According to the correlation between product quality and equipment degradation state, an equipment maintenance policy is designed by integrating periodic equipment inspection and product quality control. In this policy, np-chart is used to monitor the abnormal shift of the product quality characteristic based on periodic inspection of the equipment. By considering the inspection result of the product quality shift and equipment degradation state, the corresponding maintenance action is chosen. Furthermore, the optimal maintenance model based on product quality control is proposed and is solved with genetic algorithm. The experimental results validated the feasibility of this model.
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.
Abstract-Theoretically, CLT (communicative language teaching) aims to develop language learners' communicative competence with "whole-task practice, motivation improving, natural learning and learning supporting". But the real application of CLT still has weaknesses. This paper makes a study of classroom discourse from sociocultural theory perspective, and focus on teachers' talk, which is an important way in mediating learners' cognitive process. Discourse of reading and language structure teaching are discussed, and teachers' evaluation remarks are also included. The study points out that in CLT, more attention should be paid to the cognitive aspect of the leaning process rather than simple forms of class activities and interactions.
Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20dB noise, and keep above 99% under lower level noise.
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