The regulatory warning model of regional product quality based on the back-propagation artificial neural network

Research on the effect of government media and users' emotional experience based on LSTM deep neural network

Different government media have different communication effects and users' emotional experience. It carries on a comparative research on government media selecting three different types of government media which include China’s Police Online, Central Committee of the Communist Youth League, and China’s Fire Control in the context of public health emergencies. Based on the deep learning technique, the emotion classification model of long-term memory network is constructed to analyze the emotion of the users’ comments of different government media; taking the number of contents, the number of retweets, the number of praises, and the number of comments as evaluating indicators to do comparative analysis to cross platform government medias. Through the comparative results, it is found that different types and platforms of government media have great differences in users’ emotional experience; the emotion performance of users’ comments is strongly related to the information communication power and effectiveness of government media.

Deep learning of total electron content

One of the most notable errors in the global navigation satellite system (GNSS) is the ionospheric delay due to the total electron content (TEC). TEC is the number of electrons in the ionosphere in the signal path from the satellite to the receiver, which fluctuates with time and location. This error is one of the major problems in single-frequency (SF) GPS receivers. One way to eliminate this error is to use dual-frequency. Users of SF receivers should either use estimation models or local models to reduce this error. In this study, deep learning of artificial neural networks (ANN) was used to estimate TEC for SF users. For this purpose, the ionosphere as a single-layer model (assuming that all free electrons in the ionosphere are in this thin layer) is locally modeled by the code observation method. Linear combination has been used by selecting 24 permanent GNSS stations in the northwest of Iran. TEC was modeled independently of the geometry between the satellite and the receiver, called L4. This modeling was used to train the error ANN with two 5-day periods of high and low solar and geomagnetic activity range with a hyperbolic tangential sigmoid activation function. The results show that the proposed method is capable of eliminating ionosphere error with an average accuracy of 90%. The international reference ionosphere 2016 (IRI2016) is used for the verification, which has a 96% significance correlation with estimated TEC.