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		题名	作者	年代	出处	被引量
	1	Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder显示文摘This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.	Jaejun Kim Changhyup Park Seongin Ahn Byeongcheol Kang Hyungsik Jung Ilsik Jang	2021	Petroleum Science2021，18，5：	1
	2	A self-contained wall climbing robot with closed link mechanism显示文摘	Hyoukryeol Choi Jaejun Park Taehun Kang	2004	KSME International Journal2004，，4：	1
	3	Development of a coordinate measuring machine (CMM) touch probe using a multiaxis force sensor显示文摘	PARK Jaejun KWON Kilhwan NAHMGYOO Cho	2006	Measurement Science and Technology2006，17，9：	1

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