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Yanchun Liang
Researcher at Jilin University
Publications - 303
Citations - 7291
Yanchun Liang is an academic researcher from Jilin University. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 37, co-authored 261 publications receiving 6063 citations. Previous affiliations of Yanchun Liang include University of Trento & National University of Singapore.
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Journal ArticleDOI
Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning
Chen Yang,Chen Yang,Haishi Zhao,Lorenzo Bruzzone,Jon Atli Benediktsson,Yanchun Liang,Bin Liu,Xingguo Zeng,Renchu Guan,Chunlai Li,Ziyuan Ouyang,Ziyuan Ouyang +11 more
TL;DR: In this paper, the authors identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks, which results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters.
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Proper orthogonal decomposition and its applications—part i: theory
TL;DR: The equivalence of the matrices for processing, the objective functions, the optimal basis vectors, the mean-square errors, and the asymptotic connections of the three POD methods are demonstrated and proved when the methods are used to handle the POD of discrete random vectors.
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Particle swarm optimization-based algorithms for TSP and generalized TSP
TL;DR: A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented and it has been shown that the size of the solved problems could be increased by using the proposed algorithm.
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An improved GA and a novel PSO-GA-based hybrid algorithm
TL;DR: Inspired by the natural features of the variable size of the population, a variable population-size genetic algorithm (VPGA) is presented by introducing the ''dying probability'' for the individuals and the ''war/disease process'' forThe population.
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An ant colony optimization method for generalized TSP problem
TL;DR: Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.