Endpoint detection of distributed fiber sensing systems based on STFT algorithm

Liu, Kun; Ma, Peisheng; An, Jianchang; Li, Zhichen; Jiang, Junfeng; Li, Pengchen; Zhang, Liwang; Liu, Tiegen

doi:10.1016/j.optlastec.2019.01.036

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…This part explains the signal activity detection results of the simulated database in detail. The performance of the proposed method is compared with two representative detection methods: the LSFM [21] and the STFT methods [26]. The parameters of the three methods are shown in Table 1.…”

Section: Results Of Signal Activity Detection Of the Simulated Databasementioning

confidence: 99%

“…With a series of features emerging in time and frequency domains, researchers attempted to derive a more suitable detection method by integrating both of these domain features. As described in [26], since the amplitude of the high-frequency components had a noticeable jump when the signal occurred in a short time, a short-time Fourier transform (STFT)-based method was applied to detect the signal with the minimum runtime of 0.145 s. However, only two signals of climbing the fence and knocking the cable were verified. The signal was decomposed into intrinsic mode functions (IMFs) to realize detection using the threshold judged by the first five frames as noisy fragments of the data stream and the accuracy was 87.29% when the signal-to-noise ratio (SNR) was 0 dB [27].…”

Section: Introductionmentioning

confidence: 99%

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Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

Cao

et al. 2022

Sensors

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The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured signals is a bottleneck problem for current methods. In this article, a novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem. With the analysis of the time-varying random noise’s statistical commonality and the short-term energy (STE) of real-time data stream, the top range of the total STE distribution of the noise is found accurately for real-time data stream’s ascending STE, thus the adaptive dividing level of signals and noise is obtained as the threshold. Experiments are implemented with simulated database and urban field database with complex noise. The average detection accuracies of the two databases are 97.34% and 90.94% only consuming 0.0057 s for a data stream of 10 s, which demonstrates the proposed method is accurate and high efficiency for signal activity detection.

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Section: Results Of Signal Activity Detection Of the Simulated Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

Cao

et al. 2022

Sensors

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“…EPD memotong sinyal wicara yang diawali dengan komponen voiced paling awal hingga komponen voiced paling akhir dari sebuah sinyal utuh. Metode yang digunakan adalah spektrogram dengan nilai ambang batas seperti dalam [13] dan zero crossing rate (ZCR) seperti dalam [14]. Walaupun sinyal yang direkam sudah memenuhi kriteria centering dan normalisasi, namun untuk mengantisipasi perbedaan spesifikasi alat perekam, maka kedua langkah tersebut tetap diimplementasikan pada sistem ini.…”

Section: B Prapengolahanunclassified

Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity

Hendry¹,

Rachman²,

Zulherman³

2019

Jurnal Teknologi dan Sistem Komputer

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In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data were correct readings, and 15 other data were incorrect readings. System accuracy was measured by word-for-word recognition, which results in 100 % of recall and 98.96 % of precision for the training word data, and 100 % of recall and 99.65 % of precision for the test word data. For the overall reading of the surah, there were 15 correct readings and 14 incorrect readings that were recognized correctly.

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“…Using endpoint detection technology to intercept train vibration data is an effective method. Liu et al used short-time Fourier transform (STFT) to extract vibration signals [4]. In addition, the PSD can depict the energy distribution of vibration signal, so the endpoint detection technology based on short-time PSD is applied to extracting the train vibration signal.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of subway vibration reduction effect based on ultra-weak FBG array using deep forest

Chen

Liu

et al. 2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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Monitoring the train vibration is an important issue in subway safety management and maintenance. Aiming at the problems of traditional technology for detection and acquisition of subway vibration, such as unable to achieve dynamic detection or evaluation, and unable to give early warning to the changes of subway tunnel structure, this paper proposes a method to obtain and predict the vibration reduction effect based on grating array. The method uses short-time power spectral density (PSD) to extract train vibration signal, and uses Z-vibration level (VLz) to obtain the vibration reduction effect of subway track. A model based on deep forest (DF) is improved to predict the variation trend of vibration reduction effect. The experimental results on the actual train data illustrate that the proposed method can accurately extract the train vibration signal, and the model can effectively forecast the vibration reduction effect, which has a lower error precision and shows improved performance over other prediction models.

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Endpoint detection of distributed fiber sensing systems based on STFT algorithm

Cited by 13 publications

References 14 publications

Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity

Evaluation of subway vibration reduction effect based on ultra-weak FBG array using deep forest

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