In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring...In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.展开更多
The current study performed a finite element analysis of the strain localization behavior of a voided ductile material using a non-local plasticity formulation in which the yield strength depends on both an equivalent...The current study performed a finite element analysis of the strain localization behavior of a voided ductile material using a non-local plasticity formulation in which the yield strength depends on both an equivalent plastic strain measurement (hardening parameter) and Laplacian equivalent. The introduction of gradient terms to the yield function was found to play an important role in simulating the strain localization behavior of the voided ductile material. The effect of the mesh size and characteristic length on the strain localization were also investigated. An FEM simulation based on the proposed non-local plasticity revealed that the load-strain curves of the voided ductile material subjected to plane strain tension converged to one curve, regardless of the mesh size. In addition, the results using non-local plasticity also exhibited that the dependence of the deformation behavior of the material on the mesh size was much less sensitive than that with classical local plasticity and could be successfully eliminated through the introduction of a large value for the characteristic length.展开更多
A non-local continuum model for strain-softening simply takingplastic strain or damage vari- able as a non-local variable isderived by using the additive decomposition principle of finitedeformation gra- dient. At the...A non-local continuum model for strain-softening simply takingplastic strain or damage vari- able as a non-local variable isderived by using the additive decomposition principle of finitedeformation gra- dient. At the same time, variational equations,their finite element formulations and numerical convolutedintegration algorithm of the model in current configuration usuallycalled co-moving coordinate system are given. stability andconvergence of the model are proven by means of the weak convergencetheorem of gen- eral function and the convoluted integration theory.展开更多
Multi-seam mining often leads to the retention of a significant number of coal pillars for purposes such as protection,safety,or water isolation.However,stress concentration beneath these residual coal pillars can sig...Multi-seam mining often leads to the retention of a significant number of coal pillars for purposes such as protection,safety,or water isolation.However,stress concentration beneath these residual coal pillars can significantly impact their strength and stability when mining below them,potentially leading to hydraulic support failure,surface subsidence,and rock bursting.To address this issue,the linkage between the failure and instability of residual coal pillars and rock strata during multi-seam mining is examined in this study.Key controls include residual pillar spalling,safety factor(f.),local mine stiffness(LMS),and the post-peak stiffness(k)of the residual coal pillar.Limits separating the two forms of failure,progressive versus dynamic,are defined.Progressive failure results at lower stresses when the coal pillar transitions from indefinitely stable(f,>1.5)to failing(f,<1.5)when the coal pillar can no longer remain stable for an extended duration,whereas sud-den(unstable)failure results when the strength of the pillar is further degraded and fails.The transition in mode of failure is defined by the LMS/k ratio.Failure transitions from quiescent to dynamic as LMS/k.<1,which can cause chain pillar instability propagating throughout the mine.This study provides theoretical guidance to define this limit to instability of residual coal pillars for multi-seam mining in similar mines.展开更多
A new element tracer technique has firstly been established to estimate the contributions of mineral aerosols from both inside and outside Beijing. The ratio of Mg/Al in aerosol is a feasible element tracer to disting...A new element tracer technique has firstly been established to estimate the contributions of mineral aerosols from both inside and outside Beijing. The ratio of Mg/Al in aerosol is a feasible element tracer to distinguish between the sources of inside and outside Beijing. Mineral aerosol, inorganic pollution aerosol mainly as sulfate and nitrate, and organic aerosol are the major components of airborne particulates in Beijing, of which mineral aerosol accounted for 32%―67% of total suspended particles (TSP), 10%―70% of fine particles (PM2.5), and as high as 74% and 90% of TSP and PM2.5, respectively, in dust storm. The sources from outside Beijing contributed 62% (38%―86%) of the total mineral aerosols in TSP, 69% (52%―90%) in PM10, and 76% (59%―93%) in PM2.5 in spring, and 69% (52%―83%), 79% (52%―93%), and 45% (7%―79%) in TSP, PM10, and PM2.5, respectively, in winter, while only ~20% in summer and autumn. The sources from outside Beijing contributed as high as 97% during dust storm and were the dominant source of airborne particulates in Beijing. The contributions from outside Beijing in spring and winter are higher than those in summer, indicating clearly that it was related to the various meteorological factors.展开更多
A polarized beam of energy is usually interpreted as a set of particles, all having the same polarization state. Difference in behavior between the one and the other particle is then explained by a number of counter-i...A polarized beam of energy is usually interpreted as a set of particles, all having the same polarization state. Difference in behavior between the one and the other particle is then explained by a number of counter-intuitive quantum mechanical concepts like probability distribution, superposition, entanglement and quantized spin. Alternatively, I propose that a polarized beam is composed of a set of particles with a cosine distribution of polarization angles within a polarization area. I show that Malus’ law for the intensity of a beam of polarized light can be derived in a straightforward manner from this distribution. I then show that none of the above-mentioned counter-intuitive concepts are necessary to explain particle behavior and that the ontology of particles, passing through a polarizer, can be easily and intuitively understood. I conclude by formulating some questions for follow-up research.展开更多
基金Supported by the National Natural Science Foundation of China(61273160)the Fundamental Research Funds for the Central Universities(14CX06067A,13CX05021A)
文摘In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.
文摘The current study performed a finite element analysis of the strain localization behavior of a voided ductile material using a non-local plasticity formulation in which the yield strength depends on both an equivalent plastic strain measurement (hardening parameter) and Laplacian equivalent. The introduction of gradient terms to the yield function was found to play an important role in simulating the strain localization behavior of the voided ductile material. The effect of the mesh size and characteristic length on the strain localization were also investigated. An FEM simulation based on the proposed non-local plasticity revealed that the load-strain curves of the voided ductile material subjected to plane strain tension converged to one curve, regardless of the mesh size. In addition, the results using non-local plasticity also exhibited that the dependence of the deformation behavior of the material on the mesh size was much less sensitive than that with classical local plasticity and could be successfully eliminated through the introduction of a large value for the characteristic length.
基金the National Natural Science Foundation of China(No.19632030)
文摘A non-local continuum model for strain-softening simply takingplastic strain or damage vari- able as a non-local variable isderived by using the additive decomposition principle of finitedeformation gra- dient. At the same time, variational equations,their finite element formulations and numerical convolutedintegration algorithm of the model in current configuration usuallycalled co-moving coordinate system are given. stability andconvergence of the model are proven by means of the weak convergencetheorem of gen- eral function and the convoluted integration theory.
基金supported by the Climbling Project of Taishan Scholar in Shandong Province (No.tspd20210313)National Natural Science Foundation of China (Grant No.51874190,52079068,41941019,52090081 and 52074168)+3 种基金Taishan Scholar in Shandong Province (No.tsqn202211150)Outstanding Youth Fund Project in Shandong Province (No.ZQ2022YQ49)the State Key Laboratory of Hydroscience and Engineering,China (No.2021-KY-04)support from the G.Albert Shoemaker endowment.
文摘Multi-seam mining often leads to the retention of a significant number of coal pillars for purposes such as protection,safety,or water isolation.However,stress concentration beneath these residual coal pillars can significantly impact their strength and stability when mining below them,potentially leading to hydraulic support failure,surface subsidence,and rock bursting.To address this issue,the linkage between the failure and instability of residual coal pillars and rock strata during multi-seam mining is examined in this study.Key controls include residual pillar spalling,safety factor(f.),local mine stiffness(LMS),and the post-peak stiffness(k)of the residual coal pillar.Limits separating the two forms of failure,progressive versus dynamic,are defined.Progressive failure results at lower stresses when the coal pillar transitions from indefinitely stable(f,>1.5)to failing(f,<1.5)when the coal pillar can no longer remain stable for an extended duration,whereas sud-den(unstable)failure results when the strength of the pillar is further degraded and fails.The transition in mode of failure is defined by the LMS/k ratio.Failure transitions from quiescent to dynamic as LMS/k.<1,which can cause chain pillar instability propagating throughout the mine.This study provides theoretical guidance to define this limit to instability of residual coal pillars for multi-seam mining in similar mines.
基金the National Natural Science Foundation of China(Grant Nos.29837190,30230310,20077004&20477004)Beijing Natural Science Foundation(Grant Nos.8991002 , 8041003)+3 种基金the special fund for the doctoral s tudy of the Education Ministry of China(20010027017)“100-talent Project of CAS(dust transport)”,LAPCThe Institute of Atmospheric Phys ics,CAS the Swedish International Development Cooperation Agency(SIDA)through the Asian Regional Research Program on Environmental Technology(ARRPET)at the Asian Institute of Technology.
文摘A new element tracer technique has firstly been established to estimate the contributions of mineral aerosols from both inside and outside Beijing. The ratio of Mg/Al in aerosol is a feasible element tracer to distinguish between the sources of inside and outside Beijing. Mineral aerosol, inorganic pollution aerosol mainly as sulfate and nitrate, and organic aerosol are the major components of airborne particulates in Beijing, of which mineral aerosol accounted for 32%―67% of total suspended particles (TSP), 10%―70% of fine particles (PM2.5), and as high as 74% and 90% of TSP and PM2.5, respectively, in dust storm. The sources from outside Beijing contributed 62% (38%―86%) of the total mineral aerosols in TSP, 69% (52%―90%) in PM10, and 76% (59%―93%) in PM2.5 in spring, and 69% (52%―83%), 79% (52%―93%), and 45% (7%―79%) in TSP, PM10, and PM2.5, respectively, in winter, while only ~20% in summer and autumn. The sources from outside Beijing contributed as high as 97% during dust storm and were the dominant source of airborne particulates in Beijing. The contributions from outside Beijing in spring and winter are higher than those in summer, indicating clearly that it was related to the various meteorological factors.
文摘A polarized beam of energy is usually interpreted as a set of particles, all having the same polarization state. Difference in behavior between the one and the other particle is then explained by a number of counter-intuitive quantum mechanical concepts like probability distribution, superposition, entanglement and quantized spin. Alternatively, I propose that a polarized beam is composed of a set of particles with a cosine distribution of polarization angles within a polarization area. I show that Malus’ law for the intensity of a beam of polarized light can be derived in a straightforward manner from this distribution. I then show that none of the above-mentioned counter-intuitive concepts are necessary to explain particle behavior and that the ontology of particles, passing through a polarizer, can be easily and intuitively understood. I conclude by formulating some questions for follow-up research.