Author
Gong Li
Other affiliations: North Dakota State University
Bio: Gong Li is an academic researcher from Yanshan University. The author has contributed to research in topics: Amorphous metal & Alloy. The author has an hindex of 15, co-authored 62 publications receiving 1968 citations. Previous affiliations of Gong Li include North Dakota State University.
Papers published on a yearly basis
Papers
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TL;DR: A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.
636 citations
TL;DR: For the first time, a systematic study on fine tuning of LS-SVM model parameters for one-step ahead wind speed forecasting is presented and it is found that they can outperform the persistence model in the majority of cases.
338 citations
TL;DR: A robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network is presented.
196 citations
TL;DR: In this paper, the authors present a comprehensive literature analysis on the state-of-the-art research of bidding strategy modeling methods, including game theory, mathematical programming, game theory and agent-based models.
195 citations
TL;DR: In this paper, a comprehensive evaluation on probability density functions for the wind speed data from five representative sites in North Dakota is presented, including gamma, lognormal, inverse Gaussian, and maximum entropy principle (MEP) derived probability density function (PDFs).
157 citations
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.
29,323 citations
Journal Article•
28,685 citations
TL;DR: In this paper, first principles calculations were performed to investigate the structural, elastic, and electronic properties of IrN2 for various space groups: cubic Fm-3m and Pa-3, hexagonal P3(2)21, tetragonal P4(2)/mnm, orthorhombic Pmmn, Pnnm, and Pnn2, and monoclinic P2(1)/c.
Abstract: First principles calculations were performed to investigate the structural, elastic, and electronic properties of IrN2 for various space groups: cubic Fm-3m and Pa-3, hexagonal P3(2)21, tetragonal P4(2)/mnm, orthorhombic Pmmn, Pnnm, and Pnn2, and monoclinic P2(1)/c. Our calculation indicates that the P2(1)/c phase with arsenopyrite-type structure is energetically more stable than the other phases. It is semiconducting (the remaining phases are metallic) and contains diatomic N-N with the bond distance of 1.414 A. These characters are consistent with the experimental facts that IrN2 is in lower symmetry and nonmetallic. Our conclusion is also in agreement with the recent theoretical studies that the most stable phase of IrN2 is monoclinic P2(1)/c. The calculated bulk modulus of 373 GPa is also the highest among the considered space groups. It matches the recent theoretical values of 357 GPa within 4.3% and of 402 GPa within 7.8%, but smaller than the experimental value of 428 GPa by 14.7%. Chemical bonding and potential displacive phase transitions are discussed for IrN2. For IrN3, cubic skutterudite structure (Im-3) was assumed.
1,646 citations
TL;DR: In this paper, the most promising and appealing properties of graphene are summarized from an exponentially growing literature, with a particular attention to matching production methods to characteristics and to applications, including the high carrier mobility value in suspended and annealed samples for electronic devices, the thickness-dependent optical transparency and, in the mechanical section, the high robustness and full integration of graphene in sensing device applications, emphasizing on the high potential of graphene not only as a post-silicon materials for CMOS device application but more ambitiously as a platform for post-CMOS molecular architecture in
1,467 citations
TL;DR: The state-of-the-art of topological design and manufacturing processes of various types of porous metals, in particular for titanium alloys, biodegradable metals and shape memory alloys are reviewed.
1,393 citations