A Superconducting Half-Wave Rectifier: Principle and Experiment

International audienceThe superconducting rectifier is a low-power consumption, high-current rectifying device with significant potential for application in AC-DC conversion systems. In this study, we have clarified its operating principle from a circuit perspective. It is demonstrated that the rectification function in a superconducting rectifier is attributed to its time-varying resistance, which is controlled by external sources. An AC field-controlled superconducting half-wave rectifier was developed. By constructing an experimental platform, we measured the rectified voltage and current of the rectifier under various operating conditions. The rectifier successfully converted an AC current into a 34 A DC current, powering a superconducting coil load

Numerical study on ac loss in superconducting bulk caused by various magnetic wave conditions

This paper presents an investigation on ac losses of superconducting bulk caused by various magnetic wave conditions with the help of finite element software of Comsol Multiphysics. Three different magnetic wave conditions are analyzed, which are standing magnetic field, rotated magnetic field and traveling magnetic field. The calculation ac loss result was compared with that from analytical formulas for type-II superconducting slab and cylinder. The influences of the strength of external magnetic field on the ac losses were investigated. Effects of a relatively stable trapped-field of superconducting bulk on ac losses in time-varying magnetic field are clarified. Effect of spatially inhomogeneous distribution of the external magnetic field on the ac loss is studied. </jats:p

SBLC: a hybrid model for disease named entity recognition based on semantic bidirectional LSTMs and conditional random fields

Abstract Background Disease named entity recognition (NER) is a fundamental step in information processing of medical texts. However, disease NER involves complex issues such as descriptive modifiers in actual practice. The accurate identification of disease NER is a still an open and essential research problem in medical information extraction and text mining tasks. Methods A hybrid model named Semantics Bidirectional LSTM and CRF (SBLC) for disease named entity recognition task is proposed. The model leverages word embeddings, Bidirectional Long Short Term Memory networks and Conditional Random Fields. A publically available NCBI disease dataset is applied to evaluate the model through comparing with nine state-of-the-art baseline methods including cTAKES, MetaMap, DNorm, C-Bi-LSTM-CRF, TaggerOne and DNER. Results The results show that the SBLC model achieves an F1 score of 0.862 and outperforms the other methods. In addition, the model does not rely on external domain dictionaries, thus it can be more conveniently applied in many aspects of medical text processing. Conclusions According to performance comparison, the proposed SBLC model achieved the best performance, demonstrating its effectiveness in disease named entity recognition