Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers' experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed.
In order to adapt to the construction needs of the smart grid, smart substation need to solve the problem of protocol conversion between the conventional non-standardized condition monitoring equipment and the standardized monitoring system. This paper proposed a realization method of conversion method between Modbus and IEC61850. Object-oriented technology is used for information model on Modbus. After the analysis of IEC61850 and MMS information and service model, to establish the model mapping relationship between IEC61850, MMS and Modbus based on the principle of minimum information point which is one to one correspondence. Combined with the implementation of SISCO MMS-EASE LITE software development kit programming, giving a realization method using QT programming techniques based on the mapping model. Finally, the establishment the interval controller as an example of protocol scheme verifies the correctness and the feasibility of protocol conversion method.
Cognitive Radio (CR) is a promising technology to overcome spectrum scarcity, which currently faces lots of unsolved problems. One of the critical challenges for setting up such systems is how to coordinate multiple protocol layers such as routing and spectrum access in a partially observable environment. In this paper, a deep reinforcement learning approach is adopted for solving above problem. Firstly, for the purpose of compressing huge action space in the cross-layer design problem, a novel concept named responsibility rating is introduced to help decide the transmission power of every Secondary User (SU). In order to deal with problem of dimension curse while reducing replay memory, the Prioritized Memories Deep Q-Network (PM-DQN) is proposed. Furthermore, PM-DQN is applied to solve the joint routing and resource allocation problem in cognitive radio ad hoc network for minimizing the transmission delay and power consumption. Simulation results illustrates that our proposed algorithm can reduce the end-to-end delay, packet loss ratio and estimation error while achieving higher energy efficiency compared with traditional algorithm.
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