Search Results (6)

An important aspect of pressurized water reactor (PWR) lifetime monitoring is supporting radiation shielding analyses which can quantify various in-core and out-core effects induced in reactor materials by varying neutron–gamma fields. A good understanding of such a radiation environment during normal and accidental operating conditions is required by plant regulators to ensure proper shielding of equipment and working personnel. The complex design of a typical PWR is posing a deep penetration shielding problem for which an elaborate simulation model is needed, not only in geometrical aspects but also in efficient computational algorithms for solving particle transport. This paper presents such a hybrid shielding approach of FW-CADIS for characterization of the reactor pressure vessel (RPV) irradiation using SCALE6.2.4 code package. A fairly detailed Monte Carlo model (MC) of typical reactor internals was developed to capture all important streaming paths of fast neutrons which will backscatter the biological shield and thus enhance RPV irradiation through the cavity region. Several spatial differencing and angular segmentation options of the discrete ordinates S_N flux solution were compared in connection to a S_N mesh size and were inspected by VisIt code. To optimize MC neutron transport toward the upper RPV head, which is a particularly problematic region for particle transport, a deterministic solution of discrete ordinates in forward/adjoint mode was convoluted in a so-called contributon flux, which proved to be useful for subsequent S_N mesh refinement and variance reduction (VR) parameters preparation. The pseudo-particle flux of contributons comes from spatial channel theory which can locate spatial regions important for contributing to a shielding response. Full article

The development of underground artificial cavities plays an important role in the exploitation of urban spatial resources. As the rapidly growing number of underground artificial cavities with different depths and scales increases, the detection and identification of underground artificial cavities has become a key issue in underground engineering studies. Geophysical techniques have been widely used for the construction, management, and maintenance of underground artificial cavities. In this study, we present two identification methods for underground artificial cavities. Apparent resistivity imaging is the most popular technique for quickly identifying underground artificial cavities, using the forward simulation results of a three-dimensional earth model and comparing these with the preset positions of artificial cavities, as demonstrated in the experiment. To further improve the efficiency of underground artificial cavity identification, we developed a fast recognition approach for underground artificial cavities based on the Bayesian convolutional neural network (BCNN). Compared to a traditional convolutional neural network, the performance of the BCNN method was greatly improved in terms of the classification accuracy and efficiency of identifying underground artificial cavities with apparent resistivity image datasets. Full article

The modulation accuracy of Multi-Plane Light Conversion (MPLC) mainly depends on the positioning accuracy of the phase mask on the Spatial Light Modulator (SLM). To improve positioning accuracy, the impact of phase mask shift on modulation accuracy is investigated, and a position method is proposed. In order to investigate the influence of phase mask offset on the input light conversion effect, a convolution transmission model for the adjustable multi-pass cavity is established. Then, the positioning process for the phase masks is analyzed and simulated, and a method of positioning the phase masks is presented. This method reduces the positioning time and increases the positioning accuracy to 8 μm. Finally, experiments are performed to verify the feasibility of the method. Experimental results show that the similarity of the adjustable multi-pass cavity positioned by this method can reach 93.44%. Full article

This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone network. This model improves the fusion of high- and low-level semantic information by designing feature pyramid networks with embedded spatial attention. According to the experimental findings, the suggested detection algorithm enhances the mapped value by about 4% once compared to the YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm, and the detection capability of steel surface defects is significantly enhanced to meet the needs of real-time detection of realistic scenes in the mobile terminal. Full article

Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal cavity in traditional FCNs used for building extraction. To address these issues, this paper proposes a new building graph convolutional network (BGC-Net), which optimizes the segmentation results by introducing the graph convolutional network (GCN). The core of BGC-Net includes two major modules. One is an atrous attention pyramid (AAP) module, obtained by fusing the attention mechanism and atrous convolution, which improves the performance of the model in extracting multi-scale buildings through multi-scale feature fusion; the other is a dual graph convolutional (DGN) module, the build of which is based on GCN, which improves the segmentation accuracy of object edges by adding long-range contextual information. The performance of BGC-Net is tested on two high spatial resolution datasets (Wuhan University building dataset and a Chinese typical city building dataset) and compared with several state-of-the-art networks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches (FCN8s, DANet, SegNet, U-Net, ARC-Net, BAR-Net) in both visual interpretation and quantitative evaluations. The BGC-Net proposed in this paper has better results when extracting the completeness of buildings, including boundary segmentation accuracy, and shows great potential in high-precision remote sensing mapping applications. Full article

In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications. Full article