Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2023-07-11
Abstract： The clinical term normalization has important research significance for dealing with the problem of non-standardization of clinical terminology in electronic medical records. The current mainstream solution is to adopt a "recall-sort" strategy. Based on the dataset provided in Evaluation 3 of the China Conference of Health Information Processing, we propose a multi-strategy-based normalization method for clinical terms. In the recall phase, the full-matching strategy, standard words recommendation of similar original words, and similarity calculation based on the TF-IDF and the improved Jaccard coefficient are used to recall the candidate standard word set. At the same time, we construct a standard quantity prediction model based on the BERT model, and use adversarial training, focal loss and label smoothing strategies to effectively improve the prediction performance and generalization performance of the models. In the ranking stage, In the ranking stage, we use the BERT implicit score ranking model based on adversarial training and fusion of diagnostic information to rank the candidate word set, and then generate the final predicted standard words based on the output of the quantity prediction model. In the final evaluation test set, the method accuracy rate of our method reached 0.6356, ranking second place among the participating teams.
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science Subjects: Mechanics >> Other Disciplines of Mechanics submitted time 2023-06-15
Abstract： Topology optimization is widely used in the engineering design phase to maximize product performance by mathematically modeling and optimizing the distribution of materials in the design space. However, deep learning to solve the topology optimization problem suffers from insufficient data and weak adaptability of the training model boundary conditions. Therefore, a Topy library-based data sample generation method is used to generate 400,000 2D samples of four types of boundary conditions for random structures, cantilever beams, continuous beams and simply supported beams, each containing two types of resolution data, and to expose this dataset. An improved DoubleU-Net network is proposed for topology optimization with high accuracy prediction in real time. In the generated dataset, the average IoU accuracies of the models for four structures, namely, random beam, cantilever beam, continuous beam and simply supported beam, are 93.26%, 96.71%, 96.35% and 97.38, respectively, and the experimental results show that DoubleU-Net can better adapt to different resolution data. The model trained with the random structure dataset has strong generalization ability and has great potential for real-time structural optimization in large-scale projects.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science Subjects: Mechanics >> Oscillation and Wave submitted time 2023-06-15
Abstract： This paper proposes a modal analysis strategy based on dilated residual convolutional broad network. In modal analysis, vibration analysis of large-scale structures or complex systems usually requires processing large amounts of data and complex calculations.The dilated residual convolution width architecture can reduce the number of parameters and computational complexity of the network, reduce the computational burden, and improve the efficiency of analysis.The dilated residual convolutional broad network applied to modal analysis tasks can improve the extraction ability of vibration features, improve the accuracy of modal identification, and enhance the sensitivity of structural damage detection, and has high computational efficiency and parameter efficiency. The experimental results show that our model achieves excellent performance in the regression task of modal analysis prediction.
Peer Review Status:Awaiting Review
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