2results about How to "Convenient and effective detection" patented technology

The application discloses an application of an animal domestication behavior related RRM1 gene mutant. Through population genetics, comparative genomics and ancient genomics analysis, the cause variation of the strongest selected gene of goat domestication, namely, the RRM1 gene c.1020A>G mutation, is determined; through association analysis with a hybrid population with wild goat blood, it is found that the mutation is significantly related to the domestication behavior in the goat domestication process; and through construction of a mouse containing the RRM1 gene c.1020A>G mutation, it is proved that the corresponding mutant of the RRM1 gene obviously increases the domestication behavior of the mouse. The application provides a basis and lays a foundation for phenotype selection of the domestication behavior and preparation of a tame animal model.

The invention discloses a domain adaptive adversarial open set cross-domain intelligent fault diagnosis method, which relates to the field of mechanical intelligent fault diagnosis, and comprises the following steps: respectively constructing a feature extractor, a private class classifier, a first auxiliary classifier and a second auxiliary classifier; training a private class classifier and a feature extractor by using an adversarial training method so as to obtain good classification performance of shared classes of a source domain and a target domain, and establishing a boundary for the shared classes and the private classes in the target domain; and maximizing the prediction divergence of the first auxiliary classifier and the second auxiliary classifier so as to detect excluded outlier target domain shared class samples and forcibly generate target feature representations similar to the source domain features. The invention also discloses a domain adaptive confrontation open set cross-domain intelligent fault diagnosis system. According to the method, the private class samples of the target domain can be identified as the unknown class while the shared class samples of the target domain and the source domain are aligned, and the method has relatively high cross-domain intelligent fault diagnosis accuracy in an open set fault diagnosis scene.