Tatsuya Komatsu, Hiroto Noma, Takumi Asaoka, H. Oya, R. Miura, Koji Yoshioka
{"title":"AN EFFICIENT FEATURE ANALYSIS METHOD OF BIOLOGICAL DATA FOR IMPROVING CATTLE CONCEPTION RATE","authors":"Tatsuya Komatsu, Hiroto Noma, Takumi Asaoka, H. Oya, R. Miura, Koji Yoshioka","doi":"10.58190/icontas.2023.56","DOIUrl":null,"url":null,"abstract":"In this paper, we show an efficient feature analysis method of body surface temperature (ST) data so as to develop accurate prediction systems for artificial insemination (AI) timing of cattle. In the proposed analysis method, by using the fundamental waveform synthesis method based on the Fourier transform, approximate waveforms for the target waveform were derived. Additionally, reconstructed waveforms which does not correspond to both high frequency noise and circadian rhythm were generated. The two reconstructed waveforms derived from the approximate waveforms were used to predict the optimal AI timing and to discriminate the normal phase, respectively.","PeriodicalId":509439,"journal":{"name":"Proceedings of the International Conference on New Trends in Applied Sciences","volume":"89 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on New Trends in Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.58190/icontas.2023.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
In this paper, we show an efficient feature analysis method of body surface temperature (ST) data so as to develop accurate prediction systems for artificial insemination (AI) timing of cattle. In the proposed analysis method, by using the fundamental waveform synthesis method based on the Fourier transform, approximate waveforms for the target waveform were derived. Additionally, reconstructed waveforms which does not correspond to both high frequency noise and circadian rhythm were generated. The two reconstructed waveforms derived from the approximate waveforms were used to predict the optimal AI timing and to discriminate the normal phase, respectively.
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