Matrix Algorithm Research Articles

Secure Remote Cloud File Sharing With Attribute-Based Access Control and Performance Optimization

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. The framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on SPM, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experiments indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

Risk Identification of Critical Infrastructures Considering Dual Interdependency: A Complex Network Approach

Critical Infrastructures (CIs) are exposed to various risks, which hinder their successful operation and induce great losses. Due to the interdependency between risks and that between CIs, the identification of the most prominent risks becomes complex and challenging. However, existing studies rarely considered the dual interdependency of risks and CIs. This study proposes a double-layer network for multiple interdependent risks and CIs. The Design Structure Matrix (DSM) and Restart Random Walk (RRW) algorithm are combined to determine the impact of risks on CIs by incorporating the strength of dual interdependency. The LeaderRank algorithm is then used to rank these risk factors and an illustrative example is given to validate the model. The proposed model and algorithms can systematically quantify complex interdependencies embedded in the operation of CIs susceptible to multiple risks, and provide decision-makers with evidence to prioritize risks.

An Unambiguity and Anti-Range Eclipse Method for PD Radar Using Biphase Coded Signals

Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then pulse compression and moving target detection (MTD) are utilized to get the final velocity and range information of a target without velocity ambiguity and range ambiguity. Besides, a small transmitting pulse time width is adopted to reduce the working blind area, and two different high pulse repetition frequencies (HPRFs) are adopted to solve the problem of range eclipse. Simulation results finally prove the effectiveness and feasibility of the proposed method.

Big Data Architecture Design of Smart Grid Based On Big Data Platform

A novel big data-driven approach has been proposed to address the challenges of extensive data and user application difficulties in the smart grid. A smart grid big data architecture based on a big data platform has been designed. The random matrix theory is applied to establish a random matrix algorithm model, which samples data from the smart grid big database and undergoes training and learning to construct a data model for user requirements. Through calculations, the macroscopic data hidden in the smart grid big data is transformed into microscopic data for users' reference, enabling the identification of parameters that affect the normal operation of the smart grid at its core. The computed data can be displayed locally and also uploaded remotely to the SG186 marketing system for various user applications. Additionally, the data can be permanently stored in the cloud through wireless communication. By incorporating the random matrix theory algorithm into the smart grid big data architecture, the proposed solution not only enhances the intuitive visualization of smart grid big data but also provides technical references for future work.

Stochastic optimization of multi-capacitated vehicle routing problem with pickup and delivery using saving matrix algorithm

The Multi-Capacitated Problem is an optimization problem. To reduce the overall distance, the optimization problem seeks out vehicle routes that connect every customer to a storage facility. This article uses a saving matrix approach to propose an extended Vehicle Routing Problem that considers a stochastic environment and multiple capacitors. Stochastic customers are an essential element of the problem. A computational analysis also supports the suggested approach to obtain the best route.

Exact solutions of one-dimensional steady heat conduction analysis for multi-layered mediums by tri-diagonal matrix algorithm (TDMA) applicable to Excel program (Analytical solutions of steady heat conduction for multi-layered symmetrical plat plate, solid cylinder, sphere, unsymmetric plat plate, pipe, and spherical shell, and these applications to engineering problems)

Exact solutions of one-dimensional steady heat conduction analysis for multi-layered mediums by tri-diagonal matrix algorithm (TDMA) applicable to Excel program (Analytical solutions of steady heat conduction for multi-layered symmetrical plat plate, solid cylinder, sphere, unsymmetric plat plate, pipe, and spherical shell, and these applications to engineering problems)

Two Double Recursive Block Macaulay Matrix Algorithms to Solve Multiparameter Eigenvalue Problems

We present two double recursive block Macaulay matrix algorithms to solve multiparameter eigenvalue problems (MEPs). In earlier work, we have developed a non-recursive approach that finds the solutions of an MEP via a multidimensional realization problem in the null space of the block Macaulay matrix constructed from the coefficient matrices of that MEP. However, this approach requires an iterative increase of the degree of the block Macaulay matrix: in order to determine whether the null space contains all the (affine) solutions of the MEP, we need to compute a basis matrix of the null space for every degree and check its dimension or rank structure. In this letter, we employ a recursive/sparse technique to compute a basis matrix of the null space of the block Macaulay matrix and a recursive technique to perform the necessary rank checks. We provide two system identification examples to show our improvements in computation time and memory usage.

Identification of cashmere and wool based on LBP and GLCM texture feature selection

There are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gray level co-occurrence matrix algorithm and chi-square test was proposed to sufficiently extract the effective features of these two fibers. Firstly, the collected images of cashmere and wool fibers are processed to obtain the clear texture images with background removed by pre-processing algorithm and local binary pattern. Then, the texture features are calculated by the gray level co-occurrence matrix, and the optimal 5-dimensional features are extracted by chi-square test to represent the texture information of cashmere and wool. Finally, the two fibers are automatically classified and recognized based on the support vector machine. The experimental results show that the proposed method obtained a high recognition accuracy with the percent of 94.39. It verifies that the method based on texture feature selection is effective to identify cashmere and wool fibers.

Modeling the stress-strain state of variable-thickness composite shells and plates

A model for calculating the stress-strain state of variablethickness composite shells has been developed, based on assumptions such us the classical theory of Timoshenko-Mindlin shells. In the proposed model, the plate thickness is given by a function of curvilinear coordinates and is directly considered in the derivation of the equilibrium equations of the plate. The general equations of the theory of variable-thickness composite plates are derived. The article analyses the solution of the problem of plates bending under uniform pressure considering the variable thickness. For the numerical solution, the finite difference method (FDM) has been applied to the system of differential equations with matrix coefficients. For the resultant algebraic system, the FDM uses the tridiagonal matrix algorithm in computing the solution. The calculation results are compared with a plate of constant thickness. It is shown that the effect of thickness variability is quite significant.

Simple Algorithm for GCD of Polynomials

Based on the Bezout approach we propose a simple algorithm to determine the gcd of two polynomials which doesn’t need division, like the Euclidean algorithm, or determinant calculations, like the Sylvester matrix algorithm. The algorithm needs only n steps for polynomials of degree n. Formal manipulations give the discriminant or the resultant for any degree without needing division nor determinant calculation.

Transfer matrix algorithm: Energy eigenvalue calculation of an arbitrary potential profile

The main objective of this article is to study transfer matrix algorithm, energy eigenvalue calculation. This article gives the idea that, in quantum mechanics, the transfer matrix algorithmic a method for calculating the energy eigenvalues of an arbitrary potential profile. The algorithm is based on the transfer formalism, which expresses the wave function of a particle in a potential as a product of matrices. The transfer matrix algorithm is particularly well suited for potentials that are piecewise constant, such as the potentials often used in quantum dot and quantum well devices. The transfer matrix algorithm begins with the construction of the transfer.

A simple algorithm for GCD of polynomials

Based on the Bezout approach we propose a simple algorithm to determine the gcd of two polynomials that don't need division, like the Euclidean algorithm, or determinant calculations, like the Sylvester matrix algorithm. The algorithm needs only n steps for polynomials of degree n. Formal manipulations give the discriminant or the resultant for any degree without needing division or determinant calculation.

Temperature-decoupled hydrogen sensing with Pi-shifted fiber Bragg gratings and a partial palladium coating.

A novel, to the best of our knowledge, sensor architecture for palladium-coated fiber Bragg gratings is proposed and demonstrated that allows highly accurate multi-parameter sensing and decoupling of hydrogen concentration from temperature. By means of partly Pd-coated Pi-shifted FBGs (PSFBGs), the notch wavelength of the narrow transmission band and the flank wavelength of the broader reflection band experience different hydrogen and temperature sensitivities. PSFBGs were calibrated at hydrogen concentrations between 800 and 10,000 ppm and temperatures from 20 to 40°C, and a decreased hydrogen sensitivity at increased temperatures was found. Nonlinear temperature-dependent hydrogen calibration functions were therefore determined. An iterative matrix algorithm was used to decouple hydrogen concentration and temperature and to account for the nonlinear calibration functions. Achieved improvements and results have great importance for real field applications of FBG-based hydrogen sensing.

Foreground segmentation and location of coal and gangue under complex similar background

To improve the foreground segmentation and location accuracy of complex coal gangue images with gray histogram distribution close to the unimodal shape, a contour detection algorithm of the grayscale fluctuation matrix is proposed. The contour and non-contour pixels of coal and gangue images are investigated, and the result indicates that the gray values of the pixels around the contour exhibit the non-uniform distribution, and the gray value changes in different directions are significantly different. Accordingly, a grayscale fluctuation matrix is built by calculating the change amplitude of pixels in different directions, and multiple features are extracted from the grayscale fluctuation matrix to realize the target contour segmentation. Furthermore, the contour is optimized using the historical and future information of the contour image, thus effectively removing numerous false contours, reproducing some hidden contours and increasing segmentation accuracy. This method has high accuracy, and the maximum error rates of the pixel area and center coordinate of contour detection are 4.404% and 3.18% respectively. This study provides a feasible solution to the edge detection and segmentation of images with similar and complex backgrounds.

Cache-oblivious Hilbert Curve-based Blocking Scheme for Matrix Transposition

This article presents a fast SIMD Hilbert space-filling curve generator, which supports a new cache-oblivious blocking-scheme technique applied to the out-of-place transposition of general matrices. Matrix operations found in high performance computing libraries are usually parameterized based on host microprocessor specifications to minimize data movement within the different levels of memory hierarchy. The performance of cache-oblivious algorithms does not rely on such parameterizations. This type of algorithm provides an elegant and portable solution to address the lack of standardization in modern-day processors. Our solution consists in an iterative blocking scheme that takes advantage of the locality-preserving properties of Hilbert space-filling curves to minimize data movement in any memory hierarchy. This scheme traverses the input matrix, in O(nm) time and space, improving the behavior of matrix algorithms that inherently present poor memory locality. The application of this technique to the problem of out-of-place matrix transposition achieved competitive results when compared to state-of-the-art approaches. The performance of our solution surpassed Intel MKL version after employing standard software prefetching techniques.

Research on incremental nonlinear dynamic inverse control method for turboshaft engine based on Jacobi matrix

In order to enhance the anti-interference ability of the speed to external load and achieve the rapid response control for turboshaft engine of next-generation helicopters, an incremental nonlinear dynamic inverse (INDI) control method for turboshaft engine based on Jacobi matrix is proposed. First, the control law of turboshaft engine based on state variable model (SVM) is obtained according to the mathematical derivation of INDI. Then, based on adopting Jacobi matrix and sequential quadratic programming (SQP) algorithm, the SVM is online obtained, so as to express the steady and dynamic characteristics of turboshaft engines under different flight conditions precisely. Finally, several numerical simulations are conducted to validate the control effect of INDI control method. The results demonstrate that compared with cascade PID controller, INDI control method based on single SVM can significantly decrease the overshoot and sag of power turbine speed by more than 50% under constant flight conditions. Under variable flight conditions, INDI control method based on Jacobi matrix can effectively decrease the overshoot and sag of power turbine speed by more than 30% compared with INDI control method based on single SVM, which has more significant control effect and superior robustness, and realizes the fast response control of turboshaft engines.

Innovative Matrix Algorithm to Address the Minimal Cost-Spanning Tree Problem

Innovative Matrix Algorithm to Address the Minimal Cost-Spanning Tree Problem

Development of Security Rules and Mechanisms to Protect Data from Assaults

Cloud cryptography is the art of converting plain text into an unreadable format, which protects data and prevents the data from being misused by the attacker. Different researchers designed various Caesar cipher algorithms for data security. With the help of these algorithms, the data can be converted into a nonreadable format, but the data cannot be completely secured. In this paper, data security is provided in different phases. Firstly, data are secured through a bit-reversing mechanism in which those replace the actual values with no relation to the original data. Then the four-bit values are added at the beginning and end of bits using a salting mechanism to interlink the salting and existing bit-values and hide the original data. A Caesar cipher value is obtained by applying the Caesar cipher algorithm to the resulting bits. The Caesar cipher algorithm is used to implement number-of-shifting on the obtained values. An efficient cipher matrix algorithm is then developed in which different rules are designed to encrypt the data. Afterward, a secure cipher value is obtained by implementing Cipher XORation rules on the result obtained and the user-defined key. In the end, the proposed algorithm is compared with various papers. It identifies how much better the proposed algorithm performs than all the previous algorithms and how much the attack rate can be reduced if this algorithm is used for data security.

Quantitative Framework of Political Education Micro-communication Mode Based on Reachable Information Matrix Algorithm

Based on the generalized decision reduction and reachable information matrix algorithm of the set-valued decision information system, this paper establishes a quantitative framework for the micro-communication model of political education. First, this paper draws on the comparison between communication and psychology using matrix to extract all decision rules based on Marxist theory and ideological and political education theory. Then, taking the dissemination effect of ideological and political education of college students as the research object, by analyzing the factors affecting the effect of dissemination of ideological and political education of college students, the factors and characteristics of political education dissemination are extracted. The construction of educational discourse system.

Fault Location Method of Distribution Network with Distributed Generation

Distributed generation is widely used in power systems, but the topology becomes complicated after the grid connection. Because of the large-scale distributed generation grid, the difficulty of fault location is increasing, and the terminal is easily affected by environmental influences, which leads to the uploaded information being easily distorted. This paper proposes an improved algorithm to solve the defects of the standard intelligent algorithm in the location of high-dimensional and large-scale distributed grids. At the same time, a hybrid algorithm combined with the improved matrix algorithm is proposed to solve the location of the fault section. The experimental analysis proves that the location method studied in this paper can guarantee the distribution network’s security and stability and the power supply’s reliability. The experimental analysis has significant engineering value in the distribution network fault location.