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16篇 您的检索式：作者名="Seoho"

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		题名	作者	年代	出处	被引量
	1	Antecedents of revisit intention显示文摘	Seoho Urn Kaye Chon YoungHee Ro	2006	Annals of Tourism Research2006，33，4：	1
	2	Antecedents of revisit intention显示文摘	Seoho Um	2006	Annals of Tourism Research2006，，4：	1
	3	Antecedents of Revisit Intention显示文摘	Seoho U Kaye C	2006	Annals of Tourism Research2006，，4：	1
	4	Loss minimizing control of PMSM with the use of polynomial approximations显示文摘	JUNGGI L KWANGHEE N SEOHO C	2009	IEEE Transactions on Power Electronics2009，24，4：	1
	5	Loss minimizing control of PMSM with the use of polynomial approximations显示文摘	Junggi Lee Kwanghee Nam Seoho Choi	2009	IEEE Transactions on Power Electronics2009，24，4：	1
	6	Loss Minimizing Control of PSMS with the Use of Polynomial Approximations 显示文摘	Junggi L Kwanghee N Seoho C	2009	IEEE Transations on Power Electronics2009，24，4：	1
	7	Loss Minimizing Control of PMSM with the Use of Polynomial Approximations 显示文摘	Junggi L Kwanghee N Seoho C	2009	IEEE Transations on Power Electronics2009，24，4：	1
	8	Antecedents of revisit intention显示文摘	Seoho Um Kaye Chon Young Hee Ro	2006	Annals of Tourism Research2006，33，4：	1
	9	Antecedents Of revisit intention 显示文摘	Seoho Um Kaye Chon Young Hee Ro	2006	Annals of Tourism Research2006，33，4：	1
	10	Antecedents of revisit intention显示文摘	Seoho Um Kaye Chon YoungHee Ro	2006	Annals of Tourism Research2006，33，：	1
	11	Loss- minimizing control of PMSM with the use of polynomial approximations 显示文摘	JUNGGI L KWANGHEE N SEOHO C	2009	IEEE Transactions on Power Electronics2009，24，4：	1
	12	Antecedents of Revisit Intention显示文摘	Um Seoho Kaye Chon YoungHee Ro	2006	Annals of Tourism Research2006，33，4：	1
	13	Antecedents of Revisit In- tention 显示文摘	Seoho U Kaye C Young H R	2006	Annals of Tourism Research2006，33，4：	1
	14	Antecedents of revisit intention 显示文摘	Seoho Um Kaye Chon Young Hee Ro (2006)	2006	Annals of Tourism Research2006，33，4：	1
	15	Loss- minimizing control of PMSM with the use of polynomial approximations 显示文摘	JUNGGI L KWANGHEE N SEOHO C	2009	IEEE Transactions on Power Electronics2009，24，4：	1
	16	High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning显示文摘The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task.A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-scale fabrication processes is essential for quality control and,ultimately,their industrial adoption.In this work,we develop a high-throughput approach to rapidly identify suspended carbon nanotubes(CNTs)by using high-speed Raman imaging and deep learning analysis.Even for Raman spectra with extremely low signal-to-noise ratios(SNRs)of 0.9,we achieve a classification accuracy that exceeds 90%,while it reaches 98%for an SNR of 2.2.By applying a threshold on the output of the softmax layer of an optimized convolutional neural network(CNN),we further increase the accuracy of the classification.Moreover,we propose an optimized Raman scanning strategy to minimize the acquisition time while simultaneously identifying the position,amount,and metallicity of CNTs on each sample.Our approach can readily be extended to other types of nanomaterials and has the potential to be integrated into a production line to monitor the quality and properties of nanomaterials during fabrication.	Jian Zhang Mickael L.Perrin Luis Barba Jan Overbeck Seoho Jung Brock Grassy Aryan Agal Rico Muff Rolf Brönnimann Miroslav Haluska Cosmin Roman Christofer Hierold Martin Jaggi Michel Calame	2022	Microsystems & Nanoengineering2022，8，1：	1

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