The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to ...The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.展开更多
According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for ...According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable.At last,the analytical results of road test data of the Three Gorges prove the analytical method efficient.展开更多
Abstract: It has been inferred and proved by the remote sensing equations under rational hypotheses in atmospheric physics that there is a linear correlation between the ground reflective brightness Wij and the total ...Abstract: It has been inferred and proved by the remote sensing equations under rational hypotheses in atmospheric physics that there is a linear correlation between the ground reflective brightness Wij and the total reflective brightness Rij received in different bands with a remote sensor. Nine models delineating the ground-space correlation between the ground spectra and the optimal bands of images of the typical gold deposits have been established based on the ground-space correlativity and field measurements of the ground spectra of the typical gold deposits in the Ailaoshan area. According to the 9 correlation models, TM images were inverted into ground-space correlation images that are related to the typical gold deposits within the area and then recognized by a computer. Research on the ground spectra and TM data in the Ailaoshan area shows that the correlation analysis of the ground spectra and TM data of gold deposits can be effectively applied to the prediction of gold deposits, location of prospecting targets, and extraction of imagery information of gold mineralization.展开更多
To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method ca...To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.展开更多
The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenolog...The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.展开更多
In the present paper, the ground reaction force （GRF） acting on foot in slow squat was determined through a force measuring system, and at the same time, the kinematic data of human squat were obtained by analyzing ...In the present paper, the ground reaction force （GRF） acting on foot in slow squat was determined through a force measuring system, and at the same time, the kinematic data of human squat were obtained by analyzing the photographed image sequences. According to the height and body weight, six healthy volunteers were selected, three men in one group and the other three women in another group, and the fundamental parameters of subjects were recorded, including body weight, height and age, etc. Based on the anatomy characteristics, some markers were placed on the right side of joints. While the subject squatted at slow speed on the force platform, the ground reaction forces on the forefoot and heel for each foot were obtained through calibrated force platform. The analysis results show that the reaction force on heel is greater than that on forefoot, and double feet have nearly constant force. Moreover, from processing and analyzing the synchronously photographed image sequences in squat, the kinematic data of human squat were acquired, including mainly the curves of angle, angular velocity and angular acceleration varied with time for knee, hip and ankle joints in a sagittal plane. The obtained results can offer instructive reference for photographing and analyzing the movements of human bodies, diagnosing some diseases, and establishing in the future appropriate mathematical models for the human motion.展开更多
We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominate...We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.展开更多
Ground-Penetrating Radar(GPR)technology,with its characteristics of being fast,non-destructive,and highresolution,has become an important tool for detecting underground structures.However,GPR data inevitably suffer fr...Ground-Penetrating Radar(GPR)technology,with its characteristics of being fast,non-destructive,and highresolution,has become an important tool for detecting underground structures.However,GPR data inevitably suffer from environmental noise and electromagnetic interference during the acquisition process,leading to decreased data quality and increased complexity in data processing.Traditional filtering algorithms have limitations such as low discrimination between noise and signal,poor adaptability,and inability to process data in real time.This paper proposes a filtering model based on deep neural networks,called FilterNet.FilterNet combines Convolution Neural Networks(CNN)and recurrent neural networks(RNN)for processing multi-channel data.It can perform end-to-end filtering directly on the raw tunnel lining GPR data,achieving functions such as removing air reflection waves,denoising,and automatic gain.Using PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)as statistical indicators,it is shown that the FilterNet model improves filtering precision.The SSIM of all three models is 0.997,and the PSNR of FilterNet1D and FilterNet are 19.06 and 19.41,respectively.Furthermore,tests on the model's processing efficiency indicate that FilterNet requires less memory and is more efficient than the UNet model.FilterNet's parameters are only 48%of those of UNet.Its GFLOPS(Giga Floating Point Operations Per Second)is only one-third of UNet's,and it can process data in real time.Additionally,FilterNet performs exceptionally well in suppressing random noise.展开更多
Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically emplo...Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically employed. The dynamic changes of data series are described with the numerical features in multi dimensional space and their distributive relations instead of an unique factor. The relationship between the ground tilt data and earthquake is examined through recognition and classification.展开更多
Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environ...Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.展开更多
This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the mod...This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.展开更多
To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on ...To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.展开更多
According to the analysis of the very high frequency (VHF) self organized time division multiple access (S TDMA) aviation data link, a new dynamic slot assignment scheme is proposed in this paper, which adopts var...According to the analysis of the very high frequency (VHF) self organized time division multiple access (S TDMA) aviation data link, a new dynamic slot assignment scheme is proposed in this paper, which adopts variable data frame structure and can eliminate the effect of the idle slot on message delay. By using queue theory, the analysis models of the new scheme and previous scheme are presented, and the performance of message delay and that of system throughput are analyzed under two schemes. The simulation results show that the new scheme has a better performance than the previous one in the message delay and system throughput.展开更多
With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and...With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.展开更多
We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Se...We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.展开更多
A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data tra...A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data transmission task model and satellite data transmission scheduling problem model are established.Secondly,the conflicts in scheduling are discussed.According to the meaning of possible conflict,the method to divide possible conflict task set is given.Thirdly,a hybrid algorithm which consists of genetic algorithm and heuristic information is presented.The heuristic information comes from two concepts,conflict degree and conflict number.Finally,an example shows the algorithm's feasibility and performance better than other traditional展开更多
Oil-gas remote sensing information is obtained from satellite TM data through graph-ic treatment in the light of the hydrocarbon-microseepage theory. The nine target areas (ofthree types) selected on this basis coinci...Oil-gas remote sensing information is obtained from satellite TM data through graph-ic treatment in the light of the hydrocarbon-microseepage theory. The nine target areas (ofthree types) selected on this basis coincide well with the occurrence of natural gases and have been proved by subsequent prospecting. Plants in the target areas are characterized, as a result of hydrocarbon- microseepage, by abnormal spectral features with the absorption peaks of chlorophyll shifting toward blue light, reflectivity in the range of visible light increasing and re-flectivity in the near infrared region decreasing.展开更多
Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this stud...Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this study,we introduce a3D residual network(3D Res Net)framework based on 3D convolutional neural networks(3D CNN)to predict ground states phases in 3D dipolar spinor Bose–Einstein condensates(BECs).Our results show that the 3D Res Net framework predicts ground states with high accuracy and efficiency across a broad parameter space.To enhance phase transition predictions,we incorporate data augmentation techniques,leading to a notable improvement in the model's performance.The method is further validated in more complex scenarios,particularly when transverse magnetic fields are introduced.Compared to conventional imaginary-time evolution methods(ITEM),the 3D Res Net drastically reduces computational costs,offering a rapid and scalable solution for complex 3D multi-parameter nonlinear systems.展开更多
基金support of the“National R&D Project for Smart Construction Technology (Grant No.RS-2020-KA157074)”funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land,Infrastructure and Transport,and managed by the Korea Expressway Corporation.
文摘The widespread adoption of tunnel boring machines(TBMs)has led to an increased focus on disc cutter wear,including both normal and abnormal types,for efficient and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.
基金Foundation item:Supported by the National Nature Science Founda-tion of China(50099620)and 863 Program Foundation of China(2001AA132050-03)
文摘According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable.At last,the analytical results of road test data of the Three Gorges prove the analytical method efficient.
文摘Abstract: It has been inferred and proved by the remote sensing equations under rational hypotheses in atmospheric physics that there is a linear correlation between the ground reflective brightness Wij and the total reflective brightness Rij received in different bands with a remote sensor. Nine models delineating the ground-space correlation between the ground spectra and the optimal bands of images of the typical gold deposits have been established based on the ground-space correlativity and field measurements of the ground spectra of the typical gold deposits in the Ailaoshan area. According to the 9 correlation models, TM images were inverted into ground-space correlation images that are related to the typical gold deposits within the area and then recognized by a computer. Research on the ground spectra and TM data in the Ailaoshan area shows that the correlation analysis of the ground spectra and TM data of gold deposits can be effectively applied to the prediction of gold deposits, location of prospecting targets, and extraction of imagery information of gold mineralization.
基金Supported by the National Nature Science Founda tion of China(50099620)863 Project(2001AA132050-03)
文摘To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.
文摘The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.
基金supported by the National Natural Science Foundation of China (10702048 and 11102126)Natural Science Foundation of Shanxi (2010021004-1)
文摘In the present paper, the ground reaction force （GRF） acting on foot in slow squat was determined through a force measuring system, and at the same time, the kinematic data of human squat were obtained by analyzing the photographed image sequences. According to the height and body weight, six healthy volunteers were selected, three men in one group and the other three women in another group, and the fundamental parameters of subjects were recorded, including body weight, height and age, etc. Based on the anatomy characteristics, some markers were placed on the right side of joints. While the subject squatted at slow speed on the force platform, the ground reaction forces on the forefoot and heel for each foot were obtained through calibrated force platform. The analysis results show that the reaction force on heel is greater than that on forefoot, and double feet have nearly constant force. Moreover, from processing and analyzing the synchronously photographed image sequences in squat, the kinematic data of human squat were acquired, including mainly the curves of angle, angular velocity and angular acceleration varied with time for knee, hip and ankle joints in a sagittal plane. The obtained results can offer instructive reference for photographing and analyzing the movements of human bodies, diagnosing some diseases, and establishing in the future appropriate mathematical models for the human motion.
基金made possible by a scholarship from the Australian Government(International Postgraduate Research Scholarship-awarded in 2009)a Southern Cross University Postgraduate Research Scholarship(SCUPRS in 2009)
文摘We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.
基金supported by China Railway Construction Corporation Limited(CRCC)Major Science and Technology Special Project Research,Development,and Application Demonstraction of Gelogical Information Digital Platform(Project No.:2021-A02).
文摘Ground-Penetrating Radar(GPR)technology,with its characteristics of being fast,non-destructive,and highresolution,has become an important tool for detecting underground structures.However,GPR data inevitably suffer from environmental noise and electromagnetic interference during the acquisition process,leading to decreased data quality and increased complexity in data processing.Traditional filtering algorithms have limitations such as low discrimination between noise and signal,poor adaptability,and inability to process data in real time.This paper proposes a filtering model based on deep neural networks,called FilterNet.FilterNet combines Convolution Neural Networks(CNN)and recurrent neural networks(RNN)for processing multi-channel data.It can perform end-to-end filtering directly on the raw tunnel lining GPR data,achieving functions such as removing air reflection waves,denoising,and automatic gain.Using PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)as statistical indicators,it is shown that the FilterNet model improves filtering precision.The SSIM of all three models is 0.997,and the PSNR of FilterNet1D and FilterNet are 19.06 and 19.41,respectively.Furthermore,tests on the model's processing efficiency indicate that FilterNet requires less memory and is more efficient than the UNet model.FilterNet's parameters are only 48%of those of UNet.Its GFLOPS(Giga Floating Point Operations Per Second)is only one-third of UNet's,and it can process data in real time.Additionally,FilterNet performs exceptionally well in suppressing random noise.
文摘Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically employed. The dynamic changes of data series are described with the numerical features in multi dimensional space and their distributive relations instead of an unique factor. The relationship between the ground tilt data and earthquake is examined through recognition and classification.
文摘Groundwater is the water located beneath the earth＇s surface in the soil pore spaces and in the fractures of rock formations. As one of the most important natural resources, groundwater is associated with the environment, public health, welfare, and long-term economic growth, which affects the daily activities of human beings. In modern urban areas, the primary contaminants of groundwater are artificial products, such as gasoline and diesel. To protect this important water resource, a series of efforts have been exerted, including enforcement and remedial actions. Each year, the TGPC （Texas Groundwater Protection Committee） in US publishes a ＂Joint Groundwater Monitoring and Contamination Report＂ to describe historic and new contamination cases in each county, which is an important data source for the design of prevention strategies. In this paper, a DDM （data dependent modeling） approach is proposed to predict county-level NCC （new contamination cases）. A case study with contamination information from Harris County in Texas was conducted to illustrate the modeling and prediction process with promising results. The one-step prediction error is 1.5%, while the two-step error is 12.1%. The established model can be used at the county-level, state-level, and even at the country-level. Besides, the prediction results could be a reference during decision-making processes.
文摘This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.
基金supported by the Special Edu-cational Research Budget(Research Promotion)[FY2009]the Special Budget(Project)[FY2010 and later years]from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japansupported by the GRENE Arctic Climate Change Research Project,Japan
文摘To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.
基金Aeronautical Science F oundation of China !( N o.98E5 1116)
文摘According to the analysis of the very high frequency (VHF) self organized time division multiple access (S TDMA) aviation data link, a new dynamic slot assignment scheme is proposed in this paper, which adopts variable data frame structure and can eliminate the effect of the idle slot on message delay. By using queue theory, the analysis models of the new scheme and previous scheme are presented, and the performance of message delay and that of system throughput are analyzed under two schemes. The simulation results show that the new scheme has a better performance than the previous one in the message delay and system throughput.
基金Fundamental Research Funds for the Central Universities,China (No. 22D111207)。
文摘With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.
文摘We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.
文摘A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data transmission task model and satellite data transmission scheduling problem model are established.Secondly,the conflicts in scheduling are discussed.According to the meaning of possible conflict,the method to divide possible conflict task set is given.Thirdly,a hybrid algorithm which consists of genetic algorithm and heuristic information is presented.The heuristic information comes from two concepts,conflict degree and conflict number.Finally,an example shows the algorithm's feasibility and performance better than other traditional
文摘Oil-gas remote sensing information is obtained from satellite TM data through graph-ic treatment in the light of the hydrocarbon-microseepage theory. The nine target areas (ofthree types) selected on this basis coincide well with the occurrence of natural gases and have been proved by subsequent prospecting. Plants in the target areas are characterized, as a result of hydrocarbon- microseepage, by abnormal spectral features with the absorption peaks of chlorophyll shifting toward blue light, reflectivity in the range of visible light increasing and re-flectivity in the near infrared region decreasing.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11904309 and 12305015)the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5528)the Natural Science Foundation of Hebei Province,China(Grant No.A2024205027)。
文摘Machine learning has rapidly become a powerful tool for addressing challenges in ultracold atomic systems;however,its application to intricate three-dimensional(3D)systems remains relatively underexplored.In this study,we introduce a3D residual network(3D Res Net)framework based on 3D convolutional neural networks(3D CNN)to predict ground states phases in 3D dipolar spinor Bose–Einstein condensates(BECs).Our results show that the 3D Res Net framework predicts ground states with high accuracy and efficiency across a broad parameter space.To enhance phase transition predictions,we incorporate data augmentation techniques,leading to a notable improvement in the model's performance.The method is further validated in more complex scenarios,particularly when transverse magnetic fields are introduced.Compared to conventional imaginary-time evolution methods(ITEM),the 3D Res Net drastically reduces computational costs,offering a rapid and scalable solution for complex 3D multi-parameter nonlinear systems.
