We conduct an experimental study supported by theoretical analysis of single laser ablating copper to investigate the interactions between laser and material at different sample temperatures,and predict the changes of...We conduct an experimental study supported by theoretical analysis of single laser ablating copper to investigate the interactions between laser and material at different sample temperatures,and predict the changes of ablation morphology and lattice temperature.For investigating the effect of sample temperature on femtosecond laser processing,we conduct experiments on and simulate the thermal behavior of femtosecond laser irradiating copper by using a two-temperature model.The simulation results show that both electron peak temperature and the relaxation time needed to reach equilibrium increase as initial sample temperature rises.When the sample temperature rises from 300 K to 600 K,the maximum lattice temperature of the copper surface increases by about 6500 K under femtosecond laser irradiation,and the ablation depth increases by 20%.The simulated ablation depths follow the same general trend as the experimental values.This work provides some theoretical basis and technical support for developing femtosecond laser processing in the field of metal materials.展开更多
Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction ...The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.展开更多
The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among ...The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among trees.Therefore,an in-depth understanding of the coupling effects of these factors is important for designing sap flow measurement methods and performing accurate assessments of stand scale transpiration.This study is based on observations of sap flux density(SF_(d))of nine sample trees with different Hegyi’s competition indices(HCIs),soil moisture,and meteorological conditions in a pure plantation of Larix gmelinii var.principis-rupprechtii during the 2021 growing season(May to September).A multifactorial model of sap flow was developed and possible errors in the stand scale sap flow estimates associated with sample sizes were determined using model-based predictions of sap flow.Temporal variations are controlled by vapour pressure deficit(VPD),solar radiation(R),and soil moisture,and these relationships can be described by polynomial or saturated exponential functions.Spatial(individual)differences were influenced by the HCI,as shown by the decaying power function.A simple SF_(d)model at the individual tree level was developed to describe the synergistic influences of VPD,R,soil moisture,and HCI.The coefficient of variations(CV)of the sap flow estimates gradually stabilized when the sample size was>10;at least six sample trees were needed if the CV was within 10%.This study improves understanding of the mechanisms of spatiotemporal variations in sap flow at the individual tree level and provides a new methodology for determining the optimal sample size for sap flow measurements.展开更多
The adsorption of iodine onto silica coated magnetite nanoparticles(im-SCMNPs) that modified with imidazole was investigated for removal of high concentrations of iodine from wastewater. Modified silica magnetite nano...The adsorption of iodine onto silica coated magnetite nanoparticles(im-SCMNPs) that modified with imidazole was investigated for removal of high concentrations of iodine from wastewater. Modified silica magnetite nanoparticles showed high efficiency in removing iodine from wastewater samples. The optimum pH for iodine removal was 7.0-8.0. The adsorption capacity was evaluated using both the Langmuir and Freundlich adsorption isotherm models. The size of the produced magnetite nanoparticles was determined by X-ray diffraction analysis and scanning electron microscopy. Synthesized magnetite nanoparticles showed the high adsorption capacity and would be a good method to increase adsorption efficiency for the removal of iodine in a wastewater treatment process. The Langmuir adsorption capacity(qmax) was found to be 140.84 mg/g of the adsorbent.展开更多
Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cyc...Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.展开更多
The samples of fatigue life tests for aeroengine components are usually less than 5,so the evaluation of these samples belongs to small sample analysis. The Weibull distribution is known to describe the life data accu...The samples of fatigue life tests for aeroengine components are usually less than 5,so the evaluation of these samples belongs to small sample analysis. The Weibull distribution is known to describe the life data accurately,and the Weibayes method （developed from Bayesian method） expands on the experiential data in the small sample analysis of fatigue life in aeroengine. Based on the Weibull analysis,a program was developed to improve the efficiency of the reliability analysis for aeroengine compgnents. This program has complete functions and offers highly accurate results. A particular turbine disk＇s low cycle fatigue life was evaluated by this program. From the results,the following conclusions were drawn：（a） that this program could be used for the engineering applications,and （b） while a lack of former test data lowered the validity of evaluation results,the Weibayes method ensured the results of small sample analysis did not deviate from the truth.展开更多
The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has rec...The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.展开更多
The last three years has brought about alarming news of re-identification of coal worker’s pneumoconiosis(CWP)or‘black lung’in Australia after reporting nearly being absent for over five decades.While,the CWP stati...The last three years has brought about alarming news of re-identification of coal worker’s pneumoconiosis(CWP)or‘black lung’in Australia after reporting nearly being absent for over five decades.While,the CWP statistics in South Africa(SA)are unverifiable,but certainly CWP has not been eliminated.These events have re-kindled the need for better understanding of the dust monitoring,performance of sampling devices,and compliance determination.Over the last half century,gravimetric sampling has been the fundamental means for dust exposure monitoring using recognised respirable size-selective standards.In both South Africa and Australia,the gravimetric sampling technique in coal mines has been followed since 1988 and 1983 respectively using samplers of original Higgins-Dewell(HD)type design.This paper provides the evaluation results of currently used South African and Australian gravimetric samplers compared against the original UK SIMPEDS‘true reference’sampler.The results consistently suggested that the South African and Australian cyclones do not conform to the required size selective curve or even the‘true’reference sampler.The results show that the currently used SA and Australian samplers showed a D50 sampling bias as high as 59%and 47%respectively against the size-selective curve.Similarly,under the controlled laboratory coal dust test conditions measuring the same coal mine dust level in a chamber,the South African,Australian and UK standard SIMPEDS sampler were 7.87,9.79 and 6.71 mg/m3 respectively,which aligned with the sampling bias.The differences can in part be attributed to the‘un-auditable’inherent design and manufacturing quality,or unverifiable data on sizeselective sampling curve.This finding has significant implications towards exposure data collected over the last 25 years and their subsequent use in the arrival of the dose-response curves.Therefore,it is strongly recommended that the harmonised use of‘true reference’SIMPEDS cyclone that meets the ISO(1995)criteria uniformly across the industry would benefit the exposure assessment and compliance determination.展开更多
Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were p...Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.展开更多
Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.Ho...Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.展开更多
The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are a...The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.展开更多
A remote open-path laser-induced breakdown spectroscopy(LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the rel...A remote open-path laser-induced breakdown spectroscopy(LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the relatively simple configuration and optics were employed to measure the steel samples at a remote distance and a hot sample temperature. The system has obtained a robustness for the deviation of the sample position because of the open-path and alloptical structure. The measurement was carried out at different sample temperatures by placing the samples in a muffle furnace with a window in the front door. The results show that the intensity of the spectral lines increased as the sample temperature increased. The influence of the sample temperature on the quantitative analysis of manganese in the steel samples was investigated by measuring ten standard steel samples at different temperatures. Three samples were selected as the test sample for the simulation measurement. The results show that, at the sample temperature of 500 ℃, the average relative error of prediction is 3.1% and the average relative standard deviation is 7.7%, respectively.展开更多
The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited stand...The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.展开更多
We investigated the dependence of laser-induced breakdown spectral intensity on the focusing position of a lens at different sample temperatures(room temperature to 300 ℃) in atmosphere.A Q-switched Nd:YAG nanosecond...We investigated the dependence of laser-induced breakdown spectral intensity on the focusing position of a lens at different sample temperatures(room temperature to 300 ℃) in atmosphere.A Q-switched Nd:YAG nanosecond pulsed laser with 1064 nm wavelength and 10 ns pulse width was used to ablate silicon to produce plasma. It was confirmed that the increase in the sample's initial temperature could improve spectral line intensity. In addition, when the distance from the target surface to the focal point increased, the intensity firstly rose, and then dropped.The trend of change with distance was more obvious at higher sample temperatures. By observing the distribution of the normalized ratio of Si atomic spectral line intensity and Si ionic spectral line intensity as functions of distance and temperature, the maximum value of normalized ratio appeared at the longer distance as the initial temperature was higher, while the maximum ratio appeared at the shorter distance as the sample temperature was lower.展开更多
The K-V beam through an axisymmetric uniform-focusing channel is studied using the particle-core model. The beam halo-chaos is found, and a sample function controller is proposed based on mechanism of halo formation a...The K-V beam through an axisymmetric uniform-focusing channel is studied using the particle-core model. The beam halo-chaos is found, and a sample function controller is proposed based on mechanism of halo formation and strategy of controlling halo-chaos. We perform multiparticle simulation to control the halo by using the sample function controller. The numerical results show that our control method is effective. We also find that the radial ion density changes when the ion beam is in the channel： not only can the halo-chaos and its regeneration be eliminated by using the sample function control method, but also the density uniformity can be found at the beam＇s centre as long as an appropriate control method is chosen.展开更多
The laser-induced breakdown spectroscopy technique has irreplaceable advantages in the field of detection due to its multi-phase specimen detection ability.The development of the LIBS technique for liquid analysis is ...The laser-induced breakdown spectroscopy technique has irreplaceable advantages in the field of detection due to its multi-phase specimen detection ability.The development of the LIBS technique for liquid analysis is obstructed by its inherent drawbacks like the surface ripples and extinction of emitted intensity,which make it unpractical.In this work,an in-situ hydrogel formation sampling device was designed and used the hydrogel as the detection phase of LIBS for Cu,Cr and Al in an aqueous solution.With the measured amount of resin placed in the device,the formed hydrogel could be obtained within 20 s after putting the device into water solution.The formed hydrogel could be directly analyzed by LIBS and reflect the elemental information of the water sample.The prominent performance made this hydrogel＇s formation device especially suitable for quick in-situ environmental liquid analysis using LIBS.展开更多
A novel fuzzy support vector machine based on unbalanced samples(FSVM-US)is proposed to solve the high false positive rate problem since the gyroscope output is susceptible to unmanned aerial vehicle(UAV)airborne elec...A novel fuzzy support vector machine based on unbalanced samples(FSVM-US)is proposed to solve the high false positive rate problem since the gyroscope output is susceptible to unmanned aerial vehicle(UAV)airborne electromagnetic environment and the gyroscope abnormal signal sample is rather rare.Firstly,the standard deviation of samples projection to normal vector for SVM classifier hyper plane is analyzed.The imbalance feature expression reflecting the hyper plane shift for the number imbalance between samples and the dispersion imbalance within samples is derived.At the same time,the denoising factor is designed as the exponential decay function based on the Euclidean distance between each sample and the class center.Secondly,the imbalance feature expression and denoising factor are configured into the membership function.Each sample has its own weight denoted the importance to the classifier.Finally,the classification simulation experiments on the gyroscope fault diagnosis system are conducted and FSVM-US is compared with the standard SVM,FSVM,and the four typical class imbalance learning(CIL)methods.The results show that FSVM-US classifier accuracy is 12% higher than that of the standard SVM.Generally,FSVM-US is superior to the four CIL methods in total performance.Moreover,the FSVMUS noise tolerance is also 17% higher than that of the standard SVM.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFA0307701)the National Natural Science Foundation of China(Grant Nos.11674128,11674124,and 11974138).
文摘We conduct an experimental study supported by theoretical analysis of single laser ablating copper to investigate the interactions between laser and material at different sample temperatures,and predict the changes of ablation morphology and lattice temperature.For investigating the effect of sample temperature on femtosecond laser processing,we conduct experiments on and simulate the thermal behavior of femtosecond laser irradiating copper by using a two-temperature model.The simulation results show that both electron peak temperature and the relaxation time needed to reach equilibrium increase as initial sample temperature rises.When the sample temperature rises from 300 K to 600 K,the maximum lattice temperature of the copper surface increases by about 6500 K under femtosecond laser irradiation,and the ablation depth increases by 20%.The simulated ablation depths follow the same general trend as the experimental values.This work provides some theoretical basis and technical support for developing femtosecond laser processing in the field of metal materials.
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
基金the National Natural Science Foundation of China(Grant No.61973033)Preliminary Research of Equipment(Grant No.9090102010305)for funding the experiments。
文摘The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.
基金supported by the Fundamental Research Funds of the Chinese Academy of Forestry(CAFYBB2020QB004)the National Natural Science Foundation of China(41971038,32171559,U20A2085,and U21A2005).
文摘The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among trees.Therefore,an in-depth understanding of the coupling effects of these factors is important for designing sap flow measurement methods and performing accurate assessments of stand scale transpiration.This study is based on observations of sap flux density(SF_(d))of nine sample trees with different Hegyi’s competition indices(HCIs),soil moisture,and meteorological conditions in a pure plantation of Larix gmelinii var.principis-rupprechtii during the 2021 growing season(May to September).A multifactorial model of sap flow was developed and possible errors in the stand scale sap flow estimates associated with sample sizes were determined using model-based predictions of sap flow.Temporal variations are controlled by vapour pressure deficit(VPD),solar radiation(R),and soil moisture,and these relationships can be described by polynomial or saturated exponential functions.Spatial(individual)differences were influenced by the HCI,as shown by the decaying power function.A simple SF_(d)model at the individual tree level was developed to describe the synergistic influences of VPD,R,soil moisture,and HCI.The coefficient of variations(CV)of the sap flow estimates gradually stabilized when the sample size was>10;at least six sample trees were needed if the CV was within 10%.This study improves understanding of the mechanisms of spatiotemporal variations in sap flow at the individual tree level and provides a new methodology for determining the optimal sample size for sap flow measurements.
文摘The adsorption of iodine onto silica coated magnetite nanoparticles(im-SCMNPs) that modified with imidazole was investigated for removal of high concentrations of iodine from wastewater. Modified silica magnetite nanoparticles showed high efficiency in removing iodine from wastewater samples. The optimum pH for iodine removal was 7.0-8.0. The adsorption capacity was evaluated using both the Langmuir and Freundlich adsorption isotherm models. The size of the produced magnetite nanoparticles was determined by X-ray diffraction analysis and scanning electron microscopy. Synthesized magnetite nanoparticles showed the high adsorption capacity and would be a good method to increase adsorption efficiency for the removal of iodine in a wastewater treatment process. The Langmuir adsorption capacity(qmax) was found to be 140.84 mg/g of the adsorbent.
基金supported by the State Forestry Administration of China under the national forestry commonwealth project grant#201404309the Expert Workstation of Academician Tang Shouzheng of Yunnan Province,the Yunnan provincial key project of Forestrythe Research Center of Kunming Forestry Information Engineering Technology
文摘Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.
文摘The samples of fatigue life tests for aeroengine components are usually less than 5,so the evaluation of these samples belongs to small sample analysis. The Weibull distribution is known to describe the life data accurately,and the Weibayes method （developed from Bayesian method） expands on the experiential data in the small sample analysis of fatigue life in aeroengine. Based on the Weibull analysis,a program was developed to improve the efficiency of the reliability analysis for aeroengine compgnents. This program has complete functions and offers highly accurate results. A particular turbine disk＇s low cycle fatigue life was evaluated by this program. From the results,the following conclusions were drawn：（a） that this program could be used for the engineering applications,and （b） while a lack of former test data lowered the validity of evaluation results,the Weibayes method ensured the results of small sample analysis did not deviate from the truth.
基金partially funded by the National Natural Science Foundation of China (Grant No. 61272447)National Entrepreneurship & Innovation Demonstration Base of China (Grant No. C700011)Key Research & Development Project of Sichuan Province of China (Grant No. 2018G20100)
文摘The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.
文摘The last three years has brought about alarming news of re-identification of coal worker’s pneumoconiosis(CWP)or‘black lung’in Australia after reporting nearly being absent for over five decades.While,the CWP statistics in South Africa(SA)are unverifiable,but certainly CWP has not been eliminated.These events have re-kindled the need for better understanding of the dust monitoring,performance of sampling devices,and compliance determination.Over the last half century,gravimetric sampling has been the fundamental means for dust exposure monitoring using recognised respirable size-selective standards.In both South Africa and Australia,the gravimetric sampling technique in coal mines has been followed since 1988 and 1983 respectively using samplers of original Higgins-Dewell(HD)type design.This paper provides the evaluation results of currently used South African and Australian gravimetric samplers compared against the original UK SIMPEDS‘true reference’sampler.The results consistently suggested that the South African and Australian cyclones do not conform to the required size selective curve or even the‘true’reference sampler.The results show that the currently used SA and Australian samplers showed a D50 sampling bias as high as 59%and 47%respectively against the size-selective curve.Similarly,under the controlled laboratory coal dust test conditions measuring the same coal mine dust level in a chamber,the South African,Australian and UK standard SIMPEDS sampler were 7.87,9.79 and 6.71 mg/m3 respectively,which aligned with the sampling bias.The differences can in part be attributed to the‘un-auditable’inherent design and manufacturing quality,or unverifiable data on sizeselective sampling curve.This finding has significant implications towards exposure data collected over the last 25 years and their subsequent use in the arrival of the dose-response curves.Therefore,it is strongly recommended that the harmonised use of‘true reference’SIMPEDS cyclone that meets the ISO(1995)criteria uniformly across the industry would benefit the exposure assessment and compliance determination.
基金supported by National Natural Science Foundation of China(No.60908018)National High Technology Research and Development Program of China(No.2013AA065502)Anhui Province Outstanding Youth Science Fund of China(No.1108085J19)
文摘Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.
基金supported by National Natural Science Foundation of China under Grants 41874146 and 42030103。
文摘Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.
基金supported by the National Natural Science Foundation of China(11271088,11361011,11201088)the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
文摘The empirical likelihood is used to propose a new class of quantile estimators in the presence of some auxiliary information under positively associated samples. It is shown that the proposed quantile estimators are asymptotically normally distributed with smaller asymptotic variances than those of the usual quantile estimators.
基金supported by National Natural Science Foundation of China (Nos. 51506171 and 51675415)National Natural Science Foundation of China for Key Program (No. 51335009)+1 种基金National Key Research and Development Program of China (No. 2017YFD0700200)the joint research fund between Tokushima University and Xi’an Jiaotong University
文摘A remote open-path laser-induced breakdown spectroscopy(LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the relatively simple configuration and optics were employed to measure the steel samples at a remote distance and a hot sample temperature. The system has obtained a robustness for the deviation of the sample position because of the open-path and alloptical structure. The measurement was carried out at different sample temperatures by placing the samples in a muffle furnace with a window in the front door. The results show that the intensity of the spectral lines increased as the sample temperature increased. The influence of the sample temperature on the quantitative analysis of manganese in the steel samples was investigated by measuring ten standard steel samples at different temperatures. Three samples were selected as the test sample for the simulation measurement. The results show that, at the sample temperature of 500 ℃, the average relative error of prediction is 3.1% and the average relative standard deviation is 7.7%, respectively.
基金supported by National Natural Science Foundation of China (No. 51674032)
文摘The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.
基金support by National Natural Science Foundation of China (Grant Nos. 11674128, 11504129, and 11474129)Jilin Province Scientific and Technological Development Program, China (Grant No. 20170101063JC)the Thirteenth Five-Year Scientific and Technological Research Project of the Education Department of Jilin Province, China (2016, No. 400)
文摘We investigated the dependence of laser-induced breakdown spectral intensity on the focusing position of a lens at different sample temperatures(room temperature to 300 ℃) in atmosphere.A Q-switched Nd:YAG nanosecond pulsed laser with 1064 nm wavelength and 10 ns pulse width was used to ablate silicon to produce plasma. It was confirmed that the increase in the sample's initial temperature could improve spectral line intensity. In addition, when the distance from the target surface to the focal point increased, the intensity firstly rose, and then dropped.The trend of change with distance was more obvious at higher sample temperatures. By observing the distribution of the normalized ratio of Si atomic spectral line intensity and Si ionic spectral line intensity as functions of distance and temperature, the maximum value of normalized ratio appeared at the longer distance as the initial temperature was higher, while the maximum ratio appeared at the shorter distance as the sample temperature was lower.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 10247005 and 70071047） and the Scientific Research Foundation of China University of Mining and Technology for the Young （Grant No 2005A037）.
文摘The K-V beam through an axisymmetric uniform-focusing channel is studied using the particle-core model. The beam halo-chaos is found, and a sample function controller is proposed based on mechanism of halo formation and strategy of controlling halo-chaos. We perform multiparticle simulation to control the halo by using the sample function controller. The numerical results show that our control method is effective. We also find that the radial ion density changes when the ion beam is in the channel： not only can the halo-chaos and its regeneration be eliminated by using the sample function control method, but also the density uniformity can be found at the beam＇s centre as long as an appropriate control method is chosen.
文摘The laser-induced breakdown spectroscopy technique has irreplaceable advantages in the field of detection due to its multi-phase specimen detection ability.The development of the LIBS technique for liquid analysis is obstructed by its inherent drawbacks like the surface ripples and extinction of emitted intensity,which make it unpractical.In this work,an in-situ hydrogel formation sampling device was designed and used the hydrogel as the detection phase of LIBS for Cu,Cr and Al in an aqueous solution.With the measured amount of resin placed in the device,the formed hydrogel could be obtained within 20 s after putting the device into water solution.The formed hydrogel could be directly analyzed by LIBS and reflect the elemental information of the water sample.The prominent performance made this hydrogel＇s formation device especially suitable for quick in-situ environmental liquid analysis using LIBS.
基金supported by the Fundamental Research Fund for the Central Universities(No.56XZA12017)
文摘A novel fuzzy support vector machine based on unbalanced samples(FSVM-US)is proposed to solve the high false positive rate problem since the gyroscope output is susceptible to unmanned aerial vehicle(UAV)airborne electromagnetic environment and the gyroscope abnormal signal sample is rather rare.Firstly,the standard deviation of samples projection to normal vector for SVM classifier hyper plane is analyzed.The imbalance feature expression reflecting the hyper plane shift for the number imbalance between samples and the dispersion imbalance within samples is derived.At the same time,the denoising factor is designed as the exponential decay function based on the Euclidean distance between each sample and the class center.Secondly,the imbalance feature expression and denoising factor are configured into the membership function.Each sample has its own weight denoted the importance to the classifier.Finally,the classification simulation experiments on the gyroscope fault diagnosis system are conducted and FSVM-US is compared with the standard SVM,FSVM,and the four typical class imbalance learning(CIL)methods.The results show that FSVM-US classifier accuracy is 12% higher than that of the standard SVM.Generally,FSVM-US is superior to the four CIL methods in total performance.Moreover,the FSVMUS noise tolerance is also 17% higher than that of the standard SVM.
