A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum
This study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. The spectral conversion from th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3987 |
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| author | JeongYong Park MooHyun Kim |
| author_facet | JeongYong Park MooHyun Kim |
| author_sort | JeongYong Park |
| collection | DOAJ |
| description | This study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. The spectral conversion from the encounter- to original-frequency domain faces challenges under certain wave conditions due to the non-uniqueness of the inverse problem. To resolve these challenges, the authors developed an artificial neural network (ANN) model that transforms the encounter-frequency spectrum into the original wave spectrum at a given vessel speed and wave direction. The model was trained and validated using a large dataset mapped from various JONSWAP wave spectra to the corresponding encounter-frequency spectra for various vessel speeds and wave parameters. The hyperparameters of the ANN model were subsequently tested and optimized. The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. This approach not only enhances the reliability of wave spectrum estimation during maritime navigation but also broadens the capability of real-time operational controls and adjustments. |
| format | Article |
| id | doaj-art-144be2c493fe4ce19b070757e7e43e77 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-144be2c493fe4ce19b070757e7e43e772025-08-20T02:17:00ZengMDPI AGApplied Sciences2076-34172025-04-01157398710.3390/app15073987A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave SpectrumJeongYong Park0MooHyun Kim1Department of Ocean Engineering, Texas A&M University, Haynes Engineering Building, 727 Ross Street, College Station, TX 77843, USADepartment of Ocean Engineering, Texas A&M University, Haynes Engineering Building, 727 Ross Street, College Station, TX 77843, USAThis study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. The spectral conversion from the encounter- to original-frequency domain faces challenges under certain wave conditions due to the non-uniqueness of the inverse problem. To resolve these challenges, the authors developed an artificial neural network (ANN) model that transforms the encounter-frequency spectrum into the original wave spectrum at a given vessel speed and wave direction. The model was trained and validated using a large dataset mapped from various JONSWAP wave spectra to the corresponding encounter-frequency spectra for various vessel speeds and wave parameters. The hyperparameters of the ANN model were subsequently tested and optimized. The results demonstrate that the ANN model can effectively predict the original wave spectrum with high accuracy, as evidenced by a favorable R2 value and error distribution analysis. This approach not only enhances the reliability of wave spectrum estimation during maritime navigation but also broadens the capability of real-time operational controls and adjustments.https://www.mdpi.com/2076-3417/15/7/3987ocean wave spectrumencounter frequencyartificial neural network (ANN)Doppler effect |
| spellingShingle | JeongYong Park MooHyun Kim A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum Applied Sciences ocean wave spectrum encounter frequency artificial neural network (ANN) Doppler effect |
| title | A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum |
| title_full | A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum |
| title_fullStr | A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum |
| title_full_unstemmed | A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum |
| title_short | A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum |
| title_sort | data and machine learning based approach for the conversion of the encounter wave frequency spectrum to the original wave spectrum |
| topic | ocean wave spectrum encounter frequency artificial neural network (ANN) Doppler effect |
| url | https://www.mdpi.com/2076-3417/15/7/3987 |
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