Beijing University of Technology

Beijing Zitiao Network Technology Co., Ltd.

Suzhou Kanova Biopharmaceutical Co., Ltd.

Beijing Kanova Biopharmaceuticals Co. Ltd.

Jiaming Li

Junfei Qiao

Honggui Han

Yang Wang

Zhi Chen

Chen Dong

Li Hui Guo

Kai Li

Tiantian Sun

Xiaolong Wu

Xinghua Zhang

Xiaoxu Qi

Guanming Cai

Tianqi Chen

Ximin Zhu

Hongxu Liu

Qingyuan Hu

Zhixuan Wei

Yuan Dou

Binbin Li

Disclosed in the embodiments of the present disclosure are a video generation method, an apparatus, an electronic device and a storage medium. The method comprises: during an image editing process of subjecting an initial image to a series of image editing operations to obtain a target image, acquiring initial key frames, the initial key frames being intermediate images between the initial image and the target image during the image editing process, the different initial key frames being intermediate images generated when different image editing operations of the series of image editing operations are triggered during the image editing process, and the intermediate images showing editing effects corresponding to the image editing operations; and generating a target video according to the initial key frames so as to record the image editing process of the target image. The present disclosure can be used for recording an image editing process.

Video generation method, apparatus, electronic device and storage medium

The present invention encompasses antibodies and fragments thereof that bind to an extracellular domain of B7H3, and uses thereof.

Anti-B7-H3 antibody

A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

Type-2 fuzzy neural network-based cooperative control method for wastewater treatment process

An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI). Combined with the target-related identification algorithm, the fault type of sludge bulking is determined. Results show that the intelligent identification method can quickly obtain the sludge volume index (SVI), accurate identification fault type of sludge bulking, improve the quality and ensure the safety operation of the wastewater treatment process.

Intelligent identification method of sludge bulking based on type-2 fuzzy neural network

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Beijing, China

Jiaming Li