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- 저자 : Tangermann, Michael (검색결과 5 건)
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Some Economic Effects of EC Agricultural Trade Preferences for Central Europe
Tangermann, Stefan 세종대학교 국제경제연구소 1993 Journal of Economic Integration Vol.8 No.2
Beginning in 1988, the EC began to grant trade preferences to countries in Central Europe, first by including these countries in its GSP, later by concluding Association Agreements. Agricultural products figure prominently in the preferential arrangements. The paper describes the nature of these preferences, discusses issues involved in analyzing their economic implications. And provides preliminary quantitative estimates of the benefits accruing to the countries in Central Europe.
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( Stefan Tangermann ) 세종대학교 경제통합연구소 1993 Journal of Economic Integration Vol.8 No.2
Beginning in 1988, the EC began to grant trade preferences to countries in Central Europe, first by including these countries in its GSP, later by concluding Association Agreements. Agricultural products figure prominently in the preferential arrangements. The paper describes the nature of these preferences, discusses issues involved in analyzing their economic implications, and provides preliminary quantitative estimates of the benefits accruing to the countries in Central Europe.
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Improving zero-training brain-computer interfaces by mixing model estimators
Verhoeven, T,Hü,bner, D,Tangermann, M,Mü,ller, K R,Dambre, J,Kindermans, P J IOP 2017 Journal of neural engineering Vol.14 No.3
<P> <I>Objective</I>. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. <I>Approach</I>. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP–BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method’s strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. <I>Main results</I>. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. <I>Significance</I>. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.</P>
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Muller-Putz, Gernot,Leeb, Robert,Tangermann, Michael,Hohne, Johannes,Kubler, Andrea,Cincotti, Febo,Mattia, Donatella,Rupp, Rudiger,Muller, Klaus-Robert,Del R Millan, Jose IEEE 2015 Proceedings of the Institute of Electrical and Ele Vol.103 No.6
<P>In their early days, brain-computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.</P>
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Hubner, David,Verhoeven, Thibault,Muller, Klaus-Robert,Kindermans, Pieter-Jan,Tangermann, Michael IEEE 2018 IEEE computational intelligence magazine Vol.13 No.2
<P>One of the fundamental challenges in brain-computer interfaces (BCIs) is to tune a brain signal decoder to reliably detect a user's intention. While information about the decoder can partially be transferred between subjects or sessions, optimal decoding performance can only be reached with novel data from the current session. Thus, it is preferable to learn from unlabeled data gained from the actual usage of the BCI application instead of conducting a calibration recording prior to BCI usage. We review such unsupervised machine learning methods for BCIs based on event-related potentials of the electroencephalogram. We present results of an online study with twelve healthy participants controlling a visual speller. Online performance is reported for three completely unsupervised learning methods: (1) learning from label proportions, (2) an expectation-maximization approach and (3) MIX, which combines the strengths of the two other methods. After a short ramp-up, we observed that the MIX method not only defeats its two unsupervised competitors but even performs on par with a state-of-the-art regularized linear discriminant analysis trained on the same number of data points and with full label access. With this online study, we deliver the best possible proof in BCI that an unsupervised decoding method can in practice render a supervised method unnecessary. This is possible despite skipping the calibration, without losing much performance and with the prospect of continuous improvement over a session. Thus, our findings pave the way for a transition from supervised to unsupervised learning methods in BCIs based on eventrelated potentials.</P>
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