The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat tr...The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and ra...The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.展开更多
Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),...Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.展开更多
A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper.The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this ...A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper.The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this paper.Relatively ideal micro-Doppler parameter estimation results were obtained.The proposed micro-Doppler parameter estimation was compared with the traditional micro-Doppler parameter estimation algorithm.Requirements for return signal length were analyzed with this new algorithm and its performance was also analyzed in various environments with different SNR.展开更多
A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results ...A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results illustrate that the heat-release unit in micro-system intercross greatly affects other sensing units on the temperature.We study the method of etching process,which formed cavity to reduce the heat exchange efficiency and decrease temperature intercross effect.展开更多
By making use of the distinguishing features of repetitive imagery of the same area by satellite remote sens-ing, a method of extracting water areas at different tidal levels from the images of bays at different time...By making use of the distinguishing features of repetitive imagery of the same area by satellite remote sens-ing, a method of extracting water areas at different tidal levels from the images of bays at different times was adopted toestablish a calculating model for tidal surface and a method of finding a sum by layers was used to establish a calculatingmodel for the deposition parameter. Moreover, by making use of the calculating models for the deposition parameter andfor tidal level, the deposition parameter for the sea area less than 0 m was calculated for the Haikou Bay at two differentperiods of time: during the period of 1965 ￣ 1984 the total amount of deposits was 4 . 8 x 106 m3, and the sedimentationrate was 2 . 5 x 105 m3/a; during the period from 1984 to 1990, the total amount of deposits was - 8 . 9 x 105 m3 and thesedimentation rate was -1 . 5 x 105 m3/a.展开更多
4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving local...4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.展开更多
Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powe...Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.展开更多
Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have sign...Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have significant limitations.Current research that integrates fine and coarser spatial resolution images,using techniques such as unmixing methods,regression models,and others,usually results in coarse resolution abundance without sufficient detail within pixels,and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images.Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels.Firstly,the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper(TM)image based on the Bayesian equation.Then,the winter wheat abundance(acreage fraction in a pixel)is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer(MODIS)time series data.Finally,winter wheat is identified by the proposed Abundance-Membership(AM)model based on the spatial relationship between the two types of pixels.Specifically,winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel.In other words,this method takes advantage of the relative size of membership in a local space,rather than the absolute size in the entire study area.This method is tested in the major agricultural area of Yiluo Basin,China,and the results show that acreage accuracy(Aa)is 93.01%and sampling accuracy(As)is 91.40%.Confusion matrix shows that overall accuracy(OA)is 91.4%and the kappa coefficient(Kappa)is 0.755.These values are significantly improved compared to the traditional Maximum Likelihood classification(MLC)and Random Forest classification(RFC)which rely on spectral features.The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information.Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel,the influence of differences of environmental conditions is greatly reduced.This advantage allows the proposed method to be effectively applied in other places.展开更多
Inversion of Young’s modulus,Poisson’s ratio and density from pre-stack seismic data has been proved to be feasible and effective.However,the existing methods do not take full advantage of the prior information.With...Inversion of Young’s modulus,Poisson’s ratio and density from pre-stack seismic data has been proved to be feasible and effective.However,the existing methods do not take full advantage of the prior information.Without considering the lateral continuity of the inversion results,these methods need to invert the reflectivity first.In this paper,we propose multi-gather simultaneous inversion for pre-stack seismic data.Meanwhile,the total variation（TV）regularization,L1 norm regularization and initial model constraint are used.In order to solve the objective function contains L1norm,TV norm and L2 norm,we develop an algorithm based on split Bregman iteration.The main advantages of our method are as follows：（1）The elastic parameters are calculated directly from objective function rather than from their reflectivity,therefore the stability and accuracy of the inversion process can be ensured.（2）The inversion results are more in accordance with the prior geological information.（3）The lateral continuity of the inversion results are improved.The proposed method is illustrated by theoretical model data and experimented with a 2-D field data.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
AIM In our previous study, we have built a nine-gene(GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1 B, CXCR4, PFN1, and CALR) expression detection system based on the Ge XP system. Based on peripheral blood and Ge XP, we aimed t...AIM In our previous study, we have built a nine-gene(GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1 B, CXCR4, PFN1, and CALR) expression detection system based on the Ge XP system. Based on peripheral blood and Ge XP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma(HCC) patients and healthy people.METHODS Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fiftytwo patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.RESULTS Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.CONCLUSION Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.展开更多
Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosi...Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.展开更多
Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-...Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.展开更多
An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
This paper proposes a novel indoor positioning scheme based on visible light communication(VLC).A new indoor VLC positioning scheme using fingerprint database with multi-parameters have been raised.We conduct simulati...This paper proposes a novel indoor positioning scheme based on visible light communication(VLC).A new indoor VLC positioning scheme using fingerprint database with multi-parameters have been raised.We conduct simulation and experimental research on the illumination intensity distribution of several direction parameters.In the experiment,four LED matrixes are identified by LED-ID with room dimensions of 3.75×4.00×2.7 m^3.The results show that the mean of the location error is 0.22 m in the receiving plane,verifying the correctness and feasibility of the positioning scheme.展开更多
Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods r...Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods require lots of machining experiments and off-line tests, which may lead to high machining cost and low efficiency. This paper proposes a novel method of machining parameters optimization for an impeller based on the on-machine measuring technique. The absolute average error and standard deviation of the measured points are used to define the grey relational grade for reconstructing the objective function, and the complex problem of multi-objective optimization is simplified into a problem of single-objective optimization. Then, by comparing the values of the defined grey relational grade in a designed orthogonal experiment, the optimal combination of the machining parameters is obtained. The experiment-solving process of the objective function corresponds to the minimization of the used errors, which is advantageous to reducing the machining error. The proposed method is efficient and low-cost, since it does not require re-clamping the workpiece for off-line tests. Its effectiveness is verified by an on-machine inspection experiment of the impeller blade.展开更多
基金Supported by the National High-Tech Research Project(GJSCB-HFGDY-2024-004)National Natural Science Foundation of China(12402305)+2 种基金Postdoctoral Fellowship Program of CPSF(GZC20232200)China Postdoctoral Science Foundation(2024M762703)Sichuan Science and Technology Program(2025ZNSFSC1352)。
文摘The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金Project supported by the National Natural Science Foundation of China (No.40571115)the National High Tech-nology Research and Development Program (863 Program) of China (Nos.2006AA120101 and 2007AA10Z205)
文摘The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
基金Under the auspices of the Key Direction in Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-340)
文摘Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.
基金Supported by the National Natural Science Foundation of China(61571043)111 Project of China(B14010)
文摘A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper.The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this paper.Relatively ideal micro-Doppler parameter estimation results were obtained.The proposed micro-Doppler parameter estimation was compared with the traditional micro-Doppler parameter estimation algorithm.Requirements for return signal length were analyzed with this new algorithm and its performance was also analyzed in various environments with different SNR.
文摘A new membrane type Al_2O_3 micromachining material is used.We develop an environmental multi-parameter detection micro-system,which implements the detection to temperature,humidity,wind speed,and CO.The test results illustrate that the heat-release unit in micro-system intercross greatly affects other sensing units on the temperature.We study the method of etching process,which formed cavity to reduce the heat exchange efficiency and decrease temperature intercross effect.
基金supported by the National Natural Science Foundation of China(61374054,61203007)Natural Science Foundation Research Projection of Shaanxi Province(2013JQ8038).
文摘By making use of the distinguishing features of repetitive imagery of the same area by satellite remote sens-ing, a method of extracting water areas at different tidal levels from the images of bays at different times was adopted toestablish a calculating model for tidal surface and a method of finding a sum by layers was used to establish a calculatingmodel for the deposition parameter. Moreover, by making use of the calculating models for the deposition parameter andfor tidal level, the deposition parameter for the sea area less than 0 m was calculated for the Haikou Bay at two differentperiods of time: during the period of 1965 ￣ 1984 the total amount of deposits was 4 . 8 x 106 m3, and the sedimentationrate was 2 . 5 x 105 m3/a; during the period from 1984 to 1990, the total amount of deposits was - 8 . 9 x 105 m3 and thesedimentation rate was -1 . 5 x 105 m3/a.
基金supported in part by National Natural Science Foundation of China under Grant 52305304Jilin Youth Growth Technology Project under Grant 20230508147RC+2 种基金the Science and Technology Research Project of Jilin Provincial Education Department(No.JJKH20231193KJ)supported in part by the National Natural Science Foundation of China under Grant 52021003in part by the Natural Science Foundation of Jilin Province under Grant 20210101053JC.
文摘4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.
基金provided by the State Key Research Development Program of China (No.2016YFC0801403)Key Research Development Program of Jiangsu Provence (No.BE2015040)+1 种基金National Natural Science Foundation of China (Nos.51674253,51734009 and 51604270)Natural Science Foundation of Jiangsu Province (No.BK20171191)
文摘Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.
基金the financial support provided by the National Science & Technology Infrastructure Construction Project of China (2005DKA32300)the Key Science and Technology Project of Henan Province, China (152102110047)+2 种基金the Major Research Project of the Ministry of Education, China(16JJD770019)the Major Scientific and Technological Special Project of Henan Province, China (121100111300)the Cooperation Base Open Fund of the Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River regions and CPGIS (JOF 201602)
文摘Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have significant limitations.Current research that integrates fine and coarser spatial resolution images,using techniques such as unmixing methods,regression models,and others,usually results in coarse resolution abundance without sufficient detail within pixels,and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images.Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels.Firstly,the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper(TM)image based on the Bayesian equation.Then,the winter wheat abundance(acreage fraction in a pixel)is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer(MODIS)time series data.Finally,winter wheat is identified by the proposed Abundance-Membership(AM)model based on the spatial relationship between the two types of pixels.Specifically,winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel.In other words,this method takes advantage of the relative size of membership in a local space,rather than the absolute size in the entire study area.This method is tested in the major agricultural area of Yiluo Basin,China,and the results show that acreage accuracy(Aa)is 93.01%and sampling accuracy(As)is 91.40%.Confusion matrix shows that overall accuracy(OA)is 91.4%and the kappa coefficient(Kappa)is 0.755.These values are significantly improved compared to the traditional Maximum Likelihood classification(MLC)and Random Forest classification(RFC)which rely on spectral features.The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information.Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel,the influence of differences of environmental conditions is greatly reduced.This advantage allows the proposed method to be effectively applied in other places.
基金supported by the National Natural Science Foundation of China (Nos.61775030,61571096,41301460,61362018,and 41274127)the key projects of Hunan Provincial Department of Education (No.16A174)
文摘Inversion of Young’s modulus,Poisson’s ratio and density from pre-stack seismic data has been proved to be feasible and effective.However,the existing methods do not take full advantage of the prior information.Without considering the lateral continuity of the inversion results,these methods need to invert the reflectivity first.In this paper,we propose multi-gather simultaneous inversion for pre-stack seismic data.Meanwhile,the total variation（TV）regularization,L1 norm regularization and initial model constraint are used.In order to solve the objective function contains L1norm,TV norm and L2 norm,we develop an algorithm based on split Bregman iteration.The main advantages of our method are as follows：（1）The elastic parameters are calculated directly from objective function rather than from their reflectivity,therefore the stability and accuracy of the inversion process can be ensured.（2）The inversion results are more in accordance with the prior geological information.（3）The lateral continuity of the inversion results are improved.The proposed method is illustrated by theoretical model data and experimented with a 2-D field data.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
基金National Key R&D Program of China,No.2016YFC0106604National Natural Science Foundation of China,No.81471761 and No.81501568
文摘AIM In our previous study, we have built a nine-gene(GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1 B, CXCR4, PFN1, and CALR) expression detection system based on the Ge XP system. Based on peripheral blood and Ge XP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma(HCC) patients and healthy people.METHODS Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fiftytwo patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.RESULTS Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.CONCLUSION Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.
基金financially supported by the National Key Research and Development Program of China(Grant No.2016YFB0701204)
文摘Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.
基金supported by the National Natural Science Foundation of China(61172127)the Natural Science Foundation of Anhui Province(1408085MF121)
文摘Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
基金supported by ZTE Industry-Academia-Research Cooperation Funds under Grant No.2014ZTE02-12
文摘This paper proposes a novel indoor positioning scheme based on visible light communication(VLC).A new indoor VLC positioning scheme using fingerprint database with multi-parameters have been raised.We conduct simulation and experimental research on the illumination intensity distribution of several direction parameters.In the experiment,four LED matrixes are identified by LED-ID with room dimensions of 3.75×4.00×2.7 m^3.The results show that the mean of the location error is 0.22 m in the receiving plane,verifying the correctness and feasibility of the positioning scheme.
基金co-supported by the National Basic Research Program of China(No.2015CB057304)the National Natural Science Foundation of China(Nos.51535004 and91648111)
文摘Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods require lots of machining experiments and off-line tests, which may lead to high machining cost and low efficiency. This paper proposes a novel method of machining parameters optimization for an impeller based on the on-machine measuring technique. The absolute average error and standard deviation of the measured points are used to define the grey relational grade for reconstructing the objective function, and the complex problem of multi-objective optimization is simplified into a problem of single-objective optimization. Then, by comparing the values of the defined grey relational grade in a designed orthogonal experiment, the optimal combination of the machining parameters is obtained. The experiment-solving process of the objective function corresponds to the minimization of the used errors, which is advantageous to reducing the machining error. The proposed method is efficient and low-cost, since it does not require re-clamping the workpiece for off-line tests. Its effectiveness is verified by an on-machine inspection experiment of the impeller blade.
