Inspur Suzhou Intelligent Technology Co., Ltd

Shandong Yingxin Computer Technologies Co., Ltd.

Ruyang Li

Yaqian Zhao

Rengang Li

Dongdong Jiang

Yaqiang Zhang

Hui Wei

Zhe Xu

Chen Li

The optical device includes: a first coupler having an adjustable beam splitting ratio; a sensing arm and a programmable modulation arm which are connected to the first coupler; and a second coupler having an input port connected to the sensing arm and the programmable modulation arm and an output port connected to a photodetector. The sensing arm is used for generating, by means of a slot waveguide, a first signal from a first light wave beam outputted by the first coupler. The programmable modulation arm is used for obtaining, by utilizing a grating, a second signal according to a second light wave beam outputted by the first coupler, and the grating is a nano grating generated under a pre-programmed voltage parameter of a programmable piezoelectric transducer of the programmable modulation arm. An electronic device and a programmable photonic integrated circuit are also disclosed herein.

Optical device, an electronic device, and a programmable photonic integrated circuit

A three-dimensional reconstruction method, a system, and a non-transitory computer readable storage medium are disclosed herein. The method includes: performing local pose optimization by using a target image frame to obtain a local pose error; performing neural network prediction on the target image frame to obtain an initial reconstruction error; performing three-dimensional reconstruction according to the local pose error and the initial reconstruction error to obtain an initial reconstruction model; performing global pose optimization by using historical image frames to obtain a global optimization result and a global pose error; performing neural network completion on the global optimization result to obtain a final reconstruction error; and optimizing the initial reconstruction model according to the global pose error and the final reconstruction error to obtain a final reconstruction model. The three-dimensional reconstruction method can achieve high-quality and high-precision three-dimensional reconstruction more quickly and conveniently.

Three-dimensional reconstruction method, system, and storage medium

A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Task scheduling method and apparatus, electronic device, and readable storage medium

The present application discloses an automatic driving control method. In the method, parameters are optimally set by using a noisy and noiseless dual-strategy network, identical vehicle traffic environment state information is input into the noisy and noiseless dual-strategy network, a motion space perturbation threshold is set by using a noiseless strategy network as a comparison and a benchmark so as to adaptively adjust noise parameters, and motion noise is indirectly added by adaptively injecting noise into a strategy network parameter space, such that exploration of an environment and a motion space by a deep reinforcement learning algorithm may be effectively improved, automatic driving exploration performance and stability based on deep reinforcement learning is improved, and full consideration of influence of an environment state and driving strategies in vehicle decision-making and motion selection is ensured, thereby improving the stability and safety of an automatic vehicle.

Autonomous driving control method, apparatus and device, and readable storage medium

Provided are a turbulence field update method, apparatus, and device, and a computer-readable storage medium. The method includes: obtaining sample turbulence data; performing model training by use of the sample turbulence data to obtain a reinforcement learning turbulence model; calculating initial turbulence data of a turbulence field by use of a Reynolds Averaged Navior-Stokes (RANS) equation; processing the initial turbulence data by use of the reinforcement learning turbulence model to obtain a predicted Reynolds stress; and performing calculation on the predicted Reynolds stress by use of the RANS equation to obtain updated turbulence data.

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Jiangsu, China

Ruyang Li