Northeastern University

Jiayue Sun

Gang Wang

Huaguang Zhang

Jian Feng

Dongsheng Yang

Dazhong Ma

Lei Wang

Jinhai Liu

Senxiang Lu

Bowen Zhou

Peng Gu

Yunfei Mu

Guangdi Li

Juan Zhang

Diliyaer Hudabaierdi

Provided are an offshore floating light energy storage integrated charging station system and an operation control method thereof. The system includes a triangular floating floater structure, a control unit mounted on the floating floater structure, an energy storage tank arranged on the floating floater structure, an energy storage unit mounted in the energy storage tank, and a photovoltaic electricity generation unit paved on the energy storage tank. A battery pack in the energy storage tank on each triangular floater is one energy storage unit. The energy storage unit can independently supply electricity or charge the vessels when the photovoltaic electricity generation unit does not generate electricity and the state of charge of the energy storage unit is sufficient. The energy storage unit are controlled to switch between four control modes according to the system's needs to ensure stable operation of the system.

Offshore floating light energy storage integrated charging station system and operation control method thereof

Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.

Intelligent inversion method for pipeline defects based on heterogeneous field signals

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Shenyang, China

Jiayue Sun