Volume 7 Issue 1

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  • Research on Similar Odor Recognition Based on Bid Data Analysis

    Abstract: In the common olfactory system odor recognition is processed by the electronic nose collecting sensor data, but the odor data collection of substances is easily affected by the environment and the processing is complicated, which is prone to deviation. This paper proposes a method based on big data analysis. According to the different chemical structure characteristics of different odor substances, the BP neural network is used to build a model to classify and recognize similar odors, and compare it with the traditional PCA+LDA recognition method. The results show that the establishment of a similar odor recognition model can accurately classify substances with similar odors, and the BP neural network algorithm is used to identify different substances with a higher rate of odor recognition. This method is stable and simple, and can provide different ideas for odor identification.

    Keywords: Smell recognition; Big data analysis; BP neural network; Similar smell

  • Text Classification Based on Title Sematic Information

    Abstract: With the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory (LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.

    Keywords: Text classification; natural language processing; deep learning; LSTM

  • Design of the Automatic Control System for Restaurant Food Delivery Based On PLC

    Abstract: The paper designs an automatic control system for restaurant food delivery based on PLC, including the mechanical structure and automatic control system design. The mechanical structure of the system includes horizontal delivery subsystems and a vertical delivery subsystem. The automatic control system includes PLC control and the human-machine interface, which realizes the entire system’s automation. At the end of the paper, we analyze the whole system’s reliability and economy to reflect the characteristics and practicability of the automatic control system.

    Keywords: Food delivery system; PLC control; human-machine interface; reliability

  • License Plate Recognition Algorithm Based on Convolutional Neural Network

    Abstract: In order to improve the problem of unequal suspension positions in the traditional license plate recognition system, this paper introduces the convolutional neural network algorithm into the license plate recognition system, and conducts a series of tests and corrections to meet the current license plate recognition system. This paper proposes for the first time that the flood filling algorithm is applied to the preprocessing of the license plate image, the recognized contour is divided into regions, and then the license plate inclination angle is offset, and rough positioning and cutting are performed to make the vehicle shot from the side The picture can also fully identify the license plate, and finally judge according to the aspect ratio of the license plate and the standard aspect ratio, and get whether the recognized license plate is. The experimental results show that the model utilizes the advantages of convolutional neural network so that the model can recognize classification features more accurately.
    Keywords: License plate recognition, Convolutional neural network, Flood filling algorithm

  • Research and Implementation of FacialNet Based on Convolutional Neural Network

    Abstract: Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people’s daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition. In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.

    Keywords: Convolutional neural network, Face recognition, Gender recognition, Age recognition

  • Analysis and Prediction of COVID-19 in Xinjiang based on Machine Learning

    Abstract: Covid-19 has taken the world by storm, dramatically affecting the lives of people around the world. China is a major country in the fight against the epidemic. It has provided the world with a wealth of valuable experience in the prevention and treatment of COVID-19. Based on the data released by Xinjiang Health Commission, this study used mathematical modeling method to reasonably predict and analyze the trend of the number of coVID-19 confirmed in the recent outbreak in Xinjiang through machine learning polynomial regression under limited data conditions, aiming at the coVID-19 outbreak in Xinjiang in July.

    Keywords: COVID-19, estimates of the number of confirmed cases, Machine   learning, Polynomial regression.