Hanning Wang

A Cloud-Computing-Based Data Placement Strategy in High-Speed Railway

As an important component of China’s transportation data sharing system, high-speed railway data sharing is a typical application of data-intensive computing. Currently, most high-speed railway data is shared in cloud computing environment. Thus, there is an urgent need for an effective cloud-computing-based data placement strategy in high-speed railway. In this paper, a new data placement strategy named hierarchical structure data placement strategy is proposed. The proposed method combines the semidefinite programming algorithm with the dynamic interval mapping algorithm. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices, while the dynamic interval mapping algorithm ensures better self-adaptability of the data storage system. A hierarchical data placement strategy is proposed for large-scale networks. In this paper, a new theoretical analysis is provided, which is put in comparison with several other previous data placement approaches, showing the efficacy of the new analysis in several experiments.

A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

Study of track irregularity time series calibration and variation pattern at unit section

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.

Continuous Distributed Top-k Monitoring over High-Speed Rail Data Stream in Cloud Computing Environment

In the environment of cloud computing, real-time mass data about high-speed rail which is based on the intense monitoring of large scale perceived equipment provides strong support for the safety and maintenance of high-speed rail. In this paper, we focus on the Top-k algorithm of continuous distribution based on Multisource distributed data stream for high-speed rail monitoring. Specifically, we formalized Top-k monitoring model of high-speed rail and proposed DTMR that is the Top-k monitoring algorithm with random, continuous, or strictly monotone aggregation functions. The DTMR was proved to be valid by lots of experiments.

Dynamic metadata query algorithm of high-speed railway basic data based on heterogeneous integration

The goal of sharing high-speed railway basic data is to integrate discrete data resources in order to establish service-oriented data sharing services architecture based on distribution and centralization and to support optimum design and operations management of high-speed railway transportation organization. In this paper a common metadata dictionary pattern is designed to meet the multiple requirements. It is composed by three main parts — representation of database network location, data schema and data content. This pattern supports upwards integrated access interface, and also provides the description for semantic-level common metadata retrieval. Based on the metadata dictionary, a dynamic query algorithm for multi-table changing of relevant databases with the changes of access requirements involved in a query is presented. The correctness of the algorithm has been proved in this paper.

A Data Structure Model Supporting Railway Distributed System Integration

Railway distributed system integration needs to realize information exchange, resources sharing and coordination process across fields, departments and application systems. And railway data integration is essential to implement this integration. In order to resolve the problem of heterogeneity of data models among data sources of different railway operation systems, this paper presents a novel integration data model of spatial structure, a XML-oriented 3-dimension common data model. The proposed model accommodates both the flexibility of level relationship and syntax expression in data integration. In this model, a spatial data pattern is used to describe and express the characteristic relationship of data items among all types of data. Based on the data model with rooted directed graph and the organization of level as well as the flexibility of the expression, the model can represent the mapping between different data models, including relationship model and object-oriented model. A consistent concept and algebraic description of the data set is given to function as the metadata in data integration, so that the algebraic manipulation of data integration is standardized to support the data integration of distributed system.Copyright

Prediction of Track Geometry Status Based on Gray Forecast-Kalman Filter Analysis

Excellent condition of track geometry status is the foundation to ensure train travel security. The detection data of track inspection car contains many valuable features of the track status. The technique of gray forecast and Kalman filtering can be used to investigate the problem and predict the status change of the track geometry. In this paper, gray forecast is used in qualitative analysis of track geometry status changes, and we predict the development of track geometry status change using the Kalman filter prediction model and specific recursive algorithm, established prediction model of the track geometry to make an emulation experiment to analyze the data that track inspection car has detected, and predict changing trends of track geometry the state. Experiment results show that the application model of improved Kalman filter to predict the track geometry status changes gets a higher accuracy, and it can reflect the real change tendency of the track status.Copyright