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
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
To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The c...To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The circuit model takes both semiconductor and electrostatic effects into account, and is valid for low and medium signal frequencies. The electrostatic capacitances are extracted with a floating random walk based algorithm, and are then combined with the voltage-dependent semiconductor capacitances to form the equivalent circuit. Compared with the method used in Synopsys＇s Sdevice, which completely simulates the electro/semiconductor effects, the proposed method is more efficient and is able to handle the general TSV layout as well. For several TSV structures, the experimental results validate the accuracy of the proposed method for the frequency range from l0 kHz to 1 GHz. The proposed method demonstrated 47× speedup over the Sdevice for the largest 9-TSV case.展开更多
Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable,but such a large structural search remains a challenge if purely based on the traditional density funct...Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable,but such a large structural search remains a challenge if purely based on the traditional density functional theory(DFT)combined with Monte-Carlo(MC)methods.Herein,the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes(CNTs)under pressure.A variety of unreported ordered carbons are obtained,among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes.Most ordered carbons obtained show great thermodynamical and kinetic stability,verified by ab initio molecular dynamics simulations and phonon spectra.The ordered carbons demonstrate direct or indirect band gaps in the range of 0 to 4.4 eV,including 13 superhard(H,>40 GPa)structures and 1 ductile(Pugh's Ratio G/B<0.57)structure,in which the modulus of ordered carbons exhibits a linear correlation with the density.Our study provides a pathway to create new carbons from nanotubes and a deeper understanding of the structural evolution of CNT's under pressure.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.
基金supported by the National Natural Science Foundation of China(No.61422402)the Tsinghua University Initiative Scientific Research Program
文摘To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The circuit model takes both semiconductor and electrostatic effects into account, and is valid for low and medium signal frequencies. The electrostatic capacitances are extracted with a floating random walk based algorithm, and are then combined with the voltage-dependent semiconductor capacitances to form the equivalent circuit. Compared with the method used in Synopsys＇s Sdevice, which completely simulates the electro/semiconductor effects, the proposed method is more efficient and is able to handle the general TSV layout as well. For several TSV structures, the experimental results validate the accuracy of the proposed method for the frequency range from l0 kHz to 1 GHz. The proposed method demonstrated 47× speedup over the Sdevice for the largest 9-TSV case.
基金supported by the National Natural Science Foundation of China(Nos.52273234,52325202)The Fundamental Research Funds for the Central Universities(WK2060000098,WK9990000170)the open research fund of State Key Laboratory of Precision and Intelligent Chemistry(KY2490000300).
文摘Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable,but such a large structural search remains a challenge if purely based on the traditional density functional theory(DFT)combined with Monte-Carlo(MC)methods.Herein,the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes(CNTs)under pressure.A variety of unreported ordered carbons are obtained,among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes.Most ordered carbons obtained show great thermodynamical and kinetic stability,verified by ab initio molecular dynamics simulations and phonon spectra.The ordered carbons demonstrate direct or indirect band gaps in the range of 0 to 4.4 eV,including 13 superhard(H,>40 GPa)structures and 1 ductile(Pugh's Ratio G/B<0.57)structure,in which the modulus of ordered carbons exhibits a linear correlation with the density.Our study provides a pathway to create new carbons from nanotubes and a deeper understanding of the structural evolution of CNT's under pressure.
