Extension and parameterization of high-order density dependence in Skyrme forces

The three-body force is indispensable in nuclear energy density functionals which leads to a density dependent two-body term in the Hartree-Fock approach. Usually a single factional power of density dependency has been adopted. We consider the possibility of an additional higher-order density dependence in extended Skyrme forces. As a result, new extended Skyrme parametertizations based on the SLy4 force are obtained and the improvements in descriptions of global nuclei have been demonstrated. The higher-order term can also substantially affect nuclear properties in the high density region in general ways.Comment: 6 pages, 5 figure

Comparison of doubly-fed induction generator and brushless doubly-fed reluctance generator for wind energy applications

Phd ThesisThe Doubly-fed Induction Generator (DFIG) is the dominant technology for variable-speed wind power generation due in part to its cost-effective partially-rated power converter. However, the maintenance requirements and potential failure of brushes and slip rings is a significant disadvantage of DFIG. This has led to increased interest in brushless doubly-fed generators. In this thesis a Brushless Doubly-Fed Reluctance Generator (BDFRG) is compared with DFIG from a control performance point of view. To compare the performance of the two generators a flexible 7.5kW test facility has been constructed. Initially, a classical cascade vector controller is applied to both generators. This controller is based on the stator voltage field orientation method with an inner rotor (secondary stator) current control loop and an outer active and reactive power control loop. The dynamic and steady state performance of two generators are examined experimentally. The results confirm that the BDFRG has a slower dynamic response when compared to the DFIG due to the larger and variable inductance. Finally a sensorless Direct Power Control (DPC) scheme is applied to both the DFIG and BDFRG. The performance of this scheme is demonstrated with both simulation and experimental results.Engineering and Physical Sciences Research Council (EPSRC) and Overseas Researcher Scholarship (ORS

Continuous monitoring of volatile organic compound emissions using microtrap based injection technique and gas chromatography

A microtrap is made by packing a small diameter tubing with an adsorbent. The microtrap can be rapidly heated with a pulse of electrical current resulting in a sharp desorption that can act as an injection for GC separation. The microtrap can be used in several configurations to concentrate and inject sample in continuous, on-line monitoring system. In this research a laboratory scale catalytic incinerator was set up and volatile organic compounds in the incinerator effluents were monitored using the microtrap based injection systems. The detection systems used were gas chromatography and nonmethane organic carbon (NMOC) analyzer. Conventional sample valve, sequential valve microtrap and on-line microtrap in a backflush configuration were studied and compared as on-line injection devices. Figures of merits such as calibration curves, spike recovery and detection limits were studied. The conversion efficiencies of the catalytic incineration process at different operation conditions were also evaluated

Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been release