UDP-YOLO: High Efficiency and Real-Time Performance of Autonomous Driving Technology

In recent years, autonomous driving technology has gradually appeared in our field of vision. It senses the surrounding environment by using radar, laser, ultrasound, GPS, computer vision and other technologies, and then identifies obstacles and various signboards, and plans a suitable path to control the driving of vehicles. However, some problems occur when this technology is applied in foggy environment, such as the low probability of recognizing objects, or the fact that some objects cannot be recognized because the fog's fuzzy degree makes the planned path wrong. In view of this defect, and considering that automatic driving technology needs to respond quickly to objects when driving, this paper extends the prior defogging algorithm of dark channel, and proposes UDP-YOLO network to apply it to automatic driving technology. This paper is mainly divided into two parts: 1. Image processing: firstly, the data set is discriminated whether there is fog or not, then the fogged data set is defogged by defogging algorithm, and finally, the defogged data set is subjected to adaptive brightness enhancement; 2. Target detection: UDP-YOLO network proposed in this paper is used to detect the defogged data set. Through the observation results, it is found that the performance of the model proposed in this paper has been greatly improved while balancing the speed

Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

Improved DC-Link Voltage Regulation Strategy for Grid-Connected Converters

In this article, an improved dc-link voltage regulation strategy is proposed for grid-connected converters applied in dc microgrids. For the inner loop of the grid-connected converter, a voltage modulated direct power control is employed to obtain two second-order linear time-invariant systems, which guarantees that the closed-loop system is globally exponentially stable. For the outer loop, a sliding mode control strategy with a load current sensor is employed to maintain a constant dc-link voltage even in the presence of constant power loads at the dc-side, which adversely affect the system stability. Furthermore, an observer for the dc-link current is designed to remove the dc current sensor at the same time improving the reliability and decreasing the cost. From both simulation and experimental results obtained from a 15-kVA prototype setup, the proposed method is demonstrated to improve the transient performance of the system and has robustness properties to handle parameter mismatches compared with the input-output linearization method

Tandem mass tag-based quantitative proteomic analysis of effects of multiple sevoflurane exposures on the cerebral cortex of neonatal and adult mice

IntroductionSevoflurane is the most commonly used general anesthetic in pediatric surgery, but it has the potential to be neurotoxic. Previous research found that long-term or multiple sevoflurane exposures could cause cognitive deficits in newborn mice but not adult mice, whereas short-term or single inhalations had little effect on cognitive function at both ages. The mechanisms behind these effects, however, are unclear.MethodsIn the current study, 6- and 60-day-old C57bl mice in the sevoflurane groups were given 3% sevoflurane plus 60% oxygen for three consecutive days, each lasting 2 hours, while those in the control group only got 60% oxygen. The cortex tissues were harvested on the 8th or 62nd day. The tandem mass tags (TMT)pro-based quantitative proteomics combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, Golgi staining, and western blotting analysis were applied to analyze the influences of multiple sevoflurane anesthesia on the cerebral cortex in mice with various ages. The Morris water maze (MWM) test was performed from postnatal day (P)30 to P36 or P84 to P90 after control or multiple sevoflurane treatment. Sevoflurane anesthesia affected spatial learning and memory and diminished dendritic spines primarily in newborn mice, whereas mature animals exhibited no significant alterations.ResultsA total of 6247 proteins were measured using the combined quantitative proteomics methods of TMTpro-labeled and LC-MS/MS, 443 of which were associated to the age-dependent neurotoxic mechanism of repeated sevoflurane anesthesia. Furthermore, western blotting research revealed that sevoflurane-induced brain damage in newborn mice may be mediated by increasing the levels of protein expression of CHGB, PTEN, MAP2c, or decreasing the level of SOD2 protein expression.ConclusionOur findings would help to further the mechanistic study of age-dependent anesthetic neurotoxicity and contribute to seek for effective protection in the developing brain under general anesthesia

Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery

Objective: The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Methods: Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of complex anatomical structures is obtained. To reconstruct the 3D structure of the whole surgical environment, we first fine-tune a learning-based stereoscopic depth perception method, which is robust to the texture-less and variant soft tissues, for depth estimation. Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction. To estimate poses of new laparoscope views, we achieve a coarse-to-fine localization method, which incorporates our reconstructed 3D model. Results: We evaluate the reconstruction method and the localization module on three datasets, namely, the stereo correspondence and reconstruction of endoscopic data (SCARED), the ex-vivo phantom and tissue data collected with Universal Robot (UR) and Karl Storz Laparoscope, and the in-vivo DaVinci robotic surgery dataset, where the reconstructed 3D structures have rich details of surface texture with an accuracy error under 1.71 mm and the localization module can accurately track the laparoscope with only images as input. Conclusions: Experimental results demonstrate the superior performance of the proposed method in 3D anatomy reconstruction and laparoscopic localization. Significance: The proposed framework can be potentially extended to the current surgical navigation system