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Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data are almost always insufficient because the real-time monitoring data of a structure only reflect a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. First, disparate subsets of test data are used as training data, and the identification accuracy of the whole dataset is evaluated. The results demonstrate that the proposed approach can achieve high identification accuracy with limited types of training data, with the identification accuracy increasing up to 8.57%. Second, numerical simulation data are used as training data, and then different TL strategies and different DL architectures are compared on the performance of structural condition identification. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGNet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data. [ABSTRACT FROM AUTHOR]

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-view DCNN-based approach which can take advantage of not only multi-view images but also the relationships between them. • The proposed networks learn the integrated features of multi-view fundus images for DR detection. • We introduce the attention mechanisms for mining the relationship between different views. • In order to boost the ability to capture the tiny lesion features in the retinal images, we combine the idea of attention mechanisms with the channel dimension. Diabetic retinopathy (DR) detection has attracted much attention recently, and the deep learning algorithms have gained traction in this area. At present, DR screening by deep learning algorithms is often based on single-view fundus images, which usually leads to an unsatisfactory accuracy of DR grading due to the incomplete lesion features. In this paper, we proposed a novel diabetic retinopathy detection convolutional network for automatic DR detection by integrating multi-view fundus images. Compared to existing single-view DCNN-based DR detection methods, the proposed method has the following advantages. First, our method fully utilizes the lesion features from the retina with a field-of-view around 120 ∘ − 150 ∘. Second, by introducing the attention mechanisms, more attention will be paid on the influential view and the performance can be improved. Besides, we also assign large weights to important channels in the network for effective feature extraction. Experiments are conducted on our collected multi-view DR dataset contained 15,468 images, in which each eye sample provides four-view images. The experimental results indicate that using multi-view images is suitable for automatic DR detection and our proposed method is superior to other benchmarking methods. [ABSTRACT FROM AUTHOR]