East China Jiaotong University

Rongxiu Lu

Hui Yang

Gang Yang

Jianyong Zhu

Fangping Xu

Lizhen Dai

Yuyu Weng

Mingming Chen

Zhipeng Yang

Ying Wang

Cailing Tang

Yunchun Rao

Shuo Xu

Jie Sheng

Quanheng He

Hailong Yang

Provided is an image inpainting method and system guided by an image structure and texture information. The method includes: constructing an image inpainting model with an interaction ability between the image structure and the texture information; preprocessing a damaged image sample to obtain a mask image sample and an edge structure image sample; inputting the damaged image sample, the mask image sample, and the edge structure image sample into the image inpainting model, training the image inpainting model, and generating weight information of the image inpainting model, to obtain a target image inpainting model; obtaining a damaged image to be restored, preprocessing the damaged image to be restored, to obtain a mask image and an edge structure image, separately inputting the damaged image to be restored, the mask image, and the edge structure image into the target image inpainting model, outputting, by the target image inpainting model, a restored image.

Image inpainting method and system guided by image structure and texture information

A fault detection method and system for tunnel dome lights based on an improved localization loss function. The method includes: constructing a dataset of tunnel dome light detection images; acquiring a you only look once (YOLO) v5s neural network based on the improved localization loss function; training the YOLO v5s neural network according to the dataset to obtain a trained YOLO v5s neural network; acquiring a to-be-detected tunnel dome light image; detecting, with the trained YOLO v5s neural network, the to-be-detected tunnel dome light image to obtain position coordinates of luminous dome lights; and determining, according to the position coordinates of the luminous dome lights, whether a fault occurs in the tunnel dome lights. The present disclosure can accurately localize the tunnel dome lights and label the positions, and can detect whether the tunnel dome lights work normally according to a relative positional relationship between the labeled dome lights.

Fault detection method and system for tunnel dome lights based on improved localization loss function

Described is a method for predicting multiple components' content in a case that rare earth ions with and without color feature coexist, and relates to component content prediction in rare earth extraction process. It is difficult to quickly/accurately detect component's content in rare earth extraction process. Because of relatively large difference between images' color features of CePr/Nd mixed solution with colorless Ce ions and Pr/Nd solution, detecting content method of single rare earth element based on color feature is no longer applicable. The method includes: first searching for H and S components with maximum correlation with component content in HSI color space; establishing ELM based multi-component content soft measurement model using H and S component first-order moment as input; and for uncertainty of initial weight and ELM (extreme learning machine) model's threshold, optimizing model parameters using genetic algorithm GA to optimize ELM model for component content prediction higher precision.

Method and system for predicting content of multiple components in rare earth extraction process

The disclosure discloses a method for dynamically monitoring the content of a rare earth element (REE) component based on a time-series feature. Using an image information acquisition device to periodically acquire a time-series image of a rare earth (RE) solution to be monitored; extracting a time-series feature of the time-series image in a mixed color space; determining whether a time-series feature value of the time-series image is in an expected interval of the mixed color space; calculating a histogram intersection distance between the time-series image and a sample image in a sample data set in the HSV color space, and determining the content of the REE component corresponding to the time-series image according to a component content corresponding to a sample image with a larger histogram intersection distance, if the determination result indicates no; otherwise, directly waiting for the acquisition of a time-series image at a next sampling time point.

Method for dynamically monitoring content of rare earth element component based on time-series feature

The present invention discloses a prediction control method and system for component contents in a rare earth extraction process. The prediction control method includes: establishing an Elman neural network model of a rare earth extraction process; obtaining a predicted output value of the rare earth extraction process through the Elman neural network model of the rare earth extraction process; calculating an optimal set value through steady-state optimization; dynamically predicting an extractant flow increment and a detergent flow increment based on the predicted output value and the optimal set value; and controlling component contents in the rare earth extraction process according to the extractant flow increment and the detergent flow increment. According to the present invention, an optimal setting problem of a set point is solved through steady-state optimization calculation, and then an optimal control effect is achieved in combination with a dynamic prediction control method, thereby achieving optimal setting control over the component contents in the rare earth extraction process, and ensuring the product quality of the rare earth extraction process.

Prediction control method and system for component contents in rare earth extraction process

A method and device relate to rare earth solution image capturing, and further relate to the field of rare earth hydrometallurgical process detection technologies. According to an embodiment, a rare earth solution image capture device includes a mixer-settler, a support platform, a camera obscura, a solution collection vessel, a color camera, a light source, a computer, a programmable logic controller, a motor driver, a peristaltic pump, and a conduit pipe. According to another embodiment, the rare earth solution image capture device performs automatic sampling by using the programmable logic controller and may perform detection at any time. An image captured by the color camera may be transmitted to the computer in real time without manual intervention.

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Nanchang, China

Rongxiu Lu