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GENEVA, March 16 -- SHANGHAI MARITIME UNIVERSITY (1550 Haigang AvenuePudong New Area, Shanghai 201306), 上海海事大学 (中国上海市浦东新区海港大道1550号) filed a patent application (PCT/CN2024/082557) for "RAMAN SPECTRUM CLASSIFICATION METHOD, SPECIES BLOOD AND SEMEN CLASSIFICATION METHOD, AND SPECIES CLASSIFICATION METHOD" on Mar 20, 2024. With publication no. WO/2025/050613, the details related to the patent application was published on Mar 13, 2025.
Notably, the patent application was submitted under the International Patent Classification (IPC) system, which is managed by the World Intellectual Property Organization (WIPO).
Inventor(s): ZHOU, Rigui (1550 Haigang AvenuePudong New Area, Shanghai 201306), 周日贵 (中国上海市浦东新区海港大道1550号), REN, Pengju (1550 Haigang AvenuePudong New Area, Shanghai 201306), 任鹏举 (中国上海市浦东新区海港大道1550号), ZHOU, Hanxuan (1550 Haigang AvenuePudong New Area, Shanghai 201306), 周瀚轩 (中国上海市浦东新区海港大道1550号)
Abstract: Disclosed in the present invention are a Raman spectrum classification method, a species blood and semen classification method, and a species classification method. The Raman spectrum classification method comprises: first, acquiring a plurality of pieces of Raman spectrum data, and taking same as a training set and a test set; second, on the basis of existing spectral quality, performing a series of preprocessing on the Raman spectrum data, so as to acquire Raman spectrum data having obvious peak information; then, inputting the Raman spectrum data having obvious peak information into a neural network model in which one-dimensional convolution and a multi-head self-attention mechanism are combined, and training a classification model; and finally, inputting Raman spectrum test set data into the trained classification model, so as to obtain a final classification result. Local peak feature information can be obtained by means of performing convolution computation on a spectrum, global peak correlation information can be obtained by means of performing multi-head self-attention computation on the spectrum, and a multi-scale feature fusion effect is achieved; and local feature peaks of a Raman spectrum can be effectively combined with global peak correlations, so that a more accurate classification representation is achieved, thereby improving the classification accuracy. For more information:https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2025050613
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