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9篇 您的检索式：作者名="Kundo"

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		题名	作者	年代	出处	被引量
	1	A new method to solve general- ized multicriteria optimization problems using a simple genetic algorithm显示文摘	OSYCZKA A KUNDO S	1995	Structural and Multidisciplinary Optimization1995，10，2：	1
	2	13C NMR spectra of pentacyclic triterpenoids - A compilation of some salient features 显示文摘	Mahato SB Kundo AP	1994	Phytochemistry1994，37，：	1
	3	Zizy- beranalic acid, a pentacyclic triterpenoid of Zizyphus juju- ba显示文摘	KUNDO A B BARIK B R MONDAL D N	1989	Phytochemistry1989，28，11：	1
	4	A new challcoge of robot for harvesting strawberry grown on table top culture显示文摘	Kundo N Ninorniya K Hayashi S	1996	Transactions of the ASAE1996，39，6：	1
	5	13C NMR Spectra of pentacyclic triterpe-noids-A compilation and some salient features显示文摘	MAHATO S M KUNDO A P	1994	Phtochemistry1994，37，：	1
	6	^13C NMR spectra of pentacyclic triterpenoids--A compilation and some salient features显示文摘	MAHATO S B KUNDO A P	1994	Phytochemistry1994，37，6：	1
	7	Deep learning framework for material design space exploration using active transfer learning and data augmentation显示文摘Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space.However,the applicability of conventional generative models is limited because they cannot access data outside the range of training sets.Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain.In this study,we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set.This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations.Results show that our proposed framework can provide excellent designs close to the global optima,even with the addition of a very small dataset corresponding to less than 0.5%of the initial training dataset size.	Yongtae Kim Youngsoo Kim Charles Yang Kundo Park Grace X.Gu Seunghwa Ryu	2021	npj Computational Materials2021，，1：	1
	8	Distributed entanglement显示文摘	Coffman V Kundo J Wootters W K		0，，5：	1
	9	Production of the carotenoid lycopene,βcarotene,and astaxanthin in the food yeast Candida utilis显示文摘	Kundo K Snito T	1998	Appl Environ Microbiol1998，64，4：	1

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