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
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
A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes a...A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.展开更多
In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and no...In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.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
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
Understanding how and why assemblage dissimilarity changes along spatial gradient is a great challenge in ecology,because answers to these questions depend on the analytical types,dimensions,and components of beta div...Understanding how and why assemblage dissimilarity changes along spatial gradient is a great challenge in ecology,because answers to these questions depend on the analytical types,dimensions,and components of beta diversity we concerned.To obtain a comprehensive understanding of assemblage dissimilarity and its implications for biodiversity conservation in the Himalayas,we explored the elevational patterns and determinants of beta diversity and its turnover and nestedness components of pairwise and multiple types and taxonomic and phylogenetic dimensions simultaneously.Patterns of beta diversity and their components of different types and dimensions were calculated based on 96 sampling quadrats along an 1800-5400 m elevational gradient.We examined whether and how these patterns differed from random expectations using null models.Furthermore,we used random forest methods to quantify the role of environmental variables representing climate,topography,and human disturbance in determining these patterns.We found that beta diversity and its turnover component,regardless of its types and dimensions,shown a hump-shaped elevational patterns.Both pairwise and multiple phylogenetic beta diversity were remarkably lower than their taxonomic counterpart.These patterns were significantly less than random expectation and were mostly associated with climate variables.In summary,our results suggested that assemblage dissimilarity of seed plants was mostly originate from the replacement of closely related species determined by climate-driven environmental filtering.Accordingly,conservation efforts should better cover elevations with different climate types to maximalize biodiversity conservation,rather than only focus on elevations with highest species richness.Our study demonstrated that comparisons of beta diversity of different types,dimensions,and components could be conductive to consensus on the origin and mechanism of assemblage dissimilarity.展开更多
Based upon the diagonal loading technique and the structure of the space-time adaptive processors, a novel anti-jamming method of satellite navigation is proposed. According to matrix in- verse theorem, the range of t...Based upon the diagonal loading technique and the structure of the space-time adaptive processors, a novel anti-jamming method of satellite navigation is proposed. According to matrix in- verse theorem, the range of the diagonal loading values for space-time adaptive wideband signal pro- cessing structure is deduced, and the optimum equation of diagonal loading beam forming algorithm of space-time structure is obtained. Then, by the analysis of two-dimensional oriented vector in di- rection of the perturbation interference, the wideband interference covariance matrix obtained in the weights training period is modified. Finally, the optimum weight of multi-linear constrained space- time adaptive beam-forming alogrithm is derived for anti-interference filter processing. The new method effectively widens the null steering beams tion results prove the robustness of the proposed when discrepancy happens. The computer simula- method.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
Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)...Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)suffer from a large size,short lifespan,low power density,and poor reliability,which limits their use.In contrast,ultrafast supercapacitors(SCs)are ideal for replacing commercial AECs because of their extremely high power densities,fast charging and discharging,and excellent high-frequency re-sponse.We review the design principles and key parameters for ultrafast supercapacitors and summarize research pro-gress in recent years from the aspects of electrode materials,electrolytes,and device configurations.The preparation,structures,and frequency response performance of electrode materials mainly consisting of carbon materials such as graphene and carbon nanotubes,conductive polymers,and transition metal compounds,are focused on.Finally,future research directions for ultrafast SCs are suggested.展开更多
The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
Although guided image filtering(GIF) is known for preserving edges and fast computation,it may produce inaccurate outputs in depth map restoration.In this paper,a novel confidence-weighted GIF called mutual-structure ...Although guided image filtering(GIF) is known for preserving edges and fast computation,it may produce inaccurate outputs in depth map restoration.In this paper,a novel confidence-weighted GIF called mutual-structure weighted GIF(MSWGIF) is proposed,which replaces the mean filtering strategy in GIF during handling overlapping windows.The confidence value is composed of a depth term and a mutual-structure term,where the depth term is utilized to protect the edges of the output,and the mutual-structure term helps to select accurate windows during the structure characteristics of the guidance image are transferred to the output.Experimental results show that MSWGIF reduces the root mean square error(RMSE) by an average of 12.37%,and the average growth rate of correlation(CORR) is 0.07% on average.Additionally,the average growth rate of structure similarity index measure(SSIM) is 0.34%.展开更多
This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the acc...This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the accuracy mismatch between tem-poral low-order finite difference and spatial high-order discre tization,the ir time collocation points must increase dramatically to solve highly oscillatory solutions of structural vibration,which results in a surge in computing time and a decrease in accuracy.To address this problem,we introduced the step-by-step idea in the space-time spectral method.The Chebyshev polynomials and Lagrange's equation were applied to derive discrete spatial goverming equations,and a matrix projection method was used to map the calculation results of prev ious steps as the initial conditions of the subsequent steps.A series of numerical experiments were carried out.The results of the proposed method were compared with those obtained by traditional space-time spectral methods,which showed that higher accuracy could be achieved in a shorter computation time than the latter in highly oscillatory cases.展开更多
This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of t...This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.展开更多
Cultural filtering is deeply embedded in cross-cultural literary exchange and exerts a lasting influence on both the transmission and interpretation of literary works.This article examines the English translation of B...Cultural filtering is deeply embedded in cross-cultural literary exchange and exerts a lasting influence on both the transmission and interpretation of literary works.This article examines the English translation of Ba Jin’s The Family by Sidney Shapiro,focusing on the manifestations and underlying causes of cultural filtering in the translated text.The translator adopts a range of strategies-including the addition of cultural annotations,selective omission,and abridged translation of certain content-to implement various forms of cultural filtering.These choices are shaped by multiple filtering processes,such as the translator’s cultural identity and his understanding of traditional Chinese culture.While cultural filtering in cross-cultural translation is inevitable and may result in partial loss of meaning,it can also breathe new life into the source text and facilitate mutual understanding and dialogue between different cultural systems.展开更多
Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propos...Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propose a quasi-orthogonal spacetime block code(QOSTBC)that can achieve a full transmission code rate for backscatter communication systems with a four-antenna tag and then extend the scheme to support tags with 2i antennas.Specifically,we first present the system model for the backscatter system.Next,we propose the QOSTBC scheme to encode the tag signals.Then,we provide the corresponding maximum likelihood detection algorithms to recover the tag signals.Finally,simulation results are provided to demonstrate that our proposed QOSTBC scheme and the detection algorithm can achieve a better transmission code rate or symbol error rate performance for backscatter communication systems compared with benchmark schemes.展开更多
Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low informatio...Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low information density in dialogues,methods based on trigger enhancement have been proposed,yielding positive results.However,trigger enhancement faces challenges,which cause suboptimal model performance.First,the proportion of annotated triggers is low in DialogRE.Second,feature representations of triggers and arguments often contain conflicting information.In this paper,we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations.We first obtain representations of arguments,and triggers that contain rich semantic information through attention and gate methods.Then,we design a feature filtering mechanism that eliminates conflicting features in the encoding of trigger prototype representations and their corresponding argument pairs.Additionally,we utilize large language models to create prompts based on Chain-of-Thought and In-context Learning for automated trigger extraction.Experiments show that our model increases the average F1 score by 1.3%in the dialogue relation extraction task.Ablation and case studies confirm the effectiveness of our model.Furthermore,the feature filtering method effectively integrates with other trigger enhancement models,enhancing overall performance and demonstrating its ability to resolve feature conflicts.展开更多
Optical wireless(OW)communication systems face significant challenges such as signal attenuation due to atmospheric absorption,scattering,and noise from hardware components,which degrade detection sensitivity.To addre...Optical wireless(OW)communication systems face significant challenges such as signal attenuation due to atmospheric absorption,scattering,and noise from hardware components,which degrade detection sensitivity.To address these challenges,we propose a digital processing algorithm that combines finite impulse response filtering with dynamic synchronization based on pulse addition and subtraction.Unlike conventional methods,which typically rely solely on hardware optimization or basic thresholding techniques,the proposed approach integrates filtering and synchronization to improve weak signal detection and reduce noise-induced errors.The proposed algorithm was implemented and verified using a field-programmable gate array.Experiments conducted in an indoor OW communication environment demonstrate that the proposed algorithm significantly improves detection sensitivity by approximately 6 dB and 5 dB at communication rates of 3.5 Mbps and 5.0 Mbps,respectively.Specifically,under darkroom conditions and a bit error rate of 1×10^(-7),the detection sensitivity was improved from-38.56 dBm to-44.77 dBm at 3.5 Mbps and from-37.12 dBm to-42.29 dBm at 5 Mbps.The proposed algorithm is crucial for future capture and tracking of signals at large dispersion angles and in underwater and long-distance communication scenarios.展开更多
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Project supported by the National Natural Science Foundation of China （Grant No.60572157）, and the National High- Technology Research and Development Program of China （Grant No.2003AA123310）
文摘A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.
文摘In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.
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 in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金supported by the National Natural Science Foundation of China(grant number 31901109)Guangdong Basic and Applied Basic Research Foundation(grant number 2021A1515110744).
文摘Understanding how and why assemblage dissimilarity changes along spatial gradient is a great challenge in ecology,because answers to these questions depend on the analytical types,dimensions,and components of beta diversity we concerned.To obtain a comprehensive understanding of assemblage dissimilarity and its implications for biodiversity conservation in the Himalayas,we explored the elevational patterns and determinants of beta diversity and its turnover and nestedness components of pairwise and multiple types and taxonomic and phylogenetic dimensions simultaneously.Patterns of beta diversity and their components of different types and dimensions were calculated based on 96 sampling quadrats along an 1800-5400 m elevational gradient.We examined whether and how these patterns differed from random expectations using null models.Furthermore,we used random forest methods to quantify the role of environmental variables representing climate,topography,and human disturbance in determining these patterns.We found that beta diversity and its turnover component,regardless of its types and dimensions,shown a hump-shaped elevational patterns.Both pairwise and multiple phylogenetic beta diversity were remarkably lower than their taxonomic counterpart.These patterns were significantly less than random expectation and were mostly associated with climate variables.In summary,our results suggested that assemblage dissimilarity of seed plants was mostly originate from the replacement of closely related species determined by climate-driven environmental filtering.Accordingly,conservation efforts should better cover elevations with different climate types to maximalize biodiversity conservation,rather than only focus on elevations with highest species richness.Our study demonstrated that comparisons of beta diversity of different types,dimensions,and components could be conductive to consensus on the origin and mechanism of assemblage dissimilarity.
基金Supported by the National High Technology Research and Development Program of China("863"Program)(2011AA1569)
文摘Based upon the diagonal loading technique and the structure of the space-time adaptive processors, a novel anti-jamming method of satellite navigation is proposed. According to matrix in- verse theorem, the range of the diagonal loading values for space-time adaptive wideband signal pro- cessing structure is deduced, and the optimum equation of diagonal loading beam forming algorithm of space-time structure is obtained. Then, by the analysis of two-dimensional oriented vector in di- rection of the perturbation interference, the wideband interference covariance matrix obtained in the weights training period is modified. Finally, the optimum weight of multi-linear constrained space- time adaptive beam-forming alogrithm is derived for anti-interference filter processing. The new method effectively widens the null steering beams tion results prove the robustness of the proposed when discrepancy happens. The computer simula- method.
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.
文摘Filter capacitors play an important role in altern-ating current(AC)-line filtering for stabilizing voltage,sup-pressing harmonics,and improving power quality.However,traditional aluminum electrolytic capacitors(AECs)suffer from a large size,short lifespan,low power density,and poor reliability,which limits their use.In contrast,ultrafast supercapacitors(SCs)are ideal for replacing commercial AECs because of their extremely high power densities,fast charging and discharging,and excellent high-frequency re-sponse.We review the design principles and key parameters for ultrafast supercapacitors and summarize research pro-gress in recent years from the aspects of electrode materials,electrolytes,and device configurations.The preparation,structures,and frequency response performance of electrode materials mainly consisting of carbon materials such as graphene and carbon nanotubes,conductive polymers,and transition metal compounds,are focused on.Finally,future research directions for ultrafast SCs are suggested.
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
基金supported by the National Key Research and Development Program of China (No.2019YFB2204302)。
文摘Although guided image filtering(GIF) is known for preserving edges and fast computation,it may produce inaccurate outputs in depth map restoration.In this paper,a novel confidence-weighted GIF called mutual-structure weighted GIF(MSWGIF) is proposed,which replaces the mean filtering strategy in GIF during handling overlapping windows.The confidence value is composed of a depth term and a mutual-structure term,where the depth term is utilized to protect the edges of the output,and the mutual-structure term helps to select accurate windows during the structure characteristics of the guidance image are transferred to the output.Experimental results show that MSWGIF reduces the root mean square error(RMSE) by an average of 12.37%,and the average growth rate of correlation(CORR) is 0.07% on average.Additionally,the average growth rate of structure similarity index measure(SSIM) is 0.34%.
基金supported by the Advance Research Project of Civil Aerospace Technology(Grant No.D020304)National Nat-ural Science Foundation of China(Grant Nos.52205257 and U22B2083).
文摘This paper proposes a new step-by-step Chebyshev space-time spectral method to analyze the force vibration of functionally graded material structures.Although traditional space-time spectral methods can reduce the accuracy mismatch between tem-poral low-order finite difference and spatial high-order discre tization,the ir time collocation points must increase dramatically to solve highly oscillatory solutions of structural vibration,which results in a surge in computing time and a decrease in accuracy.To address this problem,we introduced the step-by-step idea in the space-time spectral method.The Chebyshev polynomials and Lagrange's equation were applied to derive discrete spatial goverming equations,and a matrix projection method was used to map the calculation results of prev ious steps as the initial conditions of the subsequent steps.A series of numerical experiments were carried out.The results of the proposed method were compared with those obtained by traditional space-time spectral methods,which showed that higher accuracy could be achieved in a shorter computation time than the latter in highly oscillatory cases.
文摘This study introduces an advanced recommender system for technology enhanced learning(TEL)that synergizes neural collaborative filtering,sentiment analysis,and an adaptive learning rate to address the limitations of traditional TEL systems.Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users.Utilizing bidirectional encoder representations from Transformers for sentiment analysis,our system not only understands but also respects user privacy by processing feedback without revealing sensitive information.The adaptive learning rate,inspired by AdaGrad,allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback,ensuring a dynamic response to both positive and negative sentiments.This dual approach enhances the system’s adapt-ability to changing user preferences and improves its contentment understanding.Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners,facilitating a more personalized learning experience.By dynamically adjusting recommendations based on real-time user data and behavioral analysis,our system leverages the collective insights of similar users and rele-vant content.We validated our approach against three datasets-MovieLens,Amazon,and a proprietary TEL dataset—and saw significant improvements in recommendation precision,F-score,and mean absolute error.The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems,marking a step forward in developing more responsive and user-centric educational technologies.This study paves the way for future advancements in TEL systems,emphasizing the importance of emotional intelli-gence and adaptability in enhancing the learning experience.
文摘Cultural filtering is deeply embedded in cross-cultural literary exchange and exerts a lasting influence on both the transmission and interpretation of literary works.This article examines the English translation of Ba Jin’s The Family by Sidney Shapiro,focusing on the manifestations and underlying causes of cultural filtering in the translated text.The translator adopts a range of strategies-including the addition of cultural annotations,selective omission,and abridged translation of certain content-to implement various forms of cultural filtering.These choices are shaped by multiple filtering processes,such as the translator’s cultural identity and his understanding of traditional Chinese culture.While cultural filtering in cross-cultural translation is inevitable and may result in partial loss of meaning,it can also breathe new life into the source text and facilitate mutual understanding and dialogue between different cultural systems.
基金supported by Beijing Municipal Natural Science Foundation(L222002)the Natural Science Foundation of China(U22B2004).
文摘Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propose a quasi-orthogonal spacetime block code(QOSTBC)that can achieve a full transmission code rate for backscatter communication systems with a four-antenna tag and then extend the scheme to support tags with 2i antennas.Specifically,we first present the system model for the backscatter system.Next,we propose the QOSTBC scheme to encode the tag signals.Then,we provide the corresponding maximum likelihood detection algorithms to recover the tag signals.Finally,simulation results are provided to demonstrate that our proposed QOSTBC scheme and the detection algorithm can achieve a better transmission code rate or symbol error rate performance for backscatter communication systems compared with benchmark schemes.
基金supported by the National Key Research and Development Program of China(No.2023YFF0905400)the National Natural Science Foundation of China(No.U2341229).
文摘Relation extraction plays a crucial role in numerous downstream tasks.Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue.To tackle the problem of low information density in dialogues,methods based on trigger enhancement have been proposed,yielding positive results.However,trigger enhancement faces challenges,which cause suboptimal model performance.First,the proportion of annotated triggers is low in DialogRE.Second,feature representations of triggers and arguments often contain conflicting information.In this paper,we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations.We first obtain representations of arguments,and triggers that contain rich semantic information through attention and gate methods.Then,we design a feature filtering mechanism that eliminates conflicting features in the encoding of trigger prototype representations and their corresponding argument pairs.Additionally,we utilize large language models to create prompts based on Chain-of-Thought and In-context Learning for automated trigger extraction.Experiments show that our model increases the average F1 score by 1.3%in the dialogue relation extraction task.Ablation and case studies confirm the effectiveness of our model.Furthermore,the feature filtering method effectively integrates with other trigger enhancement models,enhancing overall performance and demonstrating its ability to resolve feature conflicts.
基金supported by National Key R&D Program of China under Grants No.2022YFB3902500,No.2022YFB2903402,and No.2021YFA0718804Natural Science Foundation of Jilin Province under Grant No.222621JC010297013Education Department of Jilin Province under Grant No.JJKH20220745KJ.
文摘Optical wireless(OW)communication systems face significant challenges such as signal attenuation due to atmospheric absorption,scattering,and noise from hardware components,which degrade detection sensitivity.To address these challenges,we propose a digital processing algorithm that combines finite impulse response filtering with dynamic synchronization based on pulse addition and subtraction.Unlike conventional methods,which typically rely solely on hardware optimization or basic thresholding techniques,the proposed approach integrates filtering and synchronization to improve weak signal detection and reduce noise-induced errors.The proposed algorithm was implemented and verified using a field-programmable gate array.Experiments conducted in an indoor OW communication environment demonstrate that the proposed algorithm significantly improves detection sensitivity by approximately 6 dB and 5 dB at communication rates of 3.5 Mbps and 5.0 Mbps,respectively.Specifically,under darkroom conditions and a bit error rate of 1×10^(-7),the detection sensitivity was improved from-38.56 dBm to-44.77 dBm at 3.5 Mbps and from-37.12 dBm to-42.29 dBm at 5 Mbps.The proposed algorithm is crucial for future capture and tracking of signals at large dispersion angles and in underwater and long-distance communication scenarios.
