Ping an Technology (shenzhen) Co., Ltd.

Beijing Beikehuaxia Bio-medical Technology Co., Ltd.

Guangzhou Liushun Biotechnology Co., Ltd.

Huawei Technologies Co., Limited

Jin Ma

Jing Jing Xiao

Jianzong Wang

Chenyu Wang

Yan Wang

Ming Liu

Yu Liu

Hao Wang

Tianbo Wu

Zhangcheng Huang

Bing Liu

Xueqi Qian

Daping Li

Shuang Xie

Nengan Chen

Junjun Dong

Jingsi Dai

Xihong Liu

Leihao Xia

Lianwu Deng

Jing Xiao

Huijuan Deng

A multi-target kinase inhibitor is shown in formula (I), in which R is selected from formula (a), formula (b), formula (c), formula (d), formula (e) and formula (f). The multi-target kinase inhibitor can effectively inhibit the enzymatic activities of RET, VEGFR3 and PDGFRA, and can effectively treat diseases that are regulated and controlled by multi-target kinases and are related to abnormal signal transduction pathways of the multi-target kinases, including cancers of breast, respiratory tract, brain, reproductive organ, digestive tract, urinary tract, eye, liver, skin, head and/or neck and distant metastatic cancers thereof, and lymphoma, sarcoma, leukemia and the like. The active ingredients of the pharmaceutical composition of the present invention comprise a multi-target kinase inhibitor, which accounts for 1-50 wt % of the composition.

Multi-target kinase inhibitor, pharmaceutical composition, and preparation method for multi-target kinase inhibitor and use thereof

A method and apparatus for training a semantic segmentation model, a computer device, and a storage medium are described herein. The method includes: constructing a training sample set; inputting the training sample set into a deep network model for training; inputting the training sample set into a weight transfer function for training to obtain a bounding box prediction mask parameter; and constructing a semantic segmentation model.

Method and apparatus for training semantic segmentation model, computer device, and storage medium

Disclosed are a car damage picture angle correction method, an electronic device, and a readable storage medium. The method includes: after receiving a car damage picture to be classified and identified, identifying a rotation category corresponding to the received car damage picture by using a pre-trained picture rotation category identification model; determining a rotation control parameter corresponding to the identified rotation category according to a pre-determined mapping relation between rotation categories and rotation control parameters, the rotation control parameter including a rotation angle and a rotation direction; and rotating the received car damage picture according to the determined rotation control parameter, so as to generate an angle-normal car damage picture. The disclosure can perform car damage picture angle correction more comprehensively and more effectively with no need to artificially perform angle identification on a car damage picture and to manually rotate the picture, thereby achieving a higher efficiency and accuracy.

Car damage picture angle correction method, electronic device, and readable storage medium

A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

Deep learning based license plate identification method, device, equipment, and storage medium

A method, a device, a terminal and a storage medium for acquiring slant value of slant image are provided. The method for acquiring slant value of a slant image comprises: analyzing the slant image and acquiring coordinate information of a plurality of boundary lines of the slant image; acquiring first slant values of the boundary lines by analysing and calculating the coordinate information; acquiring a correction value; calculating difference values between the first slant values and the correction value respectively; determining the first slant value corresponding to the minimum difference value as the slant value of the slant image. The technical solution of the present disclosure can uniquely determine a slant value of slant image.

Method and device for acquiring slant value of slant image, terminal and storage medium

A video recording method is disclosed including: extracting every predetermined time period a first image captured by a monitoring camera; performing region detection on the first image to extract areas of interest covering some or all parts of a target object; performing motion area filtering on the areas of interest to select one or more target areas; dividing each target area into multiple sub-areas; extracting every predetermined time period a second image captured by the monitoring camera; comparing an image block located in each sub-area of the second image with that in the same sub-area of the second image previously extracted, to determine whether a motion event has occurred in each sub-area; and controlling the monitoring camera to start video recording from the currently extracted second image when a motion event has occurred in a sub-area. A server, system, and storage medium suitable for the method are also disclosed.

Video recording system, server, system, and storage medium

Growing community of inventors

Guangdong, China

Jin Ma