Fault text classification of on-board equipment in high-speed railway based on labeled-Doc2vec and BiGRU

Wei, Wei; Zhao, Xiaoqiang

doi:10.1016/j.jrtpm.2023.100372

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…The advantage of Bi-GRU is that it can analyze the relationship between contextual sentences. In this manner, it can make right decisions about the meaning of each sentence, potentially extracting the text features most closely related to the original one [39].…”

Section: Bidirectional Gated Recurrent Unitmentioning

confidence: 99%

Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

Yucalar

2023

Applied Sciences

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Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional (FR) and non-functional (NFR). Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual identification of these classes is a timewaster, and it needs to be automated. Methodically, machine learning (ML) approaches are applied to address this problem. In this study, twenty ML algorithms, such as Naïve Bayes, Rotation Forests, Convolutional Neural Networks, and transformers such as BERT, were used to predict FR and NFR. Any ML algorithm requires a dataset for training. For this goal, we generated a unique Turkish dataset having collected the requirements from real-world software projects with 4600 samples. The generated Turkish dataset was used to assess the performance of the three groups of ML algorithms in terms of F-score and related statistical metrics. In particular, out of 20 ML algorithms, BERTurk was found to be the most successful algorithm for discriminating FR and NFR in terms of a 95% F-score metric. From the FR and NFR identification problem point of view, transformer algorithms show significantly better performances.

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Section: Bidirectional Gated Recurrent Unitmentioning

confidence: 99%

Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

Yucalar

2023

Applied Sciences

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ZPW-2000 frequency-shift signal detection algorithm based on nonlinear least squares method

Ji,

Shan,

et al. 2024

Phys. Scr.

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Aiming at the problems of spectrum leakage, difficulty in guaranteeing demodulation accuracy, and endangering driving safety in signal detection algorithm based on periodogram with limited sampling duration, this paper proposes for the first time a kind of ZPW-20000 frequency-shift signal detection algorithm based on nonlinear least squares, so as to improve the detection performance of ZPW-2000 frequency-shift signal, to ensure the safe operation of trains, and to improve the operational efficiency. The mathematical model of ZPW-2000 frequency-shift signal is established, the Cramér-Rao bound (CRB) for carrier-frequency and low-frequency estimation are derived, and a nonlinear least squares algorithm containing three processes, namely, the coarse estimation, the grid search based on Fast Fourier Transform (FFT), and the exact search, is designed, which is applied to the detection of ZPW-2000 frequency-shift signal. The research results show that the accuracy of carrier-frequency and low-frequency estimation of the proposed algorithm is better than that of the periodogram method for different sampling durations, and the number of decoding errors of the proposed algorithm and the periodogram method is reduced to zero when the sampling durations are greater than or equal to 0.11 s and 0.19 s, respectively, and the proposed algorithm reduces the limitation on the sampling durations. As the signal-to-noise ratio (SNR) increases, the root mean square error (RMSE) of carrier-frequency and low-frequency estimation of the proposed algorithm can approach the CRB. when the SNR of −2 dB, the number of decoding errors of the proposed algorithm is reduced to zero, while the periodogram method requires the SNR of 2 dB to achieve the same effect. The proposed algorithm performs well in detection accuracy, real-time and anti-interference ability, which is of great significance for ensuring driving safety and improving the reliability of the vehicle-ground communication mode based on ZPW-2000 track circuit.

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Advancements and Challenges in Gated Recurrent Units (GRU) for Text Classification: A Systematic Literature Review

Fairuzabadi,

Kusrini,

Utami

et al. 2024

2024 7th International Conference of Computer and Informatics Engineering (IC2IE)

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Fault text classification of on-board equipment in high-speed railway based on labeled-Doc2vec and BiGRU

Cited by 6 publications

References 11 publications

Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data

ZPW-2000 frequency-shift signal detection algorithm based on nonlinear least squares method

Advancements and Challenges in Gated Recurrent Units (GRU) for Text Classification: A Systematic Literature Review

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