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- 저자 : Ya-Jun Leng (검색결과 2 건)
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Leng Ya-Jun,Huang Yuan-Hai 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4
Black-start decision making plays an important role in power system restoration after a large-area blackout. However, most studies focused on the black-start decision making problem with complete information. In this article, a novel black-start decision making method based on back-propagation neural network and genetic algorithm, called BPNN-GA, is proposed to deal with the black-start decision making problem with incomplete information. In the proposed method BPNN-GA, back-propagation neural network is used to predict the missing values in the black-start decision matrix, and a complete decision matrix is obtained. Then, the weights of indexes are assumed to be a group of variables, and two weighted normalized decision matrices are constructed. Finally, the genetic algorithm is used to determine the optimal solution of the index weights, and the best black-start scheme is selected. Based on the data of Guangdong power system of China, experiments were carried out to investigate the eff ectiveness of the proposed method. The results show that the proposed method even performs better than some black-start decision making methods considering complete information.
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APMDI-CF: An Effective and Efficient Recommendation Algorithm for Online Users
Ya-Jun Leng,Zhi Wang,Dan Peng,Huan Zhang 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.11
Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user’s unrated items, mean deviation method is used to impute values for neighbors’ missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.
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