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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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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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A Face Recognition Method Based on CNN

引用

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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