data synchronization
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Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

Abstract The developers of 1C offer a line of software products to automate the production processes of enterprises. 1C: ERP Enterprise Management and 1C: Salary and Personnel Management are the flagship solutions of 1C. In the course of the work, the difficulties that arise when it is necessary to synchronize data between the configurations of 1C: ERP Enterprise Management and 1C: Salary and Personnel Management have been analyzed. This article analyzes the functionality of the above products. An overview of the main elements underlying the mechanism for synchronizing information between 1C applied solutions is given. The process of data exchange and synchronization between the products 1C: ERP Enterprise Management and 1C: Salary and Personnel Management using the built-in tools of the 1C software platform is considered. The process of organizing interaction and data synchronization in the considered solutions is presented. Recommendations related to setting up and receiving synchronized objects are given. The analysis of the results is carried out, conclusions are drawn.

Abstract The present current differential protection for MMC-HVDC transmission lines has absolute selectivity and powerful ability to withstand high transition resistance, while it is easily affected by distributed capacitive current and data synchronization error. To solve the problem above, this article proposes a novel current differential protection scheme. The distributed capacitive current can be calculated by integrating the linear voltage distribution in real-time. Thus, the differential value of the midpoint currents of DC line, which are calculated based on the low-pass filtered measure voltages and currents on both sides, can be adopted to identify the fault. Besides, the data synchronization error can be eliminated based on the waveform matching of the calculated midpoint currents. This novel current differential protection has excellent performance and can solve the problems of traditional current differential protection for HVDC lines.