针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进IN...针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。展开更多
We present a quantum adiabatic algorithm for a set of quantum 2-satisfiability(Q2SAT)problem,which is a generalization of 2-satisfiability(2SAT)problem.For a Q2SAT problem,we construct the Hamiltonian which is similar...We present a quantum adiabatic algorithm for a set of quantum 2-satisfiability(Q2SAT)problem,which is a generalization of 2-satisfiability(2SAT)problem.For a Q2SAT problem,we construct the Hamiltonian which is similar to that of a Heisenberg chain.All the solutions of the given Q2SAT problem span the subspace of the degenerate ground states.The Hamiltonian is adiabatically evolved so that the system stays in the degenerate subspace.Our numerical results suggest that the time complexity of our algorithm is O(n^(3.9))for yielding non-trivial solutions for problems with the number of clauses m=dn(n-1)/2(d■0.1).We discuss the advantages of our algorithm over the known quantum and classical algorithms.展开更多
Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reac...Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.展开更多
文摘针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。
基金Project supported by the National Key R&D Program of China(Grant Nos.2017YFA0303302 and 2018YFA0305602)the National Natural Science Foundation of China(Grant No.11921005)Shanghai Municipal Science and Technology Major Project,China(Grant No.2019SHZDZX01)。
文摘We present a quantum adiabatic algorithm for a set of quantum 2-satisfiability(Q2SAT)problem,which is a generalization of 2-satisfiability(2SAT)problem.For a Q2SAT problem,we construct the Hamiltonian which is similar to that of a Heisenberg chain.All the solutions of the given Q2SAT problem span the subspace of the degenerate ground states.The Hamiltonian is adiabatically evolved so that the system stays in the degenerate subspace.Our numerical results suggest that the time complexity of our algorithm is O(n^(3.9))for yielding non-trivial solutions for problems with the number of clauses m=dn(n-1)/2(d■0.1).We discuss the advantages of our algorithm over the known quantum and classical algorithms.
基金supported by the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C157)。
文摘Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.
