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| 1 | In vivo analysis of oligodendrocyte lineage development in postnatal FGF2 null mice显示文摘 | Joshua C.Murtie Yong‐XingZhou Tuan Q.Le Regina C.Armstrong | 2005 | Glia2005，，4： | 1 |
| 2 | 光栅耦合外谐振腔调谐的半导体量子级连激光器显示文摘本文研究了由InGaAs/InAlAs材料组成,波长为4.6和5.1微米的量子级连中红外半导体激光器的光栅外耦合谐振腔的特性。在温度是80K时波长可调制宽度是激光中心波长的1.5%左右。对于这两个激光器而言,它们的波长可调制宽度随温度升高而减低。被调制的单模激光器的输出光功率是几个毫瓦,激光的谱线宽度是1到2个微米。激光阈值电流随波长缓慢变化,然而激光输出效率在短波长时更加优化。 | 彭川 Han Q.Le B.Ishaug J.Um James N.Baillargeon | 2003 | 光散射学报2003，15，3： | 0 |
| 3 | A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy显示文摘This paper proposes a new Deep Feed-forward Neural Network(DFNN)approach for damage detection in functionally graded carbon nanotube-reinforced composite(FG-CNTRC)plates.In the proposed approach,the DFNN model is developed based on a data set containing 20000 samples of damage scenarios,obtained via finite element(FE)simulation,of the FG-CNTRC plates.The elemental modal kinetic energy(MKE)values,calculated from natural frequencies and translational nodal displacements of the structures,are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output.The state-of-the art Exponential Linear Units(ELU)activation function and the Adamax algorithm are employed to train the DFNN model.Additionally,in order to enhance the performance of the DFNN model,the mini-batch and early-stopping techniques are applied to the training process.A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer.The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution(UD)and functionally graded-V distribution(FG-VD).Furthermore,the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated.Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data. | Huy Q.LE Tam T.TRUONG D.DINH-CONG T.NGUYEN-THOI | 2021 | Frontiers of Structural and Civil Engineering2021，15，6： | 0 |
| 4 | 利用51微米激光器测量中红外光波导损耗(英文)显示文摘一种新型的采用AlGaAs材料设计制成的光波导显示了其在中红外激光器方面的应用。波导部分包含在两个GaAs的包层之间,两个包层的掺杂材料限制光场在波导中传播并且降低损耗。三个不同长度的波导经过切入式测量得到它们的内部传播损耗为1 5dB/cm和耦合损耗为9dB。所采用的中红外激光器的波长是5 1μm,输出功率在45毫瓦以上。从光波导输出的光功率只有几个毫瓦。 | 彭川 Ryan E.Murphy Michael H.Lim Han Q.Le | 2003 | 光散射学报2003，15，2： | 0 |
| 5 | Closed-loop superconducting materials discovery显示文摘Discovery of novel materials is slow but necessary for societal progress.Here,we demonstrate a closed-loop machine learning(ML)approach to rapidly explore a large materials search space,accelerating the intentional discovery of superconducting compounds.By experimentally validating the results of the ML-generated superconductivity predictions and feeding those data back into the ML model to refine,we demonstrate that success rates for superconductor discovery can be more than doubled.Through four closed-loop cycles,we report discovery of a superconductor in the Zr-In-Ni system,re-discovery of five superconductors unknown in the training datasets,and identification of two additional phase diagrams of interest for new superconducting materials.Our work demonstrates the critical role experimental feedback provides in ML-driven discovery,and provides a blueprint for how to accelerate materials progress. | Elizabeth A.Pogue Alexander New Kyle McElroy Nam Q.Le Michael J.Pekala Ian McCue Eddie Gienger Janna Domenico Elizabeth Hedrick Tyrel M.McQueen Brandon Wilfong Christine D.Piatko Christopher R.Ratto Andrew Lennon Christine Chung Timothy Montalbano Gregory Bassen Christopher D.Stiles | 2023 | npj Computational Materials2023，，1： | 0 |
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