An optimization design of China supercritical water-cooled reactor (SCWR) with the rated electric power of 1000 MWe (CSR1000) conceptual core is proposed. Steady-state performance of the proposed core is then studied ...
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KYCORE is a 3D direct reactor calculation code capable of performing reactor hetero geneous calculation without cross section homogenization and group condense. It is developed in Nuclear Power Institute of China, inv...
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Sensitivity and Uncertainty (S&U) analysis plays a major role in reactor physics. Uncertainty in reactor engineering comes from model error, numerical error and input parameter error. Nuclear data are regarded as ...
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Because of the high temperature and high pressure characteristics of supercritical water-cooled reactor (SCWR), the thermal hydraulic performance of SCWR is greatly different from pressurized water reactor (PWR), whic...
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Knowledge of adjoint-weighted kinetics parameters plays an importance role in analyzing the reactor safety and in describing the transient behavior of nuclear reactors during normal operation or accidents. In this wor...
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In this paper, a novel model is proposed to investigate the neutron transport in scattering and absorbing medium. This solution to the linear Boltzmann equation is expanded from the idea of lattice Boltzmann method(LB...
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In this paper, a novel model is proposed to investigate the neutron transport in scattering and absorbing medium. This solution to the linear Boltzmann equation is expanded from the idea of lattice Boltzmann method(LBM) with the collision and streaming process. The theoretical derivation of lattice Boltzmann model for transient neutron transport problem is proposed for the first *** fully implicit backward difference scheme is used to ensure the numerical stability, and relaxation time and equilibrium particle distribution function are obtained. To validate the new lattice Boltzmann model, the LBM formulation is tested for a homogenous media with different sources, and both transient and steady-state LBM results get a good agreement with the benchmark solutions.
In the current practice of multi-axis machining of freeform surfaces, the interface surface between the roughing and finishing process is simply an offset surface of the nominal surface. While there have already been ...
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In the current practice of multi-axis machining of freeform surfaces, the interface surface between the roughing and finishing process is simply an offset surface of the nominal surface. While there have already been attempts at minimizing the machining time by considering the kinematic capacities of the machine tool and/or the physical constraints such as the cutting force, they all target independently at either the finishing or the roughing process alone and are based on the simple premise of an offset interface surface. Conceivably, since the total machining time should count that of both roughing and finishing process and both of them crucially depend on the interface surface, it is natural to ask if, under the same kinematic capacities and the same physical constraints, there is a nontrivial interface surface whose corresponding total machining time will be the minimum among all the possible(infinite) choices of interface surfaces, and this is the motivation behind the work of this paper. Specifically, with respect to the specific type of iso-planar milling for both roughing and finishing, we present a practical algorithm for determining such an optimal interface surface for an arbitrary freeform surface. While the algorithm is proposed for iso-planar milling, it can be easily adapted to other types of milling strategy such as contour milling. Both computer simulation and physical cutting experiments of the proposed method have convincingly demonstrated its advantages over the traditional simple offset method.
BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are suppl...
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BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities.
MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance.
RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies.
CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.
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