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

Expression of canonical WNT/β-CATENIN signaling components in the developing human lung

<p>Abstract</p> <p>Background</p> <p>The WNT/β-CATENIN signaling cascade is crucial for the patterning of the early lung morphogenesis in mice, but its role in the developing human lung remains to be determined. In this study, expression patterns of canonical WNT/β-CATENIN signaling components, including WNT ligands (<it>WNT2</it>, <it>WNT7B</it>), receptors ( <it>FZD4</it>, <it>FZD7</it>, <it>LRP5</it>, <it>LRP6</it>), transducers ( <it>DVL2</it>, <it>DVL3</it>, <it>GSK-3β</it>, <it>β-CATENIN</it>, <it>APC</it>, <it>AXIN2</it>), transcription factors ( <it>TCF4</it>, <it>LEF1</it>) and antagonists ( <it>SOSTDC1</it>) were examined in human embryonic lung at 7, 12, 17 and 21 weeks of gestation (W) by real-time qRT-PCR and in situ hybridization.</p> <p>Results</p> <p>qRT-PCR analysis showed that some of these components were gradually upregulated, while some were significantly downregulated from the 7 W to the 12 W. However, most components reached a high level at 17 W, with a subsequent decrease at 21 W. In situ hybridization showed that the canonical WNT ligands and receptors were predominantly located in the peripheral epithelium, whereas the canonical WNT signal transducers and transcription factors were not only detected in the respiratory epithelium, but some were also scattered at low levels in the surrounding mesenchyme in the developing human lung. Furthermore, Western blot, qRT-PCR and histological analysis demonstrated that the β-CATENIN-dependent WNT signaling in embryonic human lung was activated in vitro by CHIR 99021 stimulation.</p> <p>Conclusions</p> <p>This study of the expression patterns and in vitro activity of the canonical WNT/β-CATENIN pathways suggests that these components play an essential role in regulation of human lung development.</p

Research on Operation Mode and Risk Prevention of B2B Supply Chain Finance

B2B supply chain finance is the business innovation of e-commerce enterprises in the field of transforming supply chain finance. E-commerce platform, supply chain enterprises and service providers are important participants in B2B supply chain finance. Four flow coordination, credit enhancement, technology empowerment and win-win situation are the key elements of B2B supply chain finance. In view of the external environmental risks faced by the B2B supply chain finance operation at this stage and the structural risks, fund-side risks, technical risks and risks of collateral generated within the system, we put forward the formulation of external environmental risk early warning and response mechanism, the improvement of relevant legal system design, the scientific design of business processes, the strengthening of platform financial management, the improvement of technical application level and collateral supervision management system and other governance strategies in this paper

An integration fault detection method using stator voltage for marine current turbines

International audienceThe marine current turbine (MCT) is becoming more and more popular to produce eco-friendly electricity.However, its performance is negatively affected by MCT imbalance fault. In this paper, an integration faultdetection method using stator voltage for MCT is proposed. This method uses an integration way to detect theimbalance fault. The proposed method comprises three steps: First, the data conversion is based on Hilberttransform and the extreme value searching, and then the imbalance fault signature extraction based on thefrequency sequences subtraction (FSS). Last, to reduce the data dimension and to set the fault detection limit, adata vector selection method based on principal components analysis (PCA) (called preliminary-selection-basedPCA (PS-PCA)) is proposed, the adaptive fault detection is realized by calculating Hotelling T2 and SPE (squaredprediction error). Finally, a marine current prototype experimental platform was built to verify the proposedmethods. The experimental results show that the method in this paper has high detection accuracy in the faultdetection of MCT imbalance under the variable flow rate

A Dynamic Obstacle Avoidance Method for Unmanned Surface Vehicle under the International Regulations for Preventing Collisions at Sea

International audienceA new method is proposed for the dynamic obstacle avoidance problem of unmanned surface vehicles (USVs) under the international regulations for preventing collisions at sea (COLREGs), which applies the particle swarm optimization algorithm (PSO) to the dynamic window approach (DWA) to reduce the optimal trajectory finding the time and improve the timeliness of obstacle avoidance. Meanwhile, a fuzzy control algorithm is designed to dynamically adjust the weight coefficients of the velocity and obstacle distance terms in the cost function of the DWA algorithm to adapt to the changes in the environment. The proposed dynamic obstacle avoidance method is experimentally validated, in which proposed PSO combined with the DWA algorithm (PSO-CCDWA) results in a 42.1%, 11.2% and 28.0% reduction in the navigation time of the USVs in three encounter-situations of COLREGs than that of the classical DWA algorithm (CCDWA) conforming to the conventional COLREGs, respectively. The fuzzy control combined with the DWA algorithm (FUZZY-CCDWA) reduces the distance traveled by 15.8%, 0.9% and 2.8%, respectively, over the CCDWA algorithm in the three encounter scenarios. Finally, the effectiveness of the proposed dynamic obstacle avoidance method is further verified in a practical navigation experiment of a USV named “Buffalo”

Adaptive equivalent consumption minimization strategy for hybrid electric ship

International audienceIn recent years, with the development of battery technology, hybrid electric ship(HES), as a promising solution to reduce the fuel consumption and emissions, hasbecome a research hotspot. However, frequent use of the battery will acceleratethe aging of the battery, and the replacement of scrapped battery will increasethe cost of the ship. Therefore, it is necessary to consider delaying battery aginginto the energy control strategy of HES. The equivalent consumption minimizationstrategy (ECMS) is a feasible energy control strategy because it can be implementedin real time. However, under the condition of uncertain initial state ofcharge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumptionunless the equivalent factor (EF) is optimized in real time. In this paper, anadaptive equivalent consumption minimization strategy (A-ECMS)is proposed, which extracts the global optimal EF trajectory from the dynamic programming(DP) solution and uses the back propagation (BP) neural network to adjust the EFin real time. A trade-off between the fuel consumption and battery aging is madein the cost function by introducing a weight coefficient. Finally, the effectivenessand the adaptability of the proposed strategy are verified in MATLAB

A thrust allocation strategy for intelligent ships based on model prediction control

International audienceIn order to solve the problem of the traditional sequential quadratic programming thrust allocation method, such as limited feasible region and low propulsion efficiency, a thrust allocation strategy based on model predictive control is proposed in this paper. The proposed thrust allocation strategy is realized by rolling optimization of model predictive control, and moreover, considering the possibility of thruster failure, a fault-tolerant thrust allocation strategy is proposed. The simulation results show that the proposed strategy can not only make the ship run in the expected condition but also reduce the average power consumption by 16.3% compared with the traditional sequential quadratic programming method and has good fault tolerance in the face of thruster failure