In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of inform...In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.展开更多
Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people i...Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.展开更多
In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization tec...In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.展开更多
In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm base...In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.展开更多
The increase in CO_(2)injectivity and shifting of CO_(2)-absorbing layers in multilayered geological CO_(2)sequestration(GCS)reservoirs in Ordos,China indicate significantpermeability variations in certain layers.To c...The increase in CO_(2)injectivity and shifting of CO_(2)-absorbing layers in multilayered geological CO_(2)sequestration(GCS)reservoirs in Ordos,China indicate significantpermeability variations in certain layers.To capture these system changes,a numerical model incorporating all 21 aquifers and internal aquitards was developed.The monitored pressure was well matched through multiphase and thermalhydraulic-mechanical(THM)coupling numerical simulations by introducing permeability variations.The results revealed that the permeability in the second layer increased on approximately day 13 due to the abrupt pressure buildup and temperature decrease.Even such a low rate of CO_(2)(2.8 kg/s)injected into the low permeability system initiated some fractures and the permeability in the second layer around the wellbore increased by 722 times.The second critical system change occurred on approximately day 386.As demonstrated in the numerical simulation,the substantial injection of cold CO_(2)induced strong thermal stress,leading to rock contraction and the initiation of several cracks.The permeability of the firstlayer around the wellbore unexpectedly increased by 4 orders of magnitude.Since no additional pressure could drive the CO_(2)into the remaining 17 layers,the total storage capability of the multilayered system was reduced.A whole picture of the system variation is fully presented and the underlying mechanisms are analyzed.It is believed that the phenomenon of thermal-hydraulic fracturing observed in this fieldand the simulation procedures will benefitother fluidinjection and production works in various geotechnical settings.展开更多
Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivati...Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.展开更多
Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to esta...Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.展开更多
Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spec...Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.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
For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to p...For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.展开更多
The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data s...The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.展开更多
The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regressi...The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.展开更多
Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using...Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.展开更多
Data related to the pre-grouting work of a large underground project are systematically analyzed to reveal the mechanism behind,to shed some light on the execution of practical grouting,and to enrich the theory of eng...Data related to the pre-grouting work of a large underground project are systematically analyzed to reveal the mechanism behind,to shed some light on the execution of practical grouting,and to enrich the theory of engineering geology.Grouting is generally taken as an effective way for controlling nonignorable water seepage during underground rock excavation.Though various models have been developed to guide grouting design or to specify criteria for grouting stop,it does not change the fact that grouting is still highly experience-based.Therefore,explanation of the current situation due to grouting complexity is given through step-by-step data analysis,where the impact on grouting parameters from the geological and hydrogeological conditions is investigated,and the grouting features of two tunnels located at the same depth below the sea surface are compared and discussed.Then,the data from individual grout hole are used to construct the regional geological conditions via inverse analysis.It is found that grouting of fractured rock masses is accompanied with great uncertainty,and field grouting data can contribute significantly to a better understanding of the regional geological conditions around an underground tunnel or rock cavern.展开更多
Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripp...Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.展开更多
The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of win...The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.展开更多
Regarding the rapid compensation of the influence of the Earth＇ s disturbing gravity field upon trajectory calculation,the key point lies in how to derive the analytical solutions to the partial derivatives of the st...Regarding the rapid compensation of the influence of the Earth＇ s disturbing gravity field upon trajectory calculation,the key point lies in how to derive the analytical solutions to the partial derivatives of the state of burnout point with respect to the launch data.In view of this,this paper mainly expounds on two issues：one is based on the approximate analytical solution to the motion equation for the vacuum flight section of a long-range rocket,deriving the analytical solutions to the partial derivatives of the state of burnout point with respect to the changing rate of the finalstage pitch program;the other is based on the initial positioning and orientation error propagation mechanism,proposing the analytical calculation formula for the partial derivatives of the state of burnout point with respect to the launch azimuth.The calculation results of correction data are simulated and verified under different circumstances.The simulation results are as follows：（1） the accuracy of approximation between the analytical solutions and the results attained via the difference method is higher than 90%,and the ratio of calculation time between them is lower than 0.2%,thus demonstrating the accuracy of calculation of data corrections and advantages in calculation speed;（2） after the analytical solutions are compensated,the longitudinal landing deviation of the rocket is less than 20 m and the lateral landing deviation of the rocket is less than 10 m,demonstrating that the corrected data can meet the requirements for the hit accuracy of a long-range rocket.展开更多
There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that ge...There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that generates excitement and questions about how it relates to and differs from these other areas of study.展开更多
This study aims to improve knowledge of the structure of southwest Cameroon based on the analysis and interpretation of gravity data derived from the SGG-UGM-2 model. A residual anomaly map was first calculated from t...This study aims to improve knowledge of the structure of southwest Cameroon based on the analysis and interpretation of gravity data derived from the SGG-UGM-2 model. A residual anomaly map was first calculated from the Bouguer anomaly map, which is strongly affected by a regional gradient. The residual anomaly map generated provides information on the variation in subsurface density, but does not provide sufficient information, hence the interest in using filtering with the aim of highlighting the structures affecting the area of south-west Cameroon. Three interpretation methods were used: vertical gradient, horizontal gradient coupled with upward continuation and Euler deconvolution. The application of these treatments enabled us to map a large number of gravimetric lineaments materializing density discontinuities. These lineaments are organized along main preferential directions: NW-SE, NNE-SSW, ENE-WSW and secondary directions: NNW-SSE, NE-SW, NS and E-W. Euler solutions indicate depths of up to 7337 m. Thanks to the results of this research, significant information has been acquired, contributing to a deeper understanding of the structural composition of the study area. The resulting structural map vividly illustrates the major tectonic events that shaped the geological framework of the study area. It also serves as a guide for prospecting subsurface resources (water and hydrocarbons). .展开更多
The anisotropic properties of subsurface media cause waveform distortions in seismic wave propagation,resulting in a negative infl uence on seismic imaging.In addition,wavefields simulated by the conventional coupled ...The anisotropic properties of subsurface media cause waveform distortions in seismic wave propagation,resulting in a negative infl uence on seismic imaging.In addition,wavefields simulated by the conventional coupled pseudo-acoustic equation are not only aff ected by SV-wave artifacts but are also limited by anisotropic parameters.We propose a least-squares reverse time migration(LSRTM)method based on the pure q P-wave equation in vertically transverse isotropic media.A fi nite diff erence and fast Fourier transform method,which can improve the effi ciency of the numerical simulation compared to a pseudo-spectral method,is used to solve the pure q P-wave equation.We derive the corresponding demigration operator,migration operator,and gradient updating formula to implement the LSRTM.Numerical tests on the Hess model and field data confirm that the proposed method has a good correction eff ect for the travel time deviation caused by underground anisotropic media.Further,it signifi cantly suppresses the migration noise,balances the imaging amplitude,and improves the imaging resolution.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61501064Sichuan Provincial Science and Technology Project under Grant No.2016GZ0122
文摘In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.
基金Supported by the National Key Research and Development Program of China(Nos.2021YFC3101801,2023YFC3008200)the National Natural Science Foundation of China(Nos.42476219,41976200)+6 种基金the National Foreign Experts Program(No.S20240134)the Innovative Team Plan of the Department of Education of Guangdong Province(No.2023KCXTD015)the Tropical Ocean Environment in Western Coastal Waters Observation and Research Station of Guangdong Province(No.2024B1212040008)the Independent Research Project of the Southern Ocean Laboratory(No.SML2022SP301)the Shandong Innovation and Development Research Institute Think Tank Projectthe Guangdong Ocean University Scientific Research Program(No.060302032106)the Start-up Fund for Ph D Researchers(No.060302032104)。
文摘Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.
基金This work is supported by the research project (grant No. G20000467) of the Institute of Geology and Geophysics, CAS and bythe China Postdoctoral Science Foundation (No. 2004036083).
文摘In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.
基金The National Natural Science Foundation of China(No.61231002,61273266,61571106)the Foundation of the Department of Science and Technology of Guizhou Province(No.[2015]7637)
文摘In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.
基金supports from the National Natural Science Foundation of China(Grant Nos.52179095,52378323,and 42407216)are gratefully acknowledged.
文摘The increase in CO_(2)injectivity and shifting of CO_(2)-absorbing layers in multilayered geological CO_(2)sequestration(GCS)reservoirs in Ordos,China indicate significantpermeability variations in certain layers.To capture these system changes,a numerical model incorporating all 21 aquifers and internal aquitards was developed.The monitored pressure was well matched through multiphase and thermalhydraulic-mechanical(THM)coupling numerical simulations by introducing permeability variations.The results revealed that the permeability in the second layer increased on approximately day 13 due to the abrupt pressure buildup and temperature decrease.Even such a low rate of CO_(2)(2.8 kg/s)injected into the low permeability system initiated some fractures and the permeability in the second layer around the wellbore increased by 722 times.The second critical system change occurred on approximately day 386.As demonstrated in the numerical simulation,the substantial injection of cold CO_(2)induced strong thermal stress,leading to rock contraction and the initiation of several cracks.The permeability of the firstlayer around the wellbore unexpectedly increased by 4 orders of magnitude.Since no additional pressure could drive the CO_(2)into the remaining 17 layers,the total storage capability of the multilayered system was reduced.A whole picture of the system variation is fully presented and the underlying mechanisms are analyzed.It is believed that the phenomenon of thermal-hydraulic fracturing observed in this fieldand the simulation procedures will benefitother fluidinjection and production works in various geotechnical settings.
基金supported by the National Science and Technology Major Projects (2008ZX05025)the Project of National Oil and Gas Resources Strategic Constituency Survey and Evaluation of the Ministry of Land and Resources,China (XQ-2007-05)
文摘Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.
基金Project(cstc2020jcyj-bshX0106)supported by the Chongqing Postdoctoral Science Foundation,ChinaProject(2020M683247)supported by the China Postdoctoral Science Foundation+1 种基金Project(cstc2020jcyj-zdxmX0023)supported by the Key Natural Science Foundation Project of Chongqing,ChinaProject(551974043)supported by the National Natural Science Foundation of China。
文摘Source location is the core foundation of microseismic monitoring.To date,commonly used location methods have usually been based on the ray-tracing travel-time technique,which generally adopts an L1 or L2 norm to establish the location objective function.However,the L1 norm usually achieves low location accuracy,whereas the L2 norm is easily affected by large P-wave arrival-time picking errors.In addition,traditional location methods may be affected by the initial iteration point used to find a local optimum location.Furthermore,the P-wave arrival-time data that have travelled long distances are usually poor in quality.To address these problems,this paper presents a microseismic source location method using the Log-Cosh function and distant sensor-removed P-wave arrival data.Its basic principles are as follows:First,the source location objective function is established using the Log-Cosh function.This function has the stability of the L1 norm and location accuracy of the L2 norm.Then,multiple initial points are generated randomly in the mining area,and the established Log-Cosh location objective function is used to obtain multiple corresponding location results.The average value of the 50 location points with the largest data field potential values is treated as the initial location result.Next,the P-wave travel times from the initial location result to triggered sensors are calculated,and then the P-wave arrival data with travel times exceeding 0.2 s are removed.Finally,the aforementioned location steps are repeated with the denoised P-wave arrival dataset to obtain a high-precision location result.Two synthetic events and eight blasting events from the Yongshaba mine,China,were used to test the proposed method.Regardless of whether the P-wave arrival data with long travel times were eliminated,the location error of the proposed method was smaller than that of the L1/L2 norm and trigger-time-based location method(TT1/TT2 method).Furthermore,after eliminating the Pwave arrival data with long travel distances,the location accuracy of these three location methods increased,indicating that the proposed location method has good application prospects.
文摘Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.
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 National Natural Science Foundation of China(61371172)the International S&T Cooperation Program of China(2015DFR10220)+1 种基金the Ocean Engineering Project of National Key Laboratory Foundation(1213)the Fundamental Research Funds for the Central Universities(HEUCF1608)
文摘For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.
基金Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804)the National Natural Science Fundation of China(No.60743001)+1 种基金the Best National Thesis Fundation (No.2005047)the National New Century Excellent Talent Fundation (No.NCET-06-0618)
文摘The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.
文摘The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.
基金supported in part by the National Key Research and Development Program of China(No.2022YFB3305403)Project of basic research funds for central universities(2022CDJDX006)+1 种基金Talent Plan Project of Chongqing(No.cstc2021ycjhbgzxm0295)National Natural Science Foundation of China(No.52111530194)。
文摘Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.
基金the“Start-up Funding for New Faculty”provided by the Nanjing University of Aeronautics and Astronautics。
文摘Data related to the pre-grouting work of a large underground project are systematically analyzed to reveal the mechanism behind,to shed some light on the execution of practical grouting,and to enrich the theory of engineering geology.Grouting is generally taken as an effective way for controlling nonignorable water seepage during underground rock excavation.Though various models have been developed to guide grouting design or to specify criteria for grouting stop,it does not change the fact that grouting is still highly experience-based.Therefore,explanation of the current situation due to grouting complexity is given through step-by-step data analysis,where the impact on grouting parameters from the geological and hydrogeological conditions is investigated,and the grouting features of two tunnels located at the same depth below the sea surface are compared and discussed.Then,the data from individual grout hole are used to construct the regional geological conditions via inverse analysis.It is found that grouting of fractured rock masses is accompanied with great uncertainty,and field grouting data can contribute significantly to a better understanding of the regional geological conditions around an underground tunnel or rock cavern.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2016-0-00313)supervised by the IITP(Institute for Information&communication Technology Promotion)+1 种基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(2017R1E1A1A01074345).
文摘Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.
基金financially supported by the National Natural Science Foundation of China (Grant No.52378329)。
文摘The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.
文摘Regarding the rapid compensation of the influence of the Earth＇ s disturbing gravity field upon trajectory calculation,the key point lies in how to derive the analytical solutions to the partial derivatives of the state of burnout point with respect to the launch data.In view of this,this paper mainly expounds on two issues：one is based on the approximate analytical solution to the motion equation for the vacuum flight section of a long-range rocket,deriving the analytical solutions to the partial derivatives of the state of burnout point with respect to the changing rate of the finalstage pitch program;the other is based on the initial positioning and orientation error propagation mechanism,proposing the analytical calculation formula for the partial derivatives of the state of burnout point with respect to the launch azimuth.The calculation results of correction data are simulated and verified under different circumstances.The simulation results are as follows：（1） the accuracy of approximation between the analytical solutions and the results attained via the difference method is higher than 90%,and the ratio of calculation time between them is lower than 0.2%,thus demonstrating the accuracy of calculation of data corrections and advantages in calculation speed;（2） after the analytical solutions are compensated,the longitudinal landing deviation of the rocket is less than 20 m and the lateral landing deviation of the rocket is less than 10 m,demonstrating that the corrected data can meet the requirements for the hit accuracy of a long-range rocket.
文摘There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that generates excitement and questions about how it relates to and differs from these other areas of study.
文摘This study aims to improve knowledge of the structure of southwest Cameroon based on the analysis and interpretation of gravity data derived from the SGG-UGM-2 model. A residual anomaly map was first calculated from the Bouguer anomaly map, which is strongly affected by a regional gradient. The residual anomaly map generated provides information on the variation in subsurface density, but does not provide sufficient information, hence the interest in using filtering with the aim of highlighting the structures affecting the area of south-west Cameroon. Three interpretation methods were used: vertical gradient, horizontal gradient coupled with upward continuation and Euler deconvolution. The application of these treatments enabled us to map a large number of gravimetric lineaments materializing density discontinuities. These lineaments are organized along main preferential directions: NW-SE, NNE-SSW, ENE-WSW and secondary directions: NNW-SSE, NE-SW, NS and E-W. Euler solutions indicate depths of up to 7337 m. Thanks to the results of this research, significant information has been acquired, contributing to a deeper understanding of the structural composition of the study area. The resulting structural map vividly illustrates the major tectonic events that shaped the geological framework of the study area. It also serves as a guide for prospecting subsurface resources (water and hydrocarbons). .
基金financially supported by the National Key R&D Program of China (No. 2019YFC0605503)the Major Scientific and Technological Projects of CNPC (No. ZD2019-183-003)the National Natural Science Foundation of China (No. 41922028,41874149)。
文摘The anisotropic properties of subsurface media cause waveform distortions in seismic wave propagation,resulting in a negative infl uence on seismic imaging.In addition,wavefields simulated by the conventional coupled pseudo-acoustic equation are not only aff ected by SV-wave artifacts but are also limited by anisotropic parameters.We propose a least-squares reverse time migration(LSRTM)method based on the pure q P-wave equation in vertically transverse isotropic media.A fi nite diff erence and fast Fourier transform method,which can improve the effi ciency of the numerical simulation compared to a pseudo-spectral method,is used to solve the pure q P-wave equation.We derive the corresponding demigration operator,migration operator,and gradient updating formula to implement the LSRTM.Numerical tests on the Hess model and field data confirm that the proposed method has a good correction eff ect for the travel time deviation caused by underground anisotropic media.Further,it signifi cantly suppresses the migration noise,balances the imaging amplitude,and improves the imaging resolution.
