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Named Entity Recognition of Electronic Medical Records based on BERT-BiLSTM-Biaffine Model

Named Entity Recognition of Electronic Medical Records based...

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2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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Code Generation Based on Quality-Aware Reordering 9

Code Generation Based on Quality-Aware Reordering

引用

9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Song, Yuan Yu, Luo College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan430065 China

ISBN: (纸本)9798350376548

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improv.ment in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improv.ment in solving the problem of selecting the correct output from natural language to code generation, effectively improv.s the accuracy of evaluation by automating the generation of test cases, and prov.des an effective solution to improv.ng the correctness of code generation. © 2024 IEEE.

关键词： code generation natural language processing (NLP) pre-trained large models quality estimation reranking test case generation

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Wind farm combination forecasting model based on dynamic graph attention 8

Wind farm combination forecasting model based on dynamic gra...

引用

8th International Conference on Electronic Technology and information Science, ICETIS 2023

作者： Liao, Xuechao Cheng, Yiqun School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Hubei Wuhan430065 China

ISBN: (纸本)9781510666535

In recent years, wind power has become more and more important in the energy component. In order to improv. the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance. © 2023 SPIE.

关键词： Convolution

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MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fang, Shuyan Liu, Yu Liu, Song Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Prov. Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Wuhan University of Science and Technology Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan China

ISBN: (纸本)9798350337488

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improv. the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules. © 2023 IEEE.

关键词： Molecular property prediction Molecular representation learning Morgan Fingerprint Self-supervised learning

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Improv.ng energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

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2022 International Conference on Networks, Communications and information Technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improv.ng the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the time-series block data is constructed according to the data shape of the Periodic block;then the time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including prov.ding a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improv. the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Code Generation Based on Quality-Aware Reordering

Code Generation Based on Quality-Aware Reordering

引用

6th International Conference on intelligent Computing and Signal processing (ICSP)

作者： Yuan Song Luo Yu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

关键词： Codes Accuracy Computational modeling Estimation Signal processing Benchmark testing

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Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment 1

引用

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Liu, Kaibo Zheng, Wangli Min, Huasong Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China State Grid Electric Power Research Institute Jiangsu Nanjing211106 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Hubei Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method;the other is an application platform experiment, which verifies the feasibility and availability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

PL-TD3: A Dynamic Path Planning Algorithm of Mobile Robot

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Tan, Yijian Lin, Yunhan Liu, Tong Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In this paper, Prioritized Experience Replay (PER) strategy and Long Short Term Memory (LSTM) neural network are introduced to the path planning process of mobile robots, which solves the problems of slow convergence and inaccurate perception of dynamic obstacles with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. We dubbed this new method as PL-TD3. Firstly, we improv. the convergence speed of the algorithm by introducing PER strategy. Secondly, we use LSTM neural network to achieve the improv.ment of the algorithm for dynamic obstacle perception. In order to verify the method of this paper, we design static environment, dynamic environment and adaptability to dynamic experiments to compare and analyze the methods before and after improv.ment. The experimental results show that PL-TD3 outperforms TD3 in terms of execution time and execution path length in all environments. © 2022 IEEE.

关键词： Motion planning

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result prov.s that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 IEEE.

关键词： Reinforcement learning

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