提高牛受孕率的高效生物数据特征分析方法

Tatsuya Komatsu, Hiroto Noma, Takumi Asaoka, H. Oya, R. Miura, Koji Yoshioka
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引用次数: 0

摘要

本文展示了一种高效的体表温度(ST)数据特征分析方法,从而开发出精确的牛人工授精(AI)时机预测系统。在所提出的分析方法中,通过使用基于傅立叶变换的基波合成方法,得出了目标波形的近似波形。此外,还生成了与高频噪声和昼夜节律都不对应的重构波形。根据近似波形得出的两个重建波形分别用于预测最佳人工智能时间和判别正常相位。
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AN EFFICIENT FEATURE ANALYSIS METHOD OF BIOLOGICAL DATA FOR IMPROVING CATTLE CONCEPTION RATE
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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