Multi-object detection of iron foreign bodies in scraper conveyor based on improved Mask R-CNN

The scraper conveyor is the key transportation equipment in the coal mine. The iron foreign body entering the scraper conveyor will lead to wear and tear, chain breakage, and even cause serious accidents such as production stoppage and personal injury. The existing scraper conveyor foreign bodies identification method has the problems of poor adaptability to underground images and the incapability of distinguishing the types and quantities of foreign bodies. To solve the above problems, a multi-object detection method for iron foreign bodies in scraper conveyor based on improved mask region-convolutional neural network (Mask R-CNN) is proposed. The image enhancement algorithm based on the Laplace operator is used to preprocess the images collected under the environment of low illumination and high dust. The enhanced images are marked to make a data set. The ResNet-50 feature extractor of the Mask R-CNN model is used to obtain the image features of iron foreign bodies. The feature pyramid network is used for feature fusion to ensure both high-level semantic features (such as category, attribute, etc.) and low-level contour features (such as color, contour, texture, etc.), so as to improve the accuracy of small-scale iron foreign body identification. To solve the problem that the anchor point generated by the Mask R-CNN model does not correspond to the size of the iron foreign body to be detected, the Mask R-CNN model is improved. K-means Ⅱ clustering algorithm is used to replace the original anchor point generation scheme. The cluster center point is obtained by traversing the length and width information of the tag box in the data set, so as to achieve the multi-object detection of iron foreign bodies in the scraper conveyor. The experimental results show that the average detection time of the improved Mask R-CNN model is 0.732 s, which is shortened by 0.093 s and 0.002 s compared with Mask R-CNN and YOLOv5 respectively. The average precision is 91.7%, which is 11.4% and 2.9% higher than that of Mask R-CNN and YOLOv5 respectively

A Fault Tolerance Method for Multiple Current Sensor Offset Faults in Grid-Connected Inverters

International audienceThree-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control grid-connected system, which leads to difficulties for fault diagnostics and fault-tolerant control. This paper proposes a fault tolerance method based on average current compensation mode to eliminate these adverse effects of fault features. The average current compensation mode compensates the average of the three-phase current to the αβ axis current to realize the fault feature reconstruction of the current sensor. The mode does not affect the normal condition of the system. Then, the data-driven method is used for fault diagnosis, and the corresponding fault tolerant control model is selected according to the diagnosis results. Finally, the experimental results show that the proposed strategy has a good fault tolerance control performance and can improve the fault feature discrimination and diagnostic accuracy

Sulforaphane ameliorates bisphenol A-induced hepatic lipid accumulation by inhibiting endoplasmic reticulum stress

Abstract The aim of the present study was to investigate the role of endoplasmic reticulum (ER) stress in bisphenol A (BPA) – induced hepatic lipid accumulation as well as the protective effects of Sulforaphane (SFN) in this process. Human hepatocyte cell line (LO2) and C57/BL6J mice were used to examine BPA-triggered hepatic lipid accumulation and the underlying mechanism. Hepatic lipid accumulation, triglycerides (TGs) levels, the expression levels of lipogenesis-related genes and proteins in the ER stress pathway were measured. It was revealed that BPA treatment increased the number of lipid droplets, the levels of TG and mRNAs expression of lipogenesis-related genes, and activated the ER stress pathway. These changes were inhibited by an ER stress inhibitor 4-phenylbutyric acid. SFN treatment abrogated BPA-altered hepatic lipid metabolism and ameliorated BPA-induced ER stress-related markers. Together, these findings suggested that BPA activated ER stress to promote hepatic lipid accumulation, and that SFN reversed those BPA effects by alleviating ER stress

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present