Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growin...Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growing observation demands.The observation Scheduling Problem in the MEOS constellation(MEOSSP)is a challenging issue due to the large number of satellites and tasks,as well as complex observation constraints.To address the large-scale and complicated MEOSSP,we develop a Two-Stage Scheduling Algorithm based on the Pointer Network with Attention mechanism(TSSA-PNA).In TSSA-PNA,the MEOS observation scheduling is decomposed into a task allocation stage and a single-MEOS scheduling stage.In the task allocation stage,an adaptive task allocation algorithm with four problem-specific allocation operators is proposed to reallocate the unscheduled tasks to new MEOSs.Regarding the single-MEOS scheduling stage,we design a pointer network based on the encoder-decoder architecture to learn the optimal singleMEOS scheduling solution and introduce the attention mechanism into the encoder to improve the learning efficiency.The Pointer Network with Attention mechanism(PNA)can generate the single-MEOS scheduling solution quickly in an end-to-end manner.These two decomposed stages are performed iteratively to search for the solution with high profit.A greedy local search algorithm is developed to improve the profits further.The performance of the PNA and TSSA-PNA on singleMEOS and multi-MEOS scheduling problems are evaluated in the experiments.The experimental results demonstrate that PNA can obtain the approximate solution for the single-MEOS scheduling problem in a short time.Besides,the TSSA-PNA can achieve higher observation profits than the existing scheduling algorithms within the acceptable computational time for the large-scale MEOS scheduling problem.展开更多
The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rar...The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).展开更多
A novel polystyrene-poly(hydroxamic acid)interpenetrating network resin(PS-PHA IPNs)was successfully synthesized by suspension polymerization and interpenetrating network technology.The effects of various experimental...A novel polystyrene-poly(hydroxamic acid)interpenetrating network resin(PS-PHA IPNs)was successfully synthesized by suspension polymerization and interpenetrating network technology.The effects of various experimental parameters,including pH,contact time and initial concentrations of rare earth ions on the adsorption capacity were discussed in detail.Under the condition of pH 4.0(La^(3+)),1.0(Ce^(3+))and 3.0(Y^(3+)),respectively,PS-PHA IPNs can reach equilibrium adsorption in 6 h and get maximum adsorption capacities(1.08,1.43 and 1.36 mmol/g).The adsorption process of PS-PHA IPNs for La(Ⅲ),Ce(Ⅲ)and Y(Ⅲ)ions can be described by liquid membrane diffusion,particle diffusion and chemical reaction.The adsorption process is a spontaneous and endothermic process and can be better simulated by Langmuir adsorption isotherm.The studies of SEM-EDS indicate that rare earth ions are adsorbed on the surface of PS-PHA IPNs.Fourier transform infrared spectroscopy(FTIR)and X-ray photoelectron spectroscopy(XPS)analysis further prove that rare earth ions are chemisorbed on the surface of PS-PHA IPNs.These results reveal that the as-prepared PS-PHA IPNs is a promising adsorbent for adsorption of rare earth ions due to their higher adsorption capacity than other adsorbents.展开更多
As a global strategic reserve resource,rare earth has been widely used in important industries,such as military equipment and biomedicine.However,existing analyses based solely on the total volume of rare earth trade ...As a global strategic reserve resource,rare earth has been widely used in important industries,such as military equipment and biomedicine.However,existing analyses based solely on the total volume of rare earth trade fail to uncover the underlying competition and dependency dynamics.To address this gap,this paper employs the principles of trade preference and import similarity to construct dependency and competition networks.Complex network analysis is then employed to study the evolution of the global rare earth trade network from 2002 to 2018.The main conclusions are as follows.The global rare earth trade follows the Pareto principle,and the trade network shows a scale-free distribution.China has emerged as the world’s largest importer and exporter of rare earth since 2017.In the dependency network,China has become the most dependent country since 2006.The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations(ASEAN)countries.The United States of America has formed a super-strong community with European and Asian countries.In the competition network,the distribution of competition intensity follows a scale-free distribution.Most countries face low-intensity competition,but there are numerous competing countries.The competition related to China has increased significantly.Lastly,the competition source for the United States of America has shifted from Mexico to China,resulting in China,the USA,and Japan becoming the core participants in the competition network.展开更多
Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the err...Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.展开更多
The results of an expert system of lanthanide intermetallic compounds using artificial neural networks and chemical bond parameter method were reported. Two pattern recognition neural models, one for prediction of the...The results of an expert system of lanthanide intermetallic compounds using artificial neural networks and chemical bond parameter method were reported. Two pattern recognition neural models, one for prediction of the occurrence of 1 : 1 lanthanide intermetallic compounds with CsClstructure and the other for prediction of congruent or incongruent melting types, were developed. Four regression neural models were also developed for prediction of melting point of these compounds. In order to get rid of overfitting, cross-vahdation method was used for the neural models. And satisfactory results were obtained in all of the neural models in this paper.展开更多
This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient im...This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.展开更多
The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius ...The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.展开更多
The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters...The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.展开更多
Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,...Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.展开更多
This paper presents a novel transient current differential algorithm for earth fault detection in unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm uses the transien...This paper presents a novel transient current differential algorithm for earth fault detection in unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm uses the transient residual currents, which are very sensitive for earth faults detection. The transient values of residual currents are calculated for each feeder in the network and used as an earth fault indicator. The flow of residual currents is investigated. It is found that the residual current for the faulted feeder is equal to the summation of all residual currents for all other healthy feedersl Based on this investigation, a differential technique is proposed. A percentage restrain performance is proposed to ensure the selectivity and security of the algorithm. The transient algorithm is very sensitive for earth fault incidence. To apply the proposed algorithm, the residual currents can be measured easily by one sensor for each feeder with no need to voltage measurement. The proposed algorithm is less dependent on the fault resistance and the faulted feeder parameters. The network is simulated by ATP/EMTP program. Different fault conditions are covered in the simulation process： different fault inception angles, fault locations and fault resistances.展开更多
Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa...Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.展开更多
An observation network focusing on earthquakes wascompleted one year aheadof schedule and put into operationrecently. According to scientists, this135-million-yuan (U.S.$16.3million) project could also be usedfor geod...An observation network focusing on earthquakes wascompleted one year aheadof schedule and put into operationrecently. According to scientists, this135-million-yuan (U.S.$16.3million) project could also be usedfor geodetic surveying, ionosphereand sea-level observations,展开更多
Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human p...Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.展开更多
In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the...In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.展开更多
Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient lon...Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient long-distance radio links.Known for its exceptional resilience,robustness,and sustained connectivity,MBC holds significant promise for applications in emergency communications,remote area connectivity,military/defense systems,and environmental monitoring.However,the scientific exploration and application of MBC have long been highly challenging.In particular,under the combined influence of multiple physical field factors,the channel experiences superimposed multiple random fading effects,exhibiting bursty,highly time-varying,and strongly random characteristics.This persistent technical challenge has resulted in the absence of a practical statistical channel model for MBC to date.展开更多
Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS sat...Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed.展开更多
基金supported by the National Natural Science Foundation of China(No.62101587)the National Funded Postdoctoral Researcher Program of China(No.GZC20233578)。
文摘Micro-nano Earth Observation Satellite(MEOS)constellation has the advantages of low construction cost,short revisit cycle,and high functional density,which is considered a promising solution for serving rapidly growing observation demands.The observation Scheduling Problem in the MEOS constellation(MEOSSP)is a challenging issue due to the large number of satellites and tasks,as well as complex observation constraints.To address the large-scale and complicated MEOSSP,we develop a Two-Stage Scheduling Algorithm based on the Pointer Network with Attention mechanism(TSSA-PNA).In TSSA-PNA,the MEOS observation scheduling is decomposed into a task allocation stage and a single-MEOS scheduling stage.In the task allocation stage,an adaptive task allocation algorithm with four problem-specific allocation operators is proposed to reallocate the unscheduled tasks to new MEOSs.Regarding the single-MEOS scheduling stage,we design a pointer network based on the encoder-decoder architecture to learn the optimal singleMEOS scheduling solution and introduce the attention mechanism into the encoder to improve the learning efficiency.The Pointer Network with Attention mechanism(PNA)can generate the single-MEOS scheduling solution quickly in an end-to-end manner.These two decomposed stages are performed iteratively to search for the solution with high profit.A greedy local search algorithm is developed to improve the profits further.The performance of the PNA and TSSA-PNA on singleMEOS and multi-MEOS scheduling problems are evaluated in the experiments.The experimental results demonstrate that PNA can obtain the approximate solution for the single-MEOS scheduling problem in a short time.Besides,the TSSA-PNA can achieve higher observation profits than the existing scheduling algorithms within the acceptable computational time for the large-scale MEOS scheduling problem.
基金ProjectsupportedbytheNationalTenthFive Year PlanofKeyTechnology (2 0 0 2BA3 15A)
文摘The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).
基金Projects supported by the National Natural Science Foundation of China(21206199)the Natural Science Foundation of Hunan Province(2018JJ2484)+1 种基金the Doctoral Fund of Ministry of Education of China(20100162120028)the Scientific Research Project of Jiujiang University(2014KJYB012)。
文摘A novel polystyrene-poly(hydroxamic acid)interpenetrating network resin(PS-PHA IPNs)was successfully synthesized by suspension polymerization and interpenetrating network technology.The effects of various experimental parameters,including pH,contact time and initial concentrations of rare earth ions on the adsorption capacity were discussed in detail.Under the condition of pH 4.0(La^(3+)),1.0(Ce^(3+))and 3.0(Y^(3+)),respectively,PS-PHA IPNs can reach equilibrium adsorption in 6 h and get maximum adsorption capacities(1.08,1.43 and 1.36 mmol/g).The adsorption process of PS-PHA IPNs for La(Ⅲ),Ce(Ⅲ)and Y(Ⅲ)ions can be described by liquid membrane diffusion,particle diffusion and chemical reaction.The adsorption process is a spontaneous and endothermic process and can be better simulated by Langmuir adsorption isotherm.The studies of SEM-EDS indicate that rare earth ions are adsorbed on the surface of PS-PHA IPNs.Fourier transform infrared spectroscopy(FTIR)and X-ray photoelectron spectroscopy(XPS)analysis further prove that rare earth ions are chemisorbed on the surface of PS-PHA IPNs.These results reveal that the as-prepared PS-PHA IPNs is a promising adsorbent for adsorption of rare earth ions due to their higher adsorption capacity than other adsorbents.
基金supported by the Ministry of Education of the People’s Republic of China Humanities and Social Sciences Youth Foundation(Grant No.22YJC910014)the Social Sciences Planning Youth Project of Anhui Province(Grant No.AHSKQ2022D138)the Innovation Development Research Project of Anhui Province(Grant No.2021CX053).
文摘As a global strategic reserve resource,rare earth has been widely used in important industries,such as military equipment and biomedicine.However,existing analyses based solely on the total volume of rare earth trade fail to uncover the underlying competition and dependency dynamics.To address this gap,this paper employs the principles of trade preference and import similarity to construct dependency and competition networks.Complex network analysis is then employed to study the evolution of the global rare earth trade network from 2002 to 2018.The main conclusions are as follows.The global rare earth trade follows the Pareto principle,and the trade network shows a scale-free distribution.China has emerged as the world’s largest importer and exporter of rare earth since 2017.In the dependency network,China has become the most dependent country since 2006.The result of community division shows that China has separated from the American community and formed new communities with the Association of Southeast Asian Nations(ASEAN)countries.The United States of America has formed a super-strong community with European and Asian countries.In the competition network,the distribution of competition intensity follows a scale-free distribution.Most countries face low-intensity competition,but there are numerous competing countries.The competition related to China has increased significantly.Lastly,the competition source for the United States of America has shifted from Mexico to China,resulting in China,the USA,and Japan becoming the core participants in the competition network.
基金Supported by National Natural Science Foundation of P.R.China(50474020,60534010,60504006)
文摘Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.
文摘The results of an expert system of lanthanide intermetallic compounds using artificial neural networks and chemical bond parameter method were reported. Two pattern recognition neural models, one for prediction of the occurrence of 1 : 1 lanthanide intermetallic compounds with CsClstructure and the other for prediction of congruent or incongruent melting types, were developed. Four regression neural models were also developed for prediction of melting point of these compounds. In order to get rid of overfitting, cross-vahdation method was used for the neural models. And satisfactory results were obtained in all of the neural models in this paper.
文摘This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.
文摘The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.
基金sponsored by the National Natural Science Foundation of China(61333002)Open Research Foundation of the State Key Laboratory of Geodesy and Earth’s Dynamics(SKLGED2018-5-4-E)+5 种基金Foundation of the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems(ACIA2017002)111 projects under Grant(B17040)Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017A02)supported by the Three Gorges Research Center for geo-hazardMinistry of Education cooperation agreements of Krasnoyarsk Science Center and Technology BureauRussian Academy of Sciences。
文摘The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.
基金Project(202006430012)supported by the China Scholarship Council。
文摘Most earth-dam failures are mainly due to seepage,and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster.Parametric uncertainties are encountered in the seepage analysis,and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated.This work proposes an adaptive Bayesian inversion method solved using artificial neural network(ANN)based Markov Chain Monte Carlo simulation.The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples,whereby the computational workload can be greatly reduced.It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database.The enrichment samples are obtained from the posterior distribution after iteration,which allows a more accurate and rapid manner to the target posterior.The method was then applied to the hydraulic analysis of an earth dam.After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location,it was validated by the pore water pressure monitoring values at the upstream saturated location.In addition,the uncertainty in the permeability coefficient was reduced,from 0.5 to 0.05.It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
文摘This paper presents a novel transient current differential algorithm for earth fault detection in unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm uses the transient residual currents, which are very sensitive for earth faults detection. The transient values of residual currents are calculated for each feeder in the network and used as an earth fault indicator. The flow of residual currents is investigated. It is found that the residual current for the faulted feeder is equal to the summation of all residual currents for all other healthy feedersl Based on this investigation, a differential technique is proposed. A percentage restrain performance is proposed to ensure the selectivity and security of the algorithm. The transient algorithm is very sensitive for earth fault incidence. To apply the proposed algorithm, the residual currents can be measured easily by one sensor for each feeder with no need to voltage measurement. The proposed algorithm is less dependent on the fault resistance and the faulted feeder parameters. The network is simulated by ATP/EMTP program. Different fault conditions are covered in the simulation process： different fault inception angles, fault locations and fault resistances.
基金National Key Research and Development Program(2021YFB2900604)。
文摘Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
文摘An observation network focusing on earthquakes wascompleted one year aheadof schedule and put into operationrecently. According to scientists, this135-million-yuan (U.S.$16.3million) project could also be usedfor geodetic surveying, ionosphereand sea-level observations,
基金supported in part by the National Natural Science Foundation of China(Grant No.62273107,61702127,62272113)Science and Technology Program of Guangzhou(Grant No.201804010461).
文摘Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.
基金supported by the National Key Research and Development Program(2021YFB2900604).
文摘In low Earth orbit(LEO)satellite networks,on-board energy resources of each satellite are extremely limited.And with the increase of the node number and the traffic transmis-sion pressure,the energy consumption in the networks presents uneven distribution.To achieve energy balance in networks,an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor(DEF)is proposed.The DEF is defined as the function of the inter-satellite link dis-tance and the cumulative network energy consumption ratio.According to the minimum sum of DEF on inter-satellite links,an energy consumption balancing algorithm based on DEF is pro-posed,which can realize dynamic traffic transmission optimiza-tion of multiple traffic services.It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network,make full use of idle nodes with low energy consumption,and optimize the energy consumption dis-tribution of the whole network according to the continuous itera-tions of each traffic service flow.Simulation results show that,compared with the traditional shortest path algorithm,the pro-posed method can improve the balancing performance of nodes by 75%under certain traffic pressure,and realize the optimiza-tion of energy consumption balancing of the whole network.
文摘Meteor Burst Communication(MBC),a niche yet revolutionary wireless communication paradigm,exploits the transient ionized trails generated by meteors ablating in Earth’s atmosphere to enable sporadic yet resilient long-distance radio links.Known for its exceptional resilience,robustness,and sustained connectivity,MBC holds significant promise for applications in emergency communications,remote area connectivity,military/defense systems,and environmental monitoring.However,the scientific exploration and application of MBC have long been highly challenging.In particular,under the combined influence of multiple physical field factors,the channel experiences superimposed multiple random fading effects,exhibiting bursty,highly time-varying,and strongly random characteristics.This persistent technical challenge has resulted in the absence of a practical statistical channel model for MBC to date.
基金Supported by the National High Technology Research and Development Program of China(″863″Program)(2010AAxxx404)~~
文摘Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed.
