Size Prediction of Railway Switch Gap Based on RegARIMA Model and LSTM Network

Li, Chao; Zhao, Lian; Cai, Baigen

doi:10.1109/access.2020.3034687

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…The cell then connects one module from the past to another, allowing data to be transmitted from several previous instances to the present. The data in every cell can be rejected, screened or started adding as a result of the gates in every cell in preparation for the cells that come after [19,21].…”

Section: Lstmmentioning

confidence: 99%

SARIMA-LSTM Combination For COVID-19 Case Modeling

Tahyudin

Wahyudi

Nambo

2022

IIUMEJ

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The study of SARIMA method in combination with LSTM is interesting to do. This combination method can be convincing and significant because the data collected is numerical and saved based on time. In addition, the proposed method can anticipate datasets, either linear or non-linear. Based on several previous studies, the SARIMA method has the advantage of completing linear datasets while the LSTM method excels in achieving non-linear datasets. Also, both methods have been shown to have an accuracy value compared to some other methods. This study tried to combine the two through several stages of the first stage of applying the SARIMA method using fit datasets (linear data) then residual Dataset (non-linear data) analysed using the LSTM method. The result of the combination methods will be checked for the accuracy value. This research will be compared by using SARIMA and LSTM methods separately. The Dataset used as a trial is COVID-19 patient data in the United States. The results showed that the combination of SARIMA-LSTM method is better than either SARIMA or LSTM alone with RMSE of 0.33905765 and MAE of 0.29077017. ABSTRAK: Gabungan kaedah kajian SARIMA dengan LSTM adalah menarik untuk dikaji. Gabungan kaedah ini meyakinkan dan penting kerana data yang dikumpulkan bersifat numerik dan disimpan berdasarkan waktu. Selain itu, kaedah yang diusulkan ini dapat menerima set data, samada berkadar langsung atau tidak langsung. Berdasarkan beberapa penelitian sebelumnya, kaedah SARIMA mempunyai faedah dalam melengkapi set data linear, sedangkan kaedah LSTM berguna dalam mencapai set data tidak-linear. Tambahan, kedua-dua kaedah ini terbukti memiliki nilai ketepatan lebih baik berbanding beberapa kaedah lain. Kajian ini cuba menggabungkan keduanya melalui beberapa tahap. Tahap pertama mengunakan kaedah SARIMA secara set data (data linear) kemudian baki set data (data tidak-linear) dianalisa menggunakan kaedah LSTM. Dapatan dari gabungan kedua-dua kaedah tersebut akan diperiksa nilai ketepatannya. Kajian ini akan dibandingkan melalui kaedah SARIMA dan LSTM secara berasingan. Set data yang digunakan adalah merupakan data pesakit COVID-19 dari Amerika Syarikat. Dapatan kajian menunjukkan gabungan kaedah SARIMA-LSTM memiliki nilai ketepatan yang lebih baik berbanding kaedah SARIMA secara berasingan, dan LSTM dengan RMSE adalah sebanyak 0.33905765 dan MAE sebanyak 0.29077017.

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Section: Lstmmentioning

confidence: 99%

SARIMA-LSTM Combination For COVID-19 Case Modeling

Tahyudin

Wahyudi

Nambo

2022

IIUMEJ

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“…The proposed method shows high effectiveness of track circuit disturbance identification compared to the time-consuming and expensive visual analysis. The application of a long short-term memory (LSTM) network for switch gap size prediction is proposed in [ 83 ]. The regression and autoregressive integrated a moving average model to describe the change law of the switch gap size.…”

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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“…The regARIMA method focuses on the special time series regression field, which is used to estimate a time series with the help of common variables containing items with a time series structure (Clark et al 2020 ). Li et al ( 2020 ) employ the regARIMA model using the values of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percent Error (MAPE). Miswan et al ( 2016 ) use the regARIMA model, which has been proven in the context of many estimates, in estimating the electric load demand by considering the temperature-independent variable.…”

Section: Introductionmentioning

confidence: 99%

Site selection optimization for 100% renewable energy sources

Derse,

Yilmaz

2024

Environ Sci Pollut Res

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The increase in the use of Renewable Energy Sources (RES) provides many advantages such as reducing the environmental problems and sustainability. In this study, a long-term optimum RES settlement strategic plan is conducted for 81 provinces in Turkey by considering real data. Biomass energy, solar energy, hydroelectric energy, geothermal energy, and wind energy are considered RES sources. Energy consumption until the 2050 year is estimated with the regARIMA method, and then a weighted goal programming model was developed in which the outputs of the regARIMA method and risk analysis are integrated. The results of the regARIMA method are tested, and the test results indicate that an R2 value close to 1 indicates that the model is suitable, a low and negative MPE value is neutral, and a MAPE value below 4% indicates high accuracy of the model. Using GAMS 23.5 optimization software program, the developed weighted goal programming model is solved optimally. In this integrated model developed, the objectives of minimizing the installation time, minimizing the investment cost, minimizing the annual cost, maximizing the carbon emission reduction, maximizing the usage time, and minimizing the risk are considered. When the results obtained regarding the number of installations according to the model are examined, the decisions are made for 53% wind energy, 23% biomass energy, 13% hydroelectric energy, 9% solar energy, and 2% geothermal energy. Computational results show that the effective solutions are obtained by minimizing the sum of goals values, covering all provinces in Turkey, and considering real data.

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Size Prediction of Railway Switch Gap Based on RegARIMA Model and LSTM Network

Cited by 7 publications

References 37 publications

SARIMA-LSTM Combination For COVID-19 Case Modeling

SARIMA-LSTM Combination For COVID-19 Case Modeling

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Site selection optimization for 100% renewable energy sources

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