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The named entity recognition based on the epidemiological investigation of information on COVID-19 can help analyze the source and route of transmission of the epidemic to control the spread of the epidemic better. Therefore, this paper proposes a Chinese named entity recognition model BERT-BiLSTM-IDCNN-ELU-CRF (BBIEC) based on the epidemiological investigation of information on COVID-19 of the BERT pre-training model. The model first processes the unlabeled epidemiological investigation of information on COVID-19 into the character-level corpus and annotates it with artificial entities according to the BIOES character-level labeling system and then uses the BERT pre-training model to obtain the word vector with position information; then, through the bidirectional long-short term memory neural network (BiLSTM) and the improved iterated dilated convolutional neural network (IDCNN) extract global context and local features from the generated word vectors and concatenate them serially; output all possible label sequences to the conditional random field (CRF); finally pass the condition random The airport decodes and generates the entity tag sequence. The experimental results show that the model is better than other traditional models in recognizing the entity of the epidemiological investigation of information on COVID-19.INDEX TERMS chinese named entity recognition,the epidemiological investigation of information on COVID-19,bidirectional encoder representations from transformer, bidirectional long-short term memory network, iterated dilated convolutional neural network, conditional random field

This paper present a pedestrian following mobile robot with binocular vision sensor. Because Kinect is one of the most inexpensive devices of depth-cameras, it is used in our application. Human skeleton is extracted by using Kinect, and the location of human is checked by projecting the three-dimensional (3D) pose of skeleton onto 2D screen. This 2D screen is separated into three parts, left, middle and right. Mobile robot rotates and translates according to the corresponding location of pedestrian. To make the robot move forward and backward, the distance between spine point and mobile robot is calculated. Finally, a real experimental result is used to validate our proposed method.

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This paper presents a method to track and follow target person. A laser range finder (LRF) is used to measure highly accurate distance of objects with the range of 180 degrees. First of all, the erroneous data are excluded due to the error of LRF. Then all the raw sensor data are separated into many groups when the difference of the measuring distances of two adjacent laser points are beyond a limited value. For each group, the width is calculated, and it is considered as human legs if the defined conditions are satisfied. Finally, a real-time human following experiment with a SICK LRF and PIONEER mobile robot is done to validate our proposed method.