Traditional Filtering Algorithm Research Articles

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

In today’s information era, collaborative filtering algorithms are widely used and their distinct knowledge discovery techniques can effectively address numerous issues. However, conventional collaborative filtering algorithms encounter cold-start and data sparsity issues, which restrict their performance and accuracy. The study selected the multi-feature method to improve the traditional collaborative filtering algorithm, and introduced gray correlation calculation and Bayesian probability for user preference analysis. A learning resource recommendation model based on collaborative filtering was developed by comparing the target user’s characteristics with those of other users, calculating their similarity, selecting users with high similarity to the target user and forming a neighbor set. Using Bayesian probability and grey correlation to analyze user preferences in library systems can be well applied in book classification and recommendation problems in university libraries. The computing layer, which includes the collaborative filtering calculation stage and the group recommendation calculation stage, is the model’s main functional component. The smaller the value of mean absolute error, the higher the prediction accuracy of the model. The mean absolute error value of the multi-feature collaborative filtering algorithm was inferior to the traditional collaborative filtering algorithm, indicating that the classification accuracy of the former is higher than that of the latter. When the training set to test set ratio steadily became bigger, the mean absolute error value reached the lowest and smoothest point at 80%. In dataset A, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.765 and 0.809. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.044. In dataset B, the mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.796 and 0.836. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.040. In dataset C, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.815 and 0.848. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.033. When the accuracy was the highest; the mean absolute error value was the smallest at the grey correlation, which means that the technique improves the reliability of the recommendations compared with other methods. This means that the method has a positive impact on the accuracy of the recommendations compared to other methods. Grey correlation degree can comprehensively consider the interrelationships between multiple factors, handle uncertain and incomplete information, and explore potential user needs and behavior patterns. The implementation of the grey correlation degree has transformed the collaborative filtering algorithm into a group filtering algorithm, thereby enhancing its precision. The research on book classification and recommendation in university libraries, which enhances the group filtering algorithm, can address a range of issues such as improving classification accuracy, augmenting recommendation diversity, enhancing library management efficiency among others. This, in turn, enables more precise book recommendations to users.

A vision imaging compensation algorithm for quadruped robot based on walking jitter

To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better.

A recurrent neural network for adaptive filtering in terahertz time-domain spectroscopy

System interferences such as noise and background information in terahertz time-domain spectroscopy (THz-TDS) add difficulty to spectral signal analysis and affect the accuracy of component identification. In this paper, we proposed an adaptive filter based on a recurrent quantum neural network (RQNN) to process the THz-TDS signal. RQNN is a network architecture guided by quantum mechanics, for adaptive filtering of THz-TDS signal. Six pure substances, ferric sulfate, ferrous sulfate, Prussian blue, agarose, polylysine and sodium of polyaspartic acid were used as test samples. The TDS signals of the six samples were filtered with RQNN and five competing filters. Optical constants including refractive index and absorption coefficient were calculated from raw THz signal and filtered THz signal. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were calculated between the optical constants calculated before and after filtering as evaluation indicators. Results revealed that RQNN could better eliminate the noise in THz-TDS signals, and retain the characteristic peaks in the calculated optical constants simultaneously. In terms of PSNR and SSIM, RQNN-filtered THz-TDS signals demonstrated higher values than those of the competing filters. The RQNN proposed in this paper can be used as an adaptive filter for noise filtering and signal extraction of THz-TDS signal, with advantages above traditional filtering algorithms.

Enhanced dynamic state estimation of regional new energy power system under different abnormal scenarios

AbstractThe high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

An improved wavelet threshold denoising approach for surface electromyography signal

BackgroundThe surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion.PurposeThis article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality.ResultsAfter denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength.ConclusionThe empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.

Adaptive block level bilateral filtering algorithm

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integralalgorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.

An Adaptive Double Extended Kalman Filter Algorithm Based on Incremental Change Rate for Co-estimation of Battery SOC and Capacity

The battery state of charge (SOC) and capacity are important state management indicators of the battery management system, and their estimation accuracy directly affects the safety of power battery use and the driver’s driving experience. Since the increment change rate of the estimated variable can reflect the changing trend of the estimated variable, an extended Kalman filter algorithm based on the increment change rate is proposed in this paper, on this basis, an adaptive double-extended Kalman filter algorithm based on incremental change rate is constructed for the co-estimation of SOC and capacity of batteries. The tests under various operating conditions show that the target algorithm proposed in this paper has greater advantages over the traditional adaptive double-extended Kalman filter algorithm, and the maximum absolute error value (MAE) and root mean square error (RMSE) of the target algorithm can be reduced by 36.3% and 74.4% (SOC), 95.5% and 97.6% (capacity) compared with the traditional adaptive double-extended Kalman filter algorithm under DST operating conditions; The MAE and RMSE of the target algorithm can be reduced by 79.1% and 92.3% (SOC), 95.4% and 96.2% (capacity) under BBDST operating conditions.

Research on Fuzzy Energy Management Strategy of Hybrid Energy Storage System for Novel Power System

Supercapacitors have great power characteristics and can respond to high-frequency volatility and high-power load of lithium batteries. In a hybrid energy storage system (HESS), the introduction of supercapacitors is an effective scheme to reduce the load pressure and charge/discharge current fluctuation of the battery, which can reflect the high energy density characteristics of lithium batteries and extend the life of lithium batteries. To solve the problem of capacity configuration and energy allocation management of HESS, this paper first researches the dynamic programming model of HESS. Taking the minimum life decay increment of lithium batteries as the optimization objective, the energy distribution scheme of the HESS with the minimum life decay of lithium batteries under different supercapacitor configurations is obtained. Then, according to the decay rate of a unit supercapacitor to the capacity decay increment of lithium batteries, the optimal parameters of supercapacitors are selected, and the fuzzy energy management strategy based on the optimal allocation feature of dynamic programming is proposed. Finally, the simulation results show that compared with the traditional filtering algorithm, the reduction of the lithium battery life attenuation of the proposed scheme is about 2.52 times that of the filtering allocation scheme.

Power Allocation Strategy for Urban Rail HESS Based on Deep Reinforcement Learning Sequential Decision Optimization

A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage fluctuation problem caused by high power and large energy demand during the starting and braking of urban rail trains. The system is composed of on-board ultracapacitors and ground lithium batteries, aiming to smooth out the power fluctuation to realize “peak-shaving and valley-filling”. Based on deep reinforcement learning (DRL) online sequence decision, a dynamic power allocation strategy is proposed to improve the energy-saving and voltage stabilization of DC traction networks as well as HESS life protection. Furthermore, to enhance the DRL’s efficiency under time-varying operating conditions, an annealing bias - priority experience replay twin delayed deep deterministic policy gradient algorithm (A-TD3) is proposed to train the replay buffer in DRL. The online learning and optimization strategy is implemented via the mechanism of “trial and error” and “feedback” of the agent. RT-LAB semi-physical real-time simulation systems are adopted to verify the effectiveness of the proposed strategy. Compared with the traditional filtering algorithms and DRL algorithms, the results show that the proposed method converges faster and is more energy saving and stable while effectively protecting the energy storage components.

Double Extended Kalman Filter Algorithm Based on Weighted Multi-Innovation and Weighted Maximum Correlation Entropy Criterion for Co-Estimation of Battery SOC and Capacity.

Most of the traditional extended Kalman filter algorithms for the co-estimation of SOC and capacity of lithium-ion batteries are designed based on the minimum mean square error (MMSE) criterion, which may show superior performance in Gaussian noise scenes. However, due to the complexity of the battery operating environment, it is likely to face non-Gaussian noise (especially outlier noise), at which time the performance of the traditional extended Kalman filter algorithms will be seriously weakened. To solve the above problems, this paper first proposes a double extended Kalman filter algorithm based on weighted multi-innovation and weighted maximum correlation entropy (WMI-WMCC-DEKF) for the co-estimation of battery SOC and capacity. In this paper, the performance of the target algorithm is verified and compared by generating different types of noise from three noise models: weak Gaussian mixture noise, strong Gaussian mixture noise, and outlier noise. The maximum absolute error value (MAE) and root mean square error value (RMSE) of the WMI-WMCC-DEKF algorithm can achieve the highest performance improvement of 69.3 and 84.2% (SOC), 61.3, and 94.2% (capacity), respectively. The experimental results fully prove that the target algorithm has excellent performance against three kinds of noises.

Discovering planets with PLATO: Comparison of algorithms for stellar activity filtering

Context. To date, stellar activity is one of the main limitations in detecting small exoplanets via the transit photometry technique. Since this activity is enhanced in young stars, traditional filtering algorithms may severely underperform in attempting to detect such exoplanets, with shallow transits often obscured by the photometric modulation of the light curve. Aims. This paper aims to compare the relative performances of four algorithms developed by independent research groups specifically for the filtering of activity in the light curves of young active stars, prior to the search for planetary transit signals: Notch and LOCoR (N&L), Young Stars Detrending (YSD), K2 Systematics Correction (K2SC), and VARLET. Our comparison also includes the two best-performing algorithms implemented in the Wōtan package: Tukey’s biweight and Huber spline algorithms. Methods. For this purpose, we performed a series of injection-retrieval tests of planetary transits of different types, from Jupiter down to Earth-sized planets, moving both on circular and eccentric orbits. These experiments were carried out over a set of 100 realistically simulated light curves of both quiet and active solar-like stars (i.e., F and G types) that will be observed by the ESA Planetary Transits and Oscillations of stars (PLATO) space telescope, starting 2026. Results. From the experiments for transit detections, we found that N&L is the best choice in many cases, since it misses the lowest number of transits. However, this algorithm is shown to underperform when the planetary orbital period closely matches the stellar rotation period, especially in the case of small planets for which the biweight and VARLET algorithms work better. Moreover, for light curves with a large number of data-points, the combined results of two algorithms, YSD and Huber spline, yield the highest recovery percentage. Filtering algorithms allow us to obtain a very precise estimate of the orbital period and the mid-transit time of the detected planets, while the planet-to-star radius is underestimated most of the time, especially in cases of grazing transits or eccentric orbits. A refined filtering that takes into account the presence of the planet is thus compulsory for proper planetary characterization analyses.

An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising

In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF both a strong noise reduction ability and light spot detail protection ability. In our experiment, the evaluation scores of CAMF for the three indicators Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity (SSIM) were 40.47 dB, 613.28 and 0.939, respectively. The results show that CAMF is superior to other filtering algorithms in noise reduction and light spot protection.

A WLAN indoor location method based on quadratic wavelet analysis

The drift phenomenon of received signal strength may occur due to the influence of environmental change, which will affect the results of wireless local area network indoor positioning. This paper proposes an indoor location method based on quadratic wavelet analysis to solve this problem. The first wavelet analysis can obtain wavelet coefficients. The threshold interval of wavelet coefficients can be calculated by Tukey's test method, in which the mutation signal value can be filtered according to the interval. The drift signal value cannot be detected due to the limitation of primary wavelet analysis. The filtered received signal strength values can be analysed with quadratic wavelet analysis. The drift signal value can be found and filtered out by the mutation signal to obtain the new dataset of the received signal strength. The experimental results show that the influence of signal drift can be greatly reduced by the method of quadratic wavelet analysis proposed in this paper; the positioning accuracy error is lower than the traditional filtering algorithm; and the positioning accuracy can be improved.

Digital Twin-Driven Fault Diagnosis for Autonomous Surface Vehicles

This paper presents a digital twin-driven fault diagnosis approach based on a graphical model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In contrast with the traditional adaptive Kalman filter algorithm, where the fault parameters are treated as extended state variables, the newly proposed adaptive extended Kalman filter algorithm estimates the magnitude of the faults by calculating the parameter estimation gains directly from the sensor systems. To this end, the algorithm utilizes data from a global navigation satellite system receiver assuming the persistence of excitation conditions on the control inputs. The algorithm is tested in the Otter, an autonomous surface vehicle developed by Maritime Robotics, in which one of its propellers is faulty. Technically, the digital twin receives real-time data from the sensor system, estimates the magnitude of the actuator faults, and visualizes the results in a web-based application using JavaScript with Three.js library and Mapbox for real-world 3D map generation. Information regarding the magnitude of the faults is important for fault-tolerant control. Simulation and experimental results show the proposed approach is able to detect and estimate the actuator faults accurately.

A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter

Aiming at the problem of measurement-information abnormal-error and nonlinear filtering in UWB navigation and positioning, an ultra wideband position algorithm based on a maximum cross-correlation entropy unscented Kalman filter is proposed. The algorithm first obtains the predictive state estimate and the covariance matrix through traceless transformation. Then, it reconstructs observation information using the nonlinear regression method based on the maximum cross-correlation entropy criterion, which enhances the robustness of the unscented Kalman filter algorithm for heavy-tailed noise. The simulation and actual test results show that this algorithm has better positioning accuracy and stability than the traditional filter algorithm in a non Gaussian noise environment. This algorithm effectively solves the problem that UWB indoor location is easily affected by indoor environments, resulting in fixed deviation for that location.

A Filtering Method for LiDAR Point Cloud Based on Multi-Scale CNN with Attention Mechanism

Point cloud filtering is an important prerequisite for three-dimensional surface modeling with high precision based on LiDAR data. To cope with the issues of low filtering accuracy or excessive model complexity in traditional filtering algorithms, this paper proposes a filtering method for LiDAR point cloud based on a multi-scale convolutional neural network incorporated with the attention mechanism. Firstly, a regular image patch centering on each point is constructed based on the elevation information of point clouds. As thus, the point cloud filtering problem is transformed into the image classification problem. Then, considering the ability of multi-scale convolution to extract features at different scales and the potential of the attention mechanism to capture key information in images, a multi-scale convolutional neural network framework is constructed, and the attention mechanism is incorporated to coordinate multi-scale convolution kernel with channel and spatial attention modules. After this, the feature maps of the LiDAR point clouds can be acquired at different scales. For these feature maps, the weights of each channel layer and different spatial regions can be further tuned adaptively, which makes the network training more targeted, thereby improving the model performance for image classification and eventually separating of ground points and non-ground points preferably. Finally, the proposed method is compared with the cloth simulation filtering method (CSF), deep neural network method (DNN), k-nearest neighbor method (KNN), deep convolutional neural network method (DCNN) and scale-irrelevant and terrain-adaptive method (SITA) for the standard ISPRS dataset of point cloud filtering and the filter dataset of Qinghai. The experimental results show that the proposed method can obtain lower classification errors, which proves the superiority of this method in point cloud filtering.

Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information

In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information.

Sparsity-Aware Logarithmic Hyperbolic Cosine Normalized Subband Adaptive Filter Algorithm With Step-Size Optimization

To enhance the filtering accuracy of the traditional sign subband adaptive filter (SSAF) algorithm and its individual-weighting-factors variant, this brief proposes the logarithmic hyperbolic cosine normalized subband adaptive filter algorithm (LHCNSAF) and its sparsity-aware version through minimizing the LHC cost function. As the combination of mean-square-error and mean-absolute-error criterion, the presented LHCNSAF demonstrates higher filtering accuracy under white Gaussian background noise and impulse noise environments than SSAF-type algorithms, with almost no increase in computational complexity. To tackle the trade-off between filtering accuracy and convergence behavior caused by the fixed step-size, an effective variable step-size scheme is also devised based on the transient model of proposed algorithms through minimizing the mean-square deviation (MSD) at each iteration with some reasonable assumptions. Additionally, the stability and complexity analysis are also provided. Finally, the computer simulations certify the proposed algorithms possess good performance in terms of convergence speed, steady-state error, and tracking capability via contrasting with the considering algorithms under system identification application.