Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard

Real-Time Monitoring System of Seedling Amount in Seedling Box Based on Machine Vision

Conventional mat-type seedlings are still widely used in autonomous rice transplanters and automatically supplying seedling devices suited to conventional mat-type seedlings is difficult to develop. Thus, an autonomous rice transplanter carries at least one person to load the seedling pieces into the seedling box, which has led to an increase in the labor force and low operational efficiency. To solve this problem from another point of view, a machine vision-based system for the real-time monitoring of the seedling amount in a seedling box is developed. This system aims to achieve the monitoring of the fault of seedlings and seedling amount in the seedling box. According to the real-time and accuracy requirements of the image, the image acquisition platform is designed based on a previously developed autonomous rice transplanter. A camera model was developed and camera parameters for correcting the image distortion is obtained. The image processing method and segment method of seedling rows are presented. The algorithms for fault diagnosis and the calculation of the number of remaining seedlings are proposed by image analysis. The software is developed for seedling box fault diagnosis and monitoring the remaining number of seedlings. Field experiments are carried out to test the effectiveness of the developed monitoring system. The experimental results show that the image processing time is less than 1.5 s and the relative error of the seedling amount is below 3%, which indicates that the designed monitoring system can accurately realize the fault diagnosis of the seedling pieces and monitor for the remaining amount of each row. By combining the navigation information, the developed monitoring system can predict the distance from which the remaining seedlings in the seedling box can be planted, which can guarantee remaining seedlings in a seedling box are enough for transplanting until the rice transplanter returns to the supplying seedling site. This implies that one person can provide seedlings for multiple autonomous rice transplanters. This study was limited to supplying the seedling when the rice transplanter passed through the place of the seedling storage situated at the headland. In the future, we decide to conduct a study on path planning of breakpoint endurance so that the rice transplanter can automatically return to the supplying seedling place when the seedling amount in the seedling box is not enough

DDRGK1 regulates NF-κB activity by modulating IκBα stability.

NF-κB is a ubiquitously expressed transcription factor that regulates a large number of genes in response to diverse physiological and pathological stimuli. The regulation of the transcriptional activity of NF-κB is often dependent on its interaction with IκBα. Proteins that bind to IκBα are critical regulators of NF-κB activity. DDRGK1 is a member of the DDRGK domain-containing protein family with unknown function. In this study, we showed that the depletion of DDRGK1 inhibits cell proliferation and invasion. Microarray analysis indicated that the expression of NF-κB target genes showed the most significant decrease after depleting of DDRGK1, suggesting that DDRGK1 may play an important role in the NF-κB signaling pathway. We further demonstrated that DDRGK1 interacts with IκBα and regulates its stability, thereby regulates the NF-κB transcriptional activity. Our findings identify DDRGK1 as an important regulator of the NF-κB pathway

Cloning, purification, crystallization and preliminary X-ray studies of flagellar hook scaffolding protein FlgD from Pseudomonas aeruginosa PAO1

In order to better elucidate the functions of FlgD in flagellar hook biosynthesis, the three-dimensional structure of FlgD is being determined by X-­ray crystallo­graphy. Here, the expression, purification, crystallization and preliminary crystallographic analysis of FlgD from P. aeruginosa are reported

Classification of Metro Facilities with Deep Neural Networks

Metro barrier-detection has been one of the most popular research fields. How to detect obstacles quickly and accurately during metro operation is the key issue in the study of automatic train operation. Intelligent monitoring systems based on computer vision not only complete safeguarding tasks efficiently but also save a great deal of human labor. Deep convolutional neural networks (DCNNs) are the most state-of-the-art technology in computer vision tasks. In this paper, we evaluated the effectiveness in classifying the common facility images in metro tunnels based on Google’s Inception V3 DCNN. The model requires fewer computational resources. The number of parameters and the computational complexity are much smaller than similar DCNNs. We changed its architecture (the last softmax layer and the auxiliary classifier) and used transfer learning technology to retrain the common facility images in the metro tunnel. We use mean average precision (mAP) as the metric for performance evaluation. The results indicate that our recognition model achieved 90.81% mAP. Compared with the existing method, this method is a considerable improvement