In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant chal...In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional F...The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional Feature Network(MFNet)which is designed for NoS estimation by extracting features of the sampled received signals and Sampled Covariance Matrix(SCM).The MFNet treats the raw signal and the SCM as two different types of data,and is able to achieve NoS estimation under colored noise and imperfect array.MFNet employs the Gated Recurrent Unit(GRU)to capture sequential information from the original signal data and to construct the Pseudo Covariance Matrix(PCM).Subsequently,various dimensional features,including eigenvalues and the Gerschgorin disk radius,are extracted from both the PCM and SCM,which are then jointly input into the subsequent network.An overall accuracy of 82%can be achieved after network training.The ablation experimental results demonstrate the effectiveness of multiple inputs.And simulation results demonstrate that the proposed MFNet achieves higher estimation accuracy compared to existing algorithms and exhibits greater robustness against colored noise.展开更多
We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In...We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.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 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
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
Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement ...Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.展开更多
Electrochemical impedance spectroscopy(EIS)is a widely used technique to monitor the electrical properties of a catalyst under electrocatalytic conditions.Although it is extensively used for research in electrocatalys...Electrochemical impedance spectroscopy(EIS)is a widely used technique to monitor the electrical properties of a catalyst under electrocatalytic conditions.Although it is extensively used for research in electrocatalysis,its effectiveness and power have not been fully harnessed to elucidate complex interfacial processes.Herein,we use the frequency dispersion parameter,n,which is extracted from EIS measurements(C_(s)=af^(n+1),-2<n<-1),to describe the dispersion characteristics of capacitance and interfacial properties of Co_(3)O_(4) before the onset of oxygen evolution reaction(OER)in alkaline conditions.We first prove that the n-value is sensitive to the interfacial electronic changes associated with Co redox processes and surface reconstruction.The n-value decreases by increasing the specific/active surface area of the catalysts.We further modify the interfacial properties by changing different components,i.e.,replacing the proton with deuterium,adding ethanol as a new oxidant,and changing the cation in the electrolyte.Intriguingly,the n-value can identify different influences on the interfacial process of proton transfer,the decrease and blocking of oxidized Co species,and the interfacial water structure.We demonstrate that the n-value extracted from EIS measurements is sensitive to the kinetic isotope effect,electrolyte cation,adsorbate surface coverage of oxidized Co species,and the interfacial water structure.Thus,it can be helpful to differentiate the multiple factors affecting the catalyst interface.These findings convey that the frequency dispersion of capacitance is a convenient and useful method to uncover the interfacial properties under electrocatalytic conditions,which helps to advance the understanding of the interfaceactivity relationship.展开更多
When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex d...When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex dynamic operating conditions.However,the extraction of most HFs relies on complete charge-discharge cycle data,making them less adaptable to complex dynamic operating conditions.Existing mechanism HFs,while capable of characterizing capacity degradation from a mechanism perspective,suffer from limitations such as insufficient physical model expressiveness,high dimension,and redundancy of the mechanism HF.These issues increase the complexity of subsequent modeling of the relationship between HFs and capacity,thereby restricting their promotion in engineering practice.To meet this gap,this paper proposes a novel mechanism-based HF.Firstly,a multi-physical fields coupling model is developed to describe the interactions between electrochemical,thermal,and aging behaviors of the battery.Secondly,based on the aging mechanism,the accumulated charge of lithium lost during the formation of the solid electrolyte interphase(SEI)film is extracted as HF to provide a more intuitive representation of capacity degradation.Then,to reduce estimation errors caused by considering only a single aging mechanism,multiple representative regression models are employed to establish the mapping relationship between the mechanism HF and capacity,further enhancing the accuracy of final results.Finally,the proposed method is implemented and validated using real battery data under three different types of operating conditions.Experimental results demonstrate that,compared to other commonly used HFs,the proposed HF exhibits significant competitive advantages in handling incomplete cycle data,unknown operating conditions,and capacity estimation models.The minimum estimation error under ideal conditions is 0.0074,and the minimum estimation error under complex dynamic conditions is 0.0268.展开更多
Transient negative capacitance(NC),as an available dynamic charge effect achieved in resistor-ferroelectric capacitor(R-FEC)circuits,has triggered a series of theoretical and experimental works focusing on its physica...Transient negative capacitance(NC),as an available dynamic charge effect achieved in resistor-ferroelectric capacitor(R-FEC)circuits,has triggered a series of theoretical and experimental works focusing on its physical mechanism and device application.Here,we analytically derived the effects of different mechanical conditions on the transient NC behaviors in the R-FEC circuit based on the phenomenological model.It shows that the ferroelectric capacitor can exhibit either NC(i.e.,“single NC”and“double NC”)or positive capacitance,depending on the mechanical condition and temperature.Further numerical calculations show that the voltage drop caused by NC can be effectively controlled by temperature,applied stress,or strain.The relationship between NC voltage drop and system configurations including external resistance,dynamical coefficient of polarization,and input voltage are presented,showing diverse strategies to manipulate the NC effect.These results provide theoretical guidelines for rational design and efficient control of NC-related electronic devices.展开更多
The development of high-performance,reproducible carbon(C)-based supercapacitors remains a significant challenge because of limited specific capacitance.Herein,we present a novel strategy for fabricating LaCoO_(x) and...The development of high-performance,reproducible carbon(C)-based supercapacitors remains a significant challenge because of limited specific capacitance.Herein,we present a novel strategy for fabricating LaCoO_(x) and cobalt(Co)-doped nanoporous C(LaCoO_(x)/Co@ZNC)through the carbonization of Co/Zn-zeolitic imidazolate framework(ZIF)crystals derived from a PVP-Co/Zn/La precursor.The unique ZIF structure effectively disrupted the graphitic C framework,preserved the Co active sites,and enhanced the electrical conductivity.The synergistic interaction between pyridinic nitrogen and Co ions further promoted redox reactions.In addition,the formation of a hierarchical pore structure through zinc sublimation facili-tated electrolyte diffusion.The resulting LaCoO_(x)/Co@ZNC exhibited exceptional electrochemical performance,delivering a remarkable specific capacitance of 2,789 F/g at 1 A/g and outstanding cycling stability with 92%capacitance retention after 3,750 cycles.Our findings provide the basis for a promising approach to advancing C-based energy storage technologies.展开更多
Vanadium nitride(VN)is a promising pseudocapacitive material due to the high theoretical capacity,rapid redox Faradaic kinetics,and appropriate potential window.Although VN shows large pseudocapacitance in alkaline el...Vanadium nitride(VN)is a promising pseudocapacitive material due to the high theoretical capacity,rapid redox Faradaic kinetics,and appropriate potential window.Although VN shows large pseudocapacitance in alkaline electrolytes,the electrochemical instability and capacity degradation of VN electrode materials present significant challenges for practical applications.Herein,the capacitance decay mechanism of VN is investigated and a simple strategy to improve cycling stability of VN supercapacitor electrodes is proposed by introducing VO_(4)^(3-)anion in KOH electrolytes.Our results show that the VN electrode is electrochemical stabilization between-1.0and-0.4 V(vs.Hg/Hg O reference electrode)in 1.0 MKOH electrolyte,but demonstrates irreversible oxidation and fast capacitance decay in the potential range of-0.4 to0 V.In situ electrochemical measurements reveal that the capacitance decay of VN from-0.4 to 0 V is ascribed to the irreversible oxidation of vanadium(V)of N–V–O species by oxygen(O)of OH^(-).The as-generated oxidization species are subsequently dissolved into KOH electrolytes,thereby undermining the electrochemical stability of VN.However,this irreversible oxidation process could be hindered by introducing VO_(4)^(3-)in KOH electrolytes.A high volumetric specific capacitance of671.9 F.cm^(-3)(1 A.cm^(-3))and excellent cycling stability(120.3%over 1000 cycles)are achieved for VN nanorod electrode in KOH electrolytes containing VO_(4)^(3-).This study not only elucidates the failure mechanism of VN supercapacitor electrodes in alkaline electrolytes,but also provides new insights into enhancing pseudocapacitive energy storage of VN-based electrode materials.展开更多
The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction perform...The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.展开更多
Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is...Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.展开更多
The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description....The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description. There is no prior geologic model. The principal output of CRM fitting is the fraction of injected fluid （usually water） that is produced at a producer at steady-state. These fractions are interwell connectivities. Interwell connectivities are fundamental information needed to manage waterfloods in oil reservoirs. The data-driven CRM is a fast tool to estimate these parameters in mature fields and allows one to make full use of the dynamic data available. This paper considers the problem of setting an upper bound on the uncertainty of interwell connectivities for linear-constrained models. Using analytical bounds and numerical simulations, we derive a consistent upper limit on the uncertainty of interwell connections that can be used to quantify the information content of a given dataset.展开更多
During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face ...During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.展开更多
In an environment where demand for housing is growing and the supply from public authorities is virtually non-existent,several mechanisms for housing production are emerging in the formal,semi-informal and informal co...In an environment where demand for housing is growing and the supply from public authorities is virtually non-existent,several mechanisms for housing production are emerging in the formal,semi-informal and informal construction sectors.The project owner wonders how much it costs to construct a building to an acceptable standard.Cost forecasting in general faces a number of difficulties,including a lack of available information during the preliminary phase of the project.As such,estimation becomes a crucial task involving great responsibility,which can lead to either more convincing results or chaotic situations.This study proposes a quick and effective method for estimating the cost of a single-storey F4 residential building.The modelling is done using multiple linear regression based on a statistical approach applied to twenty(20)projects that have already been completed.The project data are collected from design offices in the city of Brazzaville.The method expresses the cost of an F4 construction by certain project tasks,representing five(5)variables,three(3)of which are related to structural work and two(2)to finishing work,which are easy to determine.This approach,known as MECSO(Cost Estimation Model by Sub-structure),gives good results in all statistical tests carried out with reasonable confidence intervals.This method is very practical for engineering professionals working on the evaluation and control of construction costs.展开更多
In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperatur...In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperature dependent terms from the voltages using the machine model. The estimation accuracy under low speed or load can be greatly affected by the model uncertainty and noise due to low signal-tonoise ratio. This paper presents a high frequency(HF) position offset injection-based winding and permanent magnet(PM) temperature decoupled estimation approach for PMSMs to achieve accurate and robust temperature estimation among a wide speed range especially under low-speed conditions. In the proposed approach, a small HF position offset is injected into the machine to construct a decoupled winding and PM temperature estimation model, in which the winding and PM temperatures are independently estimated from HF excitations. The temperature estimation is independent from the fundamental model and parameter variation, and it achieves high signal-tonoise ratio under low-speed conditions. Moreover, the temperature estimation is also not affected by magnetic saturation and inverter distortion, which can improve the accuracy and robustness of temperature estimation. The proposed approach is validated with experiments and comparisons on a laboratory machine under various operating conditions.展开更多
基金National Key Laboratory of Unmanned Aerial Vehicle Technology(No.202408)Key Laboratory of Smart Earth(No.KF2023ZD01-05)。
文摘In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
基金supported by the National Natural Science Foundation of China(Nos.62171469,62071029)。
文摘The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional Feature Network(MFNet)which is designed for NoS estimation by extracting features of the sampled received signals and Sampled Covariance Matrix(SCM).The MFNet treats the raw signal and the SCM as two different types of data,and is able to achieve NoS estimation under colored noise and imperfect array.MFNet employs the Gated Recurrent Unit(GRU)to capture sequential information from the original signal data and to construct the Pseudo Covariance Matrix(PCM).Subsequently,various dimensional features,including eigenvalues and the Gerschgorin disk radius,are extracted from both the PCM and SCM,which are then jointly input into the subsequent network.An overall accuracy of 82%can be achieved after network training.The ablation experimental results demonstrate the effectiveness of multiple inputs.And simulation results demonstrate that the proposed MFNet achieves higher estimation accuracy compared to existing algorithms and exhibits greater robustness against colored noise.
文摘We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.
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.
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 by the National Natural Science Foundation of China(No.32271876)the Research on Key Technologies of Intelligent Monitoring and Carbon Sink Metering of Forest Resources in Fujian Province(No.2022FKJ03)the Science and Technology Innovation Project of Fujian Agriculture and Forestry University(No.KFB23172A,KFB23173A).
文摘Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.
基金Swiss National Science Foundation through its PRIM A grant(grant No.PR00P2_193111)the NCCR MARVEL,a National Centre of Competence in Researchfunded by the Swiss National Science Foundation。
文摘Electrochemical impedance spectroscopy(EIS)is a widely used technique to monitor the electrical properties of a catalyst under electrocatalytic conditions.Although it is extensively used for research in electrocatalysis,its effectiveness and power have not been fully harnessed to elucidate complex interfacial processes.Herein,we use the frequency dispersion parameter,n,which is extracted from EIS measurements(C_(s)=af^(n+1),-2<n<-1),to describe the dispersion characteristics of capacitance and interfacial properties of Co_(3)O_(4) before the onset of oxygen evolution reaction(OER)in alkaline conditions.We first prove that the n-value is sensitive to the interfacial electronic changes associated with Co redox processes and surface reconstruction.The n-value decreases by increasing the specific/active surface area of the catalysts.We further modify the interfacial properties by changing different components,i.e.,replacing the proton with deuterium,adding ethanol as a new oxidant,and changing the cation in the electrolyte.Intriguingly,the n-value can identify different influences on the interfacial process of proton transfer,the decrease and blocking of oxidized Co species,and the interfacial water structure.We demonstrate that the n-value extracted from EIS measurements is sensitive to the kinetic isotope effect,electrolyte cation,adsorbate surface coverage of oxidized Co species,and the interfacial water structure.Thus,it can be helpful to differentiate the multiple factors affecting the catalyst interface.These findings convey that the frequency dispersion of capacitance is a convenient and useful method to uncover the interfacial properties under electrocatalytic conditions,which helps to advance the understanding of the interfaceactivity relationship.
基金supported by the National Natural Science Foundation of China(NSFC,No.62303031)the Fundamental Research Funds for the Central Universities。
文摘When estimating the capacity of lithium-ion batteries offline or online,it is essential to extract a health feature(HF)that can effectively characterize capacity degradation under both conventional ideal and complex dynamic operating conditions.However,the extraction of most HFs relies on complete charge-discharge cycle data,making them less adaptable to complex dynamic operating conditions.Existing mechanism HFs,while capable of characterizing capacity degradation from a mechanism perspective,suffer from limitations such as insufficient physical model expressiveness,high dimension,and redundancy of the mechanism HF.These issues increase the complexity of subsequent modeling of the relationship between HFs and capacity,thereby restricting their promotion in engineering practice.To meet this gap,this paper proposes a novel mechanism-based HF.Firstly,a multi-physical fields coupling model is developed to describe the interactions between electrochemical,thermal,and aging behaviors of the battery.Secondly,based on the aging mechanism,the accumulated charge of lithium lost during the formation of the solid electrolyte interphase(SEI)film is extracted as HF to provide a more intuitive representation of capacity degradation.Then,to reduce estimation errors caused by considering only a single aging mechanism,multiple representative regression models are employed to establish the mapping relationship between the mechanism HF and capacity,further enhancing the accuracy of final results.Finally,the proposed method is implemented and validated using real battery data under three different types of operating conditions.Experimental results demonstrate that,compared to other commonly used HFs,the proposed HF exhibits significant competitive advantages in handling incomplete cycle data,unknown operating conditions,and capacity estimation models.The minimum estimation error under ideal conditions is 0.0074,and the minimum estimation error under complex dynamic conditions is 0.0268.
基金This work was supported by the National Natural Science Foundation of China(Grants Nos.12222214,12132020,12002400,and 12172386)by Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices(Grant No.2022B1212010008)+1 种基金by the National Natural Science Foundation of Guangdong Province(Grant No.2021B1515020021)by the Shenzhen Science and Techonlogy Program(Grant Nos.202206193000001 and 20220818181805001).
文摘Transient negative capacitance(NC),as an available dynamic charge effect achieved in resistor-ferroelectric capacitor(R-FEC)circuits,has triggered a series of theoretical and experimental works focusing on its physical mechanism and device application.Here,we analytically derived the effects of different mechanical conditions on the transient NC behaviors in the R-FEC circuit based on the phenomenological model.It shows that the ferroelectric capacitor can exhibit either NC(i.e.,“single NC”and“double NC”)or positive capacitance,depending on the mechanical condition and temperature.Further numerical calculations show that the voltage drop caused by NC can be effectively controlled by temperature,applied stress,or strain.The relationship between NC voltage drop and system configurations including external resistance,dynamical coefficient of polarization,and input voltage are presented,showing diverse strategies to manipulate the NC effect.These results provide theoretical guidelines for rational design and efficient control of NC-related electronic devices.
基金supported financially by National Natural Science Foundation of China(NSFC)(Nos.22478115,22075083)the Programme of Introducing Talents of Discipline to Universities(No.B16017).
文摘The development of high-performance,reproducible carbon(C)-based supercapacitors remains a significant challenge because of limited specific capacitance.Herein,we present a novel strategy for fabricating LaCoO_(x) and cobalt(Co)-doped nanoporous C(LaCoO_(x)/Co@ZNC)through the carbonization of Co/Zn-zeolitic imidazolate framework(ZIF)crystals derived from a PVP-Co/Zn/La precursor.The unique ZIF structure effectively disrupted the graphitic C framework,preserved the Co active sites,and enhanced the electrical conductivity.The synergistic interaction between pyridinic nitrogen and Co ions further promoted redox reactions.In addition,the formation of a hierarchical pore structure through zinc sublimation facili-tated electrolyte diffusion.The resulting LaCoO_(x)/Co@ZNC exhibited exceptional electrochemical performance,delivering a remarkable specific capacitance of 2,789 F/g at 1 A/g and outstanding cycling stability with 92%capacitance retention after 3,750 cycles.Our findings provide the basis for a promising approach to advancing C-based energy storage technologies.
基金financially supported by the National Natural Science Foundation of China(No.U2004210)Application Foundation Frontier Project of Wuhan Science and Technology Program(No.2020010601012199)City University of Hong Kong Strategic Research Grant,Hong Kong,China(No.7005505)。
文摘Vanadium nitride(VN)is a promising pseudocapacitive material due to the high theoretical capacity,rapid redox Faradaic kinetics,and appropriate potential window.Although VN shows large pseudocapacitance in alkaline electrolytes,the electrochemical instability and capacity degradation of VN electrode materials present significant challenges for practical applications.Herein,the capacitance decay mechanism of VN is investigated and a simple strategy to improve cycling stability of VN supercapacitor electrodes is proposed by introducing VO_(4)^(3-)anion in KOH electrolytes.Our results show that the VN electrode is electrochemical stabilization between-1.0and-0.4 V(vs.Hg/Hg O reference electrode)in 1.0 MKOH electrolyte,but demonstrates irreversible oxidation and fast capacitance decay in the potential range of-0.4 to0 V.In situ electrochemical measurements reveal that the capacitance decay of VN from-0.4 to 0 V is ascribed to the irreversible oxidation of vanadium(V)of N–V–O species by oxygen(O)of OH^(-).The as-generated oxidization species are subsequently dissolved into KOH electrolytes,thereby undermining the electrochemical stability of VN.However,this irreversible oxidation process could be hindered by introducing VO_(4)^(3-)in KOH electrolytes.A high volumetric specific capacitance of671.9 F.cm^(-3)(1 A.cm^(-3))and excellent cycling stability(120.3%over 1000 cycles)are achieved for VN nanorod electrode in KOH electrolytes containing VO_(4)^(3-).This study not only elucidates the failure mechanism of VN supercapacitor electrodes in alkaline electrolytes,but also provides new insights into enhancing pseudocapacitive energy storage of VN-based electrode materials.
基金supported by National Natural Science Foundation of China(No.62201596)Research Planning Project of National University of Defense Technology(ZK22-45).
文摘The beyond fifth-generation Internet of Things requires more capable channel coding schemes to achieve high-reliability,low-complexity and lowlatency communications.The theoretical analysis of error-correction performance of channel coding functions as a significant way of optimizing the transmission reliability and efficiency.In this paper,the efficient estimation methods of the block error rate(BLER)performance for rate-compatible polar codes(RCPC)are proposed under several scenarios.Firstly,the BLER performance of RCPC is generally evaluated in the additive white Gaussian noise channels.That is further extended into the Rayleigh fading channel case using an equivalent estimation method.Moreover,with respect to the powerful decoder such as successive cancellation list decoding,the performance estimation is derived analytically based on the polar weight spectrum and BLER upper bounds.Theoretical evaluation and numerical simulation results show that the estimated performance can fit well the practical simulated results of RCPC under the objective conditions,verifying the validity of our proposed performance estimation methods.Furthermore,the application designs of the reliability estimation of RCPC are explored,particularly in the advantages of the signal-to-noise(SNR)estimation and throughput efficiency optimization of polar coded hybrid automatic repeat request.
基金National Key Research and Development Program of China(2021YFA1501302)the National Natural Science Foundation of China(22121004,22122808)+1 种基金the Haihe Laboratory of Sustainable Chemical Transformations and the Program of Introducing Talents of Discipline to Universities(BP0618007)for financial supportsupported by the XPLORER PRIZE.
文摘Noise is inevitable in electrical capacitance tomography(ECT)measurements.This paper describes the influence of noise on ECT performance for measuring gas-solids fluidized bed characteristics.The noise distribution is approximated by the Gaussian distribution and added to experimental capacitance data with various intensities.The equivalent signal strength(Ф)that equals the signal-to-noise ratio of packed beds is used to evaluate noise levels.Results show that the Pearson correlation coefficient,which indicates the similarity of solids fraction distributions over pixels,increases with Ф,and reconstructed images are more deteriorated at lower Ф.Nevertheless,relative errors for average solids fraction and bubble size in each frame are less sensitive to noise,attributed to noise compromise caused by the process of pixel values.These findings provide useful guidance for assessing the accuracy of ECT measurements of multiphase flows.
基金YPF for financial support and to the Center for Petroleum Asset Risk Management of the University of Texas at Austin for hospitality and an exciting research environment
文摘The capacitance-resistance model （CRM） is an alternative to conventional reservoir simulation. CRM, a simplification of complex numerical models, uses production and injection rates to infer a reservoir description. There is no prior geologic model. The principal output of CRM fitting is the fraction of injected fluid （usually water） that is produced at a producer at steady-state. These fractions are interwell connectivities. Interwell connectivities are fundamental information needed to manage waterfloods in oil reservoirs. The data-driven CRM is a fast tool to estimate these parameters in mature fields and allows one to make full use of the dynamic data available. This paper considers the problem of setting an upper bound on the uncertainty of interwell connectivities for linear-constrained models. Using analytical bounds and numerical simulations, we derive a consistent upper limit on the uncertainty of interwell connections that can be used to quantify the information content of a given dataset.
基金the National Natural Science Foundation of China(Grant No.52374051)the Joint Fund for Enterprise Innovation and Development of NSFC(Grant No.U24B2037).
文摘During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.
文摘In an environment where demand for housing is growing and the supply from public authorities is virtually non-existent,several mechanisms for housing production are emerging in the formal,semi-informal and informal construction sectors.The project owner wonders how much it costs to construct a building to an acceptable standard.Cost forecasting in general faces a number of difficulties,including a lack of available information during the preliminary phase of the project.As such,estimation becomes a crucial task involving great responsibility,which can lead to either more convincing results or chaotic situations.This study proposes a quick and effective method for estimating the cost of a single-storey F4 residential building.The modelling is done using multiple linear regression based on a statistical approach applied to twenty(20)projects that have already been completed.The project data are collected from design offices in the city of Brazzaville.The method expresses the cost of an F4 construction by certain project tasks,representing five(5)variables,three(3)of which are related to structural work and two(2)to finishing work,which are easy to determine.This approach,known as MECSO(Cost Estimation Model by Sub-structure),gives good results in all statistical tests carried out with reasonable confidence intervals.This method is very practical for engineering professionals working on the evaluation and control of construction costs.
基金supported by Shenzhen Science and Technology Program under Grant JCYJ20250604175412017the National Natural Science Foundation of China under Grant 62473387+1 种基金the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under Grant SML2024SP007in part by the Department of Science and Technology of Guangdong Province under Grant. 2021QN020085。
文摘In permanent magnet synchronous machine(PMSM) drives, temperature information is critical to achieve reliable and high-performance control. The popular model-based estimation methods are based on extracting temperature dependent terms from the voltages using the machine model. The estimation accuracy under low speed or load can be greatly affected by the model uncertainty and noise due to low signal-tonoise ratio. This paper presents a high frequency(HF) position offset injection-based winding and permanent magnet(PM) temperature decoupled estimation approach for PMSMs to achieve accurate and robust temperature estimation among a wide speed range especially under low-speed conditions. In the proposed approach, a small HF position offset is injected into the machine to construct a decoupled winding and PM temperature estimation model, in which the winding and PM temperatures are independently estimated from HF excitations. The temperature estimation is independent from the fundamental model and parameter variation, and it achieves high signal-tonoise ratio under low-speed conditions. Moreover, the temperature estimation is also not affected by magnetic saturation and inverter distortion, which can improve the accuracy and robustness of temperature estimation. The proposed approach is validated with experiments and comparisons on a laboratory machine under various operating conditions.
