This paper investigates ruin,capital injection,and dividends for a two-dimensional risk model.The model posits that surplus levels of insurance companies are governed by a perturbed composite Poisson risk model.This m...This paper investigates ruin,capital injection,and dividends for a two-dimensional risk model.The model posits that surplus levels of insurance companies are governed by a perturbed composite Poisson risk model.This model introduces a dependence between the two surplus levels,present in both the associated perturbations and the claims resulting from common shocks.Critical levels of capital injection and dividends are established for each of the two risks.The surplus levels are observed discretely at fixed intervals,guiding decisions on capital injection,dividends,and ruin at these junctures.This study employs a two-dimensional Fourier cosine series expansion method to approximate the finite time expected discounted operating cost until ruin.The ensuing approximation error is also quantified.The validity and accuracy of the method are corroborated through numerical examples.Furthermore,the research delves into the optimal capital allocation problem.展开更多
Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have b...Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
This paper proposes an eigenfunction expansion method to solve twodimensional （2D） elasticity problems based on stress formulation. By introducing appropriate state functions, the fundamental system of partial diffe...This paper proposes an eigenfunction expansion method to solve twodimensional （2D） elasticity problems based on stress formulation. By introducing appropriate state functions, the fundamental system of partial differential equations of the above 2D problems is rewritten as an upper triangular differential system. For the associated operator matrix, the existence and the completeness of two normed orthogonal eigenfunction systems in some space are obtained, which belong to the two block operators arising in the operator matrix. Moreover, the general solution to the above 2D problem is given by the eigenfunction expansion method.展开更多
This paper studies the eigenfunction expansion method to solve the two dimensional （2D） elasticity problems based on the stress formulation. The fundamental system of partial differential equations of the 2D problem...This paper studies the eigenfunction expansion method to solve the two dimensional （2D） elasticity problems based on the stress formulation. The fundamental system of partial differential equations of the 2D problems is rewritten as an upper tri angular differential system based on the known results, and then the associated upper triangular operator matrix matrix is obtained. By further research, the two simpler com plete orthogonal systems of eigenfunctions in some space are obtained, which belong to the two block operators arising in the operator matrix. Then, a more simple and conve nient general solution to the 2D problem is given by the eigenfunction expansion method. Furthermore, the boundary conditions for the 2D problem, which can be solved by this method, are indicated. Finally, the validity of the obtained results is verified by a specific example.展开更多
Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment a...Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.展开更多
The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making bas...The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making basis for the future urban construction land layout and regional development policy-making. Based on the night lighting data (DMSP/OLS), this paper extracts the boundary of the urban construction land of Changsha-Zhuzhou-Xiangtan urban agglomeration from 1993 to 2017, and quantitatively studies the spatial and temporal characteristics of the expansion of the metropolitan area in the past 25 years according to the methods of spatial expansion analysis, center of gravity migration measurement, landscape pattern index, spatial autocorrelation, etc. The results show that: 1) it is scientific and feasible to extract urban agglomeration construction land by the method of auxiliary data comparison for the study of urban expansion;2) the expansion of regional space in Changsha-Zhuzhou-Xiangtan metropolitan area shows a trend of “weakening first and strengthening later”. The construction land keeps increasing, and the expansion form gradually changes from extensive type to intensive type;3) the center of gravity of the metropolitan area fluctuated and repeated in part during the past 25 years, but it was always located in the municipal district of Changsha city. The eastern region, mainly Changsha city, was still the core area of urban agglomeration expansion;4) strengthening the territorial space protection and control of ecological green core in the metropolitan area is a key measure for the high-quality development of urban agglomeration.展开更多
Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method ...Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.展开更多
An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are tod...An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are today.However,the cost of obtaining abundant real-world environment is high,especially for underwater environments.It is more straightforward to simulate data that is closed to that from real environment.In this paper,a simple and easy symmetric learning data augmentation model(SLDAM)is proposed for underwater target radiate-noise data expansion and generation.The SLDAM,taking the optimal classifier of an initial dataset as the discriminator,makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation.It generates data similar to the initial dataset that can be used to supplement training data sets.This model has taken into consideration feature loss and sample loss function in model training,and is able to reduce the dependence of the generation and expansion on the feature set.We verified that the SLDAM is able to data expansion with low calculation complexity.Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy,for practical application in a production environment.展开更多
With the development of the aerospace industry,space missions are becoming more complicated and diversified,and there is a demand for antenna mechanisms with a larger physical aperture.In this paper,a planar deployabl...With the development of the aerospace industry,space missions are becoming more complicated and diversified,and there is a demand for antenna mechanisms with a larger physical aperture.In this paper,a planar deployable mechanism is proposed,which can form a flat reflection surface with a small gap between plates.To this end,a novel large-scale two-dimensional deployable nine-grid planar antenna mechanism is designed.First,two antenna folding schemes and four supporting mechanism schemes are proposed.Through comparison analysis,the antenna configuration scheme with the best comprehensive performance is selected.A kinematic model of the deployable mechanism is established,and its kinematic characteristics are analyzed.Then,the correctness of the kinematic model is verified by comparing the analytical and simulation results of the kinematic model.Subsequently,a finite element model of the antenna is developed.Based on the response surface method,the structural parameters of the support rods of the antenna are optimized,and a set of optimized solutions with lightweight and high fundamental frequency characteristics are obtained.Finally,a prototype of the proposed nine-grid planar antenna is fabricated.The feasibility of the deployment principle and the rationality of the designed mechanism are verified by deployment experiments.展开更多
D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated si...D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.展开更多
The two-dimensional(2D) pseudo-steady isothermal flow, which is isentropic and irrotational, around a convex corner is studied. The self-similar solutions for the supersonic flow around the convex corner are construct...The two-dimensional(2D) pseudo-steady isothermal flow, which is isentropic and irrotational, around a convex corner is studied. The self-similar solutions for the supersonic flow around the convex corner are constructed, where the properties of the centered simple wave are used for the 2D isentropic irrotational pseudo-steady Euler equations. The geometric procedures of the center simple waves are given. It is proven that the supersonic flow turns the convex corner by an incomplete centered expansion wave or an incomplete centered compression wave, depending on the conditions of the downstream state.展开更多
Difference expansion（DE） is one of the famous schemes in the field of reversible data hiding.With the high efficiency and simplicity,DE also has received more attention over the years.DE has a good information capac...Difference expansion（DE） is one of the famous schemes in the field of reversible data hiding.With the high efficiency and simplicity,DE also has received more attention over the years.DE has a good information capacity,but due to its major location map,the pure payload is rather low.Therefore many scholars did relevant improvements which let n pixels as a unit instead of the original two pixels as a unit and can adaptively adjust the number of embedding secret information according to the smoothness degree of the block,which achieves the result of improving the information payload or the image quality.In this paper,the study of DE-based reversible data hiding schemes is comprehensively discussed.The performance of DEbased schemes is evaluated and compared in terms of embedding capacity and stego-image quality.展开更多
The paper discusses the Edgeworth expansion for the mean direction =μ(Fn) of directional data and its 'Studentization'. As an application of the results,we give the corresponding result of von Mises population.
Because exact analytic solution is not available, we use double expansion and boundary collocation to construct an approximate solution for a class of two-dimensional dual integral equations in mathematical physics. T...Because exact analytic solution is not available, we use double expansion and boundary collocation to construct an approximate solution for a class of two-dimensional dual integral equations in mathematical physics. The integral equations by this procedure are reduced to infinite algebraic equations. The accuracy of the solution lies in the boundary collocation technique. The application of which for some complicated initialboundary value problems in solid mechanics indicates the method is powerful.展开更多
Quantification of urban expansion helps us to understand human induced effects on the environment in a temporal scale. Growing urbanization in Bangalore has resulted in demand for more space and resources. Since last ...Quantification of urban expansion helps us to understand human induced effects on the environment in a temporal scale. Growing urbanization in Bangalore has resulted in demand for more space and resources. Since last 15 years the landuse and landcover of Bangalore area has been changed drastically due to increase in settlement, urban infrastructure, opening of roads and metros etc. Using geospatial tools, we studied the changes in landuse and landcover over 19 years (1992-2011) of period and changes in transport network over 41 years (1970-2011) in parts of Bangalore. Thus, the current study shows that the built-up area has been increased drastically, tree cover areas have been converted to agricultural lands and agricultural lands to built-up areas due to urbanization. There are also changes in drainage pattern, transport network and encroachment of water bodies. Thus the whole environment is getting affected adversely due to unplanned and rapid urban sprawl.展开更多
This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text’s sentences and term expansion using WordNet [1] and a l...This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text’s sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet;and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then re-cycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.展开更多
Recently, Zhang et al. （Chin. Phys. B 26 024208 （2017）） investigated the band gap structures and semi-Dirac point of two-dimensional function photonic crystals, and the equations for the plane wave expansion metho...Recently, Zhang et al. （Chin. Phys. B 26 024208 （2017）） investigated the band gap structures and semi-Dirac point of two-dimensional function photonic crystals, and the equations for the plane wave expansion method were induced to obtain the band structures. That report shows the band diagrams with the effects of function coefficient k and medium column ra under TE and TM waves. The proposed results look correct at first glance, but the authors made some mistakes in their report. Thus, the calculated results in their paper are incorrect. According to our calculations, the errors in their report are corrected, and the correct band structures also are presented in this paper.展开更多
Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,si...Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.展开更多
Reversible data hiding(RDH)is a method to embed messages into an image that human eyes are difficult to recognize the differences between the original image and the embedded image.The method needs to make sure that th...Reversible data hiding(RDH)is a method to embed messages into an image that human eyes are difficult to recognize the differences between the original image and the embedded image.The method needs to make sure that the original image and the embedded information can be exactly recovered.The prediction-error expansion(PEE)is a successful way to realize RDH.However,it is fixed when pairing the conventional twodimensional prediction-error histogram(2D-PEH).So,the embedding capacity(EC)and embedding distortion(ED)are not satisfactory.In this study,we propose a method called greedy pairing prediction-error expansion(GPPEE)based on pairwise RDH and demonstrate GPPEE can achieve a more efficient embedding goal and reduce ED.展开更多
基金supported by the Shihezi University High-Level Talents Research Startup Project(Project No.RCZK202521)the National Natural Science Foundation of China(Grant Nos.12271066,11871121,12171405)+1 种基金the Chongqing Natural Science Foundation Joint Fund for Innovation and Development Project(Project No.CSTB2024NSCQLZX0085)the Chongqing Normal University Foundation(Grant No.23XLB018).
文摘This paper investigates ruin,capital injection,and dividends for a two-dimensional risk model.The model posits that surplus levels of insurance companies are governed by a perturbed composite Poisson risk model.This model introduces a dependence between the two surplus levels,present in both the associated perturbations and the claims resulting from common shocks.Critical levels of capital injection and dividends are established for each of the two risks.The surplus levels are observed discretely at fixed intervals,guiding decisions on capital injection,dividends,and ruin at these junctures.This study employs a two-dimensional Fourier cosine series expansion method to approximate the finite time expected discounted operating cost until ruin.The ensuing approximation error is also quantified.The validity and accuracy of the method are corroborated through numerical examples.Furthermore,the research delves into the optimal capital allocation problem.
基金Project supported by the National Key Research and Development Program of China(Grant No.2022YFB2803900)the National Natural Science Foundation of China(Grant Nos.61974075 and 61704121)+2 种基金the Natural Science Foundation of Tianjin Municipality(Grant Nos.22JCZDJC00460 and 19JCQNJC00700)Tianjin Municipal Education Commission(Grant No.2019KJ028)Fundamental Research Funds for the Central Universities(Grant No.22JCZDJC00460).
文摘Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金Project supported by the National Natural Science Foundation of China (No. 10962004)the Special-ized Research Fund for the Doctoral Program of Higher Education of China (No. 20070126002)+1 种基金the Chunhui Program of Ministry of Education of China (No. Z2009-1-01010)the Natural Science Foundation of Inner Mongolia (No. 2009BS0101)
文摘This paper proposes an eigenfunction expansion method to solve twodimensional （2D） elasticity problems based on stress formulation. By introducing appropriate state functions, the fundamental system of partial differential equations of the above 2D problems is rewritten as an upper triangular differential system. For the associated operator matrix, the existence and the completeness of two normed orthogonal eigenfunction systems in some space are obtained, which belong to the two block operators arising in the operator matrix. Moreover, the general solution to the above 2D problem is given by the eigenfunction expansion method.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20070126002)the National Natural Science Foundation of China (No. 10962004)
文摘This paper studies the eigenfunction expansion method to solve the two dimensional （2D） elasticity problems based on the stress formulation. The fundamental system of partial differential equations of the 2D problems is rewritten as an upper tri angular differential system based on the known results, and then the associated upper triangular operator matrix matrix is obtained. By further research, the two simpler com plete orthogonal systems of eigenfunctions in some space are obtained, which belong to the two block operators arising in the operator matrix. Then, a more simple and conve nient general solution to the 2D problem is given by the eigenfunction expansion method. Furthermore, the boundary conditions for the 2D problem, which can be solved by this method, are indicated. Finally, the validity of the obtained results is verified by a specific example.
基金National 973 Program of China, No.2010CB950900Swedish Research Links, No.2006-24724-44416-13
文摘Since the implementation of the reform and opening up policy in China in the late 1970s, some meteorological stations ＇entered＇ cities passively due to urban expansion. Changes in the surface and built environment around the stations have influenced observa- tions of air temperature. When the observational data from urban stations are applied in the interpolation of national or regional scale air temperature dataset, they could lead to overes- timation of regional air temperature and inaccurate assessment of warming. In this study, the underlying surface surrounding 756 meteorological stations across China was identified based on remote sensing images over a number of time intervals to distinguish the rural sta- tions that ＇entered＇ into cities. Then, after removing the observational data from these stations which have been influenced by urban expansion, a dataset of background air temperatures was generated by interpolating the observational data from the remaining rural stations. The mean urban heat island effect intensity since 1970 was estimated by comparing the original observational records from urban stations with the background air temperature interpolated. The result shows that urban heat island effect does occur due to urban expansion, with a higher intensity in winter than in other seasons. Then the overestimation of regional air tem- perature is evaluated by comparing the two kinds of grid datasets of air temperature which are respectively interpolated by all stations＇ and rural stations＇ observational data. Spatially, the overestimation is relatively higher in eastern China than in the central part of China; however, both areas exhibit a much higher effect than is observed in western China. We concluded that in the last 40 years the mean temperature in China increased by about 1.58℃, of which about 0.01℃ was attributed to urban expansion, with a contribution of up to 0.09℃ in the core areas from the overestimation of air temperature.
文摘The research purpose is to accurately reveal the temporal and spatial law of the urban expansion of Changsha-Zhuzhou-Xiangtan, one of the seven major urban agglomeration areas in China, and provide decision-making basis for the future urban construction land layout and regional development policy-making. Based on the night lighting data (DMSP/OLS), this paper extracts the boundary of the urban construction land of Changsha-Zhuzhou-Xiangtan urban agglomeration from 1993 to 2017, and quantitatively studies the spatial and temporal characteristics of the expansion of the metropolitan area in the past 25 years according to the methods of spatial expansion analysis, center of gravity migration measurement, landscape pattern index, spatial autocorrelation, etc. The results show that: 1) it is scientific and feasible to extract urban agglomeration construction land by the method of auxiliary data comparison for the study of urban expansion;2) the expansion of regional space in Changsha-Zhuzhou-Xiangtan metropolitan area shows a trend of “weakening first and strengthening later”. The construction land keeps increasing, and the expansion form gradually changes from extensive type to intensive type;3) the center of gravity of the metropolitan area fluctuated and repeated in part during the past 25 years, but it was always located in the municipal district of Changsha city. The eastern region, mainly Changsha city, was still the core area of urban agglomeration expansion;4) strengthening the territorial space protection and control of ecological green core in the metropolitan area is a key measure for the high-quality development of urban agglomeration.
基金Supported by National Natural Science Foundation of China(41804126,41604107).
文摘Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.
基金This work was funded by the National Natural Science Foundation of China under Grant(No.61772152 and No.61502037)the Basic Research Project(No.JCKY2016206B001,JCKY2014206C002 and JCKY2017604C010)the Technical Foundation Project(No.JSQB2017206C002).
文摘An important issue for deep learning models is the acquisition of training of data.Without abundant data from a real production environment for training,deep learning models would not be as widely used as they are today.However,the cost of obtaining abundant real-world environment is high,especially for underwater environments.It is more straightforward to simulate data that is closed to that from real environment.In this paper,a simple and easy symmetric learning data augmentation model(SLDAM)is proposed for underwater target radiate-noise data expansion and generation.The SLDAM,taking the optimal classifier of an initial dataset as the discriminator,makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation.It generates data similar to the initial dataset that can be used to supplement training data sets.This model has taken into consideration feature loss and sample loss function in model training,and is able to reduce the dependence of the generation and expansion on the feature set.We verified that the SLDAM is able to data expansion with low calculation complexity.Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy,for practical application in a production environment.
基金supported by the National Natural Science Foundation of China(No.52075467).
文摘With the development of the aerospace industry,space missions are becoming more complicated and diversified,and there is a demand for antenna mechanisms with a larger physical aperture.In this paper,a planar deployable mechanism is proposed,which can form a flat reflection surface with a small gap between plates.To this end,a novel large-scale two-dimensional deployable nine-grid planar antenna mechanism is designed.First,two antenna folding schemes and four supporting mechanism schemes are proposed.Through comparison analysis,the antenna configuration scheme with the best comprehensive performance is selected.A kinematic model of the deployable mechanism is established,and its kinematic characteristics are analyzed.Then,the correctness of the kinematic model is verified by comparing the analytical and simulation results of the kinematic model.Subsequently,a finite element model of the antenna is developed.Based on the response surface method,the structural parameters of the support rods of the antenna are optimized,and a set of optimized solutions with lightweight and high fundamental frequency characteristics are obtained.Finally,a prototype of the proposed nine-grid planar antenna is fabricated.The feasibility of the deployment principle and the rationality of the designed mechanism are verified by deployment experiments.
基金sponsored by the National Natural Science Foundation of China(Nos.42174149,41774144)the National Major Projects(No.2016ZX05014-001).
文摘D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.
基金Project supported by the National Natural Science Foundation of China(Nos.11371240 and11771274)
文摘The two-dimensional(2D) pseudo-steady isothermal flow, which is isentropic and irrotational, around a convex corner is studied. The self-similar solutions for the supersonic flow around the convex corner are constructed, where the properties of the centered simple wave are used for the 2D isentropic irrotational pseudo-steady Euler equations. The geometric procedures of the center simple waves are given. It is proven that the supersonic flow turns the convex corner by an incomplete centered expansion wave or an incomplete centered compression wave, depending on the conditions of the downstream state.
基金supported in part by MOST under Grants No.105-2221-E-324-015 and No.103-2632-E-324-001-MY3
文摘Difference expansion（DE） is one of the famous schemes in the field of reversible data hiding.With the high efficiency and simplicity,DE also has received more attention over the years.DE has a good information capacity,but due to its major location map,the pure payload is rather low.Therefore many scholars did relevant improvements which let n pixels as a unit instead of the original two pixels as a unit and can adaptively adjust the number of embedding secret information according to the smoothness degree of the block,which achieves the result of improving the information payload or the image quality.In this paper,the study of DE-based reversible data hiding schemes is comprehensively discussed.The performance of DEbased schemes is evaluated and compared in terms of embedding capacity and stego-image quality.
文摘The paper discusses the Edgeworth expansion for the mean direction =μ(Fn) of directional data and its 'Studentization'. As an application of the results,we give the corresponding result of von Mises population.
基金Project supported by the National Natural Science Foundation of China(No.K19672007)
文摘Because exact analytic solution is not available, we use double expansion and boundary collocation to construct an approximate solution for a class of two-dimensional dual integral equations in mathematical physics. The integral equations by this procedure are reduced to infinite algebraic equations. The accuracy of the solution lies in the boundary collocation technique. The application of which for some complicated initialboundary value problems in solid mechanics indicates the method is powerful.
文摘Quantification of urban expansion helps us to understand human induced effects on the environment in a temporal scale. Growing urbanization in Bangalore has resulted in demand for more space and resources. Since last 15 years the landuse and landcover of Bangalore area has been changed drastically due to increase in settlement, urban infrastructure, opening of roads and metros etc. Using geospatial tools, we studied the changes in landuse and landcover over 19 years (1992-2011) of period and changes in transport network over 41 years (1970-2011) in parts of Bangalore. Thus, the current study shows that the built-up area has been increased drastically, tree cover areas have been converted to agricultural lands and agricultural lands to built-up areas due to urbanization. There are also changes in drainage pattern, transport network and encroachment of water bodies. Thus the whole environment is getting affected adversely due to unplanned and rapid urban sprawl.
文摘This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text’s sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet;and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then re-cycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.
基金Project supported by the Special Grade of the Financial Support from the China Postdoctoral Science Foundation(Grant No.2016T90455)the China Postdoctoral Science Foundation(Grant No.2015M581790)the Chinese Jiangsu Planned Projects for Postdoctoral Research Funds,China(Grant No.1501016A)
文摘Recently, Zhang et al. （Chin. Phys. B 26 024208 （2017）） investigated the band gap structures and semi-Dirac point of two-dimensional function photonic crystals, and the equations for the plane wave expansion method were induced to obtain the band structures. That report shows the band diagrams with the effects of function coefficient k and medium column ra under TE and TM waves. The proposed results look correct at first glance, but the authors made some mistakes in their report. Thus, the calculated results in their paper are incorrect. According to our calculations, the errors in their report are corrected, and the correct band structures also are presented in this paper.
基金supported in part by the National Natural Science Foundation of China under Grant No.61973055Fundamental Research Funds for the Central Universities under Grant No.ZYGX2020J011Regional Innovation Cooperation Funds of Sichuan under Grant No.2024YFHZ0089.
文摘Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.
基金supported by MOST under Grants No.107-2221-E-845-002-MY3 and No.110-2221-E-845-002-。
文摘Reversible data hiding(RDH)is a method to embed messages into an image that human eyes are difficult to recognize the differences between the original image and the embedded image.The method needs to make sure that the original image and the embedded information can be exactly recovered.The prediction-error expansion(PEE)is a successful way to realize RDH.However,it is fixed when pairing the conventional twodimensional prediction-error histogram(2D-PEH).So,the embedding capacity(EC)and embedding distortion(ED)are not satisfactory.In this study,we propose a method called greedy pairing prediction-error expansion(GPPEE)based on pairwise RDH and demonstrate GPPEE can achieve a more efficient embedding goal and reduce ED.
