The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is...The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.展开更多
This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the se...This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.展开更多
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the opt...This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative.展开更多
This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this ...This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.展开更多
Characterizing the petrophysical properties holds significant importance in shale oil reservoirs.Twodimensional(2-D)nuclear magnetic resonance(NMR),a nondestructive and noninvasive technique,has numerous applications ...Characterizing the petrophysical properties holds significant importance in shale oil reservoirs.Twodimensional(2-D)nuclear magnetic resonance(NMR),a nondestructive and noninvasive technique,has numerous applications in petrophysical characterization.However,the complex occurrence states of the fluids and the highly non-uniform distributions of minerals and organic matter pose challenges in the NMR-based petrophysical characterization.A novel T_(1)-T_(2)relaxation theory is introduced for the first time in this study.The transverse and longitudinal relaxivities of pore fluids are determined based on numerical investigation and experimental analysis.Additionally,an improved random walk algorithm is proposed to,on the basis of digital shale core,simulate the effects of the hydrogen index(HI)for the organic matter,echo spacing(T_(E)),pyrite content,clay mineral type,and clay content on T_(1)-T_(2)spectra at different NMR frequencies.Furthermore,the frequency conversion cross-plots for various petrophysical parameters influenced by the above factors are established.This study provides new insights into NMRbased petrophysical characterization and the frequency conversion of petrophysical parameters measured by laboratory NMR instruments and NMR logging in shale oil reservoirs.It is of great significance for the efficient exploration and environmentally friendly production of shale oil.展开更多
针对股价预测中存在的不确定性、间断性、随机性和非线性等问题,提出一种TRSSA-ELM(Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine)股价预测模型。首先,采用自适应Tent混沌映射和随机游走策略对算法进行改...针对股价预测中存在的不确定性、间断性、随机性和非线性等问题,提出一种TRSSA-ELM(Tent Random Walk Sparrow Optimization Algorithm-Extreme Learning Machine)股价预测模型。首先,采用自适应Tent混沌映射和随机游走策略对算法进行改进,增强种群多样性和随机性,提高算法局部和全局的寻优能力。其次,使用单峰、多峰和固定维多峰测试函数对TRSSA(Tent Random Walk Sparrow Optimization Algorithm)性能进行了验证,相比于SSA(Sparrow Optimization Algorithm)、AO(Aquila Optimizer)、POA(Pelican Optimization Algorithm)和GWO(Grey Wolf Optimizer),TRSSA算法具有更好的收敛速度、精度和统计性质。最后,由于ELM(Extreme Learning Machine)模型随机生成权重和阈值,降低了预测精度和泛化能力,应用TRSSA算法优化ELM模型的权重和阈值,并用三安光电股票数据集对TRSSA-ELM模型进行了测试。实验结果表明,TRSSA-ELM模型相比于SSA-ELM、ELM、SVR(Support Vector Regression)和GBDT(Gradient Boosting Decision Tree),具有更好的预测精度和稳定性。展开更多
量子行走得益于概率幅的叠加特性,可同时出现在多条路径中,使其能以平方式乃至指数级别的速度加速扩散所携带的量子信息。文章基于无向图G=(V,E)结构,从离散时间量子随机行走(Discrete Time Quantum Walk,DTQW)搜索算法特性出发,运用幺...量子行走得益于概率幅的叠加特性,可同时出现在多条路径中,使其能以平方式乃至指数级别的速度加速扩散所携带的量子信息。文章基于无向图G=(V,E)结构,从离散时间量子随机行走(Discrete Time Quantum Walk,DTQW)搜索算法特性出发,运用幺正变换的硬币算符与迁移算符,构建了DTQW搜索算法步骤框图,在此基础上,应用SKW搜索算法对4节点无向图中的标记节点态进行搜索,通过态塌缩的观测,实现以1/4概率化读取出目标节点。研究结果表明,当有n个足够大的量子系统,并保持彼此之间的强纠缠性时,量子随机行走可以过渡到经典随机行走。文章还详细讨论了DTQW搜索算法实现左右同移的二次加速搜索机制。展开更多
文摘The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.
基金supported by the National Basic Research Program of China(Grant No.2013CB338002)
文摘This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.
基金supported by the National Basic Research Program of China(Grant No.2013CB338002)
文摘This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative.
基金Project supported by the National Basic Research Program of China(Grant No.2013CB338002)
文摘This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.
基金funded by the National Natural Science Foundation of China(42174131).
文摘Characterizing the petrophysical properties holds significant importance in shale oil reservoirs.Twodimensional(2-D)nuclear magnetic resonance(NMR),a nondestructive and noninvasive technique,has numerous applications in petrophysical characterization.However,the complex occurrence states of the fluids and the highly non-uniform distributions of minerals and organic matter pose challenges in the NMR-based petrophysical characterization.A novel T_(1)-T_(2)relaxation theory is introduced for the first time in this study.The transverse and longitudinal relaxivities of pore fluids are determined based on numerical investigation and experimental analysis.Additionally,an improved random walk algorithm is proposed to,on the basis of digital shale core,simulate the effects of the hydrogen index(HI)for the organic matter,echo spacing(T_(E)),pyrite content,clay mineral type,and clay content on T_(1)-T_(2)spectra at different NMR frequencies.Furthermore,the frequency conversion cross-plots for various petrophysical parameters influenced by the above factors are established.This study provides new insights into NMRbased petrophysical characterization and the frequency conversion of petrophysical parameters measured by laboratory NMR instruments and NMR logging in shale oil reservoirs.It is of great significance for the efficient exploration and environmentally friendly production of shale oil.
文摘量子行走得益于概率幅的叠加特性,可同时出现在多条路径中,使其能以平方式乃至指数级别的速度加速扩散所携带的量子信息。文章基于无向图G=(V,E)结构,从离散时间量子随机行走(Discrete Time Quantum Walk,DTQW)搜索算法特性出发,运用幺正变换的硬币算符与迁移算符,构建了DTQW搜索算法步骤框图,在此基础上,应用SKW搜索算法对4节点无向图中的标记节点态进行搜索,通过态塌缩的观测,实现以1/4概率化读取出目标节点。研究结果表明,当有n个足够大的量子系统,并保持彼此之间的强纠缠性时,量子随机行走可以过渡到经典随机行走。文章还详细讨论了DTQW搜索算法实现左右同移的二次加速搜索机制。
