network .This paper changes the traditional way which makes the optimal power flow, voltage deviation minimum as target, using genetic algorithm, having large data theory to generate fault .When random failure occurred, with the islanding effect of DG, making the minimum economic loss caused by the lack of power supply as the objective function, to optimize the configuration of DG from the viewpoint of reliability, proven to reduce the economic losses significantly.
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