This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the meas...This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.展开更多
This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechn...This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system.The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.展开更多
BACKGROUND: The PAWPER tape is one of the most accurate weight estimation systems available today, but international access to the tape is limited because it has no commercial distribution. For this reason, the "...BACKGROUND: The PAWPER tape is one of the most accurate weight estimation systems available today, but international access to the tape is limited because it has no commercial distribution. For this reason, the "PAWPER-on-a-page" concept was devised: downloadable image fi les that allow users to print and assemble their own tapes. However, the feasibility of this method is dependent on users being able to produce accurate tapes. This study was devised to determine whether untrained participants could print and assemble the "PAWPER-on-a-page" easily and accurately.METHODS: Doctor and nurse volunteers downloaded the "PAWPER-on-a-page" and "PAWPER XL-on-a-page" image files and printed copies on a home-printer and also at a commercial printer. One copy of each tape was then assembled according to instructions from an online video. The accuracy of printing and assembly, the time taken for assembly and the cost were then evaluated.RESULTS: There were 32 participants. The median time for assembly was 8 minutes 19 seconds and 7 minutes 31 seconds for the "PAWPER-on-a-page" and "PAWPER XL-on-a-page" respectively, with a median cost of USD 0.09 and USD 1.00 respectively. For the assembled tapes, 71.9% of the "PAWPER-on-a-page" tapes and 65.6% of the "PAWPER XL-on-a-page" achieved the required accuracy of 0.2%. Printing errors, related to scaling, were common, but easily detectable.CONCLUSION: The "PAWPER-on-a-page" system can be easily, quickly, affordably and accurately printed and assembled by end users. Stringent double checking of the printed and fully assembled tapes is essential to ensure accuracy.展开更多
A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and...A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent algorithm.Performance of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis turntable.The on-ground,turntable and flight experiments indicate that the estimation attitude has a good accuracy.Also,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.展开更多
Depth estimation is an active research area with the developing of stereo vision in recent years. It is one of the key technologies to resolve the large data of stereo vision communication. Now depth estimation still ...Depth estimation is an active research area with the developing of stereo vision in recent years. It is one of the key technologies to resolve the large data of stereo vision communication. Now depth estimation still has some problems, such as occlusion, fuzzy edge, real-time processing, etc. Many algorithms have been proposed base on software, however the performance of the computer configurations limits the software processing speed. The other resolution is hardware design and the great developments of the digital signal processor （DSP）, and application specific integrated circuit （ASIC） and field programmable gate array （FPGA） provide the opportunity of flexible applications. In this work, by analyzing the procedures of depth estimation, the proper algorithms which can be used in hardware design to execute real-time depth estimation are proposed. The different methods of calibration, matching and post-processing are analyzed based on the hardware design requirements. At last some tests for the algorithm have been analyzed. The results show that the algorithms proposed for hardware design can provide credited depth map for further view synthesis and are suitable for hardware design.展开更多
The precise estimation of the frequency of the signal is of great significance in the Radar system, the electronic warfare system and many other systems. In this paper, we propose a development and verification platfo...The precise estimation of the frequency of the signal is of great significance in the Radar system, the electronic warfare system and many other systems. In this paper, we propose a development and verification platform for the frequency estimation system in the Matlab and Simulink environment. Its open-extensibility architecture enables the performance evaluation of different frequency estimation algorithms and its graphic interface can greatly promote the system design, simulation and verification efficiency.展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.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
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper prop...To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.展开更多
Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the...Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).展开更多
Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we f...Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we focus on the previously neglected aspect of the impact of the squeezing phase on the precision of quantum phase and amplitude estimation based on a simple model of a two-level system(TLS) interacting with a squeezed reservoir. We derive the optimal squeezed phase-matching conditions for phase φ and amplitude θ parameters, which are crucial for enhancing the precision of quantum parameter estimation. The robustness of the squeezing-enhanced quantum Fisher information against departures from these conditions is examined, demonstrating that minor deviations from phase-matching can still result in remarkable precision of estimation. Additionally, we provide a geometric interpretation of the squeezed phase-matching conditions from the classical motion of a TLS on the Bloch sphere. Our research contributes to a deeper understanding of the operational requirements for employing squeezed reservoir engineering to advance quantum parameter estimation.展开更多
Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significa...Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.展开更多
The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with ...The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.展开更多
The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of applic...The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.展开更多
The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,th...The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.展开更多
Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is pro...Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.展开更多
It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only b...It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.展开更多
Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate trackin...Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.展开更多
Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,...Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.展开更多
基金supported by the National Natural Science Foundation of China(61925303,62173034,62088101,U20B2073,62173002)the National Key Research and Development Program of China(2021YFB1714800)Beijing Natural Science Foundation(4222045)。
文摘This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.
文摘This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system.The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.
文摘BACKGROUND: The PAWPER tape is one of the most accurate weight estimation systems available today, but international access to the tape is limited because it has no commercial distribution. For this reason, the "PAWPER-on-a-page" concept was devised: downloadable image fi les that allow users to print and assemble their own tapes. However, the feasibility of this method is dependent on users being able to produce accurate tapes. This study was devised to determine whether untrained participants could print and assemble the "PAWPER-on-a-page" easily and accurately.METHODS: Doctor and nurse volunteers downloaded the "PAWPER-on-a-page" and "PAWPER XL-on-a-page" image files and printed copies on a home-printer and also at a commercial printer. One copy of each tape was then assembled according to instructions from an online video. The accuracy of printing and assembly, the time taken for assembly and the cost were then evaluated.RESULTS: There were 32 participants. The median time for assembly was 8 minutes 19 seconds and 7 minutes 31 seconds for the "PAWPER-on-a-page" and "PAWPER XL-on-a-page" respectively, with a median cost of USD 0.09 and USD 1.00 respectively. For the assembled tapes, 71.9% of the "PAWPER-on-a-page" tapes and 65.6% of the "PAWPER XL-on-a-page" achieved the required accuracy of 0.2%. Printing errors, related to scaling, were common, but easily detectable.CONCLUSION: The "PAWPER-on-a-page" system can be easily, quickly, affordably and accurately printed and assembled by end users. Stringent double checking of the printed and fully assembled tapes is essential to ensure accuracy.
基金supported by the Fundamental Research Funds for the Central Universities(No.56XAA17075)
文摘A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent algorithm.Performance of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis turntable.The on-ground,turntable and flight experiments indicate that the estimation attitude has a good accuracy.Also,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.60832003)the Key Laboratory of Advanced Display and System Applications(Shanghai University),Ministry of Education,China(Grant No.P200801)the Science and Technology Commission of Shanghai Municipality(Grant No.10510500500)
文摘Depth estimation is an active research area with the developing of stereo vision in recent years. It is one of the key technologies to resolve the large data of stereo vision communication. Now depth estimation still has some problems, such as occlusion, fuzzy edge, real-time processing, etc. Many algorithms have been proposed base on software, however the performance of the computer configurations limits the software processing speed. The other resolution is hardware design and the great developments of the digital signal processor （DSP）, and application specific integrated circuit （ASIC） and field programmable gate array （FPGA） provide the opportunity of flexible applications. In this work, by analyzing the procedures of depth estimation, the proper algorithms which can be used in hardware design to execute real-time depth estimation are proposed. The different methods of calibration, matching and post-processing are analyzed based on the hardware design requirements. At last some tests for the algorithm have been analyzed. The results show that the algorithms proposed for hardware design can provide credited depth map for further view synthesis and are suitable for hardware design.
文摘The precise estimation of the frequency of the signal is of great significance in the Radar system, the electronic warfare system and many other systems. In this paper, we propose a development and verification platform for the frequency estimation system in the Matlab and Simulink environment. Its open-extensibility architecture enables the performance evaluation of different frequency estimation algorithms and its graphic interface can greatly promote the system design, simulation and verification efficiency.
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
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.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
基金supported in part by the National Natural Science Foundation of China under Grants U2001213 and 62261024in part by National Key Research and Development Project under Grant 2020YFB1807204in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education under Grant KFKT2022101.
文摘To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave(mmWave)massive Multiple-Input Multiple-Output(MIMO)systems in Internet of Vehicles(IoV)scenarios,the paper proposes a deep learning(DL)algorithm,Squeeze-and-Excitation Attention Residual Network(SEARNet),which integrates Squeeze-and-Excitation Attention(SEAttention)mechanism and residual module.Specifically,SEARNet considers the channel information as an image matrix,and embeds a SEAttention module in residual module to construct the SEAttention-Residual block.Through a data-driven approach,SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism,thereby reducing noise interference and estimating the channel in an accurate and efficient manner.The simulation results show that compared to two traditional and two DL channel estimation algorithms,the proposed SEARNet can achieve a maximum reduction in normalized mean square error(NMSE)of 97.66%and 84.49%at SNR of-10 dB,78.18%at SNR of 5 dB,and 43.51%at SNR of 10 dB,respectively.
基金Supported by the National Natural Science Foundation of China(No.62171135)the Natural Science Foundation of Fujian Province(No.2023J01399)。
文摘Channel state information(CSI)is very important to sparse code multiple access combined with orthogonal frequency division multiplexing(SCMA-OFDM)systems for data detection.The main goal of this paper is to tackle the computational complexity and pilot overhead issues when estima-ting and tracking the channel frequency response of each user in uplink SCMA-OFDM systems.To this end,a new binary pilot structure is first designed to realize the initial channel estimation with significantly reduced computational complexity.Then,a channel tracking method is proposed to update the channel estimation in time-varying channels,which exploits a modified least mean square(LMS)technique with the feedback from the detector.Simulation results show that the pro-posed pilot structure can provide accurate channel estimation results.Moreover,the average bit error rate(BER)performance of the modified LMS algorithm can approach that of a detector with perfect CSI within 2 dB at the normalized Doppler frequency up to 6×10^(-6).
基金Project supported by the National Natural Science Foundation of China (Grant No. 12265004)Jiangxi Provincial Natural Science Foundation (Grant No. 20242BAB26010)+1 种基金the National Natural Science Foundation of China (Grant No. 12365003)Jiangxi Provincial Natural Science Foundation (Grant Nos. 20212ACB211004 and 20212BAB201014)。
文摘Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we focus on the previously neglected aspect of the impact of the squeezing phase on the precision of quantum phase and amplitude estimation based on a simple model of a two-level system(TLS) interacting with a squeezed reservoir. We derive the optimal squeezed phase-matching conditions for phase φ and amplitude θ parameters, which are crucial for enhancing the precision of quantum parameter estimation. The robustness of the squeezing-enhanced quantum Fisher information against departures from these conditions is examined, demonstrating that minor deviations from phase-matching can still result in remarkable precision of estimation. Additionally, we provide a geometric interpretation of the squeezed phase-matching conditions from the classical motion of a TLS on the Bloch sphere. Our research contributes to a deeper understanding of the operational requirements for employing squeezed reservoir engineering to advance quantum parameter estimation.
基金supported in part by the National Natural Science Foundation of China under Grant 62001171in part by the Natural Science Foundation of Guangdong Province under Grant 2024A1515011172in part by the Henan Science and Technology Research and Development Program Joint Fund under Grant 235200810049。
文摘Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless network.However,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability standards.To address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is proposed.This approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift matrix.Unlike previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC scheme.The effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272123 and 12302047)the Natural Science Foundation of Jiangsu Province(Grant No.BK20231185)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0192).
文摘The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202494,52202495)Chongqing Special Project for Technological Innovation and Application Development(Grant No.CSTB2022TIAD-DEX0014).
文摘The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants 61941104,61921004the Key Research and Development Program of Shandong Province under Grant 2020CXGC010108+1 种基金the Southeast University-China Mobile Research Institute Joint Innovation Centersupported in part by the Scientific Research Foundation of Graduate School of Southeast University under Grant YBPY2118.
文摘The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.
文摘Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.
文摘It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.
基金the National Natural Science Foundation of China(No.52275062)and(No.52075262).
文摘Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.
基金supported by the Natural Science Foundation of Shanghai(No.23ZR1429300)Innovation Funds of CNNC(Lingchuang Fund,Contract No.CNNC-LCKY-202234)the Project of the Nuclear Power Technology Innovation Center of Science Technology and Industry(No.HDLCXZX-2023-HD-039-02)。
文摘Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.
