Neural Radiation Field (NeRF) is driving the development of 3D reconstruction technology. Several NeRF variants have been proposed to improve rendering accuracy and reconstruction speed. One of the most significant variants, TensoRF, uses a 4D tensor to model the radiation field, resulting in improved accuracy and speed. However, reconstruction quality remains limited. This study presents an improved TensoRF that addresses the aforementioned issues by reconstructing its multilayer perceptron network. Increasing the number of neurons in the input and network layers improves the render accuracy. To accelerate the reconstruction speed, we utilized the Nadam optimization algorithm and the RELU6 activation function. Our experiments on various classical datasets demonstrate that the PSNR value of the improved TensoRF is higher than that of the original TensoRF. Additionally, the improved TensoRF has a faster reconstruction speed (≤30min). Finally, we applied the improved TensoRF to a self-made industrial dataset. The results showed better global accuracy and local texture in the reconstructed image.
One of the main goals of material design is to sift the proper materials with the properties we want. However, the traditional method, synthesizing and testing each material in laboratory, wastes time and energy, and the actual material we want is usually one in a million which makes it more difficult. Here, we develop a generative framework to give a guidance on material design with specific properties. Our framework is mainly drove by several variants of Generative Adversarial Networks (GANs) for material data generation. Our framework is trained with 86 perovskite-type material samples including their components information, and then we compared with various networks structures and algorithm, the result shows an acceptable accuracy of materials data generation which proved a possible method of inverse design of perovskite-type electrode of SOFC.
KEYWORDS: Performance modeling, Neural networks, Surgery, Data modeling, Systems modeling, Tunable filters, Frequency modulation, Fermium, Education and training, Technology
Rational drug use prediction, which is used to estimate whether the drug use is reasonable in clinical medical treatment, is of great significance in combating excessive medical treatment. The feature interaction model based on electronic medical records is widely used in the field of medical behavior prediction. However, the features recorded in electronic medical records are very complex, and medical behavior has an obvious long-tail effect. The problem of feature sparseness makes traditional prediction models based on feature interaction impossible. It works well, we propose a novel business domain-based second-order relational graph embedding neural network model (SORGE-NN), which can distinguish the scope of features and mine the implicit propagation relationship, through the residual-multi-head attention layer , and perform high-order weighted combination of features, which effectively alleviates the challenges brought by feature sparse and complex features. We conduct experiments with real datasets, and the experimental results show that our proposed SORGE-NN achieves better results than current state-of-the-art prediction models.
A recursive principal component analysis (RPCA) method was presented in this paper for reliable fault detection of nuclear power systems equipment. Existing fault detection methods for nuclear power equipment are still stay in the theoretical research, as well as experimental analysis. Due to the special working environment of nuclear power equipment, a planned repairs and maintenance for each equipment is a normal operation. However, with the growth in installed capacity of nuclear power units, they suffer from several drawbacks. Offline detection of nuclear power system equipment can never truly reveal the actual situation, leading to maloperation or a waste use of equipment. To tackle such problems, this paper introduces RPCA methods for fault detection. A simple control chart is established for intuitive visualization of the false working condition. A recursive PCA scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. The proposed RPCA approach are verified by detecting abnormal working status occurring in a simulated nuclear power system.
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