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.

In view of the problems of ship target detection in remote sensing images, such as small ship targets, densely arranged ships, and arbitrary target directions, this paper proposes a target detection method based on improved YOLOv5. This method combines the improved DesnesNet and X-RFB modules to enhance the feature extraction of large-scale changing targets; it adds direction information on the basis of the traditional bounding box, and uses KFIOU instead of GIOU to perform curve optimization on the loss function; At the same time of quantification, high-speed and high-precision coexistence of ship target detection can be achieved. The experimental results show that: compared with the original YOLOv5 method, the map index of this method is improved from the original 91.0% to 96.5%, which is obviously better than the comparison method.

ABSTRACT Coronary post-mortem computed tomography angiography (coronary PMCTA) is a non-invasive method to evaluate the coronary artery in the forensic practice. A traditional method is to insert a urinary catheter through the carotid artery. In this study, we performed the coronary PMCTA by inserting a pigtail catheter through the femoral artery and connecting the catheter to a high-pressure syringe. We report the efficacy of this technique and factors influencing its performance, including the position of the catheter tip, aortic gas, high-pressure injector, and location of skin puncture. A total of 14 cadavers were included in our study. Two cadavers with coronary artery occlusions were identified in the PMCTA, which were confirmed by the autopsy reports. In addition, we found that adequate imaging of all the branches of the left and right coronary arteries could be achieved when the catheter tip was located in the ascending aorta around the aortic valve. Gas in the aorta could affect the image acquisitions, which could be avoided by priming the catheter with normal saline and changing the cadaver positions. The femoral artery insertion of the pigtail catheter with a high-pressure injector could achieve satisfactory images without causing obvious disfiguration of the cadavers.

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.

Whiplash injury is common in traffic accidents, and severe whiplash is characterized by cervical spinal cord injuries with cervical dislocation or fracture, that can be diagnosed by postmortem computed tomography (PMCT), postmortem magnetic resonance (PMMR), or conventional autopsy. However, for cervical spinal cord injury without fracture and dislocation, PMMR can be more informative because it provides higher resolution of soft tissues. We report the case of a 29-year-old male who died immediately following a traffic accident, in which the vehicle hit an obstacle at a high speed, causing deformation of the bumper and severe damage of the vehicle body. PMCT indicated no significant injuries or diseases related to death, but PMMR showed patchy abnormal signals in the medulla oblongata, and the lower edge of the cerebellar tonsil was herniated out of the foramen magnum. The subsequent pathological and histological results confirmed that death was caused by medulla oblongata contusion combined with cerebellar tonsillar herniation. Our description of this case of a rare but fatal whiplash injury in which there was no fracture or dislocation provides a better understanding of the potentially fatal consequences of cervical spinal cord whiplash injury without fracture or dislocation and of the underlying lethal mechanisms. Compared with PMCT, PMMR provides important diagnostic information in forensic practice for the identification of soft tissue injuries, and is therefore an important imaging modality for diagnosis of whiplash injury when there is no fracture or dislocation.

The virtual anatomy laboratory was jointly established by the Jilin Province Department of Public Security, Public Security Bureau of Changchun, Jilin University and Neusoft Medical Systems Co., Ltd. on December 30, 2019. Details of the laboratory construction, virtual autopsy process, case analysis, research and development, establishment of standards, application in teaching, and other aspects of the center are summarized in this paper. In addition, we expound the prospective and practical significance of the virtual anatomy laboratory. Based on computed tomography and magnetic resonance detection methods, the prospective applications of virtual anatomy in forensic identification are explored through the analyses of two real-life cases. Using information from traditional anatomy as the gold standard, this study expanded on and explored the application technology and scenarios of virtual anatomy in imaging, clinical diagnosis, and equipment manufacturing, among other fields. The four cooperating units have their strengths and will yield valuable scientific research results and social benefits.

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.

In order to study the thermal fatigue behavior of bionic gray cast iron with different types of graphite (from A to E), first, the effects of A to E types of graphite on the thermal fatigue properties were studied. At the same time, the crack initiation and propagation modes of different gray cast iron types were analyzed. In addition, for improving the thermal fatigue properties of gray cast iron, the anti-fatigue mechanism of the coupled bionic structure on gray cast iron was investigated. In this case, the thermal fatigue properties were measured by the number, length, and width of cracks. The results revealed that the gray cast iron with A-type graphite had the best fatigue resistance compared to others. However, C-, D-, and E-type graphite exhibited the greatest effect on the number, length, and width of crack. Interestingly, the bionic gray cast iron sample with D-type graphite exhibited improved fatigue resistance most obviously compared to the non-bionic sample.

In this study, microstructure evolution and fracture of the AZ91D magnesium alloy processed by laser surface melting (LSM) were investigated in the temperature range of room temperature (RT) ~ 350 °C and the strain rate range of 10−4 to 10−1 s−1. Macro fracture morphology of the LSM-treated sample was tortuous, and this was responsible for the improved strength compared to that of the as-received sample at different temperatures and strain rates. Micro fracture morphology of the laser-melted zone was always the ductile mode at different temperatures and strain rates, whereas micro fracture morphologies of the untreated zone were brittle fracture at RT and the ductile mode at high temperatures. Dynamic recrystallization was not observed in the laser-melted zone at different temperatures and strain rates, and this can contribute to the improved strength of the LSM-treated sample. In addition, the improved strength of the LSM-treated sample was also attributed to better thermal stability compared to that of the as-received sample.