分类： 核科学技术 >> 裂变堆工程技术 提交时间： 2023-06-01
摘要： Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving data, providing valuable information for the reactor model and data inconsistent problems. We propose a machine learning method by building a convolutional neural network based on a virtual experiment with a typical short-baseline reactor antineutrino experiment configuration: by utilizing the reactor evolution information, the major fissile isotope spectra are correctly extracted, and the uncertainties are evaluated using the Monte Carlo method. Validation tests show that the method is unbiased and introduces tiny extra uncertainties.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-27 合作期刊: 《数据智能（英文）》
摘要： With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a users blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.
分类： 物理学 >> 核物理学 提交时间： 2023-10-15
摘要： In -ray imaging, localization of the -ray interaction in the scintillator is critical. Convolutional neural network (CNN) techniques are highly promising for improving -ray localization. Our study evaluated the generalization capabilities of a CNN localization model with respect to the -ray energy and thickness of the crystal. The model maintained a high positional linearity (PL) and spatial resolution (SR) for ray energies between 591460 keV. The PL at the incident surface of the detector was 0.99, and the resolution of the central incident point source ranged between 0.521.19 mm. In modified uniform redundant array (MURA) imaging systems using a thick crystal, the CNN -ray localization model significantly improved the useful field-of-view (UFOV) from 60.32% to 93.44% compared to the classical centroid localization methods. Additionally, the signal-to-noise ratio (SNR) of the reconstructed images increased from 0.95 to 5.63.
分类： 地球科学 >> 地理学 提交时间： 2025-03-18 合作期刊: 《干旱区科学》
摘要： The evolution of land use patterns and the emergence of urban heat islands (UHI) over time are critical issues in city development strategies. This study aims to establish a model that maps the correlation between changes in land use and land surface temperature (LST) in the Mashhad City, northeastern Iran. Employing the Google Earth Engine (GEE) platform, we calculated the LST and extracted land use maps from 1985 to 2020. The convolutional neural network (CNN) approach was utilized to deeply explore the relationship between the LST and land use. The obtained results were compared with the standard machine learning (ML) methods such as support vector machine (SVM), random forest (RF), and linear regression. The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories. This variation in temperature increases across different land use types suggested that, in addition to global warming and climatic changes, temperature rise was strongly influenced by land use changes. The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C, while forest lands experienced the smallest increase of 1.19°C. The developed CNN demonstrated an overall prediction accuracy of 91.60%, significantly outperforming linear regression and standard ML methods, due to the ability to extract higher level features. Furthermore, the deep neural network (DNN) modeling indicated that the urban lands, comprising 69.57% and 71.34% of the studied area, were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030, respectively. In conclusion, the LST predictioin framework, combining the GEE platform and CNN method, provided an effective approach to inform urban planning and to mitigate the impacts of UHI.
分类： 交通运输工程 >> 水路运输 提交时间： 2024-03-28
摘要： Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness.
分类： 生物学 >> 植物学 提交时间： 2022-12-12 合作期刊: 《干旱区科学》
摘要：In recent years, deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact. Traditional plant taxonomic identification requires high expertise, which is time-consuming. Most nature reserves have problems such as incomplete species surveys, inaccurate taxonomic identification, and untimely updating of status data. Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model. Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects, this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang, such as species investigation and monitoring, by using deep learning. Since desert plant species were not included in the public dataset, the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China (PPBC). After the sorting process and statistical analysis, a total of 2331 plant images were finally collected (2071 images from field collection and 260 images from the PPBC), including 24 plant species belonging to 14 families and 22 genera. A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance, from different perspectives, to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang. The results revealed 24 models with a recognition Accuracy, of greater than 70.000%. Among which, Residual Network X_8GF (RegNetX_8GF) performs the best, with Accuracy, Precision, Recall, and F1 (which refers to the harmonic mean of the Precision and Recall values) values of 78.33%, 77.65%, 69.55%, and 71.26%, respectively. Considering the demand factors of hardware equipment and inference time, Mobile NetworkV2 achieves the best balance among the Accuracy, the number of parameters and the number of floating-point operations. The number of parameters for Mobile Network V2 (MobileNetV2) is 1/16 of RegNetX_8GF, and the number of floating-point operations is 1/24. Our findings can facilitate efficient decision-making for the management of species survey, cataloging, inspection, and monitoring in the nature reserves in Xinjiang, providing a scientific basis for the protection and utilization of natural plant resources.
分类： 物理学 >> 核物理学 提交时间： 2023-10-06
摘要： To correct spectral peak drift and obtain more reliable net counts, this study proposes a long-short memory (LSTM) model fused with a convolutional neural network (CNN) to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples. A predefined mathematical model was used to train the CNNLSTM model and generate a dataset composed of distorted pulse sequences. The trained model was validated using simulated pulses. The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping, CNN-LSTM, and LSTM models. As a result, for severely distorted pulses, the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35% compared with that of the triangular shaping algorithm. For slightly distorted pulses, the relative error of the CNN-LSTM model was reduced by 0.33% compared with that of the triangular shaping algorithm. The model was then evaluated considering two performance indicators, the correction ratio and the efficiency ratio, which represent the proportion of the increase in peak area of the two characteristic peak regions of interest (ROIs) to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI, respectively. Ten measurement results of the iron ore samples indicate that approximately 86.27% of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI, and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%. The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction, which is of great significance for X-ray spectroscopy and elemental content analyses.
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