Changchun Bureau of Science and Technology

Urban street lighting system plays an important role in urban traffic lighting and road safety. With it, the power consumption of urban street lights is increasing, and the cost is also increasing year by year. The purpose of this paper is to detect and classify targets through road monitoring radar, so as to achieve the purpose of real-time detection of pedestrians, vehicles and other targets, so as to control the brightness of street lights to save power consumption. This paper firstly establishes the radar echo model of various targets, deduces the model formula of radar echo, and analyzes the micro-Doppler characteristics produced by the micro-moving parts of the target. Secondly, Secondly, the ResNet residual neural network training method is used to analyze the target micro-Doppler information, extract the depth information, and realize the target detection and recognition task.

With the rise of the Internet of Things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. Therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. The main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. The simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm.

Text similarity is an effective metric for estimating the text matching degree between two or more texts. Vector Space Model (VSM) is employed for the text similarity calculation in most instances. However, it is insufficient and ill-suited to solve the complex tasks since the high calculation dimension and computational complexity. Therefore, it is crucial to calculate the similarity of two news text, so that whether two reported news is the identical event or the same type of information would be ascertained. According to the analysis of the news reports, five basic factors in terms of “when”, “where”, “what”, “why”, and “who” are taken into account for distinguishing a news report. By analyzing these features, in this study, a method to calculate the similarity of news text is proposed. The proposed method fully integrates the influence of the five news feature words into the evaluation of text similarity, which avoids the problem happened in the text interference and computational efficiency to a large extent. There are four steps to execute the proposed method, i.e. extraction of the news elements, classification of these elements, calculation of the similarity, and comparison with available literatures. Experimental results suggest that our proposal outperforms the vector space cosine coefficient method, Jaccard coefficient method and entropy method in terms of the time complexity and computational accuracy.

Wear and thermal cracking are the major reasons for the fatigue of a hot-work die; remarkable improvement has been achieved by laser biomimetic treatment in recent years. However, because of severe service conditions, the wear resistance and thermal fatigue behavior of a hot-work die should be further improved. Therefore, pulse current stimulation (PCS) was used to treat H13 steel processed by laser biomimetic treatment. The results indicate that the microstructures of the laser-melted zone and matrix were refined by PCS, thus increasing the hardness. Therefore, the wear resistance of laser biomimetic sample (LBS) was improved by PCS. The tensile strength of LBS at room temperature and 650°C increased with PCS. The residual compressive stress distributed on the surface of LBS is induced by PCS. Therefore, the LBS treated by PCS has the best thermal fatigue behavior.