{'fraction_non_alphanumeric': 0.022326674500587545, 'fraction_numerical': 0.004112808460634548, 'mean_word_length': 5.524904214559387, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Ballast Resistance Estimation Method Based on One Dimensional Convolutional Neural Network

Ballast resistance, as the basic transmission parameter of track circuits, is susceptible to various factors such as climate, material, environment, etc., presenting the characteristics of dynamic changes, even causing the main factor of affecting stable operation. Jointless track circuits are the mainstream apparatus widely used in China's high-speed railways, whose channels are uneven due to compensation capacitors and tuning units. Coupled with the train operation restrictions, it is difficult to achieve dynamic measurement result of ballast resistance. Under this background, considering the strong data processing ability of the one-dimensional convolution network, this paper proposes a ballast resistance estimated method by building a corresponding algorithmic model. Firstly, based on the theory of the four-terminal network and uniform transmission line, the equivalent model of track circuit under the shunting state is established. The key point is to obtain the sequence of the cab signal receiving voltage by changing the ballast resistance and transmitting level. Then, the voltage sequence plays the part of input into the one-dimensional convolutional network as a data set to train the ballast resistance estimated model. After training the network and selecting the best network parameters, the accuracies of the training set and the validation set are approach 90% and 95%, respectively, and that of the test set reaches 90.2%. The result shows that the established model has a relatively high fitting accuracy, and its application helps save manpower and costs, improving efficiency.