University of Electronic Science and Technology of China

Chengdu Di'ao Pharmaceutical Group Co., Ltd.

Ting Xue

Tao N Zhang

Yuhua Cheng

Kai Chen

Chun Yin

Jianxin Ji

Na Guo

Xuegang Huang

Bingqiang Kang

Haonan Zhang

Xin Chen

Yi Bu Li

Tao Zhang

Xinfa Ye

Anhua Shi

The present invention provides a method for separating out a defect image from a thermogram sequence based on weighted naive Bayesian classifier and dynamic multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities. The present invention extracts these features (physical quantities) and classifies the selected TTRs into K categories based on their feature vectors through a weighted naive Bayesian classifier, which deeply digs the physical meanings contained in each TTR, makes the classification of TTRs more rational, and improves the accuracy of defect image's separation. Meanwhile, the multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same category, but also considers the dissimilarities between the RTTR and the TTRs in other categories, thus the RTTR selected is more representative, which guarantees the accuracy of describing the defect outline. And the initial TTR population corresponding to the approximate solution for multi-objective optimization is chosen according to the previous TTR populations, which makes the multi-objective optimization dynamic and reduces its time consumption.

Method for separating out a defect image from a thermogram sequence based on weighted naive bayesian classifier and dynamic multi-objective optimization

The present invention provides a method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization, we find that different kinds of TTRs have big differences in some physical quantities, such as the energy, temperature change rate during endothermic process, temperature change rate during endothermic process, average temperature, maximum temperature. The present invention extract these features (physical quantities) and cluster the selected TTRs into L clusters based on their feature vectors, which deeply digs the physical meanings contained in each TTR, makes the clustering more rational, and improves the accuracy of defect separation. Meanwhile, the present invention creates a multi-objective function to select a RTTR for each cluster based on multi-objective optimization. The multi-objective function does not only fully consider the similarities between the RTTR and other TTRs in the same cluster, but also considers the dissimilarities between the RTTR and the TTRs in other clusters, the RTTR is more representative, which guarantees the accuracy of describing the defect outline.

Method for separating out a defect image from a thermogram sequence based on feature extraction and multi-objective optimization

Disclosed are a phthalic hydrazide (phthalazine ketone) compound, and a pharmaceutical composition comprising the same. As a DNA repair enzyme poly (ADP-ribozyme) polymerase inhibitor, the compound and the pharmaceutical composition can effectively treat diseases involving PARP enzymatic activity, including cancer, neural degenerative diseases, inflammation and the like.

Poly (ADP-ribose) polymerase inhibitor

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Chengdu, China

Ting Xue