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.
Machine learning has made breakthroughs in areas such as computer vision and natural language processing. In recent years, more and more research has been done on the application of machine learning on robotic grasping. This article summarizes the research progress of machine learning on robotic grasping, from the aspects of object grasping datasets, two main categories of methods that differ from the criteria for successful grasping with deep learning or reinforcement learning algorithm, discusses what current researches have done and the problems that have not yet been solved, and hopes to inspire new ideas in research of robotic grasping based on machine learning.
In the ultrasonic testing of submarine pipelines by using guided waves, wave energy leakage is a main reason of signal decay. For overcoming the decrease of energy attenuation, the propagation of guided waves of immersion plates is studied in this paper. The dispersion equations of guided waves is numerically solved. Then the appropriated modes of which phase velocity are small or large are selected for optical Schlieren visualization and propagation of leaky waves is discussed. It is shown that selecting some modes of which imaginary part are small can retard guided wave decay and extend length of testing.
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