Optimal Filter Research Articles

The Weighted Least-Squares Approach to State Estimation in Linear State Space Models: The Case of Correlated Noise Terms

In this article, a particular approach to deriving recursive state estimators for linear state space models is generalised, namely the weighted least-squares approach introduced by Duncan and Horn in 1972, for the case of the two noise processes arising in such models being cross-correlated; in this context, the fact that in the available literature two different non-equivalent recursive algorithms are presented for the task of state estimation in the aforementioned case is discussed. Although the origin of the difference between these two algorithms can easily be identified, the issue has only rarely been discussed so far. Then the situations in which each of the two algorithms apply are explored, and a generalised Kalman filter which represents a merger of the two original algorithms is proposed. While, strictly speaking, optimal state estimates can be obtained only through the non-recursive weighted least-squares approach, in examples of modelling simulated and real-world data, the recursive generalised Kalman filter shows almost as good performance as the optimal non-recursive filter.

Design Optimization and Performance Evaluation of Passive Filters for Acoustic Noise Mitigation in Locally Manufactured Small Wind Turbines

Design Optimization and Performance Evaluation of Passive Filters for Acoustic Noise Mitigation in Locally Manufactured Small Wind Turbines

Environmental-structure-perception-based adaptive pose fusion method for LiDAR-visual-inertial odometry

Environmental-structure-perception-based adaptive pose fusion method for LiDAR-visual-inertial odometry

Development of a high-order solver for large eddy simulation of turbulent heat transfer at supercritical pressure based on Nek5000

Turbulent heat transfer at supercritical pressure is a complex flow phenomenon due to drastic variations in fluid properties near the pseudocritical point. Numerical simulation is an important method to reveal the underlying physics. Currently, low-order numerical methods together with Reynolds-averaged Navier–Stokes equations are the mainstream in which empirical parameters are required, preventing high-fidelity simulations. Through inventing iterative properties updating and density-weighted explicit filtering, this work develops a high-order spectral element solver based on the open-source code Nek5000. By simulating a classical problem of supercritical CO2 flowing in a heated pipe and comparing it with benchmark data, the capability of the solver in direct numerical simulation is validated. Further results suggest lowering the mesh resolution leads to inaccurate predictions of bulk parameters and turbulent statistics. Therefore, filtering-based large eddy simulation (LES) is explored with different filter weights under a coarse mesh. Results show such a method can significantly improve most of the bulk parameters, including the bulk Nusselt number. The optimal filter weight can be determined from a simple optimization problem minimizing the deviation of overall energy conservation. Being high-order and capable of LES without empirical parameter, the current solver is a powerful tool for high-fidelity simulation of turbulent heat transfer at supercritical pressure.

Minimum nonprobabilistic entropy deconvolution for fault diagnosis of rolling element bearings

The blind deconvolution methods (BDMs) is one of the most common methods for fault diagnosis of rolling bearings, and it is essential to maintain the safe and reliable operation of mechanical equipment. However, noise interference and the need for prior periods limit the scope of application of the BDMs. In this paper, a new minimum nonprobabilistic entropy deconvolution (MNPED) method is proposed. According to the correlation between fault impact and non-Gaussianity, the Gaussian membership function in fuzzy set theory is used to map the sample points to the membership degree of Gaussian distribution, and then the nonprobabilistic entropy (NPE) is formed to measure the impact characteristics of the signal. Then NPE is incorporated into the iterative process of solving the filter coefficient. Finally, the target signal and the optimal filter coefficient are selected based on the criterion of minimum NPE. MNPED is capable of adaptively extracting the periodic pulse of a signal without requiring prior knowledge of the period, even in the presence of strong noise interference. The effectiveness and robustness of the proposed approach are validated through simulation and experimental data.

Surrogate Model-Based Filter Optimization by a Field-Circuit Model Mapping

Surrogate Model-Based Filter Optimization by a Field-Circuit Model Mapping

SOHD-LOAM: Suppress odometry height drift lidar odometry and mapping on undulating road

In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality.

Nonlinear optimization filters for stochastic time‐varying convex optimization

AbstractWe look at a stochastic time‐varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a nonlinear dynamical system and a measurement equation, respectively, yielding the notion of nonlinear filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a bilinear matrix inequality condition in the constrained case. Some special cases and variations are discussed, notably the case of parametric filters, yielding certificates based on LPV analysis and, if one wishes, matrix sum‐of‐squares relaxations. Supporting numerical results are presented from real data sets in ride‐hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant.

The Synergistic Effect of the Filtration Area Controlled by the Electromagnetic Valve and Injection Pressure on Pulse-Jet Dust Cleaning Performance

In engineering pulse-jet dust collector applications, the filtration area and injection pressure are chosen mostly based on experience. The peak pressure is tested under different injection pressures and filtration areas controlled by an electromagnetic valve, and then comprehensively analyzes the effects of dust intensity, uniformity, and air consumption on dust cleaning to obtain a better filtration area controlled by an electromagnetic valve and injection pressure. The results show that considering the uniformity and intensity of dust cleaning, the filtration area of 33 m2 under the injection pressure of 0.4 MPa should be preferentially selected, with a standard deviation of 0.246 and a variance of 0.061. The filtration area of 27 m2 under the injection pressure of 0.3 MPa should be preferentially selected considering the unit air consumption, uniformity, and intensity of dust cleaning, the standard deviation of 0.252, and the variance of 0.064. The paper presents a theoretical foundation for selecting the optimal injection pressure and filter area regulated by an electromagnetic valve in pulse-jet dust collector systems.

Fast random vector transforms in terms of pseudo-inverse within the Wiener filtering paradigm

We propose two techniques for the fast numerical implementation of a random vector transform within Wiener filtering paradigm. In signal processing terminology, the transform is interpreted as an optimal filter. The proposed transform aims to minimize the sum of distances between a reference random vector xi and its transformed counterpart yi, for all i=1,…,p. In contrast to known techniques where the transform consists of p matrices that minimize a difference between a single reference random vector and p observed random vectors, the proposed transform uses a single optimal matrix to transform p random vectors. It requires less computational work for the numerical implementation. Nevertheless, a direct numerical realization of the transform involves computation of the covariance matrix pseudo-inverse and the matrix square root which might be large, and then are computationally expensive. Proposed fast versions of the transform allow us to avoid that computation and, as a result, accelerate the numerical implementation. While the fast proposed techniques are approximate versions of the original transform, it is shown that their accuracies are very close to those of the original transform. An error analysis is provided. The effectiveness of the proposed transform is illustrated by numerical experiments in four real-world scenarios.

Fusion estimation against mixed network attacks for systems with random parameter matrices, correlated noises, and quantized measurements

This paper is concerned with the information fusion estimation problems for stochastic uncertain systems with quantized measurements and mixed network attacks including random deception attack and denial-of-service (DoS) attack. The cross-correlated random parameter matrices and addictive noises simultaneously exist in the studied system. By resorting to the optimal prediction compensation mechanism for DoS attack, an optimal centralized fusion filter in the linear minimum variance sense is proposed using an innovation analysis approach. In addition, the Kalman-like recursive distributed optimal linear fusion predictor and filter without feedback are presented based on local estimators from single-sensor subsystems. The estimation error cross-covariance matrices between two arbitrary local estimators, and those between local and prior fusion estimators are derived. They have good flexibility due to the parallel structure. However, they have lower accuracy than the centralized fusion estimators. To further improve the estimation accuracy, the distributed optimal linear fusion predictor and filter with feedback are also presented. They avoid the calculation of cross-covariance matrices. Moreover, it has been mathematically proved that they have the same estimation accuracy as the centralized fusion estimators. A simulation example demonstrates the effectiveness of the proposed algorithms.

Flexible fingerprint cuckoo filter for information retrieval optimization in distributed network

Flexible fingerprint cuckoo filter for information retrieval optimization in distributed network

A study on the characteristics and geometric optimization of a cyclone filter

Cyclone separator without filter is a common device used for separating solid–liquid/gas–solid/gas–liquid mixtures, which is widely used in chemical and household appliance industries. It has the advantages of high separation efficiency and low susceptibility to clogging. However, its separation efficiency cannot reach 100%, and some particles that enter the separator are not separated and will cause secondary pollution to downstream fluids. Combining the cyclone separator with a mesh filter to form a cyclone filter can achieve the complementary advantages of both, namely, high filtration efficiency and low susceptibility to clogging. For cyclone filters, particle shape has a significant impact on filter clogging, but there are few reports on this issue in the literature. In this paper, a new type of cyclone filter is proposed, and experimental and simulation methods are used to study its resistance characteristics. It is found that compared with the case of no filter, the flow resistance of the cyclone filter is significantly reduced, and the reason for the reduction in resistance is analyzed based on flow field analysis. The movement trajectory of flat and spherical particles in the cyclone filter and the problem of particle clogging on the filter are then studied. It is found that flat particles are more likely to cause clogging on the filter, and the reason for the clogging of the filter in the inlet area of the cyclone filter and the mechanism by which flat particles are more likely to clog the filter in the inlet area are analyzed based on the flow field and particle force in the separator. Finally, the inlet profile of the cyclone filter is optimized, and the changes in the inlet flow field and particle movement trajectory before and after optimization are compared. The problem of flat particle clogging on the filter is ultimately solved.

Single shot interferenceless coded aperture correlation holography via a learnable Wiener deconvolution network

One of the challenges in interferenceless coded aperture correlation holography (I-COACH) is high-quality single-shot reconstruction, as conventional cross-correlation reconstruction algorithm requires recording multiple holograms and complex reconstruction operations, resulting in serious background noise. Here, we present a deep learning based single-shot reconstruction by introducing a learnable Wiener deconvolution network in I-COACH. By feeding the point spread hologram (PSH) of I-COACH system and object hologram (OH) into Wiener deconvolution, a mapping relationship between Wiener deconvolution result and ground truth is generated. A Res-UNet is trained to learn optimal filter and noise regularization parameters, thereby achieving high quality reconstruction. Moreover, the training dataset used in our network are simulated using PSH that is obtained through only one experiment acquisition in advance, greatly simplifying experimental OH dataset acquisition. Compared to the conventional correlation reconstruction methods, the proposed Wiener deconvolution network can conveniently realize high-quality single-shot reconstruction in I-COACH, and the axial sensitivity in 3D reconstruction can be demonstrated by varying scattering degree of coded phase mask. Importantly, this Wiener deconvolution solution will provide an enlightening reference for dynamic imaging in I-COACH.

Oasis: An Optimal Disjoint Segmented Learned Range Filter

The learning-enhanced data structure has inspired the development of the range filter, bringing significantly better false positive rate (FPR) than traditional non-learned range filters. Its core idea is to employ piece-wise linear functions that uniformly map the entire key space into a bitmap sequentially. Nonetheless, such uniform mapping can be space-ineffective, impacting FPRs. This paper introduces Oasis, a novel learned range filter that divides the key space into disjointed intervals by excluding large empty ranges explicitly and optimally maps those unpruned intervals into a compressed bitmap. The configuration optimality in Oasis is guaranteed by a careful theoretical analysis. To enhance the versatility of Oasis, we further propose Oasis+, which integrates the design space of both learned and non-learned filters, delivering robust performance across a wide range of workloads. We evaluate the performance of both Oasis and Oasis+ when integrated into the key-value system RocksDB, using a diverse set of real-world and synthetic datasets and workloads. In RocksDB, Oasis and Oasis+ improve the performance by up to 1.4× and 6.2× when compared to state-of-the-art learned and non-learned range filters.

Measurement and Analysis of In Vivo Gastroduodenal Slow Wave Patterns Using Anatomically-Specific Cradles and Electrodes.

Bioelectrical 'slow waves' regulate gastrointestinal contractions. We aimed to confirm whether the pyloric sphincter demarcates slow waves in the intact stomach and duodenum. We developed and validated novel anatomically-specific electrode cradles and analysis techniques which enable high-resolution slow wave mapping across the in vivo gastroduodenal junction. Cradles housed flexible-printed-circuit and custom cradle-specific electrode arrays during acute porcine experiments (N = 9; 44.92 kg ± 8.49 kg) and maintained electrode contact with the gastroduodenal serosa. Simultaneous gastric and duodenal slow waves were filtered independently after determining suitable organ-specific filters. Validated algorithms calculated slow wave propagation patterns and quantitative descriptions. Butterworth filters, with cut-off frequencies (0.0167 - 2) Hz and (0.167 - 3.33) Hz, were optimal filters for gastric and intestinal slow wave signals, respectively. Antral slow waves had a frequency of (2.76 ± 0.37) cpm, velocity of (4.83 ± 0.21) mm·s-1, and amplitude of (1.13 ± 0.24) mV, before terminating at the quiescent pylorus that was (46.54 ± 5.73) mm wide. Duodenal slow waves had a frequency of (18.13 ± 0.56) cpm, velocity of (11.66 ± 1.36) mm·s-1, amplitude of (0.32 ± 0.03) mV, and originated from a pacemaker region (7.24 ± 4.70) mm distal to the quiescent zone. Novel engineering methods enable measurement of in vivo electrical activity across the gastroduodenal junction and provide qualitative and quantitative definitions of slow wave activity. The pylorus is a clinical target for a range of gastrointestinal motility disorders and this work may inform diagnostic and treatment practices.

Optimal filter assignment policy against link flooding attack

A Link Flooding (LFA) attack is a special type of Denial-of-Service (DoS) attack in which the attacker sends out a huge number of requests to exhaust the capacity of a link on the path the traffic comes to a server. As a result, user traffic cannot reach the server. As a result, DoS and degradation of Quality-of-Service (QoS) occur. Because the attack traffic does not go to the victim, protecting the legitimate traffic alone is hard for the victim. The victim can protect its legitimate traffic by using a special type of router called filter router (FR). An FR can receive server filters and apply them to block a link incident to it. An FR probabilistically appends its own IP address to packets it forwards, and the victim uses that information to discover the traffic topology. By analyzing traffic rates and paths, the victim identifies some links that may be congested. The victim needs to select some of these possible congested links (PCLs) and send a filter to the corresponding FR so that legitimate traffic avoids congested paths. In this paper, we formulate two optimization problems for blocking the least number of PCLs so that the legitimate traffic goes through a non-congested path. We consider the scenario where every user has at least one non-congested shortest path in the first problem. We extend the first problem to a scenario where there are some users whose shortest paths are all congested. We transform the original problem to the vertex separation problem to find the links to block. We use a custom-built Java multi-threaded simulator and conduct extensive simulations to support our solutions.

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis.

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn-kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.

Harmonic mitigation using optimal active power filter for the improvement of power quality for a electric vehicle changing station

The idea of electric vehicles (EVs) is relatively new in the transportation industry. EVs are becoming increasingly popular due to many advantages. Apart from various advantages, some drawbacks of these vehicles are also a concern. One of the concerns for these vehicles is the substantial power requirement to charge the batteries. During charging, EVs deteriorate the power quality by injecting harmonic current into the system. As poor power quality is undesirable, the mitigation of this harmonic current is essential. Therefore, in this study, an optimal design of a shunt active power filter (SAPF) based EV charging system is suggested. The optimal design of the SAPF ensures that the DC-link voltage is efficiently regulated. To test the performance of the suggested scheme, a simulation system is developed and tested in MATLAB Simulink. The results show that the developed optimal control can efficiently regulate the DC-link voltage. The results, pre and post-compensation, show that the total harmonic distortion in the system is reduced to 4.33 % from 26.72 % using the suggested approach. This is even better by 12.93%, 7.39%, 14.32% and 20.32% in comparison to particle swarm optimization, whale optimization algorithms, bee colony optimization and ant colony optimization used for comparison.

Design, optimization, and testing of ridge filters for proton FLASH radiotherapy at TRIUMF: The HEDGEHOG

TRIUMF has recently developed and implemented a workflow to design and fabricate application-specific ridge filters, which is a key upgrade to enable research using ultra-high (FLASH) dose rate beam delivery at its Proton Therapy Research Centre. These static ridge filters, called HEDGEHOGs, replace the rotating modulator wheels used to create a spread-out Bragg peak (SOBP) during conventional proton radiotherapy beam delivery, which are incompatible with the fast irradiation times of clinical doses at FLASH dose rates at TRIUMF. Initial beam commissioning of HEDGEHOGs demonstrated that they produce spread-out Bragg peaks matching their design parameters. Additional studies of some of the design parameters, including SOBP widths as well as different pin diameters and fabrication options (material, 3D printer technology) have now been completed. Based on those studies, the use of liquid medium printers (resin, jet) instead of filament printers is recommended. These studies also demonstrate that the lateral dose uniformity of the beam at 36.5 cm from the HEDGEHOG location is acceptable even with relatively large 7 mm base-diameter pins. The code base and workflow are made publicly available and can easily be adapted to other facilities. The feasibility of adapting the HEDGEHOG workflow to other beam lines or facilities was validated on a second proton beam line at the same facility at which the HEDGEHOG workflow was developed.