Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-lay...Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-layer parameter disturbance. The advantage of multi-layer parameter disturbance is that it not only scrambles pixel location of images, but also changes pixel values of images. Bit-plane decomposition can increase the space of key. And using chaotic sequence generated by chaotic system with different complexities to encrypt layers with different information content can save operation time. The simulation experiments show that using chaotic mapping in image encryption method based on multi-layer parameter disturbance can cover plaintext effectively and safely, which makes it achieve ideal encryption effect.展开更多
Vector-to-raster conversion is a process accompanied with errors.The errors are classified into predicted errors before rasterization and actual errors after that.Accurate prediction of the errors is beneficial to dev...Vector-to-raster conversion is a process accompanied with errors.The errors are classified into predicted errors before rasterization and actual errors after that.Accurate prediction of the errors is beneficial to developing reasonable rasterization technical schemes and to making products of high quality.Analyzing and establishing a quantitative relationship between the error and its affecting factors is the key to error prediction.In this study,land cover data of China at a scale of 1:250 000 were taken as an example for analyzing the relationship between rasterization errors and the density of arc length(DA),the density of polygon(DP) and the size of grid cells(SG).Significant correlations were found between the errors and DA,DP and SG.The correlation coefficient(R2) of a model established based on samples collected in a small region(Beijing) reaches 0.95,and the value of R2 is equal to 0.91 while the model was validated with samples from the whole nation.On the other hand,the R2 of a model established based on nationwide samples reaches 0.96,and R2 is equal to 0.91 while it was validated with the samples in Beijing.These models depict well the relationships between rasterization errors and their affecting factors(DA,DP and SG).The analyzing method established in this study can be applied to effectively predicting rasterization errors in other cases as well.展开更多
This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the cha...This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.展开更多
When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion...When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion and vice versa.A single-layer Mapping Function(MF)based on a thin-shell assumption of the Earth’s ionosphere is commonly used for TEC conversion.However,the accuracy of single-layer MF is susceptible to the inaccurate fxing of the ionospheric single-layer height.In order to fnd a mapping approach less sensitive to the choice of ionospheric efective height we defned a multi-layer ionosphere mapping function and investigated its performance in comparison with the single-layer model.We found that the multi-layer MF outperforms the single-layer MF when computing GNSS receiver Diferential Code Biases(DCBs)especially at low latitude and equatorial regions where ionosphere is highly dynamic and difcult to model.When compared with the International GNSS Services(IGS)products,we found that the mean receiver DCB estimation is improved(closer to benchmark)by about 0.14-0.27 ns and 0.30-0.78 ns during days in 2019 and 2023,respectively.We found that the receiver DCB estimation improves for about 66-87%receivers.This is also refected in Global Ionosphere Maps(GIMs)showing better performance for the multi-layer MF when comparing with IGS GIMs.Our investigation using GNSS observations onboard Low Earth Orbiting(LEO)satellites shows that the multi-layer MF can be successfully applied in computing satellite and receiver DCBs accurately.展开更多
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
文摘Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-layer parameter disturbance. The advantage of multi-layer parameter disturbance is that it not only scrambles pixel location of images, but also changes pixel values of images. Bit-plane decomposition can increase the space of key. And using chaotic sequence generated by chaotic system with different complexities to encrypt layers with different information content can save operation time. The simulation experiments show that using chaotic mapping in image encryption method based on multi-layer parameter disturbance can cover plaintext effectively and safely, which makes it achieve ideal encryption effect.
基金Under the auspices of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05050000)Special Program for Informatization of Chinese Academy of Sciences(No.INF0-115-C01-SDB3-02)
文摘Vector-to-raster conversion is a process accompanied with errors.The errors are classified into predicted errors before rasterization and actual errors after that.Accurate prediction of the errors is beneficial to developing reasonable rasterization technical schemes and to making products of high quality.Analyzing and establishing a quantitative relationship between the error and its affecting factors is the key to error prediction.In this study,land cover data of China at a scale of 1:250 000 were taken as an example for analyzing the relationship between rasterization errors and the density of arc length(DA),the density of polygon(DP) and the size of grid cells(SG).Significant correlations were found between the errors and DA,DP and SG.The correlation coefficient(R2) of a model established based on samples collected in a small region(Beijing) reaches 0.95,and the value of R2 is equal to 0.91 while the model was validated with samples from the whole nation.On the other hand,the R2 of a model established based on nationwide samples reaches 0.96,and R2 is equal to 0.91 while it was validated with the samples in Beijing.These models depict well the relationships between rasterization errors and their affecting factors(DA,DP and SG).The analyzing method established in this study can be applied to effectively predicting rasterization errors in other cases as well.
基金funded by the National Natural Science Foundation of China(Grant No.11972018,No.12002336)China Postdoctoral Science Foundation(Grant No.2021M701710)。
文摘This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.
基金funded by the LMAP(LEO Ionospheric Mapping Assessment and Derivation For Precise PVT Applications)project under the ESA Contract No.4000142821/23/NL/MGu/my。
文摘When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion and vice versa.A single-layer Mapping Function(MF)based on a thin-shell assumption of the Earth’s ionosphere is commonly used for TEC conversion.However,the accuracy of single-layer MF is susceptible to the inaccurate fxing of the ionospheric single-layer height.In order to fnd a mapping approach less sensitive to the choice of ionospheric efective height we defned a multi-layer ionosphere mapping function and investigated its performance in comparison with the single-layer model.We found that the multi-layer MF outperforms the single-layer MF when computing GNSS receiver Diferential Code Biases(DCBs)especially at low latitude and equatorial regions where ionosphere is highly dynamic and difcult to model.When compared with the International GNSS Services(IGS)products,we found that the mean receiver DCB estimation is improved(closer to benchmark)by about 0.14-0.27 ns and 0.30-0.78 ns during days in 2019 and 2023,respectively.We found that the receiver DCB estimation improves for about 66-87%receivers.This is also refected in Global Ionosphere Maps(GIMs)showing better performance for the multi-layer MF when comparing with IGS GIMs.Our investigation using GNSS observations onboard Low Earth Orbiting(LEO)satellites shows that the multi-layer MF can be successfully applied in computing satellite and receiver DCBs accurately.
