The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in...Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments.展开更多
The problem of two-dimensional(2 D)direction of arrival(DOA)estimation for double parallel uniform linear arrays is investigated in this paper.A real-valued DOA estimation algorithm of noncircular(NC)signal is propose...The problem of two-dimensional(2 D)direction of arrival(DOA)estimation for double parallel uniform linear arrays is investigated in this paper.A real-valued DOA estimation algorithm of noncircular(NC)signal is proposed,which combines the Euler transformation and rotational invariance(RI)property between subarrays.In this work,the effective array aperture is doubled by exploiting the noncircularity of signals.The complex arithmetic is converted to real arithmetic via Euler transformation.The main contribution of this work is not only extending the NC-Euler-ESPRIT algorithm from uniform linear array to double parallel uniform linear arrays,but also constructing a new 2 Drotational invariance property between subarrays,which is more complex than that in NCEuler-ESPRIT algorithm.The proposed 2 DNC-Euler-RI algorithm has much lower computational complexity than2 DNC-ESPRIT algorithm.The proposed algorithm has better angle estimation performance than 2 DESPRIT algorithm and 2 D NC-PM algorithm for double parallel uniform linear arrays,and is very close to that of 2 D NC-ESPRIT algorithm.The elevation angles and azimuth angles can be obtained with automatically pairing.The proposed algorithm can estimate up to 2(M-1)sources,which is two times that of 2 D ESPRIT algorithm.Cramer-Rao bound(CRB)of noncircular signal is derived for the proposed algorithm.Computational complexity comparison is also analyzed.Finally,simulation results are presented to illustrate the effectiveness and usefulness of the proposed algorithm.展开更多
The experimental values of the enthalpy of formation of two isomeric 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles have been obtained(261.5±5.0and 246.4±6.7kJ/mol for crystalline 3,4-and 3,5-dinitro-1-...The experimental values of the enthalpy of formation of two isomeric 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles have been obtained(261.5±5.0and 246.4±6.7kJ/mol for crystalline 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles,respectively).The ballistic effectiveness of these potential oxidizers in composite solid propellants was studied.It is shown that these two oxidizers may be successfully applied in metal-free compositions or with a small content of metal.For the bottom stage 3,4-dinitro-1-(trinitromethyl)-1H-pyrazole is a bit better than 3,5-dinitro-1-(trinitromethyl)-1H-pyrazole,for the upper stage the both oxidizers show the equal ballistic parameters.These oxidizers allow to create metal-free solid composite propellants with the binder percentage not lower than 19%(volume fraction),with I3spequal to 256.5-257.0sat density equal to 1.72-1.74g/cm^3.展开更多
New correlations based on group-contribution method for estimating critical properties have been proposed with con-tribution values of 67 groups.The new correlations have been examined by extensive comparison of the v...New correlations based on group-contribution method for estimating critical properties have been proposed with con-tribution values of 67 groups.The new correlations have been examined by extensive comparison of the values calculatedwith the experimental data of 275 substanees.The average percent deviations of estimation of T_c,P_c and V_c are respective-ly 0.76,2.73 and 2.50 which are lower than those estimated by the Lydersen method and the Ambrose method chosenfor eomparision.展开更多
In this paper,we propose a new extension of the traditional Rayleigh distribution called the modified Kies Rayleigh distribution.The new distribution contains one scale and one shape parameter and its hazard rate func...In this paper,we propose a new extension of the traditional Rayleigh distribution called the modified Kies Rayleigh distribution.The new distribution contains one scale and one shape parameter and its hazard rate function can be increasing and bathtub-shaped.Some mathematical properties of the new distribution are derived including quantiles and moments.The parameters of modified Kies Rayleigh distribution are estimated based on progressively Type-II censored data.For this purpose,we consider two estimation methods,namely maximum likelihood and maximum product of spacing estimation methods.To compare the efficiency of the proposed estimators,a simulation study is carried out.To show the applicability of the new model as well as the estimation methods,one real data for failure times of software is analyzed.Based on the empirical parts,we can conclude that the proposed model can be considered as a good model in the field of life testing and reliability analysis compared with other competing models.展开更多
Mass property of spacecraft needs to be estimated in certain circumstances.Based on the measurement of single point acceleration,angular acceleration and angular velocity,a discrete Kalman Filter(KF) based estimation ...Mass property of spacecraft needs to be estimated in certain circumstances.Based on the measurement of single point acceleration,angular acceleration and angular velocity,a discrete Kalman Filter(KF) based estimation method is developed to extract the inertia matrix and mass center.The simulation results show that the method can be used for continues online estimation without propellant consumption.Stability condition for the filter is given at last.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Background: The Poisson and the Negative Binomial distributions are commonly used to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance lar...Background: The Poisson and the Negative Binomial distributions are commonly used to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore both models are appropriate to model over-dispersed count data. Objectives: A new two-parameter probability distribution called the Quasi-Negative Binomial Distribution (QNBD) is being studied in this paper, generalizing the well-known negative binomial distribution. This model turns out to be quite flexible for analyzing count data. Our main objectives are to estimate the parameters of the proposed distribution and to discuss its applicability to genetics data. As an application, we demonstrate that the QNBD regression representation is utilized to model genomics data sets. Results: The new distribution is shown to provide a good fit with respect to the “Akaike Information Criterion”, AIC, considered a measure of model goodness of fit. The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling count data exhibiting overdispersion, arising in various fields of scientific investigation such as genomics and biomedicine.展开更多
In this article,we offer a new adapted model with three parameters,called Zubair Lomax distribution.The new model can be very useful in analyzing and modeling real data and provides better fits than some others new mo...In this article,we offer a new adapted model with three parameters,called Zubair Lomax distribution.The new model can be very useful in analyzing and modeling real data and provides better fits than some others new models.Primary properties of the Zubair Lomax model are determined by moments,probability weighted moments,Renyi entropy,quantile function and stochastic ordering,among others.Maximum likelihood method is used to estimate the population parameters,owing to simple random sample and ranked set sampling schemes.The behavior of the maximum likelihood estimates for the model parameters is studied using Monte Carlo simulation.Criteria measures including biases,mean square errors and relative efficiencies are used to compare estimates.Regarding the simulation study,we observe that the estimates based on ranked set sampling are more efficient compared to the estimates based on simple random sample.The importance and flexibility of Zubair Lomax are validated empirically in modeling two types of lifetime data.展开更多
Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore st...Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.展开更多
In this paper,a modified form of the traditional inverse Lomax distribution is proposed and its characteristics are studied.The new distribution which called modified logarithmic transformed inverse Lomax distribution...In this paper,a modified form of the traditional inverse Lomax distribution is proposed and its characteristics are studied.The new distribution which called modified logarithmic transformed inverse Lomax distribution is generated by adding a new shape parameter based on logarithmic transformed method.It contains two shape and one scale parameters and has different shapes of probability density and hazard rate functions.The new shape parameter increases the flexibility of the statistical properties of the traditional inverse Lomax distribution including mean,variance,skewness and kurtosis.The moments,entropies,order statistics and other properties are discussed.Six methods of estimation are considered to estimate the distribution parameters.To compare the performance of the different estimators,a simulation study is performed.To show the flexibility and applicability of the proposed distribution two real data sets to engineering and medical fields are analyzed.The simulation results and real data analysis showed that the Anderson-Darling estimates have the smallest mean square errors among all other estimates.Also,the analysis of the real data sets showed that the traditional inverse Lomax distribution and some of its generalizations have shortcomings in modeling engineering and medical data.Our proposed distribution overcomes this shortage and provides a good fit which makes it a suitable choice to model such data sets.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Finite Element Method (FEM), when applied to solve problems, has faced some challenges over the years, such as time consumption and the complexity of assumptions. In particular, the making of assumptions has had a sig...Finite Element Method (FEM), when applied to solve problems, has faced some challenges over the years, such as time consumption and the complexity of assumptions. In particular, the making of assumptions has had a significant influence on the accuracy of the method, making it mandatory to carry out sensitivity analysis. The sensitivity analysis helps to identify the level of impact the assumptions have on the method. However, sensitivity analysis via FEM can be very challenging. A priori error estimation, an integral part of FEM, is a basic mathematical tool for predicting the accuracy of numerical solutions. By understanding the relationship between the mesh size, the order of basis functions, and the resulting error, practitioners can effectively design and apply FEM to solve complex Partial Differential Equations (PDEs) with confidence in the reliability of their results. Thus, the coercive property and A priori error estimation based on the L1 formula on a mesh in time and the Mamadu-Njoseh basis functions in space are investigated for a linearly distributed time-order fractional telegraph equation with restricted initial conditions. For this purpose, we constructed a mathematical proof of the coercive property for the fully discretized scheme. Also, we stated and proved a cardinal theorem for a priori error estimation of the approximate solution for the fully discretized scheme. We noticed the role of the restricted initial conditions imposed on the solution in the analysis of a priori error estimation.展开更多
Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,Sou...Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
An autoregressive long-and short-term memory(ARLSTM)model was applied to develop a real-time probabilistic slope stability estimation model for the engineered barrier system(EBS)of a near surface radioactive waste dis...An autoregressive long-and short-term memory(ARLSTM)model was applied to develop a real-time probabilistic slope stability estimation model for the engineered barrier system(EBS)of a near surface radioactive waste disposal facility.The effectiveness of the developed model was verified using actual data acquired from South Korea,including precipitation,soil moisture contents,and inclinometer time-series data.The precipitation and the factor of safety(FS)ensemble results were used as the input and output variables of the AR-LSTM model,respectively,where the FS ensemble results were calculated by the Taylor model,integrating the Mualem-van Genuchten soil water retention model with consideration of the multivariate statistics on the hydrophysical properties of the soil.The estimation accuracy of the AR-LSTM model was reasonable by showing high correlation coefficient(0.9468)and low root mean squared error(0.0070)values between the actual and estimated FS values.Moreover,a significant correlation was observed between the estimated FS ensemble results and displacement events recorded by the inclinometer sensor.All the results suggest the effectiveness of the developed model for the long-term integrity assurance of the EBS.展开更多
Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction an...Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.展开更多
Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capa...Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.展开更多
Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton s...Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
基金Supported by the State Key Laboratory of Acoustics and Marine Information Chinese Academy of Sciences(SKL A202507).
文摘Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments.
基金supported by the National Science Foundation of China (No.61371169)the Aeronautical Science Foundation of China(No.20120152001)
文摘The problem of two-dimensional(2 D)direction of arrival(DOA)estimation for double parallel uniform linear arrays is investigated in this paper.A real-valued DOA estimation algorithm of noncircular(NC)signal is proposed,which combines the Euler transformation and rotational invariance(RI)property between subarrays.In this work,the effective array aperture is doubled by exploiting the noncircularity of signals.The complex arithmetic is converted to real arithmetic via Euler transformation.The main contribution of this work is not only extending the NC-Euler-ESPRIT algorithm from uniform linear array to double parallel uniform linear arrays,but also constructing a new 2 Drotational invariance property between subarrays,which is more complex than that in NCEuler-ESPRIT algorithm.The proposed 2 DNC-Euler-RI algorithm has much lower computational complexity than2 DNC-ESPRIT algorithm.The proposed algorithm has better angle estimation performance than 2 DESPRIT algorithm and 2 D NC-PM algorithm for double parallel uniform linear arrays,and is very close to that of 2 D NC-ESPRIT algorithm.The elevation angles and azimuth angles can be obtained with automatically pairing.The proposed algorithm can estimate up to 2(M-1)sources,which is two times that of 2 D ESPRIT algorithm.Cramer-Rao bound(CRB)of noncircular signal is derived for the proposed algorithm.Computational complexity comparison is also analyzed.Finally,simulation results are presented to illustrate the effectiveness and usefulness of the proposed algorithm.
基金Ministry of Education and Science of the Russian Federation(14.613.21.0043)
文摘The experimental values of the enthalpy of formation of two isomeric 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles have been obtained(261.5±5.0and 246.4±6.7kJ/mol for crystalline 3,4-and 3,5-dinitro-1-(trinitromethyl)-1H-pyrazoles,respectively).The ballistic effectiveness of these potential oxidizers in composite solid propellants was studied.It is shown that these two oxidizers may be successfully applied in metal-free compositions or with a small content of metal.For the bottom stage 3,4-dinitro-1-(trinitromethyl)-1H-pyrazole is a bit better than 3,5-dinitro-1-(trinitromethyl)-1H-pyrazole,for the upper stage the both oxidizers show the equal ballistic parameters.These oxidizers allow to create metal-free solid composite propellants with the binder percentage not lower than 19%(volume fraction),with I3spequal to 256.5-257.0sat density equal to 1.72-1.74g/cm^3.
文摘New correlations based on group-contribution method for estimating critical properties have been proposed with con-tribution values of 67 groups.The new correlations have been examined by extensive comparison of the values calculatedwith the experimental data of 275 substanees.The average percent deviations of estimation of T_c,P_c and V_c are respective-ly 0.76,2.73 and 2.50 which are lower than those estimated by the Lydersen method and the Ambrose method chosenfor eomparision.
基金the Deanship Scientific Research(DSR)King Abdulaziz University,Jeddah under Grant No.(G:337-130-1441).
文摘In this paper,we propose a new extension of the traditional Rayleigh distribution called the modified Kies Rayleigh distribution.The new distribution contains one scale and one shape parameter and its hazard rate function can be increasing and bathtub-shaped.Some mathematical properties of the new distribution are derived including quantiles and moments.The parameters of modified Kies Rayleigh distribution are estimated based on progressively Type-II censored data.For this purpose,we consider two estimation methods,namely maximum likelihood and maximum product of spacing estimation methods.To compare the efficiency of the proposed estimators,a simulation study is carried out.To show the applicability of the new model as well as the estimation methods,one real data for failure times of software is analyzed.Based on the empirical parts,we can conclude that the proposed model can be considered as a good model in the field of life testing and reliability analysis compared with other competing models.
文摘Mass property of spacecraft needs to be estimated in certain circumstances.Based on the measurement of single point acceleration,angular acceleration and angular velocity,a discrete Kalman Filter(KF) based estimation method is developed to extract the inertia matrix and mass center.The simulation results show that the method can be used for continues online estimation without propellant consumption.Stability condition for the filter is given at last.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘Background: The Poisson and the Negative Binomial distributions are commonly used to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore both models are appropriate to model over-dispersed count data. Objectives: A new two-parameter probability distribution called the Quasi-Negative Binomial Distribution (QNBD) is being studied in this paper, generalizing the well-known negative binomial distribution. This model turns out to be quite flexible for analyzing count data. Our main objectives are to estimate the parameters of the proposed distribution and to discuss its applicability to genetics data. As an application, we demonstrate that the QNBD regression representation is utilized to model genomics data sets. Results: The new distribution is shown to provide a good fit with respect to the “Akaike Information Criterion”, AIC, considered a measure of model goodness of fit. The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling count data exhibiting overdispersion, arising in various fields of scientific investigation such as genomics and biomedicine.
基金funded by the Deanship of Scientific Research(DSR),King Abdul Aziz University,Jeddah,under grant No.DF-281-305-1441This work was funded by the Deanship of Scientific Research(DSR),King Abdul Aziz University,Jeddah,under grant No.DF-281-305-1441.
文摘In this article,we offer a new adapted model with three parameters,called Zubair Lomax distribution.The new model can be very useful in analyzing and modeling real data and provides better fits than some others new models.Primary properties of the Zubair Lomax model are determined by moments,probability weighted moments,Renyi entropy,quantile function and stochastic ordering,among others.Maximum likelihood method is used to estimate the population parameters,owing to simple random sample and ranked set sampling schemes.The behavior of the maximum likelihood estimates for the model parameters is studied using Monte Carlo simulation.Criteria measures including biases,mean square errors and relative efficiencies are used to compare estimates.Regarding the simulation study,we observe that the estimates based on ranked set sampling are more efficient compared to the estimates based on simple random sample.The importance and flexibility of Zubair Lomax are validated empirically in modeling two types of lifetime data.
基金supported by the National Natural Science Foundation of China (42104121)the Scientific Research and Technology Development Project of the CNPC (2021DJ0606)。
文摘Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.
基金This project was funded by the Deanship Scientific Research(DSR),King Abdulaziz University,Jeddah under Grant No.(RG-14-130-41)The author,therefore,acknowledge with thanks DSR for technical and financial support.
文摘In this paper,a modified form of the traditional inverse Lomax distribution is proposed and its characteristics are studied.The new distribution which called modified logarithmic transformed inverse Lomax distribution is generated by adding a new shape parameter based on logarithmic transformed method.It contains two shape and one scale parameters and has different shapes of probability density and hazard rate functions.The new shape parameter increases the flexibility of the statistical properties of the traditional inverse Lomax distribution including mean,variance,skewness and kurtosis.The moments,entropies,order statistics and other properties are discussed.Six methods of estimation are considered to estimate the distribution parameters.To compare the performance of the different estimators,a simulation study is performed.To show the flexibility and applicability of the proposed distribution two real data sets to engineering and medical fields are analyzed.The simulation results and real data analysis showed that the Anderson-Darling estimates have the smallest mean square errors among all other estimates.Also,the analysis of the real data sets showed that the traditional inverse Lomax distribution and some of its generalizations have shortcomings in modeling engineering and medical data.Our proposed distribution overcomes this shortage and provides a good fit which makes it a suitable choice to model such data sets.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
文摘Finite Element Method (FEM), when applied to solve problems, has faced some challenges over the years, such as time consumption and the complexity of assumptions. In particular, the making of assumptions has had a significant influence on the accuracy of the method, making it mandatory to carry out sensitivity analysis. The sensitivity analysis helps to identify the level of impact the assumptions have on the method. However, sensitivity analysis via FEM can be very challenging. A priori error estimation, an integral part of FEM, is a basic mathematical tool for predicting the accuracy of numerical solutions. By understanding the relationship between the mesh size, the order of basis functions, and the resulting error, practitioners can effectively design and apply FEM to solve complex Partial Differential Equations (PDEs) with confidence in the reliability of their results. Thus, the coercive property and A priori error estimation based on the L1 formula on a mesh in time and the Mamadu-Njoseh basis functions in space are investigated for a linearly distributed time-order fractional telegraph equation with restricted initial conditions. For this purpose, we constructed a mathematical proof of the coercive property for the fully discretized scheme. Also, we stated and proved a cardinal theorem for a priori error estimation of the approximate solution for the fully discretized scheme. We noticed the role of the restricted initial conditions imposed on the solution in the analysis of a priori error estimation.
基金funded by the Natural Sciences and Engineering Research Council of Canada(RGPIN:2016-05964&2023-04283 to JHK)the University of Manitoba Tri-Agency Bridge Funding(#57289 to JHK)the Ricard Foundation’s Baxter Bursary(to JP)。
文摘Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by the Radioactive Waste Management of the Korea Institute of Energy Technology Evaluation and Planning grant funded by the Korea government Ministry of Knowledge(20193210100130)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.202008980000).
文摘An autoregressive long-and short-term memory(ARLSTM)model was applied to develop a real-time probabilistic slope stability estimation model for the engineered barrier system(EBS)of a near surface radioactive waste disposal facility.The effectiveness of the developed model was verified using actual data acquired from South Korea,including precipitation,soil moisture contents,and inclinometer time-series data.The precipitation and the factor of safety(FS)ensemble results were used as the input and output variables of the AR-LSTM model,respectively,where the FS ensemble results were calculated by the Taylor model,integrating the Mualem-van Genuchten soil water retention model with consideration of the multivariate statistics on the hydrophysical properties of the soil.The estimation accuracy of the AR-LSTM model was reasonable by showing high correlation coefficient(0.9468)and low root mean squared error(0.0070)values between the actual and estimated FS values.Moreover,a significant correlation was observed between the estimated FS ensemble results and displacement events recorded by the inclinometer sensor.All the results suggest the effectiveness of the developed model for the long-term integrity assurance of the EBS.
基金supported by the National Key Research and Development Plan(Grant No.2023YFB3712400)the National Key Research and Development Plan(Grant No.2020YFB1713600).
文摘Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.
基金the financial support from the National Key Research and Development Program of China(No.2023YFB3907001)the National Natural Science Foundation of China(Nos.U2233217,62371029)the UK Engineering and Physical Sciences Research Council(EPSRC),China(Nos.EP/M026981/1,EP/T021063/1 and EP/T024917/)。
文摘Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.
基金supported in part by the National Natural Science Foundation of China under Grants 61973065,U20A20197,61973063.
文摘Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.
