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Purpose: To develop a deep-learning system for automatic osteoporotic vertebral compression fractures (OVCF) detection at the thoracolumbar junction using low-dose computed tomography (CT) images. Materials and methods: 500 individuals were enrolled in this retrospective study, including 270 normal and 230 OVCF cases. The cases were divided into the training, validation, and test sets in the ratio of 6:2:2. First, a localization model using Faster R-CNN was trained to identify and locate the target thoracolumbar junction, then a 3D AnatomyNet model was trained to finely segment the target vertebrae in the localized image. Finally, 3D DenseNet model was applied for detecting OVCF on target vertebrae. Manual annotation by experienced radiologists and a clinically made diagnosis of OVCF were used as the gold standards. The performance of the detecting system was evaluated through the area under the curve (AUC) for receiver operating characteristic (ROC) analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Results Our automated segmentation method achieved a mean Dice coefficient of 0.95 for vertebral bodies (T12-L2) segmentation on the testing dataset, with dice coefficients greater than 0.9 accounting for 96.6%. For the diagnostic performance of our system for OVCF, the AUC, sensitivity, specificity, PPV and NPV for the four-fold cross-validation on the testing dataset were 98.1%, 95.7%, 92.6%, 91.7% and 96.2%, respectively. Conclusions A deep-learning system has been developed to automatically segment vertebral bodies and accurately detect OVCF using low-dose CT.

Droppers are key components of high-speed railway overhead catenary systems, which are exposed to the external environment and are prone to breakage faults due to the impact of wind force and the pantograph on moving trains day after day. How to identify dropper breakage or relaxation faults through acceleration signals installed in the carrier cable and contact wire is a challenging problem. In this study, the experimental section of the Lanzhou-Xinjiang high-speed railway was simulated on the basis of the bow-network dynamic simulation model, in which the overhead catenary system was subjected to the force of pulsating wind alone or pulsating wind and the pantograph at the same time. In the experiment, we collected 10 channel signals from five acceleration sensors when two droppers were normal or broken. We established a 1D-CNN model of four categories and then determined the hyperparameters of the deep network structure and the important parameters of the network optimization scheme through the Bayesian optimization algorithm. Furthermore, we selected the lowest sensor number to identify dropper fracture faults by a large number of experiments according to mechanics principles. The experimental results show that the proposed methods have a higher identification accuracy rate, recall rate, and robustness than the traditional artificial feature extraction approaches. Therefore, the detection methods proposed provide an effective way to identify high-speed railway catenary dropper faults on the basis of acceleration sensors.

Anomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the generator, we design a Fully-Nested Residual Encoder-decoder Network, which is used to encode and decode the images. In addition, we add residual structure into both encoder and decoder, which reduces the risk of overfitting and enhances the feature expression ability. In the test phase, a distance measurement model is used to determine whether the test sample is abnormal. The experimental results on the CIFAR-10 dataset demonstrate the excellent performance of our method. Compared with the existing models, our method achieves the state-of-the-art result.

The business knowledge of intelligent transportation inspection is scattered in the business system, database and expert experience, and it is impossible to visualize the entire business logic of transportation inspection. Aiming at the characteristics of complex and wide variety of operational inspection business relationships, an intelligent operational inspection knowledge mapping is constructed. Through the steps of operation inspection business ontology construction, knowledge acquisition, knowledge fusion, knowledge mapping display, knowledge mapping application and other steps, a logical knowledge mapping of operation inspection business is formed, which facilitates the recognition of the entire business relationship and assists the decision-making analysis of the operation inspection personnel.

In order to realize the explicit expression of the distribution network data relationship of the power grid enterprises and the efficient search of data resources, a method for modeling and intelligent searching of distribution network data resources based on knowledge mapping is proposed. This method firstly identifies the core ontology of the distribution network based on the ontology construction process and the distribution network business, then maps and extracts the identified ontology model and the relational database physical model to form a knowledge mapping of distribution network data resources. On this basis, combining the BiLSTM-CRF-based distribution network named entity recognition algorithm and the BERT-based Chinese semantic matching algorithm, a distribution network data resource search engine technology is proposed to realize the search text feature extraction and result matching. Finally, the application verification data of a power grid company shows that the distribution network knowledge mapping has a good effect on the expression and visualization of data resource relationships. At the same time, the accuracy rate of the proposed distribution network data resource search engine technology method reaches 72.2%.

A simple europium complex, Eu(TTA)3AA (TTA=4,4,4-trifluoro-1-(2-thienyl)-1,3-butanedione, AA=acrylic acid) was synthesized by a simple solution method. Then, two kinds of rubber matrix, nitrile-butadiene rubber (NBR) and silicone rubber (SiR) were used as the substrate for Eu(TTA)3AA to prepare fluorescence composites. The neat Eu(TTA)3AA complex showed the ability of self-polymerization when it was heated at 145 °C. It was found that the fluorescence intensity of the neat Eu(TTA)3AA decreased over 70% when the polymerization time was over 25 min at 145 °C. The results also revealed that the polymerizated Eu(TTA)3AA could be dispersed in nano-scale in two matrices and the luminescent intensities decreased 52% in NBR matrix, and 95% in SiR matrix compared with two relative compounds without crosslinking. To optimize the luminescence intensity of the composites, the Eu(TTA)3AA polymerization kinetic process in matrix was investigated in detail by controlling the temperature, the crosslinking agent, etc. The results showed that the peroxide could accelerate Eu(TTA)3AA self-polymerization in the rubber matrix, and therefore improved the dispersion, but not be helpful for the luminescence intensity enhancement. In addition, the relatively higher luminescence intensity in Eu(TTA)3AA/NBR in comparison to that of Eu(TTA)3AA/SiR might contribute to the interaction between nitrile group (–CN) in NBR and Eu-complexes, suggesting that the luminescence intensity of the composites also depended on the matrix characteristics.

The vertical stripe defects on silicon steel surface seriously affect the appearance and electromagnetic properties of silicon steel products. Eliminating such defects is adifficult and urgent technical problem. This paper investigates the relationship between the defects and their influence factors by classification methods. However, when the common classification methods are used in the problem, we cannot obtain a classifier with high accuracy. Byanalysis of the data set, we find that it is imbalanced and inconsistent. Because the common classification methods are based on accuracy-maximization criterion, they are not applicable to imbalanced and inconsistent data set. Thus, we propose asupport-degree-maximization criterion and anovel cost-sensitive loss function and also establish an improved L1/2 regularization approach for solution of the problem. Moreover, by employing reweighted iteration gradient boosting algorithm, we obtain a linear classifier with a high support degree. Through analyzing the classifier, we formulate a rule under which the silicon steel vertical stripe defects do not occur in the existing production environment. By applying the proposed rule to 50TW600 silicon steel production, the vertical stripe defects of the silicon steel products have been greatly decreased.

The backpropogation (BP) neural networks have been widely applied in scientific research and engineering. The success of the application, however, relies upon the convergence of the training procedure involved in the neural network learning. We settle down the convergence analysis issue through proving two fundamental theorems on the convergence of the online BP training procedure. One theorem claims that under mild conditions, the gradient sequence of the error function will converge to zero (the weak convergence), and another theorem concludes the convergence of the weight sequence defined by the procedure to a fixed value at which the error function attains its minimum (the strong convergence). The weak convergence theorem sharpens and generalizes the existing convergence analysis conducted before, while the strong convergence theorem provides new analysis results on convergence of the online BP training procedure. The results obtained reveal that with any analytic sigmoid activation function, the online BP training procedure is always convergent, which then underlies successful application of the BP neural networks.

Support vector classification (SVC) is an efficient tool to solve classification which is the fundamental problem in data mining, but its efficiency is limited on the small or middle sized data sets. In this paper a new preprocessing algorithm - clarifier method (CM) is proposed to accelerate training of SVC especially when training data are of large mode. The core idea of CM is based on the fact that of all the training vectors of SVC, only support vectors have a most significant influence on the optimal result, meanwhile the other vectors can be not taken into account with little deviation of the final result. The approach can distinguish probable support vectors directly by the labels of training vectors in its neighborhood region. Then only these training vectors are preserved to form the training set of SVC. A series of artificial and real world simulation results show the efficiency and effectiveness of CM. The number of vectors in SVC training becomes much less and the training time is decreased without loss of the generalization capability of the SVC even in the case that noisy training samples are added in