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186 results on '"Wang, Qianming"'

1. Neutron Activation Background in the NvDEx Experiment

Author

Wang, Qianming, Huang, Zeyu, Hu, Pengchong, and Ciuffoli, Emilio

Subjects
Physics - Instrumentation and Detectors
Abstract

An extremely low-background environment is a crucial requirement for any neutrinoless double beta decay experiment. Neutrons are very difficult to stop, because they can pass through the shields and activate nuclei in the detector, even inside the fiducial volume itself. Using Geant4 simulations we have studied the neutron background for N$\nu$DEx-100 and the most efficient way to reduce it. Using a 60 cm thick external HDPE shield the neutron background can be reduced down to $0.24\pm 0.06$ events/year, lower than the background rate due to natural radioactivity (0.42 events/year), which was used as a benchmark for these calculations. The amount of shielding material needed can be significantly reduced by placing HDPE in the empty space between the lead shield and the steel vessel; in this way, it is sufficient to add 20 cm external HDPE shield to reduce the neutron background down to $0.15\pm0.05$ events/year., Comment: 13 pages, 11 figures

Published
2023

9. Fast and Sensitive Determination of Iodide Based on Ternary Chalcogenides Nanoparticles.

Author

Wang, Zhitai, Wu, Nengtao, Wang, Weihao, Hu, Yaozheng, Luo, Zhijie, Zheng, Yuhui, and Wang, Qianming

Subjects
FLUORESCENCE quenching, ENVIRONMENTAL indicators, FLUORESCENT probes, MAGNETIC fields, MAGNETIC properties, QUANTUM dots
Abstract

A fluorescent probe based on ternary AgFeS2 quantum dots has been prepared for the design of ternary chalcogenides. The nanoparticles are synthesized with oleylamine as a stabilizer at a low temperature (particle size in the range of 2 to 3 nm) and they exhibit an intense blue emission in aqueous media. As for their internal structure, each nanoparticle's relative stoichiometric ratio (AgFe1.01S1.91) is very close to the theoretical value of 1:1:2. Their magnetic properties have been studied with a vibrating sample magnetometer and they have ferromagnetism between 4 K and 298 K (applied magnetic field ranging between −10,000 and 10,000 Oe). In the presence of iodide ions, the emission at 458 nm derived from AgFeS2 QDs has been observed to give rise to fluorescence quenching. The detection system is based on a static quenching process and morphological change between iodide ions and AgFeS2, which has a good linear range from 0 to 37.5 μmol/L, with a limit of detection of 0.99 μM. The nanoprobe responds within 30 s for the efficient detection of iodide. Such functional quantum dots will provide a powerful indicator in environmental and bio-sensing applications. [ABSTRACT FROM AUTHOR]

Published
2024
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30. PB2307: PHARMACOKINETICS, EFFICACY AND SAFETY OF SUBCUTANEOUS VERSUS INTRAVENOUS RITUXIMAB COMBINED WITH CHOP IN CHINESE PATIENTS WITH UNTREATED CD20-POSITIVE DIFFUSE LARGE B CELL LYMPHOMA: A RANDOMIZED TRIAL

Author

Gao, Yan, primary, Zhang, Liling, additional, Gao, Sujun, additional, Yang, Yu, additional, Zhang, Qing-Yuan, additional, Zhang, Huilai, additional, He, Pengcheng, additional, LI, Fei, additional, Jing, Hongmei, additional, Grange, Susan, additional, Bu, Lilian, additional, Wang, Qianming, additional, Wang, Yanjie, additional, and Huang, Huiqiang, additional

Published
2023
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35. A Few-Shot Learning-Based Semi-Supervised Method to Enhance NLOS Recognition Accuracy Across Multiple Locations by Cross-Spatiotemporal Information

Author

Zeng, Kungan, Li, Zhenni, Xie, Shengli, Wang, Qianming, Chen, Wuhui, and Zheng, Zibin

Abstract

Machine learning (ML)-based Global Navigation Satellite System (GNSS) signal recognition has been considered an efficient way to mitigate multipath error in positioning estimation. However, because of the spatial heterogeneity of nonline-of-sight (NLOS) interference, existing NLOS signal recognition methods struggle to achieve satisfactory accuracy across multiple locations. In particular, current methods achieve limited recognition accuracy on target domains containing unfamiliar environments. This study proposes a few-shot learning-based semi-supervised GNSS NLOS recognition method using cross-spatiotemporal information, which can achieve high-precision recognition for multiple target domains containing unfamiliar environments only requiring a limited number of labeled samples. First, we propose a self-attention mechanism-based cross-spatiotemporal information (SAM-CSI) module that captures the environmental characteristics used to model the spatial heterogeneity of NLOS interference. Next, to avoid the need for numerous labeled samples in training the model, we introduce a Siamese neural network-based few-shot learning (SNN-FSL) architecture to perform NLOS recognition using a similarity metric instead of label prediction. We then develop a semi-supervised NLOS recognition method by integrating the SAM-CSI module with the SNN-FSL architecture to enable high-precision recognition in target domains containing unfamiliar environments. Finally, we conducted comparison and few-shot learning experiments using real-world datasets, which demonstrated that our proposed method could achieve over 90% recognition accuracy in target domains containing unfamiliar environments after 50-shot learning. It represented an improvement of over 19% compared with existing methods.

Published
2024
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36. MCI-GLA Plug-In Suitable for YOLO Series Models for Transmission Line Insulator Defect Detection

Author

Wang, Yaru, Song, Xiaoke, Feng, Lilong, Zhai, Yongjie, Zhao, Zhenbing, Zhang, Shiyin, and Wang, Qianming

Abstract

In the detection of insulator defects on transmission lines, the detection precision is still not ideal, primarily attributed to the significant variation in target scale and complex image backgrounds. We propose the multiscale channel information (MCI)-global-local attention (GLA), a plug-in designed for YOLO series models, featuring two modules: the MCI extraction module and the GLA based on context information module (GLA-CI). MCI comprehensively extracts and utilizes multiscale feature map information, while GLA-CI captures both global context information and local spatial details, thereby augmenting the learning capability of networks. Experimental results indicate that the MCI-GLA plug-in improves the average precision (AP) of YOLOv4 to YOLOv8 models in detecting insulator breakage defects by 7.3%, 4.6%, 4.5%, 4.0%, and 5.3%, respectively. In particular, YOLOv7+MCI-GLA exhibits superior precision and inference time compared to other methods on self-constructed and public datasets. The code for this article can be found at https://github.com/falian0527/MCI-GLA.

Published
2024
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