Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generali...Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 1460 keV.The PL at the incident surface of the detector was 0.99,and the resolution of the central incident point source ranged between 0.52 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-ray localization model significantly improved the useful field-of-view(UFOV)from 60.32 to 93.44%compared to the classical centroid localization methods.Additionally,the signal-to-noise ratio of the reconstructed images increased from 0.95 to 5.63.展开更多
Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated ...Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.展开更多
The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan ar...The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.展开更多
Periodontitis is an inflammatory autoimmune disease. Treatment should alleviate inflammation, regulate the immune reaction and promote periodontal tissue regeneration. Icariin is the main active ingredient of Epimedii...Periodontitis is an inflammatory autoimmune disease. Treatment should alleviate inflammation, regulate the immune reaction and promote periodontal tissue regeneration. Icariin is the main active ingredient of Epimedii Folium, and it is a promising compound for the enhancement of mesenchymal stem cell function, promotion of bone formation, inhibition of bone resorption, alleviation of inflammation and regulation of immunity. The study investigated the effect of icariin on periodontal tissue regeneration in a minipig model of periodontitis. The minipig model of periodontitis was established. Icariin was injected locally. The periodontal clinical assessment index, a computed tomography(CT) scan, histopathology and enzyme-linked immune sorbent assay(ELISA)were used to evaluate the effects of icariin. Quantitative analysis results 12 weeks post-injection demonstrated that probing depth,gingival recession, attachment loss and alveolar bone regeneration values were(3.72 ± 1.18) mm vs.(6.56 ± 1.47) mm,(1.67 ± 0.59)mm vs.(2.38 ± 0.61) mm,(5.56 ± 1.29) mm vs.(8.61 ± 1.72) mm, and(25.65 ± 5.13) mm3 vs.(9.48 ± 1.78) mm3 in the icariin group and0.9% NaCl group, respectively. The clinical assessment, CT scan, and histopathology results demonstrated significant enhancement of periodontal tissue regeneration in the icariin group compared to the 0.9% NaCl group. The ELISA results suggested that the concentration of interleukin-1 beta(IL-1β) in the icariin group was downregulated compared to the 0.9% NaCl group, which indicates that local injection of icariin relieved local inflammation in a minipig model of periodontitis. Local injection of icariin promoted periodontal tissue regeneration and exerted anti-inflammatory and immunomodulatory function. These results support the application of icariin for the clinical treatment of periodontitis.展开更多
This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power ...This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.展开更多
To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively ap...To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.展开更多
In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robu...In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robust stabilization of plants under unstructured uncertainties. CE-150 helicopters are known for their varying operating conditions along with external disturbances. Therefore, local model networks are introduced for their adaptive feature and since they provide a powerful combination of fuzzy logic and conventional linear control techniques to control nonlinear systems without the added computational burden of soft-computing techniques. Using the fact that the system can be linearized at different operating points, a mixed sensitivity H-infinity controller is designed for the linearized system, and combined within a network to make transitions between them. The proposed control structure ensures robustness, decoupling of the system dynamics while achieving good performance. A comparison is carried-out against the well-known proportional-integral-derivative （PID） control technique. Results are presented to illustrate the controller＇s performance in various operating conditions.展开更多
In an earlier study, the Atmospheric Models Intercomparison Program (AMIP) simulations of African climate using the nine-layer gridpoint atmospheric general circulation model were found to be closely related to the ob...In an earlier study, the Atmospheric Models Intercomparison Program (AMIP) simulations of African climate using the nine-layer gridpoint atmospheric general circulation model were found to be closely related to the observed European Centre for Medium Range Weather Forecast (ECMWF) temperature data at 500 and 850 hPa. This paper presents the analysis of the simulation of African climate using the Global Ocean-Atmosphere-Land System Model (IAP/LASG GOALS) and the nine-layer spectral general circulation model rhomboidally truncated at zonal wave number 15 (L9R15) developed at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing. Both model simulations were not significantly different from the National Center for Environmental Prediction (NCEP) Reanalysis monthly mean data for 1980-1995 in the case of surface air temperature, sea level pressure and precipitation, with the GOALS reproducing the seasonal mean climate over Africa better. The implications of the encouraging results in developing a local area model for Nigeria have been discussed. The great role of topography in the developing of general circulation models for numerical modelling of weather and climate has been stressed.展开更多
Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of...Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.展开更多
The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems.The floor-occupant-seat local simulation mode...The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems.The floor-occupant-seat local simulation model provides an alternative solution for quickly evaluating the performance of occupant protection systems.However,the error and rationality of the loading of the thin-walled floor in the local model cannot be ignored.This study proposed an equivalent loading method for the local model,which includes two parts:the dimensionality reduction method for acceleration matrix and the joint optimization framework for equivalent node coordinates.In the dimensionality reduction method,the dimension of the acceleration matrix was reduced based on the improved kernel principal component analysis(KPCA),and a dynamic variable bandwidth was introduced to address the limitation of failing to effectively measure the similarity between acceleration data in conventional KPCA.In addition,a least squares problem with forced displacement constraints was constructed to solve the correction matrix,thereby achieving the scale restoration process of the principal component acceleration matrix.The joint optimization framework for coordinates consists of the error assessment of response time histories(EARTH)and Bayesian optimization.In this framework,the local loading error of the equivalent acceleration matrix is taken as the Bayesian optimization objective,which is quantified and scored by EARTH.The expected improvement acquisition function was used to select the new set of the equivalent acceleration node coordinates for the self-updating optimization of the observation dataset and Gaussian process surrogate model.We reduced the dimension of the acceleration matrix from 2256 to 7,while retaining 91%of the information features.The comprehensive error score of occupant's lower limb response in the local model increased from 58.5%to 80.4%.The proposed equivalent loading method provides a solution for the rapid and reliable development of occupant protection systems.展开更多
Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine...Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine the key spreaders in complex networks.The existing algorithms based on gravity model consider the shortest distance between nodes.However,nodes not only influence each other through the shortest path,which will lead the loss of information between nodes.Different from the existing gravity model,the distance considered in this model is not the shortest distance between nodes,but the reciprocal of the number of feasible paths between nodes.Conveniently,the model is called FPLGM(Local Gravity Model Based on Feasible Paths).Ten different networks are utilised to verify the effectiveness of FPLGM.Results show that the FPLGM performs best in comparison with the well-known state-of-the-art methods.展开更多
Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many v...Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many vastly different dynamic systems can be treated in this manner,and to answer why a detailed,accurate,and global mathematical model is unnecessary,is the target of this paper.Driven by a motivating example,the notions of normality and locality are introduced.Normality shows that,in ADRC,the plant is normalized to an integrator chain,which is called local nominal model and locally describes the plant’s frequency response in the neighborhood of the expected gain crossover frequency.Locality interprets why ADRC can design the controller only with the local information of the plant.With normality and locality,ADRC can be effective and robust,and obtain operational stability discussed by T.S.Tsien.Then viewing proportional-integral-derivative(PID)control as a low-frequency approximation of second-order linear ADRC,the above results are extended to PID control.A controller design framework is proposed to obtain the controller in three steps:(1)choose an integrator chain as the local nominal model of the plant;(2)select a controller family corresponding to the local nominal model;and(3)tune the controller to guarantee the gain crossover frequency specification.The second-order linear ADRC and the PID control are two special cases of the framework.展开更多
In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segme...In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.展开更多
Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent var...Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.展开更多
Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class o...Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.展开更多
We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empiri...We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
Comparison of regular(diurnal,seasonal and solar cycle)variations of high-latitude,mid-latitude and low-latitude ionospheric characteristics has been provided on basis of local empirical models of the peak electron de...Comparison of regular(diurnal,seasonal and solar cycle)variations of high-latitude,mid-latitude and low-latitude ionospheric characteristics has been provided on basis of local empirical models of the peak electron density and the peak height.The local empirical models were derived from the hand-scaled ionogram data recorded by DPS-4 digisondes located at Norilsk(69°N,88°E),Irkutsk(52°N,104°E)and Hainan(19°N,109°E)for a 6-year period from December,2002 to December,2008.The technique used to build the local empirical model is described.The primary focus is diurnal-seasonal behavior under low solar activity and its change with increasing solar activity.Both common and specific features of the high-latitude(Norilsk),mid-latitude(Irkutsk)and low-latitude(Hainan)regular variations were revealed using their local empirical models.展开更多
Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a...Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock surfaces.The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability.Based on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks.The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone.Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks.The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.展开更多
Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to ha...Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.展开更多
基金supported by the National Natural Science Foundation of China(Nos.41874121 and U19A2086)。
文摘Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 1460 keV.The PL at the incident surface of the detector was 0.99,and the resolution of the central incident point source ranged between 0.52 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-ray localization model significantly improved the useful field-of-view(UFOV)from 60.32 to 93.44%compared to the classical centroid localization methods.Additionally,the signal-to-noise ratio of the reconstructed images increased from 0.95 to 5.63.
基金financially supported by the Scientific Research Funds for Forestry Public Welfare of China(Granted No.201004026)the Program for Changjiang Scholars and Innovative Research Team in University(IRT1054)
文摘Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.
基金Sino-Italian Cooperation Project "An Integrated System for the Planning, Monitoring and Real-time Forecasting of Floods Risks"
文摘The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.
基金supported by grants from the National Natural Science Foundation of China (grant number 81625005 to Z.F.)High-level Talents of the Beijing Health System (grant number 2014-3-080 to F.Z.)the program for Beijing Science and Technology of Chinese Medicine (grant number JJ2013-11 to F.Z.)
文摘Periodontitis is an inflammatory autoimmune disease. Treatment should alleviate inflammation, regulate the immune reaction and promote periodontal tissue regeneration. Icariin is the main active ingredient of Epimedii Folium, and it is a promising compound for the enhancement of mesenchymal stem cell function, promotion of bone formation, inhibition of bone resorption, alleviation of inflammation and regulation of immunity. The study investigated the effect of icariin on periodontal tissue regeneration in a minipig model of periodontitis. The minipig model of periodontitis was established. Icariin was injected locally. The periodontal clinical assessment index, a computed tomography(CT) scan, histopathology and enzyme-linked immune sorbent assay(ELISA)were used to evaluate the effects of icariin. Quantitative analysis results 12 weeks post-injection demonstrated that probing depth,gingival recession, attachment loss and alveolar bone regeneration values were(3.72 ± 1.18) mm vs.(6.56 ± 1.47) mm,(1.67 ± 0.59)mm vs.(2.38 ± 0.61) mm,(5.56 ± 1.29) mm vs.(8.61 ± 1.72) mm, and(25.65 ± 5.13) mm3 vs.(9.48 ± 1.78) mm3 in the icariin group and0.9% NaCl group, respectively. The clinical assessment, CT scan, and histopathology results demonstrated significant enhancement of periodontal tissue regeneration in the icariin group compared to the 0.9% NaCl group. The ELISA results suggested that the concentration of interleukin-1 beta(IL-1β) in the icariin group was downregulated compared to the 0.9% NaCl group, which indicates that local injection of icariin relieved local inflammation in a minipig model of periodontitis. Local injection of icariin promoted periodontal tissue regeneration and exerted anti-inflammatory and immunomodulatory function. These results support the application of icariin for the clinical treatment of periodontitis.
基金Supported by SSFC（04BTJ002）,the National Natural Science Foundation of China（10371016） and the Post-Doctorial Grant in Southeast University.
文摘This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.
文摘To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.
文摘In this paper, a local model network H-infinity control is proposed for CE-150 helicopter stabilization. The proposed strategy capitalizes on recent developments on H-infinity control and its promising results in robust stabilization of plants under unstructured uncertainties. CE-150 helicopters are known for their varying operating conditions along with external disturbances. Therefore, local model networks are introduced for their adaptive feature and since they provide a powerful combination of fuzzy logic and conventional linear control techniques to control nonlinear systems without the added computational burden of soft-computing techniques. Using the fact that the system can be linearized at different operating points, a mixed sensitivity H-infinity controller is designed for the linearized system, and combined within a network to make transitions between them. The proposed control structure ensures robustness, decoupling of the system dynamics while achieving good performance. A comparison is carried-out against the well-known proportional-integral-derivative （PID） control technique. Results are presented to illustrate the controller＇s performance in various operating conditions.
文摘In an earlier study, the Atmospheric Models Intercomparison Program (AMIP) simulations of African climate using the nine-layer gridpoint atmospheric general circulation model were found to be closely related to the observed European Centre for Medium Range Weather Forecast (ECMWF) temperature data at 500 and 850 hPa. This paper presents the analysis of the simulation of African climate using the Global Ocean-Atmosphere-Land System Model (IAP/LASG GOALS) and the nine-layer spectral general circulation model rhomboidally truncated at zonal wave number 15 (L9R15) developed at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing. Both model simulations were not significantly different from the National Center for Environmental Prediction (NCEP) Reanalysis monthly mean data for 1980-1995 in the case of surface air temperature, sea level pressure and precipitation, with the GOALS reproducing the seasonal mean climate over Africa better. The implications of the encouraging results in developing a local area model for Nigeria have been discussed. The great role of topography in the developing of general circulation models for numerical modelling of weather and climate has been stressed.
基金The part of the project "Development of Korea Operational Oceanographic System(KOOS),Phase 2",funded by the Ministry of Oceans and Fisheries,Koreathe part of the project entitled "Cooperative Project on Korea-China Bilateral Committee on Ocean Science",funded by the Ministry of Oceans and Fisheries,Korea and China-Korea Joint Research Ocean Research Center
文摘Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272437 and 52272370)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0635)。
文摘The high cost and low efficiency of full-scale vehicle experiments and numerical simulations limit the efficient development of armored vehicle occupant protection systems.The floor-occupant-seat local simulation model provides an alternative solution for quickly evaluating the performance of occupant protection systems.However,the error and rationality of the loading of the thin-walled floor in the local model cannot be ignored.This study proposed an equivalent loading method for the local model,which includes two parts:the dimensionality reduction method for acceleration matrix and the joint optimization framework for equivalent node coordinates.In the dimensionality reduction method,the dimension of the acceleration matrix was reduced based on the improved kernel principal component analysis(KPCA),and a dynamic variable bandwidth was introduced to address the limitation of failing to effectively measure the similarity between acceleration data in conventional KPCA.In addition,a least squares problem with forced displacement constraints was constructed to solve the correction matrix,thereby achieving the scale restoration process of the principal component acceleration matrix.The joint optimization framework for coordinates consists of the error assessment of response time histories(EARTH)and Bayesian optimization.In this framework,the local loading error of the equivalent acceleration matrix is taken as the Bayesian optimization objective,which is quantified and scored by EARTH.The expected improvement acquisition function was used to select the new set of the equivalent acceleration node coordinates for the self-updating optimization of the observation dataset and Gaussian process surrogate model.We reduced the dimension of the acceleration matrix from 2256 to 7,while retaining 91%of the information features.The comprehensive error score of occupant's lower limb response in the local model increased from 58.5%to 80.4%.The proposed equivalent loading method provides a solution for the rapid and reliable development of occupant protection systems.
基金supported by National Natural Science Foundation of China[grant number 62276058,61902057,41774063]Fundamental Research Funds for the Central Universities[grant number N2217003]+1 种基金Joint Fund of Science&Technology Department of Liaoning Province and State Key Laboratory of RoboticsChina[grant number 2020-KF-12-11]。
文摘Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine the key spreaders in complex networks.The existing algorithms based on gravity model consider the shortest distance between nodes.However,nodes not only influence each other through the shortest path,which will lead the loss of information between nodes.Different from the existing gravity model,the distance considered in this model is not the shortest distance between nodes,but the reciprocal of the number of feasible paths between nodes.Conveniently,the model is called FPLGM(Local Gravity Model Based on Feasible Paths).Ten different networks are utilised to verify the effectiveness of FPLGM.Results show that the FPLGM performs best in comparison with the well-known state-of-the-art methods.
基金This work was supported by the National Nature Science Foundation of China(Grant No.61733017).
文摘Treating plant dynamics as an ideal integrator chain disturbed by the total disturbance is the hallmark of active disturbance rejection control(ADRC).To interpret its effectiveness and success,to explain why so many vastly different dynamic systems can be treated in this manner,and to answer why a detailed,accurate,and global mathematical model is unnecessary,is the target of this paper.Driven by a motivating example,the notions of normality and locality are introduced.Normality shows that,in ADRC,the plant is normalized to an integrator chain,which is called local nominal model and locally describes the plant’s frequency response in the neighborhood of the expected gain crossover frequency.Locality interprets why ADRC can design the controller only with the local information of the plant.With normality and locality,ADRC can be effective and robust,and obtain operational stability discussed by T.S.Tsien.Then viewing proportional-integral-derivative(PID)control as a low-frequency approximation of second-order linear ADRC,the above results are extended to PID control.A controller design framework is proposed to obtain the controller in three steps:(1)choose an integrator chain as the local nominal model of the plant;(2)select a controller family corresponding to the local nominal model;and(3)tune the controller to guarantee the gain crossover frequency specification.The second-order linear ADRC and the PID control are two special cases of the framework.
基金supported by the National Natural Science Foundation of China (31501229)the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP2017-AII)the Special Research Funds for Basic Scientific Research in Central Public Welfare Research Institutes, China (JBYW-AII-2017-05)
文摘In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.
文摘Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(No.61225014)the National Major Scientific Instruments Development Project(No.61527811)+2 种基金the National Natural Science Foundation of China(Nos.61304084,61374119)the Guangdong Natural Science Foundation(No.2014A030312005)the Space Intelligent Control Key Laboratory of Science and Technology for National Defense.
文摘Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.
基金supported by the Major Research Development Program of China(2016YFC0502704)National Science Foundation of China(31670645,31470578 and 31200363)+4 种基金National Forestry Public Welfare Foundation of China(201304205)Fujian Provincial Department of S&T Project(2013YZ0001-1,2015Y0083,2016Y0083,2016T3037 and 2016T3032)Key Laboratory of Urban Environment and Health of CAS(KLUEH-C-201701)Youth Innovation Promotion Association CAS(2014267)Key Program of the Chinese Academy of Sciences(KFZDSW-324)
文摘We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金Supported by Russian Foundation for Basic Research(13-05-91159-GFEN_a)Project 14.518.11.7065 and agreement N8388 of the Ministry of Education and Science of the Russian Federation+1 种基金the National Natural Science Foundation(41274146)the Specialized Research Fund for State Key Laboratory in China
文摘Comparison of regular(diurnal,seasonal and solar cycle)variations of high-latitude,mid-latitude and low-latitude ionospheric characteristics has been provided on basis of local empirical models of the peak electron density and the peak height.The local empirical models were derived from the hand-scaled ionogram data recorded by DPS-4 digisondes located at Norilsk(69°N,88°E),Irkutsk(52°N,104°E)and Hainan(19°N,109°E)for a 6-year period from December,2002 to December,2008.The technique used to build the local empirical model is described.The primary focus is diurnal-seasonal behavior under low solar activity and its change with increasing solar activity.Both common and specific features of the high-latitude(Norilsk),mid-latitude(Irkutsk)and low-latitude(Hainan)regular variations were revealed using their local empirical models.
基金supported by the National Natural Science Foundation of China(No.52074123)the Natural Science Foundation of Hebei Province(Nos.E2022209143,E2021209148 and E2021209052).
文摘Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock surfaces.The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability.Based on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks.The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone.Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks.The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.
文摘Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.
