Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter us...Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter used in the conventional repetitive controller(CRC), the complex-coefficient filter causes less change in the phase and amplitude of a signal at the frequencies of the periodic signal, especially at the fundamental frequency, when the two filters have the same cutofffrequency.展开更多
Five-phase permanent-magnet synchronous motors(PMSMs)offer high fault tolerance,low torque ripple,and high torque density.An open-circuit fault results in an asymmetric motor model,which causes direct current(DC)bias ...Five-phase permanent-magnet synchronous motors(PMSMs)offer high fault tolerance,low torque ripple,and high torque density.An open-circuit fault results in an asymmetric motor model,which causes direct current(DC)bias and harmonics,thus affecting operational performance with sensorless control under fault-tolerant conditions.To improve operational performance,this study proposes a novel sliding mode observer(SMO)based on an enhanced adaptive complex-coefficient filter(EACCF)for the sensorless vector-control strategy of a five-phase PMSM with an open-circuit fault.The novelty of the proposed strategy is the development of the EACCF,which is characterized by an effective DC bias and harmonic-attenuation capability.Additionally,the fundamental back electromotive force can be estimated without phase lag or amplitude attenuation using the SMO.By incorporating a phase-locked loop,the accuracy of position estimation can be improved under both healthy and open-circuit fault conditions.The sensorless control can not only restrain the fluctuating torque caused by open-circuit faults but also offers good steady-state and dynamic performances under healthy and open-circuit fault conditions.Experimental results are presented to demonstrate the feasibility of the proposed method.展开更多
Current quality is one of the most important issues for operating three-phase grid-connected inverter in distributed generation systems. In practice, the grid current quality is degraded in case of non-ideal utility v...Current quality is one of the most important issues for operating three-phase grid-connected inverter in distributed generation systems. In practice, the grid current quality is degraded in case of non-ideal utility voltage. A new control strategy is proposed for the three-phase gridconnected inverter. Different from the traditional method,our proposal utilizes the unique abc-frame complex-coefficient filter and controller to achieve the balanced, sinusoidal grid current. The main feature of the proposed method is simple and easy to implement without any frame transformation. The theoretical analysis and experimental test are presented. The experimental results verify the effectiveness of the proposed control strategy.展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a c...Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.展开更多
1 Introduction Recently,the increasing demand for advanced telecommunication systems has spurred extensive research into bandpass filters(BPFs),with particular emphasis on miniaturization,reduction of insertion loss(I...1 Introduction Recently,the increasing demand for advanced telecommunication systems has spurred extensive research into bandpass filters(BPFs),with particular emphasis on miniaturization,reduction of insertion loss(IL),and enhancement of upper stopband rejection(Huang et al.,2021;Snyder et al.,2021;Lin et al.,2023;Zeng et al.,2023).展开更多
In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the...In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.展开更多
Color filters are essential components for optical modulation.However,conventional filters are restricted to operating exclusively in either reflective or transmissive mode.Furthermore,they suffer from limited UV and ...Color filters are essential components for optical modulation.However,conventional filters are restricted to operating exclusively in either reflective or transmissive mode.Furthermore,they suffer from limited UV and thermal stability,low color purity,and exhibit identical coloration on both surfaces.Herein,we propose a novel design strategy for trans-reflective color filters by integrating the absorptive properties of dye-doped polysulfone(PSU)with the diffractive capabilities of photonic crystals.This composite filter achieved broad-spectrum transmission with deep color outputs—yellow(0.410,0.510),magenta(0.446,0.231),and cyan(0.201,0.425)—closely aligned with standard color space coordinates.By tuning the refractive index of CeO_(2)@SiO_(2)nanoparticles to match dye-based PSU matrix,the transmittance of filters exceeded 70%.Moreover,dye-mediated absorption reduces the scattering light,thereby enhancing reflection color purity(full width at half maxima(FWHM)=25 nm)and producing vibrant blue,green,and red hues.The incorporation of UV-absorbing CeO_(2)@SiO_(2)nanoparticles effectively mitigated dye photodegradation,yielding exceptional UV stability(ΔT<2%under prolonged UV exposure).The filters also exhibited outstanding thermal stability(ΔT<1%after 30 min heat treatment at 230°C).This work establishes a robust materials design framework for multifunctional optical filters,advancing the development of highfidelity dual-mode color systems for next-generation display technologies.展开更多
Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used i...Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and int...The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.展开更多
This paper demonstrated the generation of multi-wavelength bound state noise-like pulse(BNLP)in a dispersion-managed composite-filtered fiber laser consisting of nonlinear polarization rotation(NPR)and loop.In the cas...This paper demonstrated the generation of multi-wavelength bound state noise-like pulse(BNLP)in a dispersion-managed composite-filtered fiber laser consisting of nonlinear polarization rotation(NPR)and loop.In the case of BNLP,the generation is caused by the interaction between two noise-like pulses(NLPs)induced by the comb-filtering effect,and bound state level can be artificially controlled in the researches.Our work provides a new method for generating low-coherence pulses and establishes a research idea for the study of the comb-filtering effects.展开更多
In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenario...In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.展开更多
The integration of surface filtration and catalytic decomposition functions in catalytic bags enables the synergistic removal of multiple pollutants(such as dust,nitrogen oxide,acid gases,and dioxins)in a single react...The integration of surface filtration and catalytic decomposition functions in catalytic bags enables the synergistic removal of multiple pollutants(such as dust,nitrogen oxide,acid gases,and dioxins)in a single reactor,thus effectively reducing the cost and operational difficulties associated with flue gas treatment.In this study,Mn-Ce-Sm-Sn(MCSS)catalysts were prepared and loaded onto hightemperature resistant polyimide(P84)filter through ultrasonic impregnation to create composite catalytic filter.The results demonstrate that the NO conversion rates of the composite catalytic filter consistently achieve above 95%within the temperature range of 160-260℃,with a chlorobenzene T_(90)value of 230℃.The ultrasonic impregnation method effectively loaded the catalyst onto the filter,ensuring high dispersion both on the surface and inside the filter.This increased exposure of catalyst active sites enhances the catalytic activity of the composite catalytic filter.Additionally,increasing the catalyst loading leads to a gradual decrease in permeability,an increase in pressure drops and the long residence time of the flue gas,thereby improving catalytic activity.Compared to ordinary impregnation methods,ultrasonic impregnation improves the bonding strength between the catalyst and filter,as well as the permeability of the composite catalytic filter under the same loading conditions.Overall,this study presents a novel approach to prepare composite catalytic filter for the simultaneous removal of NO and chlorobenzene at low temperatures.展开更多
A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,whic...A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.展开更多
Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection metho...Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.展开更多
The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses ...The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses both matched and mismatched disturbances is formulated.Due to the fact that only position information can be measured,a linear Extended State Observer(ESO)is introduced to estimate unknown states and matched disturbances,while a dedicated disturbance observer is constructed to estimate mismatched disturbances.Different from the traditional observer results,the design of the disturbance observer used in this study is carried out under the constraint of output feedback.Furthermore,an output feedback nonlinear controller is proposed leveraging the aforementioned observers to achieve accurate trajectory tracking.To mitigate the inherent differential explosion problem of the traditional backstepping framework,a finite-time stable command filter is incorporated.Simultaneously,considering transient filtering errors,a set of error compensation signals are designed to counter their negative impact effectively.Theoretical analysis affirms that the proposed control strategy ensures the boundedness of all signals within the closed-loop system.Additionally,under the specific condition of only time-invariant disturbances in the system,the conclusion of asymptotic stability is established.Finally,the algorithm’s efficacy is validated through comparative experiments.展开更多
Brackish water(BW)irrigation may cause soil quality deterioration and thereby a decrease in crop yields.Here we examined the impacts of applying gasification filter cake(GFC),intercropping with Portulaca oleracea(PO),...Brackish water(BW)irrigation may cause soil quality deterioration and thereby a decrease in crop yields.Here we examined the impacts of applying gasification filter cake(GFC),intercropping with Portulaca oleracea(PO),and their combination on soil quality,nutrient uptake by plants and tomato yields under BW irrigation.The treatments evaluated included(i)freshwater irrigation(Control),(ii)BW irrigation,(iii)GFC application under BW irrigation(BW+GFC),(iv)intercropping with PO under BW irrigation(BW+PO),and(v)the combined application of GFC and PO under BW irrigation(BW+PO+GFC).Overall,the use of BW for irrigation resulted in a decline in both soil quality(assessed by a soil quality index(SQI)integrating a wide range of key soil properties including salinity,nutrient availability and microbial activities)and crop yields.Nevertheless,when subjected to BW irrigation,the application of GFC successfully prevented soil salinity.Additionally,the intercropping of PO decreased the soil sodium adsorption ratio and improved the absorption of nutrients by plants.As a result,the BW+GFC+PO treatment generally showed higher tomato yield as compared to other BW-related treatments(i.e.BW,BW+GFC and BW+PO).Compared to BW,the BW+GFC+PO treatment had an average increase of 24.7% in the total fruit yield of four Cropping Seasons.Furthermore,the BW+GFC+PO treatment consistently exhibited the highest fruit quality index(FQI).Taken together,the combined application of GFC and PO is effective in promoting soil quality and crop yields under BW irrigation.展开更多
Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively r...Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.展开更多
In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utili...In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.展开更多
基金supported in part by the National Natural Science Foundation of China(61873348,6230 3266,62273200)JSPS(Japan Society for the Promotion of Science) KAKENHI(22H03998,23K25252)
文摘Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter used in the conventional repetitive controller(CRC), the complex-coefficient filter causes less change in the phase and amplitude of a signal at the frequencies of the periodic signal, especially at the fundamental frequency, when the two filters have the same cutofffrequency.
基金Supported by the National Natural Science Foundation of China(52277052).
文摘Five-phase permanent-magnet synchronous motors(PMSMs)offer high fault tolerance,low torque ripple,and high torque density.An open-circuit fault results in an asymmetric motor model,which causes direct current(DC)bias and harmonics,thus affecting operational performance with sensorless control under fault-tolerant conditions.To improve operational performance,this study proposes a novel sliding mode observer(SMO)based on an enhanced adaptive complex-coefficient filter(EACCF)for the sensorless vector-control strategy of a five-phase PMSM with an open-circuit fault.The novelty of the proposed strategy is the development of the EACCF,which is characterized by an effective DC bias and harmonic-attenuation capability.Additionally,the fundamental back electromotive force can be estimated without phase lag or amplitude attenuation using the SMO.By incorporating a phase-locked loop,the accuracy of position estimation can be improved under both healthy and open-circuit fault conditions.The sensorless control can not only restrain the fluctuating torque caused by open-circuit faults but also offers good steady-state and dynamic performances under healthy and open-circuit fault conditions.Experimental results are presented to demonstrate the feasibility of the proposed method.
基金supported by the National Natural Science Foundation of China(No.51307149)
文摘Current quality is one of the most important issues for operating three-phase grid-connected inverter in distributed generation systems. In practice, the grid current quality is degraded in case of non-ideal utility voltage. A new control strategy is proposed for the three-phase gridconnected inverter. Different from the traditional method,our proposal utilizes the unique abc-frame complex-coefficient filter and controller to achieve the balanced, sinusoidal grid current. The main feature of the proposed method is simple and easy to implement without any frame transformation. The theoretical analysis and experimental test are presented. The experimental results verify the effectiveness of the proposed control strategy.
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
基金supported by the Royal Academy of Engineering,UK,in the scheme of Distinguished International Associate(DIA-2424-5-134).
文摘Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.
基金supported by the National Natural Science Foundation of China(No.62371263)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCK25_1995).
文摘1 Introduction Recently,the increasing demand for advanced telecommunication systems has spurred extensive research into bandpass filters(BPFs),with particular emphasis on miniaturization,reduction of insertion loss(IL),and enhancement of upper stopband rejection(Huang et al.,2021;Snyder et al.,2021;Lin et al.,2023;Zeng et al.,2023).
基金Supported by the Guangxi Special Program for Technological Innovation Guidance(No.GuiKeAC25069006).
文摘In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability.
基金supported by the Program of the National Natural Science Foundation of China(22238002)the Fundamental Research Funds for the Central Universities(DUT22-LAB610)Research and Innovation Team Project of Dalian University of Technology(DUT2022TB10).
文摘Color filters are essential components for optical modulation.However,conventional filters are restricted to operating exclusively in either reflective or transmissive mode.Furthermore,they suffer from limited UV and thermal stability,low color purity,and exhibit identical coloration on both surfaces.Herein,we propose a novel design strategy for trans-reflective color filters by integrating the absorptive properties of dye-doped polysulfone(PSU)with the diffractive capabilities of photonic crystals.This composite filter achieved broad-spectrum transmission with deep color outputs—yellow(0.410,0.510),magenta(0.446,0.231),and cyan(0.201,0.425)—closely aligned with standard color space coordinates.By tuning the refractive index of CeO_(2)@SiO_(2)nanoparticles to match dye-based PSU matrix,the transmittance of filters exceeded 70%.Moreover,dye-mediated absorption reduces the scattering light,thereby enhancing reflection color purity(full width at half maxima(FWHM)=25 nm)and producing vibrant blue,green,and red hues.The incorporation of UV-absorbing CeO_(2)@SiO_(2)nanoparticles effectively mitigated dye photodegradation,yielding exceptional UV stability(ΔT<2%under prolonged UV exposure).The filters also exhibited outstanding thermal stability(ΔT<1%after 30 min heat treatment at 230°C).This work establishes a robust materials design framework for multifunctional optical filters,advancing the development of highfidelity dual-mode color systems for next-generation display technologies.
基金funded by the Directorate General of Research and Development,Ministry of Higher Education,Science and Technology of the Republic of Indonesia,with grant number 2.6.63/UN32.14.1/LT/2025.
文摘Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
基金supported by the National Natural Science Foundation of China under Grant No.62172132.
文摘The surge of large-scale models in recent years has led to breakthroughs in numerous fields,but it has also introduced higher computational costs and more complex network architectures.These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization.To address this issue,various network compression techniques have been developed,such as network pruning.A typical pruning algorithm follows a three-step pipeline involving training,pruning,and retraining.Existing methods often directly set the pruned filters to zero during retraining,significantly reducing the parameter space.However,this direct pruning strategy frequently results in irreversible information loss.In the early stages of training,a network still contains much uncertainty,and evaluating filter importance may not be sufficiently rigorous.To manage the pruning process effectively,this paper proposes a flexible neural network pruning algorithm based on the logistic growth differential equation,considering the characteristics of network training.Unlike other pruning algorithms that directly reduce filter weights,this algorithm introduces a three-stage adaptive weight decay strategy inspired by the logistic growth differential equation.It employs a gentle decay rate in the initial training stage,a rapid decay rate during the intermediate stage,and a slower decay rate in the network convergence stage.Additionally,the decay rate is adjusted adaptively based on the filter weights at each stage.By controlling the adaptive decay rate at each stage,the pruning of neural network filters can be effectively managed.In experiments conducted on the CIFAR-10 and ILSVRC-2012 datasets,the pruning of neural networks significantly reduces the floating-point operations while maintaining the same pruning rate.Specifically,when implementing a 30%pruning rate on the ResNet-110 network,the pruned neural network not only decreases floating-point operations by 40.8%but also enhances the classification accuracy by 0.49%compared to the original network.
基金supported by the Research Fund of Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology(No.2020B1212030010)。
文摘This paper demonstrated the generation of multi-wavelength bound state noise-like pulse(BNLP)in a dispersion-managed composite-filtered fiber laser consisting of nonlinear polarization rotation(NPR)and loop.In the case of BNLP,the generation is caused by the interaction between two noise-like pulses(NLPs)induced by the comb-filtering effect,and bound state level can be artificially controlled in the researches.Our work provides a new method for generating low-coherence pulses and establishes a research idea for the study of the comb-filtering effects.
基金supported by the National Science and Technology Council,Taiwan under grants NSTC 111-2221-E-019-047 and NSTC 112-2221-E-019-030.
文摘In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.
基金Project supported by the National Key Research and Development Program of China(2021YFB3500600,2021YFB3500605)Natural Science Foundation of Jiangsu Province(BK20220365)+5 种基金Key R&D Program of Jiangsu Province(BE2022142)Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB610002)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_1419)Science and Technology Plan of Yangzhou(YZ2022030,YZ2023020)the State Key Laboratory of Clean and Efficient Coal-fired Power Generation and Pollution Control(D2022FK098)。
文摘The integration of surface filtration and catalytic decomposition functions in catalytic bags enables the synergistic removal of multiple pollutants(such as dust,nitrogen oxide,acid gases,and dioxins)in a single reactor,thus effectively reducing the cost and operational difficulties associated with flue gas treatment.In this study,Mn-Ce-Sm-Sn(MCSS)catalysts were prepared and loaded onto hightemperature resistant polyimide(P84)filter through ultrasonic impregnation to create composite catalytic filter.The results demonstrate that the NO conversion rates of the composite catalytic filter consistently achieve above 95%within the temperature range of 160-260℃,with a chlorobenzene T_(90)value of 230℃.The ultrasonic impregnation method effectively loaded the catalyst onto the filter,ensuring high dispersion both on the surface and inside the filter.This increased exposure of catalyst active sites enhances the catalytic activity of the composite catalytic filter.Additionally,increasing the catalyst loading leads to a gradual decrease in permeability,an increase in pressure drops and the long residence time of the flue gas,thereby improving catalytic activity.Compared to ordinary impregnation methods,ultrasonic impregnation improves the bonding strength between the catalyst and filter,as well as the permeability of the composite catalytic filter under the same loading conditions.Overall,this study presents a novel approach to prepare composite catalytic filter for the simultaneous removal of NO and chlorobenzene at low temperatures.
文摘A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.
基金Supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(22KJB520012)the Research Project on Higher Education Reform in Jiangsu Province(2023JSJG781)the College Student Innovation and Entrepreneurship Training Program Project(202313571008Z)。
文摘Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.
基金supported by the National Key R&D Program of China(No.2021YFB2011300)the Special Funds Project for the Transformation of Scientific and Technological Achievements of Jiangsu Province,China(No.BA2023039)+1 种基金the National Natural Science Foundation of China(No.52075262)the Fundamental Research Funds for the Central Universities,China(No.30922010706).
文摘The output feedback active disturbance rejection control of a valve-controlled cylinder electro-hydraulic servo system is investigated in this paper.First,a comprehensive nonlinear mathematical model that encompasses both matched and mismatched disturbances is formulated.Due to the fact that only position information can be measured,a linear Extended State Observer(ESO)is introduced to estimate unknown states and matched disturbances,while a dedicated disturbance observer is constructed to estimate mismatched disturbances.Different from the traditional observer results,the design of the disturbance observer used in this study is carried out under the constraint of output feedback.Furthermore,an output feedback nonlinear controller is proposed leveraging the aforementioned observers to achieve accurate trajectory tracking.To mitigate the inherent differential explosion problem of the traditional backstepping framework,a finite-time stable command filter is incorporated.Simultaneously,considering transient filtering errors,a set of error compensation signals are designed to counter their negative impact effectively.Theoretical analysis affirms that the proposed control strategy ensures the boundedness of all signals within the closed-loop system.Additionally,under the specific condition of only time-invariant disturbances in the system,the conclusion of asymptotic stability is established.Finally,the algorithm’s efficacy is validated through comparative experiments.
基金supported by the Key Research and Development Program of Ningxia(Grant No.2023BCF01046)。
文摘Brackish water(BW)irrigation may cause soil quality deterioration and thereby a decrease in crop yields.Here we examined the impacts of applying gasification filter cake(GFC),intercropping with Portulaca oleracea(PO),and their combination on soil quality,nutrient uptake by plants and tomato yields under BW irrigation.The treatments evaluated included(i)freshwater irrigation(Control),(ii)BW irrigation,(iii)GFC application under BW irrigation(BW+GFC),(iv)intercropping with PO under BW irrigation(BW+PO),and(v)the combined application of GFC and PO under BW irrigation(BW+PO+GFC).Overall,the use of BW for irrigation resulted in a decline in both soil quality(assessed by a soil quality index(SQI)integrating a wide range of key soil properties including salinity,nutrient availability and microbial activities)and crop yields.Nevertheless,when subjected to BW irrigation,the application of GFC successfully prevented soil salinity.Additionally,the intercropping of PO decreased the soil sodium adsorption ratio and improved the absorption of nutrients by plants.As a result,the BW+GFC+PO treatment generally showed higher tomato yield as compared to other BW-related treatments(i.e.BW,BW+GFC and BW+PO).Compared to BW,the BW+GFC+PO treatment had an average increase of 24.7% in the total fruit yield of four Cropping Seasons.Furthermore,the BW+GFC+PO treatment consistently exhibited the highest fruit quality index(FQI).Taken together,the combined application of GFC and PO is effective in promoting soil quality and crop yields under BW irrigation.
文摘Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.
基金supported in part by the National Natural Science Foundation of China(12171124,61933007)the Natural Science Foundation of Heilongjiang Province of China(ZD2022F003)+2 种基金the National High-End Foreign Experts Recruitment Plan of China(G2023012004L)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany
文摘In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
