Filter Matrices Research Articles

Dynamic sum-based event-triggered H∞ filtering for networked T-S fuzzy wind turbine systems with deception attacks

This article is concerned with the dynamic sum-based event-triggered H∞ filtering issue for networked wind turbine systems subject to deception attacks and communication delays. By considering the time-varying wind power case rather than the existing maximum power case, a more general T-S fuzzy system with two premise variables is modeled for nonlinear wind turbine system. In order to save the communication cost, a novel dynamic sum-based event-triggered scheme is proposed, which has the following three merits. First, some past sampled measurements are utilized to reduce redundant transmissions. Second, an auxiliary dynamic variable based on the past sampled measurements is introduced in the triggering condition to further enlarge the triggering intervals. Third, a dynamic triggering threshold is designed, which can be regulated adaptively along with the system evolution. With the help of Lyapunov method and linear matrix inequality technique, some sufficient co-design conditions of H∞ filter and triggering matrices are derived for the T-S fuzzy filtering error system with deception attacks and communication delays. Lastly, some simulations are carried out to illustrate the advantages of the proposed strategy.

H−/H∞ filter‐based fault detection application to PEM fuel cell air‐feed system

AbstractThis paper focuses on diagnosing faults and disturbances in the polymer electrolyte membrane (PEM) fuel cell air‐feed system. The main objective is to design a robust filter, specifically of the type, to improve fault detection and attenuate perturbations. The norm is employed to assess the fault sensitivity performance of the detection process, while the norm guarantees the robustness of the proposed filter against disturbances. The approach utilized in this work is based on Lyapunov stability analysis, and the filter matrices are determined by solving a convex optimization problem formulated as linear matrix inequalities (LMIs). To illustrate the effectiveness of our results, a simulation is conducted under various disturbance and faulty scenarios. By integrating robust filtering techniques and addressing fault detection and disturbance attenuation, this study offers valuable insights for enhancing the performance and reliability of the PEM fuel cell air‐feed system.

Accurate near-wall measurements in wall bounded flows with optical flow velocimetry via an explicit no-slip boundary condition

High fidelity near-wall velocity measurements in wall bounded fluid flows continue to pose a challenge and the resulting limitations on available experimental data cloud our understanding of the near-wall velocity behavior in turbulent boundary layers. One of the challenges is the spatial averaging and limited spatial resolution inherent to cross-correlation-based particle image velocimetry (PIV) methods. To circumvent this difficulty, we implement an explicit no-slip boundary condition in a wavelet-based optical flow velocimetry (wOFV) method. It is found that the no-slip boundary condition on the velocity field can be implemented in wOFV by transforming the constraint to the wavelet domain through a series of algebraic linear transformations, which are formulated in terms of the known wavelet filter matrices, and then satisfying the resulting constraint on the wavelet coefficients using constrained optimization for the optical flow functional minimization. The developed method is then used to study the classical problem of a turbulent channel flow using synthetic data from a direct numerical simulation (DNS) and experimental particle image data from a zero pressure gradient, high Reynolds number turbulent boundary layer. The results obtained by successfully implementing the no-slip boundary condition are compared to velocity measurements from wOFV without the no-slip condition and to a commercial PIV code, using the velocity from the DNS as ground truth. It is found that wOFV with the no-slip condition successfully resolves the near-wall profile with enhanced accuracy compared to the other velocimetry methods, as well as other derived quantities such as wall shear and turbulent intensity, without sacrificing accuracy away from the wall, leading to state of the art measurements in the region of the turbulent boundary layer when applied to experimental particle images.

Investigating the influence of pore size, pore fluid and wavelength on backscattering images with sintered glass filter matrices as experimental models

Laser backscattering imaging (LBI) is an economic, energy-saving method, which can be applied to characterise bio-organisms in postharvest processes. When applying LBI to horticultural crops, scientists seldom report the cell size of their samples, and often consider the scattering coefficient as constant. The objective of this research was to investigate the influence of pore size, the pore filling for different solutes and concentrations, and wavelength under controlled conditions. Porous glass discs with five different pore diameters from 1 to 160 µm were used as reproducible experimental models for the experiment. NaCl aqueous solutions (0, 1, 2, 3 and 4 mol/L) and NaH2PO4 aqueous solutions (0, 0.8, 1.6, 2.4 and 3.2 mol/L) were applied as pore fills. The LBI system used laser modules at three wavelengths (405, 635, 780 nm). The results revealed that three of the four investigated experimental factors influence LBI, specifically: (i) the pore diameter, (ii) the solutes and (iii) their concentrations. In contrast, the three wavelengths applied did not show clear differences in the LBI patterns. Thus, when applying LBI on fruit for example, the cell size at different depths from the fruit surface needs to be carefully considered.

Modeling multiple latent information graph structures via graph convolutional network for aspect-based sentiment analysis

AbstractAspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of aspects in a sentence. Recently, graph convolution network (GCN) model combined with attention mechanism has been used for ABSA task over graph structures, achieving promising results. However, these methods of modeling over graph structure fail to consider multiple latent information in the text, i.e., syntax, semantics, context, and so on. In addition, the attention mechanism is vulnerable to noise in sentences. To tackle these problems, in this paper, we construct an efficient text graph and propose a matrix fusion-based graph convolution network (MFLGCN) for ABSA. First, the graph structure is constructed by combining statistics, semantics, and part of speech. Then, we use the sequence model combined with the multi-head self-attention mechanism to obtain the feature representation of the context. Subsequently, the text graph structure and the feature representation of context are fed into GCN to aggregate the information around aspect nodes. The attention matrix is obtained by combining sequence model, GCN and the attention mechanism. Besides, we design a filter layer to alleviate the noise problem in the sentence introduced by the attention mechanism. Finally, in order to make the context representation more effective, attention and filtering matrices are integrated into the model. Experimental results on four public datasets show that our model is more effective than the previous models, demonstrating that using our text graph and matrix fusion can significantly empower ABSA models.

Unexpected air pollutants with potential human health hazards: Nitrification inhibitors, biocides, and persistent organic substances

To better understand the influence of land use and meteorological parameters on air pollutants, we deployed passive air samplers in 15 regions with different land use in eastern Austria. The samplers consisted of polyurethane PUF and polyester PEF filter matrices, which were analyzed for 566 substances by gas-chromatography/mass-spectrometry. In a previous article, we highlighted a widespread contamination of ambient air with pesticides that depends on the surrounding land use and meteorological parameters. Here we report that, in addition to agricultural pesticides, eight other substances were frequently detected in ambient air: Nitrapyrin, a nitrification inhibitor used to increase nitrogen use efficiency of fertilizers and banned in Austria since 1993; biocides against insects (DEET and transfluthrin) used mainly outside agriculture; piperonyl butoxide (PBO), a synergist mixed into pesticide formulations; and four industrially used polychlorinated biphenyls (PCBs), long banned worldwide. Concentrations of the detected substances were positively related to air temperature, but only slightly related to agricultural land use in the sampler's vicinity. The city center showed the highest concentrations of biocides, PCBs and PBO, but also medium concentrations of nitrapyrin. Four sites had no air contamination with these substances; including two national parks dominated by grassland or forest, but also two sites with mixed land use. The potential human toxicity of the detected substances based on globally harmonized hazard classifications was high: seven substances had specific organ toxicity, six were cancerogenic, and two were acutely toxic; however, several substances had incomplete information of hazard profiles. Moreover, all substances were acutely and chronically toxic to aquatic life. We recommend that substances of different origins be included in the air pollution monitoring portfolio to comprehensively assess the potential hazards to humans and the environment.

Asynchronous fault detection filtering for Markovian jump systems with output sensor saturation

In this paper, a robust asynchronous filter is proposed to detect faults in Markovian jump systems with sensor saturations. The proposed filter has a mode different from that of the original system, and is tolerant of external disturbances. The residual is generated to construct an evaluation function that can successfully perform fault detection. Detailed filter matrices can be computed from the existing conditions. Furthermore, practical and comparative simulations are performed to verify the efficiency of the proposed technique.

Wood-Inspired Filter with a Lignin-Reinforced Nanocomposite Layer for Enhanced Oil/Water Separation and Selective Dye Removal

Nanocomposite filter membranes have attracted a great deal of attention in water purification. However, the weak interaction between nanomaterials and filter matrices severely reduces the stability and causes secondary pollution. Herein, inspired by the structure and function of natural wood, we report a simple strategy for fabricating a filter that uses alkaline lignin as a multifunctional modifier to tailor graphene oxide (GO) and bridge from GO to the surface of the glass fiber (GF). The obtained lignin-GO-modified GF filter was covered by a thin nanocomposite layer (∼2 μm) and exhibited hydrophilic and underwater oleophobic properties. It performed well in separating different oil-in-water emulsions (>92% for n-hexadecane, n-dodecane, and engine oil) under gravity and in selectively removing cationic dyes due to interception and adsorption via electrostatic effects and π–π interactions. Moreover, the filter had good stability and could be regenerated by washing without a significant loss of removal efficiency of n-dodecane-in-water emulsions during five consecutive cycles (1.86% per cycle). Therefore, this study shows that the wood-inspired filter membrane holds considerable promise for wastewater treatment, especially for oil/water separation and selective dye removal.

Pesticides in ambient air, influenced by surrounding land use and weather, pose a potential threat to biodiversity and humans

Little is known about (i) how numbers and concentrations of airborne pesticide residues are influenced by land use, interactions with meteorological parameters, or by substance-specific chemo-physical properties, and (ii) what potential toxicological hazards this could pose to non-target organisms including humans. We installed passive air samplers (polyurethane PUF and polyester PEF filter matrices) in 15 regions with different land uses in eastern Austria for up to 8 months. Samples were analyzed for 566 substances by gas-chromatography/mass-spectrometry. We analyzed relationships between frequency and concentrations of pesticides, land use, meteorological parameters, substance properties, and season. We found totally 67 pesticide active ingredients (24 herbicides, 30 fungicides, 13 insecticides) with 10–53 pesticides per site. Herbicides metolachlor, pendimethalin, prosulfocarb, terbuthylazine, and the fungicide HCB were found in all PUF samplers, and glyphosate in all PEF samplers; chlorpyrifos-ethyl was the most abundant insecticide found in 93% of the samplers. Highest concentrations showed the herbicide prosulfocarb (725 ± 1218 ng sample−1), the fungicide folpet (412 ± 465 ng sample−1), and the insecticide chlorpyrifos-ethyl (110 ± 98 ng sample−1). Pesticide numbers and concentrations increased with increasing proportions of arable land in the surroundings. However, pesticides were also found in two National Parks (10 and 33 pesticides) or a city center (17 pesticides). Pesticide numbers and concentrations changed between seasons and correlated with land use, temperature, radiation, and wind, but were unaffected by substance volatility. Potential ecotoxicological exposure of mammals, birds, earthworms, fish, and honeybees increased with increasing pesticide numbers and concentrations. Human toxicity potential of detected pesticides was high, with averaged 54% being acutely toxic, 39% reproduction toxic, 24% cancerogenic, and 10% endocrine disrupting. This widespread pesticide air pollution indicates that current environmental risk assessments, field application techniques, protective measures, and regulations are inadequate to protect the environment and humans from potentially harmful exposure.

Distributed cubature Kalman filtering for nonlinear systems with stochastic communication protocol

AbstractThis paper is concerned with the distributed cubature Kalman filtering (DCKF) for a class of discrete time‐varying nonlinear systems subject to stochastic communication protocol (SCP). In order to avoid the data collisions, the SCP is introduced to randomly schedule each sensor node information from one of the neighboring nodes to the filter which is presented via not only the information of itself sensor but also the information of neighboring sensors. The considered scheduling probability of the selected node is unknown. Therefore, the exact filtering error covariance is not available. The purpose of this paper is to design a DCKF under the spherical–radial cubature rule such that an upper bound of the filtering error covariance is guaranteed by utilizing fundamental inequality. Such an upper bound is dependent on known upper and lower bounds of the scheduling probabilities. Subsequently, the desired filter matrices are given by minimizing the upper bound of the filtering error. A sufficient condition is derived by using Riccati‐like difference equations method. Besides, a recursive form of filter algorithm is designed for online computation. Finally, the usefulness of the DCKF is verified by utilizing a simulation example on the induction machines.

THE INFLUENCE OF THE COVID-19 PANDEMIC ON ALLOCATION OF ASSETS IN INVESTORS PORTFOLIO

Investing during a pandemic is very challenging. Even in these difficult times, the investor must appropriately allocate assets into his portfolio. In this article, we discuss investing in the stock market. We are interested in creating portfolios of shares that consist of financial assets. The individual methods we use are designed to provide an allocation of funds in between individual shares. In the modern portfolio theory, the Markowitz model (Markowitz, 1952) is being used to solve these problems. The paper's main goal is to propose an efficient, robust approach to solve the Markowitz optimization problem adjusted for periods of a global decline in financial markets. In our research, we focus on robust optimization. Instead of precisely given input parameters, we propose a set of parameters from which we always select the worst possible parameter (so-called worst-case optimization). The robustness of optimization is achieved using so-called filter matrices. These matrices are used to modify historical data directly during optimization. The proposed model modifies the data by using different lengths of historical returns. Our proposed model is then compared with the original Markowitz non-robust model. We compare these two models using the properties of the second derivative of the optimization problem. Our results are visualized for different levels of investor’s risk aversion. We present our methods on historical price data of five randomly selected companies traded on the US market. By comparing the proposed robust approach with the non-robust one, we show that different lengths of historical returns capture volatility changes earlier. The investor can thus reduce his risk aversion and increase his expected returns.

Bell inequalities for entangled qubits: quantitative tests of quantum character and nonlocality on quantum computers.

This work provides quantitative tests of the extent of violation of two inequalities applicable to qubits coupled into Bell states, using IBM's publicly accessible quantum computers. Violations of the inequalities are well established. Our purpose is not to test the inequalities, but rather to determine how well quantum mechanical predictions can be reproduced on quantum computers, given their current fault rates. We present results for the spin projections of two entangled qubits, along three axes A, B, and C, with a fixed angle θ between A and B and a range of angles θ' between B and C. For any classical object that can be characterized by three observables with two possible values, inequalities govern relationships among the probabilities of outcomes for the observables, taken pairwise. From set theory, these inequalities must be satisfied by all such classical objects; but quantum systems may violate the inequalities. We have detected clear-cut violations of one inequality in runs on IBM's publicly accessible quantum computers. The Clauser-Horne-Shimony-Holt (CHSH) inequality governs a linear combination S of expectation values of products of spin projections, taken pairwise. Finding S > 2 rules out local, hidden variable theories for entangled quantum systems. We obtained values of S greater than 2 in our runs prior to error mitigation. To reduce the quantitative errors, we used a modification of the error-mitigation procedure in the IBM documentation. We prepared a pair of qubits in the state |00〉, found the probabilities to observe the states |00〉, |01〉, |10〉, and |11〉 in multiple runs, and used that information to construct the first column of an error matrix M. We repeated this procedure for states prepared as |01〉, |10〉, and |11〉 to construct the full matrix M, whose inverse is the filtering matrix. After applying filtering matrices to our averaged outcomes, we have found good quantitative agreement between the quantum computer output and the quantum mechanical predictions for the extent of violation of both inequalities as functions of θ'.

A Novel Low-Complexity GFDM Relay Communication System: Relay Selection and Filter-and-Forward

In this paper, we develop a low-complexity cooperative relay network based on generalized frequency division multiplexing (GFDM) system. In this network, multiple relay nodes (RNs) are used to relay GFDM symbols from a source node (SN) toward a destination node (DN). We propose three methods to design the RNs based on the minimization of the total power of the noise after symbol estimation at the DN. In the first method, only one node is selected among all RNs to forward the signal from the SN to the DN. In the second and third methods, a filter-and-forward (FF) scheme is used, such that at each RN, the received GFDM signal from the SN is circularly convolved with a finite impulse response (FIR) filter and then, forwarded to the DN. The FIR filter's coefficients are represented as a circulant matrix. Since a circulant matrix is characterized by its eigenvalues, we obtain the eigenvalues of the FIR coefficient matrices for all RNs. The difference between the second and third methods is that in the former, the eigenvalues of the filter matrices are restricted to be one or zero, however, in the later, the eigenvalues are determined with no restrictions. Simulation results are provided to demonstrate the performance of the proposed methods in terms of bit error rate (BER). Moreover, we show that because of using circular convolution, the proposed methods can be implemented with a low complexity.

Treatment of the uncontrolled landfill of Casablanca city by naturals materials

This work treats the leachate from the uncontrolled landfill of Casablanca city using the infiltration-percolation technique. To do this, we carried out a comparative study of the filtrations on different matrices to choose the best filtering medium. We used silt, fly ash, bottom ash, agricultural soil and sea sands from the Casablanca coast. The parameters studied are: Chemical Oxygen Demand (COD), pH and heavy metals. The results obtained from adsorption technique show that these matrices used can be an effective adsorbent for the reduction of physicochemical parameters and the elimination of heavy metals. A revaluation of the filtering sludge made in the field of pottery. The results observed are satisfactory at all levels, whether in the reuse of this liquid waste, in the field of irrigation or the recycling of filter matrices.

Construction of Spatial Filtering Matrices for Norm-Optimal ILC in Single Point Incremental Forming

Construction of Spatial Filtering Matrices for Norm-Optimal ILC in Single Point Incremental Forming

State-Feedback Filtering for Delayed Discrete-Time Complex-Valued Neural Networks.

This article explores a new filtering problem for the class of delayed discrete-time complex-valued neural networks (CVNNs) via state-feedback control design. The novelty of this article comes from the consideration of the newly developed complex-valued reciprocal convex matrix inequality as well as the complex-valued Jensen-based summation inequalities (JSIs). By employing an appropriate Lyapunov-Krasovskii functional (LKF) and by using newly proposed complex-valued inequalities, attention is concentrated on the design of a state-feedback filter such that the associated filtering error system is asymptotically stable with prescribed filter and control gain matrices. The proposed theoretical results are presented in terms of complex-valued linear matrix inequalities (LMIs) that can be solved numerically by using the YALMIP toolbox in MATLAB software. Additionally, one numerical example is given to confirm the validity of the resulting sufficient conditions with the availability of the suitable control and filter design.

Vertex‐dependent filtering of retarded uncertain state‐multiplicative noisy systems

SummaryThe problem of robust H∞ estimation of stochastic, state‐delayed, uncertain, linear, discrete‐time systems is solved by applying a vertex dependent Lyapunov function. In this problem, a cost function is defined, which is the expected value of the standard H∞ performance index with respect to the uncertain parameters. The optimal estimator is obtained by solving a simple set of linear matrix inequalities (LMIs) for either the case where the filter matrices are constant or for the case where these matrices are gain scheduled. The solutions to the corresponding minimum error variance estimation problems are also obtained for the nominal and the uncertain cases, where an additional set of conditions is introduced that guarantees the minimization of the estimation error variance. Two examples are given. The first is a numerical example that demonstrates the advantage of the new design method. The second is an example of a practical nature that is taken from the field of guidance control and estimation.

Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises

This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed. It is proved that the RLMVE-based filter and the existing de-correlated filter have the functional equivalence. Then, for multi-sensor cases, cross-covariance matrices between any two local filters are derived. Based on the RLMVE-based filter and cross-covariance matrices, a DF filter weighted by matrices is proposed in the sense of linear minimum variance. Finally, based on the existing de-correlated filter, the algorithm of cross-covariance for de-correlated systems and the DF algorithm weighted by matrices, a de-correlated DF filtering algorithm is proposed. An example verifies the effectiveness of the proposed RLMVE-based DF filter.

Online Data Dimensionality Reduction and Reconstruction Using Graph Filtering

Graphs have become a popular toolbox for capturing similarity relationships among data vectors in a variety of networks. Diffusion processes are used to model the data using the graph topology. A novel approach is put forth that exploits data similarity and diffusion models, in a graph, to improve upon the reconstruction mean-square error (MSE) performance of principal component analysis. The tasks of data dimensionality reduction and reconstruction are formulated as graph matrix filtering operations, that are utilized in a dimensionality reduction framework optimal in the MSE sense. The novel formulation is seeking MSE-optimal filter matrices that minimize the reconstruction MSE of all the graph data. Block coordinate descent techniques are employed in the graph spectral domain MSE cost to recursively determine the MSE-optimal dimensionality reducing and reconstruction filters. Online implementations are put forth to work with continuous streams of graph data, while almost sure convergence to a stationary point of the ensemble reconstruction MSE cost is established. Analysis of the reconstruction MSE reveals that the lowest MSE achieved by the novel approach is not larger than the standard PCA MSE, while there is a bound for the filter orders after which no more MSE reduction occurs. Extensive numerical tests using synthetic data, as well as real image datasets demonstrate the advantage of the novel graph-based framework over standard PCA and related methods.

Analytic design of directional and square-shaped 2D IIR filters based on digital prototypes

This paper proposes an analytic design method for a class of 2D recursive filters, namely with directional and square-shaped frequency response. The design technique is based on a specified 1D digital prototype filter and a complex frequency transformation which is determined using various rational approximations. Several design examples are presented for given specifications. The resulted filters are efficient and adjustable, being at the same time of low complexity and relatively high selectivity. These filters may have useful applications in image processing, like detecting lines with a given orientation from an image, as shown through simulation results on test images. The novelty of the proposed method consists in deriving the transfer function of the desired 2D filter only by applying successive frequency transformations to a prototype filter. Its advantages over most state-of-the-art design methods, which generally use global optimization numerical algorithms, is the filter tunability, and also its versatility; since the filter parameters, namely orientation and selectivity appear explicitly in the 2D filter matrices, they result directly for any specified parameters, therefore the design procedure does not need to be resumed every time from the start.