http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Yingming Cao (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Optimized inverse distance weighted interpolation algorithm for γ radiation field reconstruction
Zhang Biao,Cao Jinjia,Lin Shuang,Li Xiaomeng,Zhang Yulong,Zheng Xiaochang,Chen Wei,Song Yingming 한국원자력학회 2024 Nuclear Engineering and Technology Vol.56 No.1
The inversion of radiation field distribution is of great significance in the decommissioning sites of nuclear facilities. However, the radiation fields often contain multiple mixtures of radionuclides, making the inversion extremely difficult and posing a huge challenge. Many radiation field reconstruction methods, such as Kriging algorithm and neural network, can not solve this problem perfectly. To address this issue, this paper proposes an optimized inverse distance weighted (IDW) interpolation algorithm for reconstructing the gamma radiation field. The algorithm corrects the difference between the experimental and simulated scenarios, and the data is preprocessed with normalization to improve accuracy. The experiment involves setting up gamma radiation fields of three Co-60 radioactive sources and verifying them by using the optimized IDW algorithm. The results show that the mean absolute percentage error (MAPE) of the reconstruction result obtained by using the optimized IDW algorithm is 16.0%, which is significantly better than the results obtained by using the Kriging method. Importantly, the optimized IDW algorithm is suitable for radiation scenarios with multiple radioactive sources, providing an effective method for obtaining radiation field distribution in nuclear facility decommissioning engineering.
-
Shu Wang,Houpu Yang,Jiajia Guo,Miao Liu,Fuzhong Tong,Yingming Cao,Bo Zhou,Peng Liu,Lin Cheng,Fei Xie,Deqi Yang,Jiaqing Zhang 한국바이오칩학회 2011 BioChip Journal Vol.5 No.1
Neo-adjuvant chemotherapy for breast cancer substantially benefits patients who achieve pathological response. However, clinical or pathological response information can only be obtained a period of time after chemotherapy. The identification of novel bio-markers or the application of new technique that can be used to predict treatment response before che-motherapy would allow therapy to be tailored on an individual patient basis. The purpose of this study is to identify the chemo-sensitivity and chemo-resistance related proteins using antibody microarray profiling, and to develop a multi-protein predictive model for breast cancer. Total protein was extracted from core needle biopsy samples obtained from 15 patients before treatment with neo-adjuvant TA(combination of taxanes and anthracycline) chemotherapy. Protein profiling was analyzed by antibody microarray. 10 pati-ents were used as training set to develop the predictive model using the software PAM(prediction analysis of microarray). Another 5 patients were used as a validation set to test the model. In cross-validation, the mole-cular predictive model showed an accuracy of 90%, in independent validation, the model classified the cases with an accuracy of 80%. In conclusion, the proteomic predictive model has the potential to predict pathological response to neo-adjuvant TA chemotherapy.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
