针对火电机组故障预测准确率低的问题,提出基于BERT-STAM1DCNN的火电机组故障预测模型。在预处理阶段,利用随机森林算法对原始数据进行处理,筛选重要的特征,提高故障预测准确率;利用BERT(bidirectional encoder representations from tr...针对火电机组故障预测准确率低的问题,提出基于BERT-STAM1DCNN的火电机组故障预测模型。在预处理阶段,利用随机森林算法对原始数据进行处理,筛选重要的特征,提高故障预测准确率;利用BERT(bidirectional encoder representations from transformers)模型对特征数据进行训练,并提出一种融合二次加权时空注意力机制的一维卷积神经网络(STAM1DCNN)模型,提高关键信息对预测结果的影响程度。以火电机组实际运行数据作为数据集,与其他模型相比,所提出的模型具有良好的性能和稳定性。展开更多
AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,com...AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.展开更多
文摘针对火电机组故障预测准确率低的问题,提出基于BERT-STAM1DCNN的火电机组故障预测模型。在预处理阶段,利用随机森林算法对原始数据进行处理,筛选重要的特征,提高故障预测准确率;利用BERT(bidirectional encoder representations from transformers)模型对特征数据进行训练,并提出一种融合二次加权时空注意力机制的一维卷积神经网络(STAM1DCNN)模型,提高关键信息对预测结果的影响程度。以火电机组实际运行数据作为数据集,与其他模型相比,所提出的模型具有良好的性能和稳定性。
基金supported by the Key Project of International Cooperation of Qilu University of Technology(Grant No.:QLUTGJHZ2018008)Shandong Provincial Natural Science Foundation Committee,China(Grant No.:ZR2016HB54)Shandong Provincial Key Laboratory of Microbial Engineering(SME).
文摘AlphaPanda(AlphaFold2[1]inspired protein-specific antibody design in a diffusional manner)is an advanced algorithm for designing complementary determining regions(CDRs)of the antibody targeted the specific epitope,combining transformer[2]models,3DCNN[3],and diffusion[4]generative models.
