Efficient Algorithm Research Articles

Optimal allocation of solar photovoltaic distributed generation for performance enhancement of electrical distribution networks considering optimal volt‐var regulation under uncertainty and high load variation

AbstractThis article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt‐var regulation control of PV inverters compared to several valid and robust optimization algorithms.

Legendre-Galerkin spectral algorithm for fractional-order BVPs: Application to the Bagley-Torvik equation

<p>Herein, we provide an efficient spectral Galerkin algorithm, according to a special type of shifted Legendre basis for finding a semi-analytic solution to the Liouville-Caputo fractional boundary value problem. The algorithm’s main goal is to transform the fractional differential problem into a linear system with efficiently invertible, well-structured matrices. The convergence rates of the algorithm are carefully obtained as well as the error bound.</p>

Virtual synchronous generator control with parameters optimization based on new WaOA algorithm for grid-feeding inverters

The widespread integration of renewable energy sources (RESs) within traditional power systems causes a considerable impact, such as a decrease in total inertia, damping properties, and large frequency deviation. To tackle this issue, numerous research have been proposed to introduce the virtual synchronous generator (VSG) control strategy as an effective solution applied to power electronics inverters operating in grid-forming or grid-feeding control modes. However, the proper tunning of the inertia and damping parameters is considered as a significant challenge to ensure the system stability and reliability. In this paper, an efficient meta-heuristic Walrus Optimization Algorithm (WaOA) -based VSG control intended for grid-feeding inverters within a weak grid is proposed. The objective of this work is to offer a proper tuning of the VSG parameters, thereby improving the dynamic characteristics of the microgrid (MG) and guaranteeing a stable operation. In addition, it highlights the adopted optimization technique which is introduced on VSG control while ensuring minimum objective function value. Through offline simulations in MATLAB/Simulink, the superiority of the suggested WaOA method is first confirmed, in which it offers the lowest Integral Square Error (ISE) fitness function value of 0.026, compared to the Mountain Gazelle Optimizer (MGO) and Tunicate Swarm Algorithm (TSA) with 0.027 9 and 0.0281, respectively. Second, the performance of the WaOA-based VSG control is compared to the ones based on MGO and TSA techniques under active and reactive power variations. The obtained results demonstrate that the proposed VSG control ensures a better transient performance, especially in terms of under and overshoots.

Solving fractional differential equations on a quantum computer: A variational approach

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in terms of time complexity but also has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation costs scale economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering applications, such as the subdiffusion equation, the nonlinear Burgers' equation, and a coupled diffusive epidemic model. We assess quantum hardware performance under realistic noise conditions, further validating the practical utility of our algorithm.

An efficient algorithm for text encryption on android devices

An efficient algorithm for text encryption on android devices

Multi-source partial multi-label learning via tensor decomposition and nonconvex regularization

Multi-source partial multi-label learning (MSPMLL) aims to learn a multi-label classifier from training examples with multi-source features and multiple labels. Most of the existing MSPMLL models tackle each source independently to derive classifiers. Those methods cannot efficiently reveal the intrinsic structure and relationships among instances across different sources, nor deal with inconsistent labels caused by noise. To address those challenges, this paper proposes a tensor decomposition and nonconvex regularization approach to multi-source partial multi-label learning, nominated as MSPML-TLR. In this model, a hypergraph based affinity graph is constructed from each of multi-source data in the feature space. A tensor representation of multi-source multi-label data is proposed based on the affinity graphs. A multi-linear projection is learned to map the feature affinity space to a latent ground-truth label space via tensor robust principal component analysis. Furthermore, a notion called the parameterized tensorial logarithmic rank is designed to approximate the rank of the multi-linear projection and the latent ground-truth label tensor. Moreover, a hypergraph-based Laplacian regularization is imposed on each frontal slice of the latent ground-truth label tensor to preserve the topological structure of instances across multi-source feature spaces. An efficient algorithm is designed to implement MSPML-TLR and the convergence of the algorithm is rigorously proven. Extensive experiments on various real-world benchmark datasets demonstrate that the proposed model outperforms state-of-the-art methods in multi-label classification tasks.

Tensor nonconvex unified prior for tensor recovery

Tensor data, such as hyperspectral images and multi-frame videos, have gained significant attention in practical applications. However, the inherent degradation phenomena during data acquisition, including noise and missing pixels, give rise to a series of ill-posed inverse problems that need to be addressed. Currently, the rational exploration of prior knowledge for tensor recovery, including global low-rankness and local smoothness, has emerged as a common concern. Inspired by recent notable works, this paper proposes a novel tensor non-convex unified prior term, which employs weighted tensor Schatten p-norm as a rank surrogate function in the gradient domain. The new prior can yield a regularizer that effectively captures low-rankness and smoothness, and is applied to tensor completion and tensor robust principal component analysis models. An efficient algorithm is developed by using the alternating direction method of multipliers and its convergence analysis is also provided. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods, particularly in cases of high missing rates and strong noise levels.

Blockchain-based Management for Organ Donation and Transplantation

Organ donation and transplantation systems now face a variety of requirements and obstacles in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation, all of which are hampered by legal, clinical, ethical, and technical restrictions. As a result, a comprehensive organ donation and transplantation system is essential to provide a fair and efficient procedure that improves patient experience and confidence. In this work, we present a private Ethereum blockchain-based system for managing organ donation and transplantation in a completely decentralised, secure, traceable, auditable, private, and trustworthy manner. We create smart contracts and offer six algorithms, along with information on their implementation, testing, and validation. We assess the performance of the suggested solution by conducting privacy, security, and confidentiality assessments and comparing it to current solutions.

Unmanned aerial vehicle variable radius set covering problem for emergency wireless network

The utilization of unmanned aerial vehicles (UAVs) has garnered increasing interest across various disciplines. The idea of smart cities led to the exploration of using UAVs in the public service industry, with a focus on integrating all aspects of the system through information and physical items. In this paper, we introduce and model the operational problem of using UAVs to construct an emergency wireless network in a disaster situation using the set covering approach. Unique to our study is the consideration that UAVs do not require predetermined positioning or altitude specifications. Consequently, the problem described in this paper can be classified as a planar variable radius covering problem, which involves a nonconvex continuous relaxation feasible set. We introduce the application of the Dantzig–Wolfe decomposition alongside a branch-and-price algorithm to tackle this problem. Additionally, the pricing subproblem’s solvable equivalent formulation is ingeniously derived from existing theories on the minimum covering circle. For large-scale instances, we propose a heuristic derived from an extended formulation. Comparative computational experiments demonstrate that our proposed algorithms significantly surpass the efficiency of the benchmark genetic algorithm previously reported in the literature.

DETERMINING OPTIMAL COMPRESSION ALGORITHM FOR FILES OF DIFFERENT FORMATS

The aim of the article is to highlight possible areas of the use of compression algorithms in various fields. The article investigates the efficiency of the compression of files of different formats and provides recommendations on the optimal compression algorithms for specific scenarios. The research was carried out empirically: compression algorithms were implemented at the software level, the programs were used to compress files of various formats, the size of the resulting files was determined, and a comparison of the efficiency of the methods was made. Research results. The article presents a table of file sizes after compression using different compression algorithms, calculates compression ratios for each case, determines the average compression ratios for each compression algorithm, analyses the efficiency of compression algorithms, and identifies the optimal compression algorithms for files of different formats. The scientific novelty of this work is an integrated approach to comparing compression algorithms by compression ratios on different file formats and the study of the combination of Huffman and LZ78 algorithms, which has not been widely studied before. This allows us to gain a deeper understanding of the process of compressing files of different formats and identify effective algorithms for specific data types. The analysis can contribute to the development and improvement of file compression methods and have practical applications in various fields, such as data storage and transmission, file compression, and improving the performance of information processing systems. The practical significance of the work lies in its potential usefulness for various fields. It provides recommendations and conclusions on the selection of efficient file compression algorithms for different file formats. This can have a positive impact on data storage and transmission, data processing speed, software development, and multimedia data. Using optimal compression algorithms helps reduce file size, saves resources, and improves user experience.

Improving the computational efficiency of lock-in algorithms through coherent averaging

The lock-in amplifier and coherent averaging, established techniques for processing periodic signals, are combined in a novel system. In this approach, the signal is first averaged coherently for a number of periods and then passed through a lock-in with moving average filter of a whole number of periods as low-pass filter. Results match those obtained with traditional lock-in with moving average filter, but maximizing the number of cycles used for the coherent average significantly reduces the multiplications involved in the calculation. Any lock-in system with moving average filter can take advantage of these results. Particularly in this study, they are employed to nearly double the achievable clock frequency of a lock-in system implemented on a Cyclone V FPGA, taking it from 119.39 MHz to 216.54 MHz without the use of hardware embedded multipliers.

Quantum machine learning with Qiskit: Evaluating regression accuracy and noise impact

AbstractQuantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.

Learning an interpretable end-to-end network for real-time acoustic beamforming

Recently, many forms of audio industrial applications, such as sound monitoring and source localization, have begun exploiting smart multi-modal devices equipped with a microphone array. Regrettably, model-based methods are often difficult to employ for such devices due to their high computational complexity, as well as the difficulty of appropriately selecting the user-determined parameters. As an alternative, one may use deep network-based methods, but these are often difficult to generalize, nor can they generate the desired beamforming map directly. In this paper, a computationally efficient acoustic beamforming algorithm is proposed, which may be unrolled to form a model-based deep learning network for real-time imaging, here termed the DAMAS-FISTA-Net. By exploiting the natural structure of an acoustic beamformer, the proposed network inherits the physical knowledge of the acoustic system, and thus learns the underlying physical properties of the propagation. As a result, all the network parameters may be learned end-to-end, guided by a model-based prior using back-propagation. Notably, the proposed network enables an excellent interpretability and the ability of being able to process the raw data directly. Extensive numerical experiments using both simulated and real-world data illustrate the preferable performance of the DAMAS-FISTA-Net as compared to alternative approaches.

Phase field modeling and numerical algorithm for two-phase dielectric fluid flows

We develop a method for modeling and simulating a class of two-phase flows consisting of two immiscible incompressible dielectric fluids and their interactions with imposed external electric fields in two and three dimensions. We first present a thermodynamically-consistent and reduction-consistent phase field model for two-phase dielectric fluids. The model honors the conservation laws and thermodynamic principles, and has the property that, if only one fluid component is present in the system, the two-phase formulation will exactly reduce to that of the corresponding single-phase system. In particular, this model accommodates an equilibrium solution that is compatible with the zero-velocity requirement based on physics. This property leads to a simpler method for simulating the equilibrium state of two-phase dielectric systems. We further present an efficient numerical algorithm, together with a spectral-element (for two dimensions) or a hybrid Fourier-spectral/spectral-element (for three dimensions) discretization in space, for simulating this class of problems. This algorithm computes different dynamic variables successively in an un-coupled fashion, and involves only coefficient matrices that are time-independent in the resultant linear algebraic systems upon discretization, even when the physical properties (e.g. permittivity, density, viscosity) of the two dielectric fluids are different. This property is crucial and enables us to employ fast Fourier transforms for three-dimensional problems. Ample numerical simulations of two-phase dielectric flows under imposed voltage are presented to demonstrate the performance of the method herein and to compare the simulation results with theoretical models and experimental data.

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Modular construction of boolean networks

Boolean networks have been used in a variety of settings, as models for general complex systems as well as models of specific systems in diverse fields, such as biology, engineering, and computer science. Traditionally, their properties as dynamical systems have been studied through simulation studies, due to a lack of mathematical structure. This paper uses a common mathematical technique to identify a class of Boolean networks with a “simple” structure and describes an algorithm to construct arbitrary extensions of a collection of simple Boolean networks. In this way, all Boolean networks can be obtained from a collection of simple Boolean networks as building blocks. The paper furthermore provides a formula for the number of extensions of given simple networks and, in some cases, provides a parametrization of those extensions. This has potential applications to the construction of networks with particular properties, for instance in synthetic biology, and can also be applied to develop efficient control algorithms for Boolean network models.

Modular construction of boolean networks

Boolean networks have been used in a variety of settings, as models for general complex systems as well as models of specific systems in diverse fields, such as biology, engineering, and computer science. Traditionally, their properties as dynamical systems have been studied through simulation studies, due to a lack of mathematical structure. This paper uses a common mathematical technique to identify a class of Boolean networks with a “simple” structure and describes an algorithm to construct arbitrary extensions of a collection of simple Boolean networks. In this way, all Boolean networks can be obtained from a collection of simple Boolean networks as building blocks. The paper furthermore provides a formula for the number of extensions of given simple networks and, in some cases, provides a parametrization of those extensions. This has potential applications to the construction of networks with particular properties, for instance in synthetic biology, and can also be applied to develop efficient control algorithms for Boolean network models.

A new adaptive elastic loss for robust unsupervised feature selection

The sparse-based unsupervised dimension reduction technique has been demonstrated to be an effective tool for dealing with high-dimensional data. Among them, the sparse regularization technique is used to introduce the prior knowledge of the model and helps unsupervised dimension reduction algorithms to complete the feature selection. However, the sparse regularization only considers the sparsity of representation. It ignores the group effect of variables among which the pairwise correlations are very high. To solve these issues, we present a novel regularization approach based on adaptive elastic loss in this study. Then, utilizing the regularization method, an efficient unsupervised feature selection methodology is given. Additionally, we devise an efficient alternative optimization algorithm to resolve the complex optimization issue of our proposed method and conduct a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments to test the validity of our proposed method, and real-world data and a simulation study demonstrate that our proposed method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering and classification.

Uniformly monotone partitioning of polygons

We study the problem of partitioning a simple polygon P with n vertices (including R reflex vertices) and no holes into a minimum number of uniformly monotone subpolygons using diagonals that lie inside P, each connecting two distinct vertices of P. A set of polygons is uniformly monotone if there exists a line such that all of the polygons are monotone with respect to the line. We present an O(nRlog⁡n+R5)-time algorithm for the problem. When Steiner points can be placed on the boundary of P and each Steiner point is considered as a vertex of P, we present an O(n+R5)-time algorithm for the problem. We present an O(n+R4)-time algorithm when Steiner points can be placed anywhere in P. Our algorithms improve upon the previously best ones. We also present simple and efficient 2-approximation algorithms.

Assessing the application and removal of temporary restorations in endodontic practice: A survey-based study

Background: This survey delved into the diverse practices regarding the placement and removal of temporary restorations in the field of endodontics. Employing a Google Forms questionnaire containing 16 inquiries, the study sought to capture varied trends prevalent among practitioners. The survey was conducted for 2 months, engaging 250 participants through social media platforms by sharing a generated link for the Google survey. Materials and Methods: The questionnaire covered a spectrum of aspects related to temporary restoration procedures, aiming to gather insights into the methodologies, techniques, and protocols adopted by practitioners during these processes. By employing an Internet-based survey tool, the researchers efficiently collated responses from a diverse pool of professionals. Results: The study’s focus encompassed understanding the nuances in temporary restoration practices, emphasizing the significance of structured guidelines or protocols in ensuring efficient and standardized procedures. Through the collected responses, the survey aimed to identify prevalent trends, disparities, and potentially innovative approaches in the placement and removal of temporary restorations within the domain of endodontics. Conclusion: Ultimately, the survey results are anticipated to provide valuable insights into the varied methodologies employed in temporary restoration procedures, potentially paving the way for standardization or enhancement of protocols in this critical aspect of endodontic therapy.