Accuracy metrics have been widely used for the evaluation of predictions in machine learning. However, the selection of an appropriate accuracy metric for the evaluation of a specific prediction has not yet been specified. In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics. Test results showed that the training algorithm of “TRAINGLM” exhibited the best performance for predicting damage locations in deep piles. Subsequently, the artificial neural networks were trained using three different datasets collected from three acoustic emission sensor groups, and the prediction accuracies of three models were evaluated with the seven different accuracy metrics. The test results showed that the dataset collected from the pile body-installed sensors group exhibited the highest accuracy for predicting damage locations in deep piles. Subsequently, the correlations between the seven accuracy metrics and the sensitivity of each accuracy metrics were discussed based on the analysis results. Eventually, a novel selection method for an appropriate accuracy metric to evaluate the accuracy of specific predictions was proposed. This novel method is useful to select an appropriate accuracy metric for wide predictions, especially in the engineering field.
This paper presents a theoretical and experimental study on the ampacity of the prefabricated straight-through joint of a 110 kV high voltage cable. Thermal modelling revealed that the critical spot limiting the ampacity of this type of cable joint is located on its crosslinked polyethylene (XLPE) insulation section. The axial distribution of the thermal field in this type of cable joint was also determined. An algorithm for assessing ampacity in this type of cable joint was developed. Experiments were conducted on a real cable system with a prefabricated straight-through joint under different loading conditions. The experiments show a good agreement with the thermal modelling results.
Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.
The lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technology and intelligent algorithm, the computer has a hominine visual perception system which understands, analyzes, and determines input image information, thus recognizing and detecting specific objectives. However, the effect of image recognition for tunnel crack now cannot satisfy the demands of practical engineering. SSD algorithm has been used when analyzing features of lining surface image, while comparison analysis has been made from image recognition results, error rate, and running time. The results indicate that the SSD algorithm can accurately and rapidly detect and mark the position of the tunnel crack. The tunnel information obtained from image recognition is subsequently imported into the team independently developed software GeoSMA-3D, which is useful for determining tunnel grade.
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