Beihang University

Beijing Bytedance Network Technology Co., Ltd.

Thomson Licensing

Yumeng Li

Xianbin Cao

Wenbo Du

Xi Bo Zhu

Wei Lu

Wei Lin

Chi Fang

Biyue Li

Hui Zhang

Lei Zheng

Yonghao Luo

Jiheng Yang

Huayun Miao

Guibin Chen

Haichao An

Jintong Zhang

This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.

Deep unsupervised learning approach, device and storage medium for airspace complexity evaluation

A computer-based airplane flight path planning method based on the pigeon-inspired optimization (PIO) algorithm includes steps of establishing an uncertainty prediction model, determining the path to be optimized, and obtaining an optimal path using the PIO algorithm for a flight controller onboard to execute. The PIO algorithm treats a pigeon flock as a scale-free network, applies map and compass operators to the scale-free network, and performs landmark operations to obtain the optimal path. The device that performs the path planning includes an access module for obtaining the regional environment information and a flight controller onboard the airplane. The flight controller includes a building module for setting up the trajectory prediction model including uncertainties; a determining module to determine the trajectories which need optimization; an optimization module, which uses the PIO algorithm to optimize the flight path; and a computer memory module.

Airplane flight path planning method and device based on the pigeon-inspired optimization

A sector situation (SS) evaluation framework is based on knowledge transfer and is specifically applicable for small-training-sample environment. The SS evaluation framework is able to effectively mine knowledge hidden within the samples of both target and non-target sectors, and properly handle the integration between the knowledge derived from different sectors. This framework includes three main steps: (1) sufficiently mine the knowledge within the samples of the target sector using the strategies of multi-factor subset generation and multi-base evaluator construction, and build target base evaluators; (2) precisely learn the knowledge in the samples of the non-target sectors using similar strategies for the target sector, together with a sample transformation, and build non-target base evaluators; and (3) efficiently integrate the target and non-target base evaluators based on evaluation confidence analysis of those base evaluators.

Knowledge-transfer-based learning framework for airspace situation evaluation

An interaction method, device and equipment for operable object. The mobile terminal acquires a moving direction and a moving displacement when moving (S); an initial position of an operable object is an original position of the operable object before the mobile terminal moves; the mobile terminal determines, according to the moving direction, the moving displacement, and the initial positions of the operable objects, determines an application to be launched corresponding to a screen of the mobile terminal; the application to be launched is displayed on the screen of the mobile terminal (S); and the application to be launched is launched (S).

Interaction method, device and equipment for operable object

Provided are flight conflict resolution method and apparatus based on ultimatum game theory. The method includes: obtaining a first priority of a first aircraft and a second priority of a second aircraft when it is determined that a minimum distance between the first aircraft and the second aircraft within a preset time period is less than a preset distance; determining a first angle and a second angle of the first aircraft and a fourth angle and a fifth angle of the second aircraft according to the first priority, the second priority, and a preset limiting deflection angle; determining a third angle of the first aircraft and a sixth angle of the second aircraft; determining a first deflection angle of the first aircraft and a second deflection angle of the second aircraft according to the first and second priorities, the first, second, third, fourth, fifth and sixth angles.

Flight conflict resolution method and apparatus based on ultimatum game theory

A method, a device and a mobile terminal for data transmission are provided. The transmission method is applied in the mobile terminal, when a user wants to share data, target data can be determined in a present application by the mobile terminal, then the mobile terminal is moved to obtain a moving direction and a moving displacement when the mobile terminal moves; an application currently corresponding to the screen of the mobile terminal is determined according to the moving direction, the moving displacement and an initial position of each application; when a predetermined condition is satisfied, an application corresponding to the screen of the mobile terminal is determined to be a target application, and the determined target data are transmitted to the target application; it only needs to move the mobile terminal to determine the target application when a predetermined condition is met for transmitting data to the target application.

Data transmission method, device and mobile terminal

In video/image coding, macroblocks (MBs) are often intra coded in raster scan order, starting from one seed MB. The invention improves intra prediction for optimized usage of multi-core processors. Encoding starts from multiple intra coded seed MBs per frame, and continues with adjacent MBs. A predefined prediction spread pattern (growth pattern) is used that comprises simultaneous prediction of two or more adjacent MBs per MB. Adjacent MBs with high coding cost are called 'Hold-MB' and can be held from being processed, until another neighboring edge is available for prediction. Encoding comprises marking a MB with high coding cost for deferred prediction. This MB is skipped while the other MBs are encoded. When a further adjacent MB was encoded, the marked MB is predicted based on the adjacent MBs. Since a decoder receives the deferment marks and uses the same growth pattern, it follows the encoder, and predicts and decodes correctly.

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Beijing, China

Yumeng Li