Distribution Of Matrix Research Articles

Improved Sparrow Search Algorithm for Rectangular Planar Array Synthesis

To optimize sparse rectangular planar array antennas under multiple constraints, including array aperture, number of elements, and minimum element spacing, an improved sparrow search algorithm (ISSA) is proposed. Initially, a position distribution matrix is generated using the Blackman window weighting method. The sparrow search algorithm (SSA) is then enhanced by incorporating Kent mapping for population initialization, which improves the initial population’s diversity. Additionally, the strategy for updating the discoverer’s position integrates elements from the sine cosine algorithm (SCA), along with a nonlinear sine learning factor, thereby enhancing global search capability. Finally, a crossover strategy is embedded into the SSA to refine the optimization accuracy by improving the search methodology used by the vigilantes. To verify the efficacy of our approach, we carried out simulation experiments. The results demonstrate that this method significantly enhances the performance of the array antenna by reducing the peak sidelobe level and minimizing the zero-trap depth. These findings validate the reliability and effectiveness of the suggested method.

Comparative investigation of fillers loading effect on morphological, micromechanical, and thermal properties of polyvinyl alcohol/cellulosicfillers-based composites

Polyvinyl alcohol (PVA)-based biocomposites were fabricated by the incorporation of chitosan (Ch), cellulose fibers (CS), and their mixture (1:1 ratio). Fillers with various loading (2, 4, 8, and 10 wt.-%) were incorporated into PVA employing the solution casting method. The fillers and biocomposites were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), brightfield microscopy, tensile and microindentation tests, contact angle measurement and thermogravimetric analysis (TGA). FTIR spectra revealed the removal of lignin, and intermolecular H-bonding between PVA and fillers promoting their filler–matrix interfacial interactions. Crystallographic results showed varied crystallite sizes and crystallinity of composites. Microscopic techniques revealed a uniform filler distribution, attributed to their compatibility with PVA. Tensile and microindentation tests demonstrated a decreased tensile strength (3.3–8.2 MPa of the composites compared to 15.7 MPa of the matrix) and Martens hardness (HM) of biocomposites. However, their value was increased with higher filler concentration, signifying the mechanical reinforcement. Contact angle analysis confirms the decreased wettability (hydrophilicity) of biocomposites, attributed to higher compatibility of fillers with PVA and intermolecular H-bonding between them. A slightly decreased thermal stability of biocomposites with filler incorporation is implied by TGA results despite their uniform distribution and strong matrix–filler interfacial interactions.

Influence of Central Coke Ratio on the Internal State of Blast Furnace

Central coke charging (CCC) is a widely used burden distribution method for blast furnaces (BFs). Adjusting the central coke ratio can change the burden temperature field and affect the smooth operation of BF. This study presents a coupled physical and mathematical model, incorporating particle motion, gas flow, and heat transfer between the burden and gas in two 5500 m3 BFs. The central coke ratios of the blast furnace A (BFA) and blast furnace B (BFB) is 15% and 20%, respectively. The root positions of the cohesive zone in the BFA and BFB are in the lower part of the stack and bosh zones, respectively. In the central area of the BF, the gas flow rate, gas temperature, and burden temperature of the BFB are higher. In the edge area of the BF, the gas flow rate, gas temperature, and burden temperature of the BFA are higher. The actual top gas temperature and gas pressure verify the accuracy of the proposed model. This model investigates the influence of the central coke ratio on the position of the cohesive zone, gas flow rate, gas temperature, and burden temperature, providing a cost‐effective method for studying the effect of the burden distribution matrix on the internal state of the BF.

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Exposure Levels, Health Risks, Spatially Distribution, Multivariate Statistics and Positive Matrix Factorization Model of Heavy Metals from Wild solid Waste Dumpsites

Steady-State Fault Propagation Characteristics and Fault Isolation in Cascade Electro-Hydraulic Control System

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems and lays the foundation for guiding component repair and maintenance.

Bayesian estimation for mean vector of multivariate normal distribution on the linear and nonlinear exponential balanced loss based on wavelet decomposition

This paper addresses the problem of Bayesian wavelet estimating the mean vector of multivariate normal distribution under a multivariate normal prior distribution based on linear and nonlinear exponential balanced loss functions. The covariance matrix of multivariate normal distribution is considered known. Bayes estimators of mean vector parameter of multivariate normal distribution are achieved. Then two soft shrinkage wavelet threshold estimators based on Stein’s unbiased risk estimate (SURE) and Bayes estimators are provided. Finally, the performance of the soft shrinkage wavelet estimators was checked through simulation study and Electrical Grid Stability Simulated data set. Simulation and real data results showed the better performance of SURE thresholds based on linear and nonlinear exponential balanced loss functions compared to other classical wavelet methods. Also, they showed better performance for SURE threshold based on nonlinear exponential balanced loss function in multivariate normal distribution with small dimensions.

Unscented recursive three-step filter based unbiased minimum-variance estimation for a class of nonlinear systems

For the direct feedthrough system where unknown input affects both process equation and measurement equation, an unscented recursive three-step filter based unbiased minimum-variance (URTSF-UMV) estimation algorithm is proposed. The algorithm is based on the unbiased minimum-variance (UMV) method and uses statistical linearisation technology to approximate the nonlinear system and the nonlinear measurement function to linear regression form. The estimation of unknown input is obtained from innovation through weighted least-squares (WLS) estimation. The approximate linear regression form allows us to process the nonlinear system with unknown input direct feedthrough by using the UMV state estimation framework, and derive the filter by minimising the trace of the state error covariance of the measurement update under the unbiased condition. The URTSF-UMV requires that the unknown input distribution matrix is full column rank. We use the full rank decomposition method to deal with the nonlinear system containing the unknown input rank-deficient distribution matrix. The effectiveness of the URTSF-UMV algorithm is demonstrated through an illustrative simulation example.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

Impact of vehicles at the roadside of expressway in urban area: Simultaneous measurement of particle size distribution and positive matrix factorization

This study conducted real-time monitoring of size-resolved particle concentrations ranging from 9 nm to 10 μm simultaneously at four sites on the park ground and the roof of a five-story apartment buildings in the upwind and downwind areas of the Olympic Expressway next to apartment complex areas of Seoul, Korea. Using a positive matrix factorization model for source apportionment, eight factors were resolved at each monitoring site: four exhaust emissions of vehicles, one non-exhaust emission of vehicle, two regional sources, and one unknown source. After categorizing monitoring data into three cases by wind conditions, impact and contribution of each vehicle-related source on the local road to the roadside pollution was quantified and characterized by subtracting the urban background concentrations. Throughout the measurement period, the contribution of vehicle-related sources to the particle number concentration at each monitoring site ranged from 61 % to 69 %, while that to the particle mass concentration ranged from 39 % to 87 %. During periods of steady traffic flow and wind blowing from the road to three downwind sites at speeds exceeding >0.5 m/s during working hours, the particle number concentrations at the downwind sites were 2.2–2.5 times higher than the average levels. Among vehicle-related sources, gasoline vehicles with multiple injections or high-emitting diesel vehicles showed the highest contribution to particle number concentrations at all sites. As wind speed increased, the number concentrations of particles from vehicle exhaust and non-exhaust emissions decreased and increased, respectively, probably due to enhanced dilution and transport, respectively. In addition, particle number concentrations showed a parabolic curve-like trend with traffic volumes increasing to approximately 10,000 vehicles/h, and then decreasing for both vehicle exhaust and non-exhaust emissions. These results can be utilized in numerical modeling studies and in establishing traffic-related environmental policies to reduce seasonal and temporal particle exposure near the roadsides.

Investigation and calculation of the longitudinal compressive strength of unidirectional glass fiber reinforced polymer considering the fiber orientation distribution

In this study, undulations and their influence on the longitudinal compressive strength of a unidirectional glass fiber reinforced polymer (GFRP) composite are investigated theoretically and experimentally. The objective of this research is to explore the failure mechanisms in FRP and to characterize the mechanical properties of FRP as a function of fiber orientation. For this purpose, a multiscale material model is developed that considers a stochastic fiber orientation distribution (FOD) and models matrix fracture-initiated failure. The relationship between compressive strength and undulation is investigated experimentally on standardized specimens made of unidirectional GFRP. The fiber orientations are measured using X-ray computed tomography and ImageJ image analysis, resulting in a binormal distribution of fiber orientations in the series of samples tested. To examine the failure process in detail, the compression tests are simulated using finite element analysis (FEA). Both the FEA results and the measured compressive strengths confirm the model assumption of matrix fracture-initiated failure under longitudinal compressive loading. The presented analytical model realistically represents the correlation of compressive strength with the FOD.

A discrete-module-beam hydroelasticity method with finite element theory in analyzing VLFS in different engineering scenarios

A numerical tool is developed in the framework of the Discrete-Module-Beam method to analyze hydroelasticity of very large floating structures (VLFS) such as floating offshore photovoltaic platforms (FOPV). The proposed method utilizes finite element theory to perform structural analysis. The 3D potential flow theory is used to perform a hydrodynamic analysis after macro-submodule division. For the structural analysis part, the platform is divided into macro-submodules and each macro-submodule is represented by a beam, which is then further discretized into micro beam elements with the nodes categorized into three groups, that is, the lumped mass, the boundary nodes and the inner nodes. Derivation of the lumped-mass stiffness matrix and recovery of the displacement distribution are given. Procedures to deal with interconnections, complex boundary conditions and unsteady loads are elaborated. Good agreement is obtained in different loading scenarios to the numerical and experimental results.

Visible and infrared lab spectroscopy for soil texture classification: Analysis of entire spectra v/s reduced spectra

Soil, directly or indirectly, impacts seven of the Sustainable Development Goals set by the United Nations. Soil texture is a fundamental physical property that controls other properties ranging from soil stability, erodibility, compaction to water holding capacity, nutrient availability and fertility. While Visible Near-Infrared and Shortwave-Infrared (VNIR/SWIR) laboratory spectroscopy has been already used for soil texture classification, Mid Infrared (MIR) region and unison of the VNIR/SWIR and MIR region warrant quantification. Thus, the objectives of this work were: (1) to investigate the critical spectral regions for soil texture classification, (2) to compare the use of entire spectral bands v/s subset of spectral bands obtained via the feature selection method, and (3) to quantify the degree of misclassification in the neighbouring classes for a given textural class. This study utilized an ICRAF–ISRIC global soil spectral library consisting of 3643 VNIR/SWIR, MIR soil spectra and the USDA soil textural classes. Two classifiers were used for soil texture classification using the three databases (VNIR/SWIR, MIR, VNIR + MIR) with entire spectral bands and three databases with only the Partial Information Correlation (PIC) selected bands (VNIR/SWIR_PIC, MIR_PIC, VNIR + MIR_PIC). The mean confusion matrix, overall accuracy and kappa were calculated to evaluate classifiers' performances over 100 iterations. Two additional measures were proposed for partitioning inaccuracies between neighbouring and far classes: Correct-Neighbouring-Far classes distribution matrix and added neighbourhood accuracy. This work highlighted that: (1) the combined VNIR/SWIR + MIR region provided the best texture classification, followed by MIR and then VNIR/SWIR region, (2) the use of PIC selected bands provided lower classification performances when compared to using all bands but a massive reduction in the number of bands allowing to reduce model complexity, (3) the misclassifications were predominantly in the neighbouring classes rather than far classes, for a given texture class. Finally, the textural classes with poor classification performance had low areal and sample representation. These insights may launch a number of soil texture classification studies worldwide based on global soil spectral databases, further helping map soils and the associated ecosystem services it provides.

An accurate and low-cost vehicle-induced deflection prediction framework for long-span bridges using deep learning and monitoring data

Accurate vehicle-induced deflection prediction is critical to bridge structural management and maintenance. However, most existing studies use finite element models and weigh-in-motion (WIM) system to predict the deflection of bridges, the cost of building finite element models is expensive. An accurate and low-cost vehicle-induced deflection prediction framework is developed in this study. In this framework, the calculation method of the spatio-temporal distribution matrix of the vehicle loads is proposed. A fusion attention mechanism bidirectional long short-term memory (ATT-BiLSTM) model is used to predict vehicle-induced deflection. Taking a suspension bridge in China as an example, the accuracy of short- and long-term vehicle-induced deflection prediction is tested, and different deep learning models are used for comparison. The results show that the proposed framework can predict short-term vehicle-induced deflection with a mean absolute error (MAE) of less than 2 mm, which can also accurately predict the extrema of vehicle-induced deflection within a certain time window. The proposed framework is independent of finite element models and lowers the cost of bridge deflection prediction.

Adaptive dynamic programming and distributionally robust optimal control of linear stochastic system using the Wasserstein metric

SummaryIn this paper, we consider the optimal control of unknown stochastic dynamical system for both the finite‐horizon and infinite‐horizon cases. The objective of this paper is to find an optimal controller to minimize the expected value of a function which depends on the random disturbance. Throughout this paper, it is assumed that the mean vector and covariance matrix of the disturbance distribution is unknown. An uncertainty set in the space of mean vector and the covariance matrix is introduced. For the finite‐horizon case, we derive a closed‐form expression of the unique optimal policy and the opponents policy that generates the worst‐case distribution. For the infinite‐horizon case, we simplify the Riccati equation obtained in the finite‐hozion setting to an algebraic Riccati equation, which can guarantee the existence of the solution of the Riccati equation. It is shown that the resulting optimal policies obtained in these two cases can stabilize the expected value of the system state under the worst‐case distribution. Furthermore, the unknown system matrices can also be explicitly computed using the adaptive dynamic programming technique, which can help compute the optimal control policy by solving the algebraic Riccati equation. Finally, a simulation example is presented to demonstrate the effectiveness of our theoretical results.

Matrix-Network Modeling of Translation Communication from Perspective of Eco-Translatology

The article presents the results of a search study conducted based on ecosystemic and network approaches in translation studies within the framework of the concept of “human — network environment”. The relevance is determined by the importance of considering the socio-communicative aspect in translation activities and viewing the translator as a functionally integrated element of the network environment. Communicative-network interaction of participants in a translation project is described. The research methodology is based on the potential of matrix and network modeling methods for processing and visualizing data of translation communication. The analysis is carried out using two models: a responsibility distribution matrix and an undirected graph compiled using a social network visualizer program as a hypothetical projection of a more complex model, verified in practice. Examples of analyzing project interaction between written and oral translations based on a protocol of pilot interviews with English language translators about their experience working in a translation department at an industrial enterprise in the Chelyabinsk region are used as materials. The comprehensive methodology of matrix-network modeling allows for explicitating the specifics of work in a translation project at an enterprise, identifying control-network nodes that accumulate the main communicative load, and determining strategies to enhance communication efficiency.

A New Class of Bayes Minimax Estimators of the Mean Matrix of a Matrix Variate Normal Distribution

Bayes minimax estimation is important because it provides a robust approach to statistical estimation that considers the worst-case scenario while incorporating prior knowledge. In this paper, Bayes minimax estimation of the mean matrix of a matrix variate normal distribution is considered under the quadratic loss function. A large class of (proper and generalized) Bayes minimax estimators of the mean matrix is presented. Two examples are given to illustrate the class of estimators, showing, among other things, that the class includes classes of estimators presented by Tsukuma.

GUARANTEED ORIGIN OF ELECTRICITY IN THE LOCAL POWER SYSTEM WITH RENEWABLE ENERGY SOURCES

The article is focused on the development of a method for determining the part of electricity generated by renewable energy sources (RES) in the electricity flows in the branches of the electric grid. The method is based on a mathematical model of the electric grid, in which the coefficients of current distribution in the branches of the circuit from RES nodes and nodal voltages are used to determine the electricity flows in the branches of the electric grid. As a result, a matrix of power distribution coefficients of RES generation units to load nodes of the power grid is formed. This way, the component of the load power that depends on the power in the RES nodes is determined. The voltages used to form the RES power distribution matrix are determined based on the results of calculating the steady-state modes of the power grid or on the measurement data of the ACEMS. Since the forming of local electric power systems (LES) based on RES is actual, the article considers LES as a balancing group within the electric power system (EPS), which can operate as an autonomous system according to certain conditions. It is shown that for this purpose, a RES reservation system should be created in the power system due to their dependence on natural conditions and, accordingly, the instability of their electricity generation. When a transmission system operates as part of an EPS, a general system reserve is usually used (mainly maneuverable capacities of thermal and hydroelectric power plants). In order for the LES can operate as an autonomous one, it is proposed to use other means of power reserve available today: electrochemical energy storage, hydrogen and biogas technologies. An effective way is to coordinate the schedules of RES generation in the LES with the load schedules of active consumers. The example shows that in the case of a power deficit in the LES, RES located in the power grid can be used. The values of the necessary power at the LES connection node, power flows in the branches connecting the LES and RES grid, and power losses in them are determined by the offered method.

ECT image reconstruction based on sensitive field expansion and optimization

Electrical capacitance tomography (ECT) is an efficient method for addressing the issue of two-phase flow monitoring. Most current methods result in low image reconstruction accuracy due to soft field issues. This paper propose an ECT image reconstruction method based on sensitive field expansion and optimization, which improve reconstruction efficiency and accuracy. Firstly, a sensitivity field optimization method based on flow pattern identification was proposed. The flow pattern recognition determines the flow pattern to which the input signal belongs and selects the sensitive field for the corresponding flow pattern. Secondly, a sensitive field expansion method based on feature extraction is proposed. The ECT image is modeled as a finite rate of innovation signal, sampled using an exponential reproducing kernel. Feature information is extracted from the input signal for data fusion, and the optimized sensitive field is expanded into a new sensitive field distribution matrix by zero-padding and random reorganization. Then, based on optimized and expanded sensitive fields, a sparse image reconstruction method is proposed. Image reconstruction by constructing comprehensive observation equation to obtain the original permittivity distribution vector of the ECT system using the sparsity of the sensitivity matrix and capacitance signal. Finally, the ECT hardware system and the upper computer measurement and imaging integration software are designed. The experimental results show that the method outperforms other existing algorithms in terms of imaging indexes, has better imaging results.

Programmatically Localizing Diabetic Retinopathy Features in 45-Degree Retinal Photographs Using Anatomical Colocation

Background: The aim in this study was to investigate the localization of diabetic retinopathy features at the posterior pole. Methods: This study extracted diabetic retinopathy feature locations from 757 macula-centered 45-degree fundus photographs in the publicly available DDR dataset. Arteriole and venule locations were also extracted from the RITE (n = 35) and IOSTAR (n = 29) datasets. Images were normalized to collocate optic disc and macula positions, and feature positions were collated to generate a frequency distribution matrix. Sørensen–Dice coefficients were calculated to compare the location of different features. Results: Arterioles occurred in two main, distinct arcuate patterns. Venules showed a more diffuse distribution. Microaneurysms were diffusely located around the posterior pole. Hemorrhages and exudates occurred more frequently at the temporal aspect of the macula. Cotton wool spots occurred in a region approximating the radial peripapillary capillaries. Intraretinal microvascular abnormalities and neovascularization were seen throughout the posterior pole, with neovascularization at the disc (n = 65) being more common than neovascularization elsewhere (n = 46). Venous beading occurred primarily between the first and third bifurcations of the venules. Diabetic retinopathy overall was more frequent in the temporal aspect of the macula. The location of cotton wool spots and exudates showed moderate similarity (0.52) when all data were considered, reducing to low similarity (0.18) when areas of low frequency were removed. Conclusions: Diabetic retinopathy occurs throughout the posterior pole but is more frequent in the temporal aspect of the macula. Understanding the location of diabetic retinopathy features may help inform visual search strategies for diabetic retinopathy screening.

Efficient History-Driven Adversarial Perturbation Distribution Learning in Low Frequency Domain

The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, \(\mathcal {N}\text{-HSA}_{LF}\) . It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed \(\mathcal {N}\text{-HSA}_{LF}\) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under \(L_2\) -norm and \(L_\infty\) -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent.