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Now the measurement means of access capacity at all levels of distribution network are insufficient and it is difficult to calculate effectively based on limited data. This paper proposes a hierarchical evaluation method of access capacity of distribution network with multiple prosumer group load forecasting. Firstly, a load dynamic prediction model is constructed. According to the typical load model of historical data mining, a unit prediction model of typical users is established by using the conjugate gradient RBF neural network learning algorithm. Then, according to the charge-discharge dynamic model of the energy storage power station and the load dynamic prediction results, an assessment model of access capacity at the 10kV side was constructed. By analyzing the known load type, load type ratio and transformer capacity on the low-voltage side, the conversion coefficient of 10kV side load to 0.4kV side transformer capacity is obtained by weighting different types of loads proportionally. Then, access distribution capacity of different prosumer group can be obtained based on conversion coefficient. It can ensure the effective measurement of access capacity at all levels of distribution network under the premise of meeting N-1 safety criteria. Finally, an example is given to demonstrate the effectiveness of the proposed method.

The clutters of rebar in the ground penetrating radar (GPR) images may mask the echoes of the inner defects under the rebars, which adversely affects the identification of the inner structural defects in the reinforced concrete (RC). In this study, a deep learning-based method for rebar clutters removal and defect echoes enhancement in GPR B-scan images was proposed. The residual-inception blocks and attention modules were designed in the network as per the characteristics of the task. Validation of the method has been performed at three levels: first using synthetic data, next by synthetic data superimposed with actual concrete background noise, and finally using a sandbox model test in a realistic scenario. The results indicate that the proposed method had the ability to effectively remove rebar clutters and reconstruct complete defect echoes, with performance superior to those of the traditional deep-learning based methods. A RC structural defect identification experiment was performed to verify the effectiveness of the proposed method. The results indicated that after the rebar signals were removed using the method proposed, the accuracy for identifying the defects under the rebars had been improved from 0.208 to 0.850. The proposed method is promising for applying to practical engineering due to its fast processing and better performance.

To meet the requirements of high accuracy and high efficiency in three-dimensional (3D) measurement, a phase difference-3D coordinate mapping model is proposed based on extreme learning machine (ELM) network. First, the reconstruction model of the ideal measurement system is set following the geometric structure of the system. Subsequently, by generalizing camera and world coordinates, a generalized measurement model is built. Lastly, ELM network is employed to solve the mapping coefficients. During measurement, only one phase difference map is required to complete the 3D reconstruction of the object, which simplifies the data processing process and saves time. The result indicates that the mean square errors (MSEs) of the X, Y and Z of the testing sample are 3.5955×10-4 mm, 9.5113×10-4 mm and 4.4×10-3 mm, respectively. Moreover, the reconstruction experiments of objects with different geometric structures are performed to demonstrate the general application of the proposed method.

Diagnosing tunnel lining structural damages is vital to ensure safe tunnel operations. However, the detection of multiple defect is challenging task due to the size imbalance between cracks, spalling, and backgrounds. Currently, deep-learning-based methods for multiple defect are dependent on multiple-stage networks, which have limited their scalability and complex frame working processes. To accurately recognize the multiple defect at the pixel-level using only one-stage networks, a new method was proposed, which integrated the basic SegNet with a focal loss function, and was referred to as an FL-SegNet method. The focal loss function was adopted to address the problem of the size imbalance by down-weighing the losses assigned to the well-classified samples, and then the training was focused on the hard samples. Furthermore, comparative experiments were performed to evaluate the performances of the different methods. The experimental results demonstrated that FL-SegNet method was capable of accurately predicting the profiles of small-sized cracks and overlapping damages even under various noise conditions, and successfully outperformed the two-stream method and the basic SegNet method in this regard. The performance metrics (MPA and MIoU) of the FL-SegNet method were significantly higher than those of other multiple defect detection approaches in different scenarios (images with small-sized damages attained to 81.53% and 69.86%, increased by 11.99 % and 4.88% compared with two-stream method, and increased by 17.78% and 7.69% compared with basic SegNet). Therefore, this paper provides an effective solution for the future detection of multiple defect in tunnel linings.