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Rail weld defects are major threats to railroad transportation. Enormous resources have been required for related maintenance. This paper presents a creative solution to predict weld defects and to classify railroads into different conditions based on the predictions. The results are based on features extracted from manufacturing technologies of welds, from related materials and from influential factors in the environments. Features such as marks for welding engineers are defined. Maintenance can be selectively implemented based on the predicted conditions. Safety is the foundation of the railroad business, and a very strict safety requirement is utilized as one of the main constraints in this research. Additionally, 11 key risk factors leading to rail defects and their risk levels are identified. Extreme learning machine (ELM), random forest, logistic regression, principal component analysis (PCA), support vector machine (SVM) and other data science approaches are utilized. The evaluation results show that the related rail maintenance workload can decrease significantly under high safety standards. Labor costs of weld inspection will be reduced substantially because of the decreased workload for the sections predicted to not have any defects with a 100% recall rate (approximately 30% of the total sections), contributing to a massive cost reduction. Consequently, rail companies are expected to achieve enhanced management and operation.

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Background: As the multimedia service develops and the transmission rate in terrestrial communication systems increases rapidly, satellite communication needs to improve the transmission rate and throughput. Multiple Input Multiple Output (MIMO) techniques can increase the system capacity significantly by introducing the space dimension, as the system bandwidth remains the same. Therefore, utilization of MIMO for satellite communications to increase the capacity is an important research topic. So MIMO techniques for multibeam satellite communications are researched in the dissertation. Objective: The goal of this work is improving the capacity of the satellite system. Multi-beam and dual-polarized technologies are applied to a satellite system to improve the capacity further. Methods: In this paper, we first introduce a multi-beam dual-polarized satellite multi-put and multiout (MBDP-S-MIMO) system which combines the full frequency multiplexing and dual-polarization technologies. Then the system model and channel model are first constructed. At last, to improve the capacity further, BD and BD-ZF precoding algorithms are applied to MBDP-S-MIMO and their performance is verified by simulation. Results: Simulation results show the performance of the BD precoding algorithm gets better with the growth of the XPD at the receiver and is almost not affected by the growth of the channel polarization correlation coefficient. In addition, with the growth of the users’ speed, the performance becomes worse. Conclusion: The multi-beam dual-polarized satellite MIMO system has high capacity, and it has certain application prospects for satellite communication.