Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in undergroun...Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.展开更多
Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial c...Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement rates.The Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock failure.Then the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is proposed.The results show that the probability density function(PDF)of EPs follows the q-Gaussian distribution.The displacement rate is positively correlated with q value.With the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes denser.The influence of rock type on q value is related to the burst intensity of energy release and the crack fracture mode.The q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.展开更多
To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence...To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.展开更多
Recently, giant intrinsic anomalous Hall effect(AHE) has been observed in the materials with kagome lattice.Here, we systematically investigate the influence of high pressure on the AHE in the ferromagnet LiMn_(6)Sn_(...Recently, giant intrinsic anomalous Hall effect(AHE) has been observed in the materials with kagome lattice.Here, we systematically investigate the influence of high pressure on the AHE in the ferromagnet LiMn_(6)Sn_(6) with clean Mn kagome lattice. Our in situ high-pressure Raman spectroscopy indicates that the crystal structure of LiMn_(6)Sn_(6) maintains a hexagonal phase under high pressures up to 8.51 GPa. The anomalous Hall conductivity(AHC) σ_(xy)^(A) remains around 150 Ω^(-1)·cm^(-1), dominated by the intrinsic mechanism. Combined with theoretical calculations, our results indicate that the stable AHE under pressure in Li Mn_(6)Sn_(6) originates from the robust electronic and magnetic structure.展开更多
基金supported by National Key R&D Program of China(No.2022YFC3004705)the National Natural Science Foundation of China(Nos.52074280,52227901 and 52204249)National Natural Science Foundation of China Youth Fund(No.52104230).
文摘Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.
基金supported by National Key R&D Program of China(2022YFC3004705)the National Natural Science Foundation of China(Nos.52074280,52227901 and 52204249)+1 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_2913)the Graduate Innovation Program of China University of Mining and Technology(No.2024WLKXJ139).
文摘Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement rates.The Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock failure.Then the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is proposed.The results show that the probability density function(PDF)of EPs follows the q-Gaussian distribution.The displacement rate is positively correlated with q value.With the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes denser.The influence of rock type on q value is related to the burst intensity of energy release and the crack fracture mode.The q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.
基金supported in part by the National Key R&D Program of China No.2020YFB1806905the National Natural Science Foundation of China No.62201079+1 种基金the Beijing Natural Science Foundation No.L232051the Major Key Project of Peng Cheng Laboratory(PCL)Department of Broadband Communication。
文摘To facilitate emerging applications and demands of edge intelligence(EI)-empowered 6G networks,model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence(AI)models that provide abilities of semantic extraction and recovery.Nevertheless,it is not feasible to preload all AI models on resource-constrained terminals.Thus,in-time model transmission becomes a crucial problem.This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication.The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data.We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability(MTOP)over the Rayleigh channel.Besides,we define the effective model accuracy(EMA)to evaluate the model transmission performance of both communication and intelligence.Then we propose a joint model selection and resource allocation(JMSRA)algorithm to maximize the average EMA of all users.Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%.
基金supported by the National Natural Science Foundation of China (Grant No. 52272265)the National Key R&D Program of China (Grant Nos. 2023YFA1607400 and 2018YFA0704300)+4 种基金the support from the National Natural Science Foundation of China (Grant Nos. 52271016 and 52188101)the support from Analytical Instrumentation Center (# SPST-AIC10112914), SPST, Shanghai Tech Universitythe European Research Council (ERC Advanced Grant No. 742068 ‘TOPMAT’)the DFG through SFB 1143 (Project ID 247310070)the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC2147,Project ID 390858490)。
文摘Recently, giant intrinsic anomalous Hall effect(AHE) has been observed in the materials with kagome lattice.Here, we systematically investigate the influence of high pressure on the AHE in the ferromagnet LiMn_(6)Sn_(6) with clean Mn kagome lattice. Our in situ high-pressure Raman spectroscopy indicates that the crystal structure of LiMn_(6)Sn_(6) maintains a hexagonal phase under high pressures up to 8.51 GPa. The anomalous Hall conductivity(AHC) σ_(xy)^(A) remains around 150 Ω^(-1)·cm^(-1), dominated by the intrinsic mechanism. Combined with theoretical calculations, our results indicate that the stable AHE under pressure in Li Mn_(6)Sn_(6) originates from the robust electronic and magnetic structure.
