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
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
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 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly fa...The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.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
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
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
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
A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily s...A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.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
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
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
Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,Sou...Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.展开更多
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
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widel...Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.展开更多
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.
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 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 Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2023010201020443)the School-Level Scientific Research Project Funding Program of Jianghan University(Grant No.2022XKZX33)the Natural Science Foundation of Hubei Province(Grant No.2024AFB466).
文摘The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.
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.
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.
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 National Natural Science Foundation of China(Grant Nos.12104190,12104189,12204312)the Natural Science Foundation of Jiangsu Province(Grant No.BK20210874)+2 种基金General project of Natural Science Research in Colleges And Universities of Jiangsu Province(Grant No.20KJB140008)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20224BAB211014 and 20232BAB201042)Key Laboratory of Tian Qin Project(Sun Yat-sen University)。
文摘A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.
基金Supported by the State Key Laboratory of Acoustics and Marine Information Chinese Academy of Sciences(SKL A202507).
文摘Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
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funded by the Natural Sciences and Engineering Research Council of Canada(RGPIN:2016-05964&2023-04283 to JHK)the University of Manitoba Tri-Agency Bridge Funding(#57289 to JHK)the Ricard Foundation’s Baxter Bursary(to JP)。
文摘Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.
基金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Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
文摘Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.
