Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,ter...Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.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
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
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
In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields loc...In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.展开更多
A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in t...A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout.Such structural properties are modeled as an aggregation of parcels,buildings,and road networks.We introduce several computational measures(Ratio Minimum Distance,Minimum Ratio Minimum Distance,and Metric Compactness)that respectively evaluate the capability for a given road to be connected with the whole road network.These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network.Based on these local and global properties derived from the topological and graph-based representation,and on building density metrics,this paper proposes an analysis of road and building layouts at different levels of granularity.The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment.Overall,and by integrating the parcels and buildings layouts,this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout.展开更多
Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale...Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.展开更多
The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based fea...The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.展开更多
Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involve...Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic approaches.Dragonfly Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal solutions.Moreover,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network size.Thus,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO performance.In(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking model.To measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network lifetime.The simulation was performed in MATLAB environment.展开更多
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes...Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.展开更多
Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrast...Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.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
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
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
基金National Natural Science Foundation of China(No.62372100)。
文摘Owing to the constraints of depth sensing technology,images acquired by depth cameras are inevitably mixed with various noises.For depth maps presented in gray values,this research proposes a novel denoising model,termed graph-based transform(GBT)and dual graph Laplacian regularization(DGLR)(DGLR-GBT).This model specifically aims to remove Gaussian white noise by capitalizing on the nonlocal self-similarity(NSS)and the piecewise smoothness properties intrinsic to depth maps.Within the group sparse coding(GSC)framework,a combination of GBT and DGLR is implemented.Firstly,within each group,the graph is constructed by using estimates of the true values of the averaged blocks instead of the observations.Secondly,the graph Laplacian regular terms are constructed based on rows and columns of similar block groups,respectively.Lastly,the solution is obtained effectively by combining the alternating direction multiplication method(ADMM)with the weighted thresholding method within the domain of GBT.
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 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.
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 Science and Technology Major Project of China(No.2011ZX05029-003)CNPC Science Research and Technology Development Project,China(No.2013D-0904)
文摘In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.
文摘A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science.The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout.Such structural properties are modeled as an aggregation of parcels,buildings,and road networks.We introduce several computational measures(Ratio Minimum Distance,Minimum Ratio Minimum Distance,and Metric Compactness)that respectively evaluate the capability for a given road to be connected with the whole road network.These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network.Based on these local and global properties derived from the topological and graph-based representation,and on building density metrics,this paper proposes an analysis of road and building layouts at different levels of granularity.The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment.Overall,and by integrating the parcels and buildings layouts,this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout.
基金the Project Fund for Key Discipline of the Shanghai Municipal Education Commission(No.J50104)the Major State Basic Research Development Program of China(No.2017YFB0403500)。
文摘Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.
文摘The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.
文摘Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense circumstances.The involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic approaches.Dragonfly Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal solutions.Moreover,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network size.Thus,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO performance.In(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking model.To measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network lifetime.The simulation was performed in MATLAB environment.
基金supported by the DOD National Defense Science and Engineering Graduate(NDSEG)Research Fellowshipsupported by the NGA under Contract No.HM04762110003.
文摘Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.
文摘Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.
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 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.
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.
