Development of On-Line Tritium Monitor Based Upon Artificial Diamond for Fusion Applications

In this paper a novel on-line tritium monitor is presented. It is made with a single crystal diamond detector (SCD) covered with a thin layer of LiF 95% enriched in ⁶Li. Thermal neutrons impinging on the LiF layer produce α and T ions which are detected by the active diamond. The pulse height spectrum shows two separated peaks due to α and T ions respectively. By a proper calibration in a reference thermal flux the number of ⁶Li atoms and thus the absolute n+⁶Li→α+T reaction rate per unitary flux can be established. Once calibrated the detector can be used to measure the tritium production. Due to the many outstanding properties of diamond this detector could operate in the harsh working conditions of a fusion breeding blanket. A test of this detector was performed at the 14 MeV Frascati Neutron Generator (FNG). The detector was inserted inside a mock-up of the European Helium Cooled Lithium Lead (HCLL) Tritium Blanket Module (TBM), designed to validate the neutronic database for fusion application. The mock-up of the TBM was designed to perform a full set of experiments to validate tritium production code prediction comparing the experimental results with calculations. The measured tritium rates with the Li-Diamond detector are described in this paper. Comparison with calculations is in progress and will be reported in a future paper

Human Pose Estimation using Global and Local Normalization

In this paper, we address the problem of estimating the positions of human joints, i.e., articulated pose estimation. Recent state-of-the-art solutions model two key issues, joint detection and spatial configuration refinement, together using convolutional neural networks. Our work mainly focuses on spatial configuration refinement by reducing variations of human poses statistically, which is motivated by the observation that the scattered distribution of the relative locations of joints e.g., the left wrist is distributed nearly uniformly in a circular area around the left shoulder) makes the learning of convolutional spatial models hard. We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation. In addition, our empirical results show that incorporating multi-scale supervision and multi-scale fusion into the joint detection network is beneficial. Experiment results demonstrate that our method consistently outperforms state-of-the-art methods on the benchmarks.Comment: ICCV201