Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermo...This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link.It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform(rnSDFT)and an RLS filter improved with a forgetting factor(oSDFT-RLS)to accurately estimate the capacitance and equivalent series resistance(ESR).Importantly,this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems,ensuring its noninvasiveness.Furthermore,the oSDFTRLS estimates capacitor parameters in the time-frequency domain,enabling the tracking of capacitor degradation and predicting potential faults.Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.展开更多
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
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref...The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.展开更多
In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes fu...In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.展开更多
Micro-Doppler parameter estimation is crucial for moving targets.However,conventional methods face limitations like inadequate time-frequency(TF)resolution and poor generalization,while existing deep learning approach...Micro-Doppler parameter estimation is crucial for moving targets.However,conventional methods face limitations like inadequate time-frequency(TF)resolution and poor generalization,while existing deep learning approaches often treat TF analysis as a fixed preprocessing step.To overcome these challenges,this paper introduces a radar micro-Doppler parameter estimation method based on a gated dual-path dynamic-wavelet convolutional network(GDWCN).The GDWCN is an end-to-end deep learning framework that maps raw radar signals to micro-motion parameters by integrating clutter suppression,gated dual-path module,feature extraction,and parameter regression.Its core innovation is a gated dual-path module that combines dynamic convolution and learnable wavelet convolution,selecting the optimal processing path based on input signal characteristics.For the Inspire 2 drone,GDWCN reduced the mean absolute error(MAE)of frequency estimation by approximately 38%compared to the enhanced time-frequency micro-Doppler network,and its relative error by approximately 69%compared to the short-time Fourier transform(STFT),and 58%over the local maximum synchroextracting transform.Ablation studies further confirm the efficacy of the clutter suppression module and the attention mechanism.展开更多
Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter esti...Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation.Firstly,a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics.Then,an estimator based on parameter estimation is designed,and its stability is verified.This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient.Finally,the approach is validated through joint simulations on the MATLAB/Simulink and AMESim platforms,as well as a hardware-in-the-loop semi-physical simulation experimental platform that accounts for system delay and noise conditions.The results indicate that the proposed algorithm effectively tracks changes in the adhesion coefficient during train braking,including the decrease in adhesion when the train brakes and slides,and the overall increase as the train speed decreases.The effectiveness of the algorithm was verified by setting different test conditions.The results show that the estimation algorithm can accurately estimate the adhesion coefficient,and through error analysis,it is found that the error between the estimated value of the adhesion coefficient and the theoretical value of the adhesion coefficient is within 5%.The adhesion coefficient obtained through the online estimation method based on the parameter estimation proposed in this paper demonstrates strong followability in both simulation and practical applications.展开更多
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource man...In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.展开更多
The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths,serving as a crucial prototype for achieving high-precision measurements in various scientific and technological...The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths,serving as a crucial prototype for achieving high-precision measurements in various scientific and technological applications.In this study,we analyze different models for estimating the relative phase shift in a general two-arm Mach-Zehnder interferometer.We demonstrated that single-parameter estimation models can be reduced from the two-parameter estimation model by imposing appropriate constraints on the parameter space.To make quantum Fisher information of the single-parameter estimation models meaningful,the corresponding constraints must be guaranteed in the experiment implementation.Furthermore,we apply the quantum Fisher information approach to analyze the Mach-Zehnder interferometer with the an input state composed of a displaced squeezed vacuum state and a coherent state,providing insights into the precision limits of such configurations.展开更多
The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogram...The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.展开更多
The existence and uniqueness of the maximum likelihood estimator(MLE)of parameter for the exponential-Poisson distribution is discussed by Ku s[2007.A new lifetime distribution.Computational Statistics and Data Analys...The existence and uniqueness of the maximum likelihood estimator(MLE)of parameter for the exponential-Poisson distribution is discussed by Ku s[2007.A new lifetime distribution.Computational Statistics and Data Analysis 51(9):4497-4509]in simple random sampling(SRS).As an alternative to the MLEs in SRS,Joukar et al.[2021.Parameter estimation for the exponential-poisson distribution based on ranked set samples.Communication in Statistics-Theory and Methods 50(3):560-581]discussed the MLE of parameter for this distribution in ranked set sampling(RSS).However,they did not discuss the existence and uniqueness of the MLE in RSS and did not provide explicit expressions for the Fisher information in RSS.In this article,we discuss the existence and uniqueness of the MLE of parameter in RSS and give explicit expressions for the Fisher information in RSS.The MLEs will be compared in terms of asymptotic efficiencies.Numerical studies and a real data application show that these MLEs in RSS can be real competitors for those in SRS.展开更多
The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variabilit...The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variability.To analyze sea ice changes in the Barents Sea,sea ice data from the National Snow and Ice Data Center were utilized.A remarkable decline in sea ice has been witnessed in the northern and eastern regions.This phenomenon has expanded the ice-free operational area for marine structures,highlighting the significance of wave factors.A site within this area was chosen to estimate the wave parameters.The wave data from ERA5 were categorized according to wave energy in each season.Four mixture joint distribution models for the wave height and period were constructed based on the mixture distribution method and copula theory,and environmental contours were developed and compared with the conditional probability method.Despite differences in the design parameter results,the mixture models demonstrate good performance in sample fitting,particularly in the distribution tails.Among these models,the Gaussian copula offers the best fit.展开更多
Weighted exponential distribution W ED(α,λ)with shape parameterαand scale parameterλpossesses some good properties and can be used as a good fit to survival time data compared to other distributions such as gamma,...Weighted exponential distribution W ED(α,λ)with shape parameterαand scale parameterλpossesses some good properties and can be used as a good fit to survival time data compared to other distributions such as gamma,Weibull,or generalized exponential distribution.In this article,we proved the existence and uniqueness of the maximum likelihood estimator(MLE)of the parameters of W ED(α,λ)in simple random sampling(SRS)and provided explicit expressions for the Fisher information number in SRS.Moreover,we also proved the existence and uniqueness of the MLE of the parameters of W ED(α,λ)in ranked set sampling(RSS)and provided explicit expressions for the Fisher information number in RSS.Simulation studies show that these MLEs in RSS can be real competitors for those in SRS.展开更多
The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and...The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and makes full use of the sparse characteristics of DD domain,it has been widely studied to design efficient channel estimation and signal detection schemes.In this paper,we design a novel superimposed pilot pattern with transition band,which replaces the traditional embedded pilot(EP)guard zero-symbols,and perform a two-stage channel estimation.In the first stage,we fully utilize the dispersion characteristics of OTFS signal in DD domain,and use threshold decision to make coarse channel estimation.In the second stage,we use the results of the coarse estimation for iterative signal detection and accurate channel estimation.During the second stage,we make full use of the sparsity of the channel in DD domain,remodel the received signal into the form of sparse channel vector multiplied by channel coefficient matrix,and introduce Doppler index segmentation factor(DISF)to subdivide the Doppler index to solve the problem of fractional Doppler.Simulations reveal that,the scheme proposed in this paper has higher spectral efficiency compared with traditional EP scheme and lower peak-to-average power ratio(PAPR)compared with traditional superimposed pilot scheme.展开更多
Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical applicati...Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.展开更多
In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorit...In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system.An output-based event-triggered scheme is first employed to alleviate transmission burden.Accounting for the limited-communication-induced measurement bias,a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation,thereby achieving accurate system state estimates.Subsequently,the Field Programmable Gate Array(FPGA)implementation of the proposed algorithm is also realized with the hope of providing fast bias calibration in practical scenarios.A simulation about a numerical example and a practical example(for gyroscope’s angular velocity bias calibration)on MATLAB is provided to demonstrate the feasibility and effectiveness of the proposed algorithm.展开更多
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
基金funded by the Brazilian National Council for Scientific and Technological Development—CNPq(CNPq grant number 405997/2022-1)supported by the EMBRAPII VIRTUS Competence Center in Intelligent Hardware for Industry—VIRTUS-CC(MCTI grant number 055/2023).
文摘This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters(3L-NPC-VSI)used in grid-tied systems.The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link.It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform(rnSDFT)and an RLS filter improved with a forgetting factor(oSDFT-RLS)to accurately estimate the capacitance and equivalent series resistance(ESR).Importantly,this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems,ensuring its noninvasiveness.Furthermore,the oSDFTRLS estimates capacitor parameters in the time-frequency domain,enabling the tracking of capacitor degradation and predicting potential faults.Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.
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The National Natural Science Foundation of China under contract No. 42274159the Project supported by Key Laboratory of Space Ocean Remote Sensing and Application,MNR under contract No.2023CFO016。
文摘The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.
文摘In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
基金supported in part by the National Natural Science Foundation of China(No.62222120)the National Key Research and Development Program of China(No.2024YFB3909804)the Shandong Provincial Natural Science Foundation(No.ZR2024JQ003).
文摘Micro-Doppler parameter estimation is crucial for moving targets.However,conventional methods face limitations like inadequate time-frequency(TF)resolution and poor generalization,while existing deep learning approaches often treat TF analysis as a fixed preprocessing step.To overcome these challenges,this paper introduces a radar micro-Doppler parameter estimation method based on a gated dual-path dynamic-wavelet convolutional network(GDWCN).The GDWCN is an end-to-end deep learning framework that maps raw radar signals to micro-motion parameters by integrating clutter suppression,gated dual-path module,feature extraction,and parameter regression.Its core innovation is a gated dual-path module that combines dynamic convolution and learnable wavelet convolution,selecting the optimal processing path based on input signal characteristics.For the Inspire 2 drone,GDWCN reduced the mean absolute error(MAE)of frequency estimation by approximately 38%compared to the enhanced time-frequency micro-Doppler network,and its relative error by approximately 69%compared to the short-time Fourier transform(STFT),and 58%over the local maximum synchroextracting transform.Ablation studies further confirm the efficacy of the clutter suppression module and the attention mechanism.
基金supported by the National Natural Science Foundation of China(grant/award number 52072266).
文摘Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation.Firstly,a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics.Then,an estimator based on parameter estimation is designed,and its stability is verified.This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient.Finally,the approach is validated through joint simulations on the MATLAB/Simulink and AMESim platforms,as well as a hardware-in-the-loop semi-physical simulation experimental platform that accounts for system delay and noise conditions.The results indicate that the proposed algorithm effectively tracks changes in the adhesion coefficient during train braking,including the decrease in adhesion when the train brakes and slides,and the overall increase as the train speed decreases.The effectiveness of the algorithm was verified by setting different test conditions.The results show that the estimation algorithm can accurately estimate the adhesion coefficient,and through error analysis,it is found that the error between the estimated value of the adhesion coefficient and the theoretical value of the adhesion coefficient is within 5%.The adhesion coefficient obtained through the online estimation method based on the parameter estimation proposed in this paper demonstrates strong followability in both simulation and practical applications.
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
基金supported in part by the National Natural Science Foundation of China(No.62271248)the Natural Science Foundation of Jiangsu Province(No.BK20230090)the Key Laboratory of Land Satellite Remote Sensing Application through the Ministry of Natural Resources of China(No.KLSMNR-K202303).
文摘In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.
基金supported by the National Natural Science Foundation of China(Grants Nos.92476118 and 12275062).
文摘The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths,serving as a crucial prototype for achieving high-precision measurements in various scientific and technological applications.In this study,we analyze different models for estimating the relative phase shift in a general two-arm Mach-Zehnder interferometer.We demonstrated that single-parameter estimation models can be reduced from the two-parameter estimation model by imposing appropriate constraints on the parameter space.To make quantum Fisher information of the single-parameter estimation models meaningful,the corresponding constraints must be guaranteed in the experiment implementation.Furthermore,we apply the quantum Fisher information approach to analyze the Mach-Zehnder interferometer with the an input state composed of a displaced squeezed vacuum state and a coherent state,providing insights into the precision limits of such configurations.
基金Natural Science Foundation of Hunan Province,China(No.2024JJ8335)Open Topic of Hunan Geospatial Information Engineering and Technology Research Center,China(No.HNGIET2023004).
文摘The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.
基金Supported by the National Natural Science Foundation of China(11901236,12261036)Scientific Research Fund of Hunan Provincial Education Department(21A0328)Young Core Teacher Foundation of Hunan Province([2020]43).
文摘The existence and uniqueness of the maximum likelihood estimator(MLE)of parameter for the exponential-Poisson distribution is discussed by Ku s[2007.A new lifetime distribution.Computational Statistics and Data Analysis 51(9):4497-4509]in simple random sampling(SRS).As an alternative to the MLEs in SRS,Joukar et al.[2021.Parameter estimation for the exponential-poisson distribution based on ranked set samples.Communication in Statistics-Theory and Methods 50(3):560-581]discussed the MLE of parameter for this distribution in ranked set sampling(RSS).However,they did not discuss the existence and uniqueness of the MLE in RSS and did not provide explicit expressions for the Fisher information in RSS.In this article,we discuss the existence and uniqueness of the MLE of parameter in RSS and give explicit expressions for the Fisher information in RSS.The MLEs will be compared in terms of asymptotic efficiencies.Numerical studies and a real data application show that these MLEs in RSS can be real competitors for those in SRS.
基金the National Natural Science Foundation of China(No.52171284)the Natural Science Foundation of Shandong Province(No.ZR2023QE016).
文摘The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variability.To analyze sea ice changes in the Barents Sea,sea ice data from the National Snow and Ice Data Center were utilized.A remarkable decline in sea ice has been witnessed in the northern and eastern regions.This phenomenon has expanded the ice-free operational area for marine structures,highlighting the significance of wave factors.A site within this area was chosen to estimate the wave parameters.The wave data from ERA5 were categorized according to wave energy in each season.Four mixture joint distribution models for the wave height and period were constructed based on the mixture distribution method and copula theory,and environmental contours were developed and compared with the conditional probability method.Despite differences in the design parameter results,the mixture models demonstrate good performance in sample fitting,particularly in the distribution tails.Among these models,the Gaussian copula offers the best fit.
基金Supported by the National Science Foundation of China(11901236,12261036)Scientific Research Fund of Hunan Provincial Education Department(21A0328)+2 种基金Provincial Natural Science Foundation of Hunan(2022JJ30469)Young Core Teacher Foundation of Hunan Province([2020]43)Provincial Postgraduate Innovation Foundation of Hunan(CX20221113)。
文摘Weighted exponential distribution W ED(α,λ)with shape parameterαand scale parameterλpossesses some good properties and can be used as a good fit to survival time data compared to other distributions such as gamma,Weibull,or generalized exponential distribution.In this article,we proved the existence and uniqueness of the maximum likelihood estimator(MLE)of the parameters of W ED(α,λ)in simple random sampling(SRS)and provided explicit expressions for the Fisher information number in SRS.Moreover,we also proved the existence and uniqueness of the MLE of the parameters of W ED(α,λ)in ranked set sampling(RSS)and provided explicit expressions for the Fisher information number in RSS.Simulation studies show that these MLEs in RSS can be real competitors for those in SRS.
基金supported by National Natural Science Foundation(NNSF)of China under Grant 62001351the Foundation of National Key Laboratory of Electromagnetic Environment(6142403220202)the Stability Support Fund for Basic Military Industrial Research Institutes(A240104130).
文摘The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and makes full use of the sparse characteristics of DD domain,it has been widely studied to design efficient channel estimation and signal detection schemes.In this paper,we design a novel superimposed pilot pattern with transition band,which replaces the traditional embedded pilot(EP)guard zero-symbols,and perform a two-stage channel estimation.In the first stage,we fully utilize the dispersion characteristics of OTFS signal in DD domain,and use threshold decision to make coarse channel estimation.In the second stage,we use the results of the coarse estimation for iterative signal detection and accurate channel estimation.During the second stage,we make full use of the sparsity of the channel in DD domain,remodel the received signal into the form of sparse channel vector multiplied by channel coefficient matrix,and introduce Doppler index segmentation factor(DISF)to subdivide the Doppler index to solve the problem of fractional Doppler.Simulations reveal that,the scheme proposed in this paper has higher spectral efficiency compared with traditional EP scheme and lower peak-to-average power ratio(PAPR)compared with traditional superimposed pilot scheme.
文摘Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.
基金support from the National Natural Science Foundation of China(Grant Nos.U2330206,U2230206,62173068)Sichuan Science and Technology Program(Grants Nos.2024NSFSC1483,2024ZYD0156,2023NSFC1962,DQ202412).
文摘In data communication,limited communication resources often lead to measurement bias,which adversely affects subsequent system estimation if not effectively handled.This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system.An output-based event-triggered scheme is first employed to alleviate transmission burden.Accounting for the limited-communication-induced measurement bias,a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation,thereby achieving accurate system state estimates.Subsequently,the Field Programmable Gate Array(FPGA)implementation of the proposed algorithm is also realized with the hope of providing fast bias calibration in practical scenarios.A simulation about a numerical example and a practical example(for gyroscope’s angular velocity bias calibration)on MATLAB is provided to demonstrate the feasibility and effectiveness of the proposed algorithm.
