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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Tu, Lan Lv, Shuojun Shi, Hui Shanwei Polytechnic Department of Information Engineering Shanwei516600 China Shanwei Innovation Industrial Design and Research Institute Research Institute of Cloud Computing and Data Center Project Design Shanwei516600 China

ISBN: (纸本)9798350352719

In view of the high professionalism and complexity of medicine patent text, traditional patent retrieval mainly relies on manual operation and simple text Processing methods, this paper aims to use Natural Language Processing (NLP) technology to efficiently obtain valuable information in medicine patent retrieval. To transform unstructured text into meaningful information. Through in-depth research on the characteristics and structure of medicine patent text, a new structured feature extraction algorithm of patent text is proposed, and a patent retrieval system based on NLP is developed, which can carry out instant structured processing, and can automatically identify and translate text into corresponding patented medicines, and nearly 300 medicine patent abstracts are tested by experiment. The system performance is good. The system achieves the expected effect. © 2024 IEEE.

关键词： Patents and inventions

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Application and research of V2X Vehicle Road Collaboration Technology 24

Application and Research of V2X Vehicle Road Collaboration T...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Tu, Lan Shi, Hui Department of Information Engineering Shanwei Polytechnic Guangdong Shanwei516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Guangdong Shanwei516600 China

ISBN: (纸本)9798400716942

Traditional end-to-end mobile communication must go through wireless access networks, core networks, platforms, and application layers, which ultimately leads to longer end-to-end delays and cannot meet the performance requirements of V2X (Vehicle-to-everything) applications with high latency requirements. This paper proposes a V2X vehicle-road collaborative communication system architecture diagram and an optimized traffic signal control algorithm model, dynamically optimizes the duration of traffic lights to maximize traffic flow, and designs a CRCU (Car-side, Road-side, cloud-side, User-side) model architecture and a vehicle-road collaborative autonomous driving application networking architecture diagram to reduce end-to-end communication delay. It provides local vehicle networking services combined with geographic information, which helps to achieve an airport delay of 13.888 microseconds and a transmission reliability of 99.999%. © 2024 ACM.

关键词： Mobile telecommunication systems

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research on the Application of Natural Language Processing Technology in Medicine Patent Retrieval

Research on the Application of Natural Language Processing T...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Lan Tu Shuojun Lv Hui Shi Department of Information Engineering Shanwei Polytechnic Shanwei China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

关键词： Training Patents Adaptation models Transfer learning Transforms Search problems Natural language processing

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A Face Recognition Method Based on CNN

A Face Recognition Method Based on CNN

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Xie, Zhiming Li, Junjie Shi, Hui Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer;the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Small Evolution Antenna design Method Based on Dynamic Hill Climbing Algorithm and Orthogonal Experiment

Small Evolution Antenna Design Method Based on Dynamic Hill ...

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2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Shi, Hui Li, Junjie Xie, Zhiming Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

Small-scale evolution antennas have the advantages of high gain, wide bandwidth and small size, and have broad application prospects in the aerospace field. Aiming at the complex high-dimensional and dynamic problems of aerial antenna modeling, a dynamic hill-climbing algorithm based on orthogonal design is designed and implemented to simulate and optimize the single-span antenna in high-frequency operation mode. In the optimization problem, this paper defines a new individual comparison mechanism, which can reflect the number of violations of constraints and the degree of violation of each constraint, making the comparison of individuals more reasonable. The antenna simulation results demonstrate the effectiveness and feasibility of using dynamic evolutionary algorithms for antenna design. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

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A Face Recognition Method Based on CNN

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Journal of Physics: Conference Series 2019年第1期1395卷

作者： Zhiming Xie Junjie Li Hui Shi Department of Information Engineering Shanwei Polytechnic Shanwei 516600 Guangdong China The Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design & Research Institute Shanwei 516600 Guangdong China The Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei 516600 Guangdong China

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer; the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model.

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Small Evolution Antenna design Method Based on Dynamic Hill Climbing Algorithm and Orthogonal Experiment

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Journal of Physics: Conference Series 2019年第1期1395卷

作者： Hui Shi Junjie Li Zhiming Xie Department of Information Engineering Shanwei Polytechnic Shanwei 516600 Guangdong China The Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design & Research Institute Shanwei 516600 Guangdong China The Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei 516600 Guangdong China

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