The Experts below are selected from a list of 48612 Experts worldwide ranked by ideXlab platform
M A Irzak - One of the best experts on this subject based on the ideXlab platform.
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globus m plasma physics research for Fusion Application and compact neutron source development
Plasma Physics and Controlled Fusion, 2016Co-Authors: V K Gusev, N N Bakharev, Ya B Ber, V V Bulanin, F V Chernyshev, V V Dyachenko, P R Goncharov, E Z Gusakov, A D Iblyaminova, M A IrzakAbstract:During the past decade, plasma physics research promoting the physics base of ITER and developing novel concepts such as a compact Fusion neutron source has been conducted on the Globus-M spherical tokamak (ST) (R = 36 cm, a = 24 cm, I p ≤ 250 kA, B T ≤ 0.4 T). Tokamak reconstruction is imminent. The upgraded tokamak Globus-M2 will have the same vacuum chamber and an enhanced magnetic system to provide B T = 1 T and I p = 500 kA. In this paper we outline the most important research directions and the main results obtained on Globus-M and make some predictions about the possibilities and parameters of Globus-M2.
Andreas Keil - One of the best experts on this subject based on the ideXlab platform.
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Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials
Frontiers Media S.A., 2019Co-Authors: Badong Chen, Nathan M. Petro, Zejian Yuan, Nanning Zheng, Andreas KeilAbstract:Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics Application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI Fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus
Keil Andreas - One of the best experts on this subject based on the ideXlab platform.
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Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials
DigitalCommons@University of Nebraska - Lincoln, 2019Co-Authors: Ji Hong, Chen Badong, Petro, Nathan M., Yuan Zejian, Zheng Nanning, Keil AndreasAbstract:Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics Application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI Fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus
T Nagasaka - One of the best experts on this subject based on the ideXlab platform.
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The development of advanced vanadium alloys for Fusion Applications
Journal of Nuclear Materials, 2004Co-Authors: J.m. Chen, T. Muroga, T Nagasaka, S.y. Qiu, W.g Huang, Y. ChenAbstract:Abstract As an alternative choice for Fusion Application, V alloyed with 6–8 wt%W has been studied. The results showed that W could strengthen the alloy at room temperature, more effectively than Ti or Cr. With both additions of Ti and W, the recovery and recrystallization of the alloys behaved similarly to V–4Cr–4Ti, but the behavior was strongly influenced by the concentration of the interstitial impurities in solid solution. Similar behavior to V–4Cr–4Ti was also found for V–6W–1Ti on hydrogen embrittlement. V–6W–1Ti seemed to have better properties for hydrogen embrittlement due to its low hydrogen absorption rate. Significant differences in oxidation behavior in air were not observed for the alloys investigated.
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Reduction of impurity levels of vanadium and its alloys for Fusion Application
International Journal of Refractory Metals & Hard Materials, 2000Co-Authors: T. Muroga, T NagasakaAbstract:Abstract Reduction of interstitial impurity (C, N and O) levels is essential for Application of vanadium alloys to low activation structural materials of Fusion reactors. This paper summarizes the effort of reducing the impurity levels of vanadium and vanadium alloys at the National Institute for Fusion Science (NIFS) Japan, program of fabricating high purity V–4Cr–4Ti alloy ingots.
Florence Pipelier - One of the best experts on this subject based on the ideXlab platform.
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Information Fusion : Application to data and model Fusion for ultrasound image segmentation
IEEE Transactions on Biomedical Engineering, 1999Co-Authors: Basel Solaiman, Renaud Debon, Florence PipelierAbstract:Nowadays, information Fusion constitutes a challenging research topic. The authors' study proposes to achieve the Fusion of several knowledge sources. This, in order to detect the esophagus inner wall from ultrasound medical images. After a brief description of information Fusion concepts, the authors propose a system architecture including both model and data Fusion. The data Fusion is accomplished using fuzzy modeling, which can be seen as a monosensor/multiple sources data Fusion system. The model Fusion is performed using a full-adapted snake theory, which projects the fuzzy decision into the binary decision space.
